rafmacalaba/datause-encoder-data huggingface dataset:
||capita figure is the weighted average over the<br>calendar years covered (lagged by 2 years),<br>where the weights used are the proportion of<br>firms covered in the calendar year (lagged by<br>two years).<br>Source: World Development Indicators,<br>World Bank.|
|---|---|
|Age of Firm (logs)|Log of age of the firm. The age of the firm is<br>defined as the current year minus the year the<br>firm first started operations.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Manager Experience (logs)|Log of the number of years of experience the<br>top manager of the firm has working in the<br>industry.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Exports (proportion of sales)|The proportion of the firms’ annual sales made<br>abroad in the last fiscal year. Only direct<br>exports are included.<br>Source: Enterprise Surveys.<br>|
||www.enterprisesurveys.org|
|Female Top Manger Y:1 N:0|Dummy variable equal to 1 if the top manager<br>of the firm is a female and 0 otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Multi-establishment firm Y:1 N:0|Dummy variable equal to 1 if the firm is part<br>of a larger parent firm and 0 otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Crime Losses Y:1 N:0|Dummy variable equal to 1 if the firm suffered<br>losses due to crime (as reported by the firm)<br>during the last fiscal year and 0 otherwise.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Courts Obstacle Y:1 N:0|Dummy variable equal to 1 if the firm reports<br>functioning of courts as a moderate, major or<br>very severe obstacle to its current operations<br>and 0 if it reports no obstacle or minor<br>obstacle.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Power Outages (hours)|Total hours of power outages experienced by<br>the firm in a typical month over the last fiscal<br>year.|

38
# **Table A13. Survey Methods’ Impacts on the Measurement of Labor Market Outcomes, Addressing the Substitution Protocol’s Effects**

||(1)<br>Employed|(2)<br>Working|
|---|---|---|
|LOA|0.040*|0.044**|
||(0.023)|(0.022)|
||[0.081]|[0.043]|
|ESR|-0.001|-0.011|
||(0.024)|(0.023)|
||[0.966]|[0.620]|
|Observations|2,480|2,480|
|Outcome(Control Group)Mean|0.571|0.670|

_Notes:_ This table shows the estimated effects of including the LOA module (LOA) or enforcing self-reporting (ESR) on the reporting of employment or work status. We present estimated coefficients from Equation (1). 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. All estimations include strata fixed effects and are controlled by variables selected using a doubleLASSO procedure (Table A3 in the Appendix shows the selected variables). 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 50
Table F.7. Heterogeneous Effects on Voting Intentions by PEP Benefits Knowledge

|Vote intention<br>in 2023|Vote intention<br>in 2026|
|---|---|
|Mayoral<br>elections<br>(1)|Presidential<br>elections<br>(2)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)<br>0.086<br>(0.053)|0.102**<br>(0.051)|
|_β_2=I(Treatment)<br>-0.087*|-0.100**|
|(0.046)|(0.044)|
|Knowledge PEP Diff. Effect =_β_1+_β_2<br>-0.001|0.002|
|(0.027)|(0.027)|
|R-squared<br>0.024|0.022|
|Observations<br>1,002|1,002|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. Dependent variables in Columns (i)–(ii) are indicator variables [=1] if the respondent had the intention to vote in the next mayoral election in 2023 and in the next presidential election in 2026. All the columns control for sex (female and male), two age groups (21–28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

94
Policy Research Working Paper 11010

# **Abstract**

In 2019, 21.9 percent of the population in Pakistan lived below the national poverty line. Since then, the COVID-19 pandemic, devastating floods in 2022, and a macro-fiscal crisis with record inflation have profoundly impacted economic activity and income-earning opportunities. The absence of a new household survey limits the ability to ascertain the implications of these different shocks on household welfare and poverty. Up-to-date welfare information is crucial for formulating appropriate policy responses to crises that directly affect households’ socioeconomic well-being. To overcome the lack of current data, this paper proposes a microsimulation tool that combines microdata from the latest national household survey with high-frequency macro indicators to produce nowcasts and forecasts of poverty. The tool models household consumption by using individual and household characteristics and accounts for changes in labor markets, inflation, social transfers, and remittances. The results account for household-specific inflation rates, which reflect systematic variation in household consumption patterns and sector-specific growth rates. Using the preferred specification, the findings suggest that in 2024, the poverty rate stood at 25.3 percent, an increase of seven percentage points compared to 2023, with about 13 million additional people falling into poverty. Moreover, in addition to the projected increase in poverty, poor households face disproportionately higher welfare losses and get pushed deeper into poverty.

