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<article xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.3" article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">IJAR</journal-id>
      <journal-title-group>
        <journal-title>Indonesian Journal of Advanced Research</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2986-0768</issn>
      <publisher>
        <publisher-name>Formosa Publisher</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.55927/ijar.v4i6.14664</article-id>
      <title-group>
        <article-title>The Influence of Population, Open Unemployment Rate and Human Development Index on Poverty Levels in West Kalimantan Province</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Firmansyah</surname>
            <given-names>Riki Zogik</given-names>
          </name>
          <aff>Universitas Pembangunan Nasional Veteran Jawa Timur</aff>
          <email>21011010128@student.upnjatim.ac.id</email>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Huda</surname>
            <given-names>Syamsul</given-names>
          </name>
          <aff>Universitas Pembangunan Nasional Veteran Jawa Timur</aff>
        </contrib>
      </contrib-group>
      <pub-date pub-type="epub">
        <day>16</day>
        <month>06</month>
        <year>2025</year>
      </pub-date>
      <history>
        <date date-type="received">
          <day>30</day>
          <month>04</month>
          <year>2025</year>
        </date>
        <date date-type="rev-recd">
          <day>14</day>
          <month>05</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>16</day>
          <month>06</month>
          <year>2025</year>
        </date>
      </history>
      <volume>4</volume>
      <issue>6</issue>
      <fpage>627</fpage>
      <lpage>638</lpage>
      <abstract>
        <p>This study analyzes the effect of population, open unemployment rate (TPT), and human development index (HDI) on the poverty rate in West Kalimantan Province. Using a quantitative approach with secondary data from BPS for the period 2009–2023, the method used is multiple linear regression. The results show that population and HDI have a negative significant effect on poverty, while TPT has no significant effect. This finding confirms the importance of improving the quality of human resources and population management in reducing poverty.</p>
      </abstract>
      <kwd-group>
        <kwd>Poverty</kwd>
        <kwd>Population</kwd>
        <kwd>TPT</kwd>
        <kwd>HDI</kwd>
        <kwd>West Kalimantan</kwd>
      </kwd-group>
      <permissions>
        <license>
          <ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">http://creativecommons.org/licenses/by/4.0/</ali:license_ref>
          <license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License.</license-p>
        </license>
      </permissions>
    </article-meta>
  </front>
  <body>
 <sec>
  <title>INTRODUCTION</title>
  <disp-quote>
    <p>Poverty is a structural issue that remains a fundamental
    challenge in Indonesia's social and economic development. As a
    multidimensional phenomenon, poverty is not only related to a lack
    of income to fulfill basic needs, but also involves aspects of
    accessibility to essential public services, such as education,
    health, decent housing, clean water, and sanitation. From a
    sustainable development perspective, poverty also reflects
    inequalities in the distribution of resources, economic
    opportunities, and socio-political representation that impact the
    quality of life of the community in the long term. Although
    Indonesia has shown a downward trend in the national poverty rate in
    recent years, spatial inequality in the distribution of poverty
    remains high, indicating problems in equitable distribution of
    development outcomes between regions.</p>
    <p>West Kalimantan Province is one of the regions that still faces
    major challenges in poverty reduction efforts. Based on data from
    the Central Statistics Agency (BPS) in 2024, the number of poor
    people in West Kalimantan reached</p>
    <p>336.08 thousand people or 6.71% of the total population of the
    province. This percentage indicates that more than one in every
    fifteen residents lives below the poverty line. In fact, the poverty
    line value in West Kalimantan was recorded at Rp595,509 per capita
    per month, which is higher than the national average of Rp582,932.
    This fact indicates that the minimum cost of living in the province
    is relatively higher, while the community's ability to fulfill basic
    needs is still low. This inequality further emphasizes the need for
    a contextual and regionally- based policy approach in effectively
    addressing poverty.</p>
  </disp-quote>
  <sec id="figure-1.-graph-of-the-percentage-of-poverty-level-in-kalimantan-island-in-2023">
    <title>Figure 1. Graph of the Percentage of Poverty Level in
    Kalimantan Island in 2023</title>
    <disp-quote>
      <graphic mimetype="image" mime-subtype="jpeg" xlink:href="vertopal_ba8c0e97a9de48c490e86dfcc8238599/media/image3.jpeg" />
      <p>West Kalimantan, which in 2023 has a population of 5,623,328
      people, is the most populous province on the island of Kalimantan.
