rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9409

# **Abstract**

Using World Bank PovcalNet data from 1974 –2018 for 135 countries, this paper approximates the identity that links growth in mean incomes and changes in the distribution of relative incomes to reductions in absolute poverty, and, in turn, examines the role of income inequality for poverty reduction. The analysis finds that the assumption that income is log-normally distributed allows one to approximate the identity well. Using this approximation, both the growth and inequality elasticities of poverty reduction are calculated. The inequality elasticity of poverty reduction is larger, on average, compared to the (absolute) growth elasticity of poverty reduction. Moreover, the (absolute) growth elasticity declines steeply with a country’s initial level of inequality. However, despite these results, most of the observed changes in poverty can be explained by changes in mean incomes. This is a consequence of changes in income inequality (as measured by percentage changes in the standard deviation of log-income) being an order of magnitude smaller than changes in mean incomes. Overall, the results highlight the important role income inequality can play in reducing poverty even if prior poverty changes have, in large part, been a consequence of economic growth.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at kbergstrom@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
eight WBL indicatorsj,t plus the aggregated WBL index in order to capture each specific aspect of gendered laws covered by Women, Business and the Law. Femalei,t ∗WBL indicatorsj,t is the interaction variable of interest, because it captures the likelihood of a woman being financially included depending on legal gender equality. Finally, Controlsi,j,t is a matrix of individual and country-specific control variables, details of which are discussed in the data section below. We control for potential omitted variable bias by including year fixed effects to capture time-specific shocks and country-level fixed effects to control for country characteristics. Standard errors are clustered at the country level, following Abadie et al. (2017). All reported estimations are weighted using individual weights that are provided in the Global Findex database to ensure national representativeness.

# **3.2. Individual-level variables**

Our individual-level variables are extracted from the World Bank’s Global Findex database.<sup>5</sup> The survey was conducted by Gallup, Inc., in conjunction with its annual Gallup World Poll Survey. Global Findex covers approximately 150,000 respondents belonging to 144 countries, thus representing more than 97% of the population of the world. Using random selection, roughly 1,000 people of age 15 and above in each economy have been questioned using over 140 languages. A stratified random sampling technique was used, and a stratum was developed based on population size, geography or both to ensure representativeness. The Global Findex is composed of four waves of data (2011, 2014, 2017 and 2021).

Using variables from the Global Findex database, we control for potential cofounders of financial inclusion at the individual level. First, _Age_ is the respondent’s age in years and is expected to be positively correlated with financial inclusion (Zins and Weill, 2016). Second, we introduce four dummies to capture if the respondent is in the first income quintile ( _incomeQ1_ ), second income quintile ( _incomeQ2_ ), third income quintile ( _incomeQ3_ ), or fourth income quintile ( _incomeQ4_ ), and consider the richest income quintile dummy as the omitted variable. Individuals with higher incomes are more likely to have access to the formal financial system (Aslan et al., 2017). Existing literature shows that individuals with higher levels of education are more likely to have better financial education and to be more financially included (Allen et al, 2016; De la Rica et al., 2008). We thus consider two dummies to control for educational attainment, one equal to one if the individual has completed elementary education

> 5 The database is freely available at the World Bank website: https://datatopics.worldbank.org/financialinclusion.

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# **Table B1 – Variables Means and Means Tests for Balanced and Unbalanced Panels**