This paper is a product of the Poverty and Equity Global Department. 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 authors may be contacted at obarriga@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
|31151|Manufacture of crude vegetable and animal cooking<br>oil|0|0|0|0.016|0.007|-0.009|
|---|---|---|---|---|---|---|---|
|38441|Manufacture of motor cycle and motorized tricycles|0.016|0.080|0.064|0.016|0.080|0.064|
|39040|Manufacture of toys|0.015|0.013|-0.002|0.015|0.013|-0.002|
|35523|Manufacture of crumb rubber|0.015|0.011|-0.003|0.015|0.011|-0.003|
|32111|Spinning mills|0|0|0|0.014|0.007|-0.008|
|31112|Processing and preserving of meat|0.013|0.021|0.008|0.013|0.021|0.008|
|35511|Manufacture of tire and inner tubes|0.012|0.008|-0.004|0.012|0.008|-0.004|
|38241,<br>38242,<br>|Manufacture of textile and printing machineries, of<br>shore construction equipment, of other industrial|||||||
|38243,<br>38245,<br>38246,<br>38247|<br>machinery and equipment n.e.c, of component and<br>parts and alteration and repair of special industrial<br>machineries|0.010|0.027|0.017|0.010|0.027|0.017|
|35609|Manufacture of plastic products n.e.c|0|0|0|0.008|0.012|0.004|
|31144|Manufacture of frozen fish and other similar<br>products|0|0|0|0.006|0.011|0.004|
|31142|<br>Manufacture of salted /dried fish and other similar<br>products|0|0|0|0.006|0.002|-0.004|
|38131|Manufacture of fabricated structural metal products<br>other than aluminum|0|0|0|0.005|0.139|0.133|
|32210|Manufacture of wearing apparel made of textile<br>(garments)|0|0|0|0.004|0.001|-0.002|
|38323,<br>38511,<br>38512,<br>38513,<br>38514|Manufacture of x-ray apparatus and equipment,<br>professional, scientific, measuring and controlling<br>manual, electric and electronic equipment, instruments<br>for practicum purposes|0|0|0|0.003|0.007|0.004|
|38113,||||||||
|<br>38114,<br>38120|Manufacture of kitchen ware made of aluminum|0|0|0|0.003|0.001|-0.001|
|31246,<br>31249|Manufacture of chip and other food made of soya<br>bean / other nuts|0.001|0.002|0.001|0.003|0.004|0.002|
|36321|Manufacture of structural cementproducts|0|0|0|0.002|0.029|0.027|

_Note_ : Table includes only industries in which at least one politically connected firm was active in 1996-97. N.e.c. Not otherwise classified.

**<u>Table C1.2: Descriptive statistics industry outcomes before and after the crisis</u>**

||All y<br>('93-'96,'|ears<br>00-'09)|Suhar<br>('93|to era<br>-'96)|Post-Su<br>('00|harto era<br>-'09)|
|---|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Mean|SD|
|entry|0.09|0.11|0.13|0.10|0.07|0.10|
|exit|0.07|0.07|0.07|0.06|0.07|0.07|
|PCM|0.31|0.14|0.29|0.12|0.31|0.14|
|PE|-3.06|1.50|-2.97|1.56|-3.09|1.47|
|HHIY|0.18|0.17|0.20|0.19|0.18|0.16|
|MS4|0.60|0.24|0.61|0.26|0.59|0.24|
|lnN|3.86|1.21|3.73|1.27|3.91|1.18|
|Z|0.00|3.92|-1.23|4.03|0.49|3.76|
|lnP|0.31|0.61|1.16|0.28|-0.03|0.30|
|N|2,8|00|8|00|2,|000|
|Industries|20|0|2|00|2|00|

55
The larger this distance, the weaker the upstream instrument will be as a result of pollution decay. On the other hand, monitors that are closer together are more likely to be subject to common shocks such as rainfall, which would create spurious correlation.

