rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 5: Welfare impacts of economic contraction FY22-FY23**

<!-- Start of picture text --> 0.0%<br>-1.0% -0.2%<br>-2.0%<br>-3.0%<br>-4.0%<br>-5.0%<br>-6.0% -5.1%<br>-7.0%<br>-6.5%<br>-8.0%<br>-7.5%<br>-9.0% -8.0%<br>Bottom 20% 2 3 4 Top 20%<br>(PAE)<br>FY23-FY22 Change in Real Household Consumption<br><!-- End of picture text --> Source: Authors’ calculation from HIES 2018/19 data.

Note: Projections use preferred specification following equation (1), including information from nominal growth from eleven economic sectors to model labor income and headline inflation.

# **4. ROBUSTNESS CHECKS**

Our modeling assumptions are generally aligned with the overall forecasting/nowcasting literature. Alternative methods are broadly divided into distributionally neutral (poverty growth elasticity, neutral distribution growth) and non-distributionally neutral, where heterogenous growth rates across households introduce changes in the welfare distribution (quintile growth contribution, sectoral growth models). A detailed description of each method is provided in Appendix A. Poverty-growth elasticity (PGE) uses the ratio between the percent change in the poverty headcount and the percent change in real per capita GDP to project poverty. Evidence shows that the value of the PGE is dependent on the period used. Meanwhile, the neutral distribution method (NDG) uses the growth in real per capita consumption (with a pass-through) and applies it to every household. These methods keep inequality constant as they only shift the welfare distribution to the right (left) as positive (negative) growth occurs.

Non-neutral distribution methods use additional information to model changes in the welfare distribution. Quintile growth contribution (QGC) models build on the NDG approach but allow different income distribution quintiles to grow at different rates. For this, total growth equals the real GDP per capita, but it is distributed across the initial welfare quintiles, weighting it by their past contribution to growth, as informed by the previous Growth Incidence Curve (GIC). Finally, sectoral models recognize that GDP per capita growth differs across sectors of the economy and, therefore, does not translate into similar income growth rates across the income distribution. For instance, GDP growth led by labor-intensive sectors will have a more considerable impact on household welfare than economic growth prompted by sectors intensive on capital. Usual modeling approaches use the employment sector of the household member with the highest income and apply the growth rate of the three main economic sectors (agriculture, manufacturing, services) or more disaggregated data, such as eleven-sector models.

14
<!-- Start of picture text --> 0.30<br>0.25 +<br>0.20<br>rra3<br>8==8 0.15— A$—S_<br>A<br>ge5<br>$0.10<br>0.05 +<br>0.00<br>2001 2002 += 2003S 2004 «= 2005S 2006 «= 2007 «Ss 2008 «= 2009-2010<br><!-- End of picture text --> <!-- Start of picture text --> 04s —nm<br>44<br>0.40 iit<br>se&5z035030 oamtmtn<br>& '<br>2©g 025 wtitmt<br>88s3 oss020 “4nt44ve<br>%, i<br>58 010 4it<br>3 "mt<br>0.05 +ait<br>a<br>0.00 e&§ +—; 22s£255 a655 FsEFS5 3S$ 5G SSPRPSESERERF ——FBR HOSat ESE2S——6SEB<br>BU Re a F< ew eT RTE sk eR Se ETS B<br>“" 2¢ ®§ 42= 6 2we 2Le =ese2 Ft3 BH zFg 32 sa 2em se<br>5 - a“ 2a Fe8 8 Fa8 Rg 38 =5<br><!-- End of picture text -->
# **I. Introduction**

The literature on the effect of migration on source countries has typically focused on the direct effects of remittances on household consumption and investment. However, the emigration of labor and the growing volume of remittances open up many indirect general equilibrium issues for further research, many of which have not been fully explored. These include the labor market shock from reduced labor supply, which has considerable policy importance for developing countries.

To the best of our knowledge, Mishra (2007) is the first econometric study to model the impact of _emigration_ (i.e., a negative labor supply shock) on individual wages in a source country, building on an approach introduced by Borjas (2003) using the supply shifts in education-experience groups to assess the labor market impact of _immigration_ . Using U.S. census data to track the volume of Mexican emigration to the United States combined with Mexican census data on individuals in the Mexican labor market, this study finds that a 10 percent increase in emigration, on average, increases wages in Mexico by almost 4 percent.