      Geographically, the region is dominated by large rivers and vast
      tropical forest areas, and has abundant natural resource
      potential, such as agricultural, plantation, forestry and mining
      products. However, these potential resources have not been
      optimally managed to support inclusive local economic development.
      Limited infrastructure, low value-added economic sectors, and
      underdeveloped processing industries mean that communities are
      still heavily dependent on</p>
      <p>traditional agriculture and subsistence economies that are
      vulnerable to fluctuations in weather and commodity prices. In
      addition, classic issues such as accessibility to quality
      education and job skills training also worsen employment
      conditions in the region.</p>
      <p>West Kalimantan's Open Unemployment Rate (TPT) in 2023 was
      recorded at 5.05%, reflecting the mismatch between labor supply
      and demand in the labor market. The high unemployment rate is also
      an indication of the low quality of human resources that have not
      been able to meet the needs of modern industry and
      technology-based economic sectors. In addition to unemployment,
      the low productivity of workers in the informal sector also
      exacerbates income inequality, which in turn has an impact on
      increasing social vulnerability and economic exclusion for
      marginalized groups of society.</p>
      <p>The Human Development Index (HDI) is another important
      indicator that can provide a comprehensive picture of the quality
      of life of the population. West Kalimantan's HDI in 2023 was
      recorded at 69.41, an increase from 68.63 in the previous year.
      While this increase reflects improvements in health, education and
      living standards, it is still below the national average. The
      disparity in HDI between districts/cities in the province is also
      striking, with urban areas such as Pontianak City and Singkawang
      City having relatively high HDIs, while inland areas such as
      Ketapang, Melawi and Kapuas Hulu Districts are still significantly
      behind. Limited access to education and health infrastructure,
      geographical remoteness, and the lack of policy interventions that
      are responsive to local needs are factors that widen the
      development gap between regions within the province. By looking at
      these various data, it can be concluded that population, open
      unemployment rate, and HDI play a significant role in the poverty
      rate in West Kalimantan. The problem of poverty cannot be solved
      with a single approach, but requires a holistic and contextual
      policy strategy, including local economic development, improving
      the quality of education and job training, and equitable
      infrastructure development. This study aims to analyze the effect
      of the three variables on poverty, as an effort to provide
      data-based recommendations in</p>
      <p>order to encourage sustainable and inclusive development in
      West Kalimantan.</p>
    </disp-quote>
  </sec>
</sec>












<sec>
  <title>LITERATURE REVIEW</title>
  <disp-quote>
    <p><italic><bold>Poverty Teory</bold></italic></p>
    <p>According to Suparlan (2004), poverty can be defined as a
    condition in which the standard of living of a person or group of
    people is at a very low level, resulting in significant shortcomings
    when compared to the standard of living that is normatively
    considered decent and adequate. This poverty condition does not only
    affect the material aspects, but also affects the health, quality of
    life, and moral and psychological aspects of the individuals who
    experience it. Furthermore, poverty is closely related to economic
    productivity, where low levels of production and consumption
    characterize the poor, which in turn hinders the social and economic
    progress of the region.</p>
    <p>In this context, Mudrajad Kuncoro (2010) explains the concept of
    a vicious cycle of poverty that outlines how poverty can become a
    cycle that is difficult to break. This cycle starts with the low
    income of the poor, which leads to limited</p>
    <p>capital for productive investment. As a result, their
    productivity remains low and incomes do not increase, so poverty
    continues from generation to generation. This theory is in line with
    Nurkse's (1953) view that poverty is a structural problem due to a
    lack of capital. Nurkse emphasized that the poor do not have
    sufficient capital to increase productivity and income, resulting in
    economic stagnation that reinforces poverty itself.</p>
    <p><italic><bold>Total Population</bold></italic></p>
    <p>Population reflects the total number of individuals living in an
    area in a given period, influenced by birth, death, and migration
    (immigration and emigration) rates. Changes in population are
    important to analyze because they impact economic, educational,
    health, and spatial aspects. According to Malthus' classic
    demographic theory, population growth is exponential, while
    resources such as food only increase arithmetically, so this
    imbalance has the potential to cause food shortages and poverty.