|||**2**|**001**|||**2**|**002**|||**2**|**003**|||**2**|**004**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|**Unbal.**|**Bal.**|**_% Diff._**|**_t-test_**|**Unbal.**|**Bal.**|**_% Diff._**|**_t-test_**|**Unbal.**|**Bal.**|**_% Diff._**|**_t-test_**|**Unbal.**|**Bal.**|**_% Diff._**|**_t-test_**|
|y = Real cons./Pov. Line|1.12|1.12|_0.3_|0.12|1.38|1.40|_-1.8_|-0.62|1.65|1.56|_5.3_|1.83|1.64|1.58|_4.0_|1.40|
|b = Real Soc. Ben./Pov. line|0.01|0.01|_-13.5_|-0.52|0.03|0.04|_-18.0_|-1.12|0.04|0.04|_10.4_|0.61|0.03|0.05|_-22.7_|-1.98|
|poor (1=poor; 0=Non poor)|0.58|0.58|_-0.5_|-0.17|0.42|0.38|_9.2_|1.99|0.27|0.31|_-10.7_|-2.02|0.29|0.31|_-8.7_|-1.66|
|sbd (1= soc. ben. Receipient; 0=Others)|0.06|0.05|_7.8_|0.49|0.14|0.15|_-3.0_|-0.35|0.18|0.18|_-0.9_|-0.11|0.19|0.22|_-13.2_|-2.01|
|hfem = Head of household, Female|0.38|0.35|_9.4_|1.87|0.41|0.37|_10.8_|2.24|0.41|0.39|_5.3_|1.17|0.43|0.41|_2.9_|0.67|
|hage30_45 = Head of household, age 30-45|0.30|0.27|_8.2_|1.36|0.28|0.28|_1.7_|0.29|0.27|0.26|_5.8_|0.92|0.27|0.24|_9.6_|1.47|
|hage46_60 = Head of household, age 46-60|0.29|0.30|_-2.0_|-0.36|0.29|0.31|_-4.2_|-0.78|0.31|0.32|_-4.6_|-0.89|0.30|0.33|_-7.0_|-1.36|
|hage60plus = Head of household, more than 60|0.32|0.35|_-6.1_|-1.23|0.33|0.35|_-4.8_|-0.98|0.34|0.36|_-5.4_|-1.12|0.34|0.37|_-7.4_|-1.58|
|hprim = Head of household, primary education|0.12|0.11|_2.1_|0.20|0.10|0.12|_-11.9_|-1.25|0.10|0.13|_-18.9_|-2.14|0.11|0.13|_-10.6_|-1.16|
|hsec = Head of household, secondary education|0.72|0.74|_-2.6_|-1.19|0.73|0.74|_-1.7_|-0.79|0.72|0.72|_-0.4_|-0.17|0.73|0.74|_-2.0_|-0.90|
|htert = Head of household, tertiary education|0.11|0.09|_33.3_|2.50|0.12|0.09|_39.3_|2.90|0.13|0.10|_27.4_|2.27|0.11|0.08|_34.7_|2.58|
|nchild = Number of children in the household|0.60|0.61|_-0.6_|-0.11|0.58|0.59|_-1.0_|-0.19|0.55|0.56|_-1.4_|-0.25|0.53|0.52|_2.9_|0.49|
|dep3_4 = 1 if Household size/No. of Earners = Between 3 and 4|0.15|0.11|_38.2_|3.23|0.14|0.12|_12.1_|1.18|0.14|0.12|_14.3_|1.38|0.14|0.13|_6.1_|0.65|
|dep5plus = 1 if Household size/No. of Earners = Five or more|0.35|0.35|_-0.6_|-0.12|0.34|0.35|_-1.4_|-0.28|0.33|0.35|_-5.8_|-1.18|0.33|0.35|_-4.6_|-0.94|
|rural = Rural areas|0.60|0.69|_-13.3_|-5.17|0.60|0.69|_-12.7_|-4.96|0.60|0.69|_-12.4_|-4.83|0.60|0.69|_-12.5_|-4.86|
|summer = Household interviewed in quarter 2 and 3|0.50|0.65|_-23.7_|-8.60|0.50|0.65|_-23.8_|-8.64|0.50|0.65|_-23.9_|-8.68|0.50|0.65|_-23.5_|-8.53|
|terremprate = District Average No. Of earners/No. Of adults|0.58|0.59|_-2.5_|-0.90|0.58|0.59|_-1.7_|-0.62|0.56|0.55|_1.2_|0.44|0.55|0.54|_2.4_|0.84|
|growth = GDP annual growth rate|1.00|1.00|_0.0_|na|1.08|1.08|_0.0_|_na_|1.07|1.07|_0.0_|_na_|1.07|1.07|_0.0_|_na_|
|**Sample**|**6217**|**866**|||**6159**|**866**|||**6125**|**866**|||**6121**|**866**|||