Some papers predate Mishra (2007) but they focus on geographic averages (or sector averages), rather than individuals. Lucas (1987), for example, uses annual time series data from 1946 to 1978 on agricultural wage and employment and finds that mine worker emigration to South Africa has raised wages in Malawi and Mozambique.<sup>3</sup> Hanson et al (2002) finds a marginal negative impact of border enforcement on wages in cities along the U.S.-Mexican border. Robertson (2000), Chiquiar (2004), and Hanson (2004) provide evidence that those Mexican states that have greater international trade and migration links have enjoyed faster growth in average income and labor earnings. In addition, the impact of emigration on wages in Mexico has been largest in states with well-developed U.S. emigrant networks (Munshi, 2003). In yet another study, Hanson (2006) suggests that average hourly earnings in states with high emigration rates increased by 6 to 9 percent, compared to states with low emigration rates.

> 3 See also Lucas (2005, pp. 99-100).

2
The URA was clearly not able to validate its taxpayer registry. Nor was it able to strengthen its TIN system by purging it of inactive entries. Though not all of the blame should be attributed to the URA (problems, including alleged corruption, in the Registry of Companies have undermined URA’s taxpayer registry), the failure to expand the tax base (many small firms and informal traders have escaped the presumptive tax, which has proved costly to administer) and document taxpayer registration were two of the principal failures of the URA reform.

In terms of compliance management SUNAT, the KRA, and SARS made significant progress, while other ARAs have made only minor headway at best. It should be noted, however, that of all the performance indicators evaluated, the number and quality of audits seem to have fared the worst across the board.

Peru’s audit presence for its largest taxpayers was estimated at nearly 11% in 1999. SUNAT’s audit presence at the national level, however, was only 1.45%, yet in comparative terms this was an impressive rate (given that this figure includes only actual field audits and not other kinds of minor audit acts). These data are even more impressive when compared with the pre-reform audit presence. The audit presence in 1991 was 0.17%, indicating an increase of more than 8,000%.

An analysis of SUNAT’s audits and audit findings in February 1999 revealed that some form of non-compliance (defined as non-compliance with any formal requirement) was quite common (the overall non-compliance rate was 75.4%). The non-compliance rate was even higher for the largest taxpayers: 90%. While SUNAT’s audits were able to uncover non-compliance they were clearly not able to thwart it.

Mexico, with over six million taxpayers, relied more on mass operations and certified public accountant (CPA) audits than it the traditional tax agency audit. On average (1996-1998) less than 10% of all the SAT’s audit acts were actual comprehensive or specific item field audits. Nearly 61% of the SAT’s audit acts were either reviews of CPA reports (including cross-checking of information) or letters to taxpayers requesting more information, and over 30% were perfunctory visits to taxpayers.

The SAT’s audit presence, based on office revisions as well as visits, averaged 2.84% over the period 1996-1998, which was quite high in comparative terms for the 34
22

# **_Preliminary data analysis: the ‘parallel trends’ assumption_**

For all the generalizations of the DD approach discussed in the methods section, we will obtain unbiased estimates of SHI impacts only insofar the crucial “parallel trends” identifying assumption holds in our data, that is, only if the change in outcomes in non-SHI “control” countries constitutes an adequate representation of what the change in outcomes in SHI “treatment” countries would have been in the absence of SHI adoption. We obtain evidence that the (directly untestable) “parallel trends” assumption cannot be discarded to hold in our context through two alternative procedures. Firstly, we performed _t_ -tests for both (i) baseline differences in average outcomes—year 1990—between eventual SHI countries and non-adopters (excluding from the sample countries which adopted SHI in 1990 or before) and (ii) baseline differences in average _changes_ in outcomes (1990-1991) between the same groups of countries (excluding countries which adopted SHI in 1991 or before). The first test shows statistically significant differences between baseline outcomes only in a few cases, and trend differences (1990-91) occurred in even fewer cases—only four instances, namely for overall and male life expectancy, and death rates by all causes and female breast cancer.

Secondly, we ran “placebo” DD as a checking strategy: we created a false treatment dummy variable which took on the value of one for eventual SHI adopters in the sample and zero for countries that never adopted SHI, and used only 1990-1991 data (excluding countries that adopted SHI in 1991 or before) for performing a DD regression comparing years 1991 and 1990. Since there was in fact no “treatment” (SHI adoption) in either year, rejecting the null hypothesis of individual insignificance of the false treatment variable for changes in a given outcome— conditional on covariates—casts doubt on the unbiasedness of estimates obtained by DD methods for that variable. These “placebo” regressions showed significant effects for only 9
- 18 -

Under plausible assumptions about how the Lorenz curves have shifted over time, it can also be shown that, for these data, the (absolute) elasticity of the FGT class of poverty measures with respect to the mean (again holding the Lorenz curve constant) is a monotonic decreasing function of the initial Gini measure of inequality;18 differentiating (3) with respect to the Gini coefficient G oae finds that (analogously to (4)): where Ea denotes the elasticity of the Pa poverty measure to Lorenz dominating changes in the Gini coefficient.