    However, technological developments and modernization have changed
    some of Malthus' assumptions, such as increased food production and
    decreased birth rates in many countries, so the theory remains
    relevant to understanding issues of overpopulation and resource
    distribution.</p>
    <p>In contrast, according to Karl Marx, demographic pressure is
    caused more by limited employment opportunities than the
    availability of needs. An increase in population can encourage the
    production of goods and services and economic growth, but it needs
    to be controlled through population policy so that the balance
    between resources and needs is maintained.</p>
    <p>H1: It is suspected that population has an effect on the level of
    poverty in the province of West Kalimantan.</p>
    <p><italic><bold>The Open Unemployment Rate</bold></italic></p>
    <p>The Open Unemployment Rate (TPT) is an important labor market
    indicator that measures the proportion of the labor force that is
    currently not working but is actively seeking employment. This
    indicator provides insights into the efficiency and inclusivity of a
    region’s labor market. According to Sukirno (2011), unemployment is
    defined as a condition in which individuals, despite having the
    capacity and willingness to work, are unable to secure employment.
    Thus, the TPT focuses on those who are actively looking for work but
    have not yet succeeded in finding suitable opportunities. A
    persistently high TPT signals structural problems within the labor
    market, such as limited availability of decent jobs, mismatches
    between skills and labor market demands, or macroeconomic
    instabilities that hinder job creation. This condition can lead to
    serious socio-economic consequences, including reduced household
    incomes, lower consumption, increased poverty rates, and heightened
    social tensions. In the context of Indonesia, unemployment remains
    one of the critical challenges for the government in its efforts to
    achieve inclusive and sustainable economic development.</p>
    <p>From a theoretical perspective, classical economists such as Adam
    Smith and David Ricardo believed that poverty could be mitigated if
    labor markets functioned efficiently and without significant
    distortions. According to this</p>
    <p>classical view, supply and demand for labor would naturally reach
    equilibrium, and unemployment would be temporary. However, in
    reality, labor markets often face frictions—such as barriers to
    entry, information asymmetry, and institutional rigidities—that
    prevent optimal allocation of labor. In Indonesia, data on TPT is
    primarily sourced from periodic labor force surveys conducted by the
    Central Statistics Agency (Badan Pusat Statistik – BPS). These
    surveys provide vital information for policy formulation,
    particularly in addressing employment challenges and aligning labor
    policies with national development goals.</p>
    <p>H2: It is suspected that the level of open unemployment affects
    the level of poverty in the province of West Kalimantan.</p>
    <p><italic><bold>Human Development Index</bold></italic></p>
    <p>The Human Capital Theory proposed by Gary S. Becker emphasizes
    that human resources are strategic assets that can be improved
    through investments in education, training, and health. Human
    capital includes abilities and knowledge acquired through improved
    health and education, which contribute to productivity and economic
    growth (Becker, 1993).</p>
    <p>The Human Development Index (HDI) is an indicator that measures
    the quality of human development based on three main dimensions:
    health (life expectancy), education (years of schooling and
    schooling expectations), and decent living standards (income per
    capita). HDI is used to evaluate the effectiveness of development
    programs and illustrate the potential and human productivity in a
    region (Susanti, 2013). Increasing human capital through investment
    will result in a more competent and productive workforce, supporting
    sustainable economic development.</p>
    <p>H3: It is suspected that the human development index affects the
    level of poverty in the province of West Kalimantan.</p>
    <p>Figure 1. Conceptual Framework</p>
  </disp-quote>
</sec>













<sec>
  <title>METHODOLOGY</title>
  <disp-quote>
    <p>This study uses quantitative methods to analyze the effect of
    Population, Open Unemployment Rate, and Human Development Index on
    Poverty Level in West Kalimantan Province. The research was
    conducted in West Kalimantan with data from 2009-2023, using
    secondary data in the form of time series from BPS and other
    official sources. the analysis used is multiple linear regression
    analysis with the help of SPSS software.</p>
  </disp-quote>
</sec>













<sec>
  <title>RESEARCH RESULT</title>
  <disp-quote>
    <p><italic><bold>Normality Test</bold></italic></p>
  </disp-quote>
  <sec id="table-1.-normality-test">
    <title>Table 1. Normality Test</title>
    <table-wrap>
      <table>
        <colgroup>
          <col width="49%" />
          <col width="51%" />
        </colgroup>