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- 15 -

# **<u>Table 4 Ordinary Least Squares Estimation</u>**

||<br>(1)||<br>(2)||<br>(3)||(4)|(5)||(6)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_RAI_|0.023<br>(0.114)|<br>||||||||||
|ln_RDDEN_|||–0.183<br>(0.088)|**||||||||
|ln_GDRD_|||||0.022<br>(0.130)|<br>||||||
|ln_TCMK_|||||||–0.341<br>(0.097)|***||||
|ln_TCPT_||||||||–0.544<br>(0.651)|<br>|<br>||
|ln_MAI_||||||||||0.643<br>(0.140)|***<br>|
|ln_N15_|0.818|***|0.870|***|0.820|***|0.751|***<br>0.804||0.726|***|
||(0.073)||(0.076)||(0.074)||(0.074)|(0.072)||(0.074)||
|_POPDEN_|0.2490|*|0.2680|*|0.2490|*|0.3280|***<br>0.2740|*|0.2800|***|
||(0.1370)|<br>|<sup>(0.1520)</sup>||<sup>(0.1380)</sup>|<br>|<sup>(0.1130)</sup>|<sup>(0.1530)</sup>||<sup>(0.0929)</sup>||
|_INFO_|–0.006||–0.010||–0.006||0.003|0.000||0.006||
||(0.027)||(0.027)||(0.027)||(0.026)|(0.027)||(0.026)||
|_UNEMP_|–0.004||–0.005||–0.004||–0.009|–0.001||–0.005||
||(0.015)||(0.014)||(0.015)||(0.014)|(0.015)||(0.014)||
|_EDU_|0.020||0.010||0.019||0.015|0.025||0.012||
||(0.098)||(0.098)||(0.098)||(0.095)|(0.099)||(0.095)||
|Constant|–13.370|**|–12.280|**|–13.340|**|–13.090|***<br>–13.440|**|–12.010|**|
||(5.214)||(5.132)||(5.280)||(4.983)|<br>(5.198)||(5.032)||
|Observations|136||136||136||134|<br>134||134||
|R-squared|0.64||0.643||0.64||0.654|0.643||0.66||
|<br>F-statistic|103.5||110||103.9||157.04|102.5||186.86||
|Number of<br>region||||||||||||
|dummy||||||||||||
|<br>variables|5||5||5||5|<br>5||5||

Instrumental variable regression is performed to address the endogeneity related to transport infrastructure investment (table 5). The results are broadly similar; the data exhibit significant economies of agglomeration and show that market access is important to attracting firms. More firms are located where transport costs to markets are lower and the market access index is higher. The results indicate that transport connectivity seems largely exogenous, although in the model in which _MAI_ is regressed, the exogeneity test can be rejected at the 10 percent significance level. In this case, our instruments are found to be
# **Table 7.  Survey Methods’ Differential Effects on Employment and Work Status by Exposure to Social Norms Related to Housework**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Wo|men|Me|n|
||Employed|Working|Employed|Working|
|LOA|0.035|-0.019|-0.033|0.004|
||(0.084)|(0.083)|(0.087)|(0.076)|
||[0.691]|[0.839]|[0.712]|[0.957]|
|ESR|-0.040|-0.052|-0.042|-0.003|
||(0.085)|(0.086)|(0.090)|(0.066)|
||[0.638]|[0.546]|[0.612]|[0.974]|
|LOA x Average time spent on housework|0.025|0.084|0.038|0.001|
|(women)|(0.067)|(0.065)|(0.070)|(0.060)|
||[0.757]|[0.225]|[0.591]|[0.995]|
|ESR x Average time spent on housework|0.048|0.045|0.012|-0.020|
|(women)|(0.065)|(0.067)|(0.076)|(0.052)|
||[0.480]|[0.510]|[0.864]|[0.724]|
|LOA + LOA x Average time spent on housework|<br>0.059*|0.065*|0.004|0.005|
|(women)|(0.035)|(0.035)|(0.033)|(0.029)|
||[0.084]|[0.048]|[0.904]|[0.869]|
|ESR + ESR x Average time spent on housework|0.008|-0.007|-0.030|-0.023|
|(women)|(0.037)|(0.036)|(0.034)|(0.029)|
||[0.803]|[0.849]|[0.358]|[0.419]|
|Observations|1,361|1,361|1,119|1,119|
|Outcome(Control Group)Mean|0.436|0.537|0.735|0.832|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing selfreporting (ESR) by exposure to social norms related to housework. This exposure measure is estimated as the average time that other women in the EA (excluding the respondent) devoted to housework the previous day (Columns [1] and [2]). This variable is estimated using the Time Use module. We present ITT estimates from Equation (2), where is the exposure measure in hours, and the estimations of the total effect of each survey method (Table A4 contains the descriptive statistics and definition of this measure). The vector includes the iihss variable and the list of control variables selected using a double-LASSO procedure for each outcome (Table DD iihss XX A3 in the Appendix shows the variables selected). All estimations include EA (stratification variable) fixed effects. iihss DD Each column is a separate dependent variable as defined in Section 3. The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses, and the randomization inference _p_ -values are shown in brackets. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 34
where _dij_ is the Euclidean distance between the geographic centers of prefectures _i_ and _j_ , _diport_ is the Euclidean distance between the center of region _i_ and that of the nearest prefecture which has a major international port, and _area_ is a prefecture's land area (in km<sup>2</sup> ). The data on land areas is taken from _Regional Economic Statistical Yearbook 2008_ . Eight major international ports are considered: Shanghai, Ningbo, Tianjin, Guangzhou, Qingdao City, Dalian City, Qinhuangdao City and Shenzhen City. These correspond to the eight Chinese ports which had the largest throughput (measured in million tonnes of traffic) in ISL (2006). Pre-multiplication of a variable by **W** * **W** represents the second-order spatial lag of that variable.