It can thus be argued that a past history of fairly equitable growth (resulting in a relatively "high" mean and "low" inequality) resulted in a 'high' elasticity of poverty to future growth in Indonesia. The growth experience over the 1970s and early 1980s would appear to have created a sound foundation for maintaining poverty alleviation from the more modest growth of the mid-1980s. This finding also has implications for Indonesia's poverty alleviation prospects after 1987, as will be considered further in Section 6.

# 5. An Alternative Poverty Assessment.

The methods we have used so far will have overestimated poverty alleviation in Indonesia if growth rates ir. mean real consumption have been underestimated. The SUSENAS does imply a considerably higher growth rate of real consumption per capita than do the national accounts; 15 percent over
(higher levels are associated with lower health safety). To quantify the later, we use the index based on O*NET surveys task descriptions created by Avdiu and Nayyar (2020).

The relationships between these four measures and the share of workers in the labor force who fall into each category are shown in Figure 1 below. For example, 38% of the workers in our sample are considered essential (Measure #1 – blue circle). Similarly, 38% of the workers are able to work from home (Measure #2 – red circle). Some 12 % of these teleworkable and essential jobs overlap, which means 64% of the workers in our sample have “income-safe” jobs (Measure #3 – combined area of blue and red circles). We find that 48% of the jobs do not require face-to-face interaction (black circle). An estimated 15% of these jobs are also essential, and 20% are telework jobs. In total, 17% of all jobs are exposed to health risks (Measure #4 – area of the blue circle that does not overlap with either of the red of black circles). These are the essential jobs that require face-toface interaction but cannot be performed from home via telework. In our sample, 6% of jobs can be considered the “best” jobs with the minimal economic and health risk exposure to COVID-19. That is, they are essential, can be done from home, and do not require face to face interaction. These jobs fall into the overlap of the three circles. By contrast, 17% of the jobs (those outside the three circles) can be considered to have the highest risk level. These jobs are non-essential, cannot be done from home, and require extensive face-to-face interactions.

The measures of income safety by occupation are strongly correlated with the relative task content of those occupations, as shown by the literature (Autor, Levy and Murnane, 2003; Autor and Handel, 2013). To calculate the task content of a given occupation, we use the US Department of Labor O*NET abilities survey, which provides information on various characteristics of occupations. We follow the same “basic” definitions of manual and communication skills used in the literature. We thus define manual tasks as those that require “movement and strength,” and we define communication tasks as those that require oral and written expression and comprehension. We then determine the extent to which various jobs entail such tasks by merging these measures with the EU-LFS data and constructing concordances between SOC and ISCO categories of occupations.

12
households. Visitors to the household were dropped from the sample as were women married more than once, and women with inconsistent information on age of marriage or missing information on education. Women interviewed in NFHS-3 who were never married or where gauna had not been performed were also dropped. Women who had at least one multiple birth, reported having a birth before age 12, had a birth before marriage or a duration between births less than nine months were dropped. There are a number of women for whom the space between marriage and first birth was less than nine months. These women are in the sample unless they are dropped for any of the other reasons. Finally, only Hindu women are used here. About 80 percent of India’s population are Hindu and focusing on this group ensures fewer issues with estimating the hazards functions. Specifically, it avoids imposing the assumption that the baseline hazard is the same for Hindu women and other women, such as Sikhs, which would be problematic if use of sex selection varies across groups.<sup>17</sup>

# **6.1 Recall Error**

A data issue that has not received the attention it deserves is the reliability of the birth histories. Of primary concern is the potential for systematic recall error, i.e. children who are missed nonrandomly, for example, because of those children’s early death. Interviewers therefore probe for any missed births if there are four or more years between two births reported as consecutive. Although probing catches many missed births, recall error is still likely to be a substantial problem in India. First, there is a significantly higher mortality risk for girls than for boys. Secondly, the preference for boys also leads to more boys than girls being remembered among those children who died. Finally, the probing works of long intervals between observed births, but given short durations between births, especially after the birth of a girl, it is unlikely to pick up all children.

Figure 2 shows that differential recall of boys and girls is, indeed, a problem. The solid line is the ratio of boys to children reported as first born by the number of years between the survey 17 Furthermore, the sample sizes for other groups, such as Sikhs, are small relative to Hindu, potentially introducing excessive noise in the results.

16
Note: Figures in parenthesis are the percentage of the total sample of male workers in each category.

_Source: Mazumdar (1994), Table 1, p. 4._ Of the five countries considered, Zimbabwe seems to stand apart on its own. A larger percentage of the workers are to be found in very large firms, and earnings levels seem to increase through successive levels of size groups to the largest group employing 1500 or more workers. All the other countries attain the highest level of wages in much lower size groups. Zambia, Cameroon and Ghana have the highest wage level in the 200+ size group, while Kenya has it in the 400+ group. The definitions of the micro, small and medium sized enterprises differ in detail from country to country, but generally they are all within a generally accepted range.