        <thead>
          <tr>
            <th colspan="2">One Sample Kolmogorov Smirnov Test</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td></td>
            <td>Unstandardized Residual</td>
          </tr>
          <tr>
            <td>Asymp.Sig (2-tailed)</td>
            <td>0,200</td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Source: Data processed</p>
      <p>Based on the table above, the results of the One-Sample
      Kolmogorov- Smirnov test show the Asymp.Sig. (2-tailed) for each
      variable that exceeds 0.05 according to the established
      experimental level. This finding indicates that the data used in
      the study has a distribution pattern that follows a normal
      distribution.</p>
      <p><italic><bold>Multikolinieritas Test</bold></italic></p>
    </disp-quote>
  </sec>
  <sec id="table-2.-multicollinearity-test">
    <title>Table 2. Multicollinearity Test</title>
    <table-wrap>
      <table>
        <colgroup>
          <col width="37%" />
          <col width="29%" />
          <col width="33%" />
        </colgroup>
        <thead>
          <tr>
            <th></th>
            <th>Tolerence</th>
            <th>VIF</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Total Population</td>
            <td>0,305</td>
            <td>3,282</td>
          </tr>
          <tr>
            <td><p>The Open</p>
            <p>Unemployment Rate</p></td>
            <td>0,807</td>
            <td>1,239</td>
          </tr>
          <tr>
            <td><p>The Human</p>
            <p>Development Index</p></td>
            <td>0,346</td>
            <td>2,886</td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Source: Data processed</p>
      <p>Referring to Table 4.2, the multicollinearity test results
      indicate that each independent variable has a VIF value that is
      still below the 10 threshold. It is known that total population is
      3,282, the open unemployment rate is 1,239, and human development
      index is 2,886. Because all VIF values &lt; 10 and tolerance
      values &gt; 0.01, it can be concluded that there is no
      multicollinearity problem in the regression model used in this
      study.</p>
      <p><italic><bold>Heteroscedasticity Test</bold></italic></p>
    </disp-quote>
  </sec>
  <sec id="table-3.-glejser-test-results">
    <title>Table 3. Glejser Test Results</title>
    <table-wrap>
      <table>
        <colgroup>
          <col width="67%" />
          <col width="33%" />
        </colgroup>
        <thead>
          <tr>
            <th></th>
            <th>Sig</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>Total Population</td>
            <td>0,851</td>
          </tr>
          <tr>
            <td>Open unemployment rate</td>
            <td>0,198</td>
          </tr>
          <tr>
            <td><p>Human Development</p>
            <p>Index</p></td>
            <td>0,644</td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Source: Data processed</p>
      <p>Referring to the Glejser test results listed in Table 4.3, all
      independent variables show a significance value (Sig.) above 0.05.
      This means that there is no systematic pattern of relationship
      between the independent variables and the absolute value of the
      residuals. These results indicate that the regression model used
      does not experience heteroscedasticity problems.</p>
      <p><italic><bold>Autocorrelation Test</bold></italic></p>
    </disp-quote>
  </sec>
  <sec id="table-4.-autocorrelation-test">
    <title>Table 4. Autocorrelation Test</title>
    <table-wrap>
      <table>
        <colgroup>
          <col width="33%" />
          <col width="33%" />
          <col width="33%" />
        </colgroup>
        <thead>
          <tr>
            <th><bold>Durbin Watson</bold></th>
            <th><bold>test results</bold></th>
            <th><p specific-use="wrapper">
              <disp-quote>
                <p><bold>Description</bold></p>
              </disp-quote>
            </p></th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>1, 960</td>
            <td>0,8140 &lt; 1,581 &lt; 1,7501</td>
            <td></td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Source: Data processed</p>
      <p>Based on the output, a Durbin-Watson value of 1.960 is
      obtained. This value does not allow us to definitively conclude
      the existence of positive autocorrelation. Because the DW test
      results do not provide a firm decision, the runs test is then
      conducted as a further step to detect the presence of
      autocorrelation.</p>
    </disp-quote>
  </sec>
  <sec id="table-5.-runs-test-results">
    <title>Table 5. Runs Test Results</title>
    <table-wrap>
      <table>
        <colgroup>
          <col width="50%" />
          <col width="50%" />
        </colgroup>
        <thead>
          <tr>
            <th colspan="2"><bold>Runs Test</bold></th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td></td>
            <td>Unresd</td>
          </tr>
          <tr>
            <td>Sig Value</td>
            <td>0,603</td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Source: Data processed</p>
      <p>Referring to Table 4.4 regarding the test results, the Asymp.