We thus adopt a variety of definitions of “upstream”, and check the robustness of our results to the definition used. To assign an upstream counterpart to a given pollution monitor, we use the following protocol: first, we follow the river upstream until it reaches a new district; then, we locate the nearest monitor along the river that falls within a distance range (in km) of [ _X, Y_ ] from the original monitor, where _X ∈{_ 0 _,_ 20 _,_ 50 _,_ 75 _,_ 100 _}_ and _Y ∈{_ 200 _,_ 300 _}_ . When a river splits upstream of a given monitor, so that there is an upstream monitor on each of two tributaries, we take the unweighted average pollution reading of these monitors as our upstream measure.<sup>5</sup> When there is no upstream monitor of any kind to be found, we use the river’s origin as an upstream location (subject to the distance-range requirement) and assign the sample-wide minimum value of pollution as our upstream measure.

## **2.3 Other data**

We include several types of variables as controls in many of our regression analyses. The main body of these controls consists of cross-sectional birth, mother, and child characteristics taken from RCH-2. A pair of climate controls are created using monthly, gridded rainfall totals from the University of Delaware and air temperature averages from the Indian Meteorological Institute; we use these gridded averages to interpolate monthly rainfall and temperature values at each district (in mortality-only regressions) or monitor (in regressions involving pollution). Finally, we add time-varying measures of common effluent treatment plant (CETP) capacity and the incidence of the NRCP, which capture the non-uniform intensity of environmental cleanup and policy efforts within the Ganga Basin.<sup>6</sup>

# **3 Empirical Strategy**

Our aim is to determine whether or not the 1987 Supreme Court decision affected environmental quality or health outcomes. Our analysis has two broad parts. In the first part, we use difference-in-differences (DD) **–** comparing outcomes in the affected area to those elsewhere in the Ganga Basin, before and after the verdict **–** to estimate the impacts of _Mehta vs. Union of India_ on infant mortality and river pollution. In the second, we employ instrumental variables (IV) to identify the direct relationship between river pollution and neonatal mortality. The IV strategy serves two purposes: first, it quantifies an important component of the external damages of river pollution; and second, it sheds some light

> 5We are unable to weight tributaries by their contribution to downstream water flow, due to a lack of data on volumetric flow rates at specific locations.

> 6We do not have any other information about the placement, utilization or other operational details of this CETP, or others like it, in India. All the results in this paper however, are robust to the exclusion of these policy controls.

10
based on lost-years. In the case of Sweden, ignoring the age distribution of deaths would inate the importance of mortality by a factor of 4.5.<sup>19</sup> <!-- Start of picture text --> 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99<br>2,000<br>1,500<br>1,000<br>500<br>0<br>Covid-19 Deaths<br><!-- End of picture text --> Figure 1: Distribution of Covid-19 deaths per age in Sweden as of early June.

To estimate each country's number of poverty-years, we rst take the income distribution for each country from PovcalNet for 2018, which is the latest year for which data are available. Next, we scale these distributions to 2020 by assuming that all household incomes grow in accordance with growth rates in real GDP per capita, meaning that the growth is distribution-neutral. We do so under two dierent growth scenarios: (1) using GDP growth estimates for 2019 and 2020 from around June 2020, which incorporate the expected impacts of the pandemic and associated policy responses, and (2) using GDP growth estimates for 2019 and 2020 from around January 2020, before Covid-19 took o. Central Bank growth estimates are used for Sweden, Belgium and the United Kingdom while growth estimates from the January and June 2020 edition of the World Bank's Global Economic Prospects (GEP) are used for Pakistan, Peru and Philippines (The World Bank, 2020).

Finally, following Lakner et al. (2020) but using each country's most recent national poverty line, the impact of Covid-19 on poverty is computed by comparing the number of poor under the two growth scenarios.<sup>20</sup> Denoting the number of poor people in year

> 19If the same number of deaths were to be distributed at random in the population, they would generate 4.5 as many lost-years as the current distribution. This factor is equal to the average residual life-expectancy in Sweden (43 years) divided by the average residual life-expectancy of those dying (9.5 years).