Earnings fimctions were estimated for each country, using the size groups defined in Table 4.2. We performed the exercise by using the size group dummies which were identified in the first step for each country as explanatory variables, together with the control variables defined above: Experience; (experience)<sup>2</sup> ; education dummies; industry dummies; and dummies defining if the worker had an apprenticeship or not, and if the worker was permanent or not. The values of the enterprise size coefficients from the estimated equations are given in Table 4.2.

**Table 4.2: Coefficients of Enterprise Size Dummies**

_(with human capital control variables in the regression)_

|**Category |**|<br>**Kenya**<br>|**|**<br>**Zambia**|**| Zimbabwe**|**Cameroon**|**Ghana**|
|---|---|---|---|---|---|
|Small|0.21|0.15|0.45|0.3|0.31|
|Medium|0.34|0.25|0.56|0.35|0.43|
|Large 1|0.64||0.61|0.43|0.53|
|Large 2|0.53|0.63|1.08|0.64|0.65|
|Very Large|0.6||1.32|||
|Adjusted R2|0.313|_0.455_|0.362|0.492|0.298|

_Source: Mazumdar (1994), p._<sup>_6_</sup> _._ Except for Kenya, the values of the coefficients increase successively with the size groups. Our visual procedure of distinguishing the size categories have been largely successful.

The base used for the size group dummies is the "micro" category for all countries. The values of the coefficients in Table 4.2 thus show the relative increase in earnings with respect to the micro enterprises, and the incremental values of the 22
# **Per Capita Income and Tax Revenue Linkage**

Figure 1 indicates that in 1994, 1998, and 2003, higher income countries collected higher tax revenues and the pattern holds for all the three groups of countries of different levels of income per capita. In the high-income countries, the tax-GDP ratio increased dramatically from 21 percent to approximately 30 percent over the decade. The levels of collection efforts, however, did not significantly change for the groups of low- and middle-income countries. Low-income countries’ collections stayed relatively flat, at around 14 percent, while middleincome countries experienced a modest increase from 20 percent to 21 percent. The observed trend in tax collection implies that low-income countries are being trapped in a structural dilemma: they typically have low taxable capacity – coupled with inefficient collection structure overwhelmed by trade and consumption taxes – while having enormous needs for resources to finance development needs.

A general established trend is that, in aggregate, tax intake increases over time: the average of tax-GDP in all groups of countries rose from approximately 19 percent in 1994 to 22 percent in 2003. A number of demand and supply factors account for the tax-income relationship. For example, the demand for public goods and services may rise faster in higher income countries where urbanization tends to rise at a faster speed compared to the one in lower income countries; and it is usually easier to collect taxes in urban areas with higher concentration of the formal sector (Bird 2007). In addition, there are other issues related to good governance and application of voluntary tax compliance philosophy on the basis of improved taxpayer service and enforcement in advanced tax administrations that help enhance revenue collections.

**Figure 1: Central Tax Revenue as a Percentage of GDP by Income Group (1994-2003)**

<!-- Start of picture text --> 30 %GDP 28.3 29.6<br>25<br>21.0 21.1<br>20.1<br>20 18.7<br>15 14.4 13.9 14.5<br>10<br>5<br>0<br>1994 1998 2003<br>Low income Middle income High income<br><!-- End of picture text --> <!-- Start of picture text --> 1998<br><!-- End of picture text --> Source: IMF GFS; WDI.

- 3 -
Policy ReseaRch WoRking PaPeR 4346

# **Abstract**

The causal role that income plays in determining child health has been discussed and contested in much previous work. The authors revisit this question with an investigation of short-term fluctuations in aggregate income and infant mortality using an unusually large dataset of 59 developing countries, covering over 1.7 million births. The authors show that there is a large, negative association between per capita GDP and infant mortality—on average, a one-unit decrease in log GDP is associated with an increase in mortality of between 18 and 44 infants per 1,000 children born. Given our use of unit data we are able to control for changes in the characteristics of women giving birth, and for various possible confounding factors, including weather shocks, conflict, female education, and the quality of institutions. None of these factors have an appreciable effect on our results. Further, female infant mortality is more sensitive than male infant mortality to economic fluctuations, especially during negative shocks to GDP, suggesting that policies that protect the health status of female infants may be especially important during economic downturns in much of the developing world.

This paper—a product of the Research Division, Development Economics Department—is part of a larger effort in the department to determine the influence of economic policy on health outcomes. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jfriedman@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