      Sig. (2-tailed) value of 0,603. This value is used to evaluate
      whether the residuals in the regression model spread randomly or
      follow a certain pattern. In the run test, if the Asymp. Sig.
      (2-tailed) is greater than the significance threshold of 0.05,
      then the residuals are considered non-patterned or random. Since
      0,603 exceeds 0.05, it can be concluded that the residuals in this
      study are random. Therefore, the regression model used does not
      experience autocorrelation problems, so the assumption of residual
      independence can be fulfilled.</p>
      <p><italic><bold>Multiple Linear Regression
      Test</bold></italic></p>
    </disp-quote>
  </sec>
  <sec id="table-6.-multiple-linier-regression-test">
    <title>Table 6. Multiple Linier Regression Test</title>
    <table-wrap>
      <table>
        <colgroup>
          <col width="17%" />
          <col width="16%" />
          <col width="16%" />
          <col width="21%" />
          <col width="16%" />
          <col width="15%" />
        </colgroup>
        <thead>
          <tr>
            <th rowspan="2">Model</th>
            <th colspan="2"><p>Unstandardized</p>
            <p>Coefficients</p></th>
            <th><p specific-use="wrapper">
              <disp-quote>
                <p>Standardized</p>
                <p>Coefficients</p>
              </disp-quote>
            </p></th>
            <th rowspan="2"><p specific-use="wrapper">
              <disp-quote>
                <p>T</p>
              </disp-quote>
            </p></th>
            <th rowspan="2"><p specific-use="wrapper">
              <disp-quote>
                <p>Sig</p>
              </disp-quote>
            </p></th>
          </tr>
          <tr>
            <th>B</th>
            <th><p specific-use="wrapper">
              <disp-quote>
                <p>Std,Error</p>
              </disp-quote>
            </p></th>
            <th><p specific-use="wrapper">
              <disp-quote>
                <p>Beta</p>
              </disp-quote>
            </p></th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td>(Constant)</td>
            <td>16,040</td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>0,760</p>
              </disp-quote>
            </p></td>
            <td></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>21,009</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>.000</p>
              </disp-quote>
            </p></td>
          </tr>
          <tr>
            <td>X1</td>
            <td>-0,904</td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>0,259</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>-0,554</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>-3,488</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>.005</p>
              </disp-quote>
            </p></td>
          </tr>
          <tr>
            <td>X2</td>
            <td>-0,084</td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>0,101</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>-0,082</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>-0,839</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>.421</p>
              </disp-quote>
            </p></td>
          </tr>
          <tr>
            <td>X3</td>
            <td>-0,490</td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>0,173</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>-0,421</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>-2.830</p>
              </disp-quote>
            </p></td>
            <td><p specific-use="wrapper">
              <disp-quote>
                <p>.016</p>
              </disp-quote>
            </p></td>
          </tr>
        </tbody>
      </table>
    </table-wrap>
    <disp-quote>
      <p>Source: Data processed</p>
      <p>The results of the multiple linear regression equation are as
      follows:</p>
    </disp-quote>
  </sec>
  <sec id="y-16.040-0904x1-0084x2-0490x3-e">
    <title>Y= 16.040 <italic>–</italic> 0,904X1 <italic>–</italic>
    0,084X2 <italic>–</italic> 0,490X3 + e</title>
    <disp-quote>
      <p>From the multiple linear regression equation above, it can be
      explained with the following explanation:</p>
    </disp-quote>
    <list list-type="order">
      <list-item>
        <p specific-use="wrapper">
          <disp-quote>
            <p>βα = The constant value of 16,040 means that if all
            independent variables are</p>
          </disp-quote>
        </p>
      </list-item>
    </list>
    <disp-quote>
      <p>considered constant, the Poverty Level will be 16,040.</p>
    </disp-quote>
    <list list-type="order">
      <list-item>
        <label>2.</label>
        <p specific-use="wrapper">
          <disp-quote>
            <p>β₁ = -0.904 is the regression coefficient X1 means that