# _Additional parameters used in the numerical solution of the theoretical model:_

As outlined in section 3.2, to obtain a numerical solution for the system of equations [3] – [7], which describe the short-run equilibrium of our theoretical model, we also require measures for the parameters  ,  ,  . For 2007, the measures for  and  , which correspond to the regional shares of the national urban and rural workforces respectively, are constructed using data on "urban employed persons" and "total employed persons" from the _Regional Economic Statistical Yearbook 2008_ . For  , rural employment is calculated as total employed persons minus urban employed persons<sup>35</sup> According to the _China Statistical Yearbook 2008_ , employed persons refer to " _people aged 16 and over who are engaged in gainful employment and thus receive remuneration payment or earn business income_ ".  , the national share of urban workers in total employment, is measured using the same data.

The data on "total employed persons" is also used in the calculation of GDP per worker for 2007, which is used to help assess the fit of the "after" equilibrium solution. GDP per capita for 2007, which is also used in assessing the fit, is measured using population data from _Regional Economic Statistical Yearbook 2008_ .

Table A1 provides summary statistics for both the variables used in the estimation of the wage equations and the additional parameters used in obtaining the "before" and "after" numerical solutions to our model.

> 35 In the various provincial statistical yearbooks, the numbers of urban and rural employed persons exactly sum to the number of total employed persons at the provincial level.

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_JULY 2018_ _6_ standard probit model to estimate the propensity score for each observation in the loan (treated) and the no loan (control) group samples. The conditional independence assumption that underlies the validity of the PSM methodology requires that conditional on observable characteristics, receiving a WEDP loan is independent of potential outcomes and unobservable heterogeneity is assumed to play no role in participation (Dehejia and Sadek, 2002). The conditions for our study are relatively promising for propensity score matching to be reliable, since both the loan recipients and the control group were all registered with the WEDP program and are from the same localities so are likely to have similar observable backgrounds. It is possible that the MFI lenders may have access to more information about the borrower than what is included in our survey instrument and unobservable lender information may still influence the estimation results. However, we will use a well-specified probit regression to estimate the probability of receiving a loan, grounded on empirical evidence in the financial and entrepreneurship space.

The PSM method will be used in the DID model such that the control group outcome is matched to a treated observation using characteristics of the entrepreneur (PSM Min) and entrepreneur characteristics plus a measure of firm size (PSM Max). Specifically, the propensity score specification matches on age of business owner, age squared, higher than secondary education, household size, a measure of cognition (digitspan memory recall), an enterpreneurial identity index as a proxy for motivation and passion and number of employees as a proxy for firm size at baseline (variables for gender and marital status are excluded since the sample is all female and the majority are married). The chosen variables were collected in the same baseline survey and are theorized to be important covariates of receiving a WEDP loan with measures of firm size, demographic information and entrepreneurial skill all included. Using the estimated propensity scores, matched-pairs are constructed on the basis of how close the scores are across the two samples where the matching estimator is based on a kernel-weighted average of control outcomes. Prior to matching, the estimated propensity scores for those with and without WEDP loans were respectively 0.3526 (standard error of 0.079) and 0.324 (0.078) and the region of common support was [0.144, 0.606]. Figure 3 in the Appendix displays the distribution of estimated propensity scores for the two groups and the region of common support. In addition, Figure 4 in the Appendix gives details of the probit regression to establish the propensity score and presents the balance test to verify the second identifying assumption for PSM of the presence of a common support.