            the Population Growth Rate has a negative effect on the
            poverty rate. With an increase in the population growth rate
            by 1 percent, the poverty rate is expected to decrease by
            0.904 percent.</p>
          </disp-quote>
        </p>
      </list-item>
      <list-item>
        <label>3.</label>
        <p specific-use="wrapper">
          <disp-quote>
            <p>β₂ = -0.084 is the regression coefficient X2 means that
            the Population Growth Rate has a negative effect on the
            poverty rate. For every 1 percent increase in the Open
            Unemployment Rate, the poverty rate is expected to decrease
            by</p>
          </disp-quote>
        </p>
      </list-item>
    </list>
    <disp-quote>
      <p>0.084 percent.</p>
    </disp-quote>
    <list list-type="order">
      <list-item>
        <label>4.</label>
        <p specific-use="wrapper">
          <disp-quote>
            <p>β₃ = -0.490 is the regression coefficient X3 means that
            HDI has a negative effect on the poverty rate. If the HDI
            increases by 1 percent, the Poverty Rate will decrease by
            0.490 percent.</p>
          </disp-quote>
        </p>
      </list-item>
    </list>
  </sec>
</sec>










<sec>
  <title>DISCUSSION</title>
  <disp-quote>
    <p><italic><bold>The Effect of Total Population on
    Poverty</bold></italic></p>
    <p>This test found a t-value of 3.488 for population and a
    probability of 0.005,</p>
  </disp-quote>
  <p>lower than the significance level of α = 0.05 (since 0.005 &lt;
  0.05). hence, the</p>
  <disp-quote>
    <p>researcher rejects (H0) and accepts (Ha). This shows that
    population has a negative and substantial impact on the poverty rate
    in West Kalimantan. This means that an increase in population in the
    area tends to be followed by a decrease in the poverty rate, as a
    result the researcher found that the correlation between the two
    variables is negative and substantial in accordance with the
    research hypothesis. The researcher suspects that this effect occurs
    due to an increase in employment opportunities, the implementation
    of government social assistance programs, and improvements in
    infrastructure and public services that encourage community
    participation in productive economic activities.</p>
    <p>This is in accordance with the findings of Iilmiah et al. (2021)
    who revealed that the size of the population does not have a
    substantial effect on the number of poor people. The success of the
    family planning program, which began to show results through the
    2000 Population Census (SP2000), resulted in the composition of the
    population being dominated by the productive age group. This
    dominance of the productive age group does not directly increase the
    number of poor people, because productive age offers many employment
    opportunities that can be utilized to improve welfare.</p>
    <p><italic><bold>The Effect of The Open Unemployment Rate on
    Poverty</bold></italic></p>
    <p>The researcher determined that the t-value for the open
    unemployment rate variable is 0.836, while the t-value for the
    table. Furthermore, this study found that the probability of the
    open unemployment rate affecting the poverty rate is 0.421, which is
    higher than the significance level α = 0.05 (0.421 &gt; 0.05). As a
    result, we accept the null hypothesis (Ho) and reject the
    alternative hypothesis (Ha). As a result, we conclude that there is
    no substantial difference between the growth rate and consumption
    rate in West Kalimantan Province. This condition indicates that an
    increase in the open unemployment rate does not have a substantial
    impact on changes in the poverty rate, as a result the researcher
    found that the relationship between these variables is contrary to
    the hypothesis that has been proposed. The researcher suspects that
    this phenomenon may be due to the fact that most of the individuals
    who fall into the unemployment category are still at a
    non-productive age or are in the process of looking for work, as a
    result they have not had a direct effect on increasing the poverty
    rate. This opinion is not in line with the concept of classical
    theory introduced by Adam Smith and David Ricardo, which states that
    unemployment will not occur in the long run if the labor market
    operates freely.</p>
    <p>The results of this study are not in line with several theories
    adopted by scientists, but rather with previous studies conducted by
    Safuridar and Natasya Ika (2019) and Ristika, Primandana, W, and
    Wahed, M. (2021) which state that the level of turbulence (TPT) has
    no effect. The absence of a significant correlation between TPT and
    the poverty rate indicates that those who are unemployed are not
    necessarily among the high-income group.</p>
    <p><italic><bold>The Effect of Human Development Index on
    Poverty</bold></italic></p>
    <p>The researcher determined the value of t -2.830 for the HDI
    variable, t table 2.2009. Furthermore, this study found that the
    significance level of the human</p>
    <p>development index variable on the Poverty Level is 0.421, higher
    than α = 0.05. Therefore, the researcher rejected the null
    hypothesis (Ho) and accepted the alternative hypothesis (Ha). The
    researcher found a substantial effect of HDI on the Poverty rate in
    West Kalimantan Province. This finding indicates that an increase in
    HDI can reduce the poverty rate, which means that researchers found
    a substantial negative relationship between the two variables as
    previously hypothesized.</p>
    <p>This reduction in poverty occurs because a high HDI signifies
    improvements in the aspects of education, health services, and
    decent living standards. Improvements in these aspects play a role
    in increasing people's productivity and providing greater access to
    employment and income opportunities. The results of this review are
    consistent with the findings of Ardian, R., Yulmardi, Y., &amp;
    Bhakti, A. (2021), as well as Ashari, R. T., &amp; Athoillah,</p>
    <p>M. (2023), which show that an increase in HDI has a substantial
    impact in reducing the poverty rate in West Kalimantan Province.
    This can be explained because the HDI reflects key dimensions of
    development, such as better access to education, improved health
    services, and improved living standards.</p>
  </disp-quote>
</sec>









<sec>
  <title>CONCLUSIONS</title>
  <disp-quote>
    <p>Based on the results of data analysis that has been carried out
    through quantitative methods using secondary data from the period
    2009 to 2023 in West Kalimantan Province, as well as through testing
    the independent variables consisting of population, open
    unemployment rate (TPT), and human development index (HDI) on the
    dependent variable, namely the poverty rate, it can be concluded
    that:</p>
  </disp-quote>
  <list list-type="order">
    <list-item>
      <p specific-use="wrapper">
        <disp-quote>
          <p>Total population has a significant effect on the poverty
          rate in West Kalimantan Province with a negative relationship
          direction. This shows that the larger the population, the
          lower the poverty rate. This finding indicates that an
          increase in population can be a potential productive human
          resource if managed properly.</p>
        </disp-quote>
      </p>
    </list-item>
    <list-item>
      <p specific-use="wrapper">
        <disp-quote>
          <p>The open unemployment rate has no significant effect on the
          poverty rate. This means that fluctuations in the open
          unemployment rate do not have a strong direct impact on the
          poverty rate in the context of the region and period
          studied.</p>
        </disp-quote>
      </p>
    </list-item>
    <list-item>
      <p specific-use="wrapper">
        <disp-quote>
          <p>The Human Development Index (HDI) has a significant effect
          on the poverty rate with a negative relationship. The higher
          the HDI, which reflects improvements in the quality of
          education, health, and income, the lower the poverty rate
          tends to be.</p>
        </disp-quote>
      </p>
    </list-item>
  </list>
</sec>










<sec>
  <title>RECOMMENDATIONS</title>
  <disp-quote>
    <p>For the Regional Government of West Kalimantan Province, it is
    recommended to increase investment in the education and health
    sectors to encourage the improvement of the quality of human
    resources. In addition, it is necessary to strengthen job skills
    training that is relevant to market needs to reduce labor
    mismatches. Increasing HDI must be done evenly, not only
    concentrated in urban areas, in order to reduce the overall poverty
    rate.</p>
  </disp-quote>
</sec>









<sec>
  <title>ADVANCED RESEARCH</title>
  <disp-quote>
    <p>For future researchers, it is recommended to add other variables
    that could potentially affect the poverty rate, such as income
    distribution, inflation, or the level of urbanization. In addition,
    qualitative or mixed-methods can also be used to provide a deeper
    understanding of social and structural factors that are not covered
    by quantitative analysis alone.</p>
  </disp-quote>
</sec>












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