# 2. Level differences

An OLS regression specification will be used to compare the average differences of those firms that received a WEDP loan to those that did not across a larger variety of outcome measures that were collected in the 2016 follow-up survey round only.
decline in labor earnings. The decline in employment is captured by the microsimulation model. However, the declines in labor earnings are larger than the loss predicted by the private consumption component of National Accounts.<sup>18</sup> In fact, in 2020 Brazil implemented the Emergency Employment and Income Preservation Program <mark>consisting in a reduction of the working day (by 70, 50, or 25 percent) with a proportional reduction in earnings in exchange for employment stability. The evidence is also in line with previous findings that show earnings reductions are an important margin of adjustment strategy during shocks in comparison to employment reductions (Gaurav et al., 2020).</mark>

**Figure 11. Observed changes in employment and labor earnings between 2019 and 2020**

Employment declines (%) Labor earnings declines (%) <!-- Start of picture text --> Q1 -17.1 Q1 -7.9<br>Q2 -10.3 Q2 -10.1<br>Q3 -10.1 Q3 -9.5<br>Q4 -4.5 Q4 -12.5<br>Q5 -6.5 Q5 -14.8<br><!-- End of picture text --> Notes: Estimations obtained using the PNAD-Continua 2019 and 2020.

These results suggest that the macro-micro simulation model requires additional fine-tuning of the assumptions to better take into account labor income losses. For example, protecting employment positions can go hand in hand with a reduction in working hours and a corresponding reduction in labor income that needs to be taken into account. As explained in Section 3.1, workers in some sectors (public administration, utilities, health, and extraterritorial agencies) were protected from any shock in the model. However, the actual data shows that although these jobs were protected, labor incomes did decline in the utilities and health sectors. Similarly, although public sector nominal wages cannot decline, their purchasing power in real terms can. Another assumption that would need to be fine-tuned is the overall private consumption shock applied in the model. When the simulation does not consider the emergency public transfers that governments distributed to households, then the private consumption shock applied in the model needs to be larger. A similar

> 18 While the simulation assigns a value of labor earnings equal to zero for workers losing a job, predicts a value of labor earnings for those gaining a job, and adjusts the value for labor incomes for workers who remained in their jobs by the overall change in private consumption per capita, the change in hours of work is not modeled.

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here for comparison with more objective measures defined above. About 28% of our sample claim access to finance to be a major or severe obstacle to the operation of their business. It is also more likely to be claimed by small firms than large (30% vs. 24% for large). The access obstacle is slightly higher in 2007 9at 31%) than it was in the previous years (27% in 2003 and 25% in 2005).

Finally, as savings and investment are likely to be sensitive to firm perception of uncertainty, we consider two available subjective perceptions of uncertainty – **macroeconomic uncertainty** (such as inflation, exchange rate and others) and **regulatory policy uncertainty** . We define two indicator variables for firms that claim either of the two obstacles as a major or severe obstacle to their business. They are referred as Macro obstacle and Policy obstacle.

# **4.2 Construction of Variables for the Listed Firms Dataset**

The first key variable of interest is financial savings, which is defined as a year to year change in the stock of cash. This definition follows Riddick and Whited (2009). For example, if the cash stock of a company has increased by 1 million relative to the previous year, we consider this increase as a new financial savings. To compare financial savings across companies of different size we scale financial savings by total assets of the previous year.<sup>9</sup> The second key variable of interest is physical savings, i.e. investment in property, plant and equipment (PPE). Unfortunately we do not have a direct measure of investment in our balance sheet or income statements data because this is a supplemental data item often not required for companies to file with the exchange. To construct a proxy for investment we measured a change in gross value of PPE from year to year, scaled by the previous year value of net PPE.<sup>10</sup> We obtain IK (investment to capital ratio), which shows the change in gross value of PPE. This value should be highly correlated with new investment (or disinvestment if the value goes down).

In addition we calculate the ratio of stock of liquid assets (i.e. cash plus marketable securities) over total assets. This is a stock measure representing total cash (relative to assets)

> 9 In other words, Financial Savings to Assets (t) = ( Cash(t) – Cash(t-1) )/Total assets (t-1).

> 10 Physical Savings, or Investment is defined as IK (t) = ( PPE(t) – PPE(t-1) )/PPE(t-1).

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