> 20In all projections, it is assumed that only 85 percent of growth in GDP per capita is passed through to growth in welfare observed in household surveys in line with historical evidence (Lakner et al., 2020). In contrast to the data from PovcalNet which are expressed in 2011 PPPs and in per capita terms, the national poverty lines for Pakistan, Peru and the Philippines are expressed in local currency units and per adult equivalents. To convert the national poverty lines into 2011 PPPs and per capita terms we follow the approach of Jollie and Prydz (2016), which is to nd the poverty line in per capita 2011 PPPs that gives the same poverty rate as the national poverty line in local currency units and per adult

9
Table 4: Comparison of Hazard and Logit Models for Second Birth for Women with 8 or More Years of Education and 1 Girl

||Ur|ban|Ru|ral|
|---|---|---|---|---|
||Logit|Hazard|Logit|Hazard|
|||1985–|1994||
|Starting population<sup>a</sup>|5011|5011|2967|2967|
|Second birth<sup>b</sup>|3725|4059|2427|2665|
|Number of abortions|250|282|90|100|
|Sex ratio (boys per 100 girls)|120_._5|121_._1|113_._3|113_._4|
|||1995–|2006||
|Starting population<sup>a</sup>|3827|3827|2740|2740|
|Second birth<sup>b</sup>|1957|2763|1659|2328|
|Number of abortions|296|393|176|252|
|Sex ratio (boys per 100 girls)|143_._7|140_._9|130_._7|131_._3|

**Note.** The logit models are estimated using only uncensored observations. The hazard models are estimated using all observations and covers the period from beginning of spell to 6 years (24 quarters) after the birth of the first child. a Number of women in period who had a girl as their first child. b For logit model the number of women who are observed to have a second birth. For hazard model the predicted number of births that will occur between beginning of the spell and 6 years (24 quarters)

number of censored women predicted to exit with a birth.

Not surprisingly there are substantially more censored observations for the 1995–2006 period. For urban women about 21 percent of the starting population are predicted to have a second birth but have not yet had one and for rural women it is 24 percent. This allows for potentially larger differences in predicted sex ratios and number of abortions. Despite the large number of censored observations for rural women there is relatively little difference in the predicted sex ratio with the logit model predicting 130.7 boys and the hazard model 131.3 per 100 girls as we would expect with the relatively stable predicted probability of having a boy over the spell shown in Figure 8, panel (c). The more interesting difference is for urban women in the 1995–2006 period. The hazard model predicts almost 100 more abortions by the end of the spell but shows that the logit model is likely to _overestimate_ the final sex ratio with the logit model predicting 143.7 boys and the hazard model 140.9 boys per 100 girls. This is consistent with the very high probability of having a son early on in the spell followed by a somewhat lower probability for later births seen in panel (c) in 43
The sophistication and complexity of new regulations and bank operations require highly specialized, well-trained and experienced supervisors to oversee banks. As reported in Panel B of Figure 12, there was some improvement in the education levels of supervisory personnel as a higher share of them now hold more advanced degrees than in the previous survey for all the developing regions.<sup>17</sup> Conversely, on-the-job training appears to be less prevalent than in round 4 in all regions except MENA though the number of countries included in that region is low (Figure 12, Panel C). This reduction in on-the-job training is statistically significant for high-income OECD countries. The mean percentage of bank supervisors with more than 10 years’ experience in bank supervision also declined significantly in highincome OECD countries between round 4 and round 5 (Figure 12, Panel D). In contrast, this figure increased significantly in developing countries overall and, specifically, in MENA. For all the other developing regions changes on this dimension were not statistically significant, however. Overall, despite some increases in supervisory powers and indications that supervisory personnel are better educated, the BRSS 5 survey data indicate that regulatory complexity has advanced more quickly than supervisory capacity.

# **3 Capital reforms**

The global financial crisis (GFC) revealed structural weaknesses in capital regulations that were in place prior to the crisis. A significant number of financial institutions lacked high-quality capital to absorb losses, necessitating bailouts using public funds. Following policy goals set by the Basel Committee on Banking Supervision, several countries have introduced legislation and regulatory reforms to strengthen capital requirements. These reforms include increasing regulatory capital requirements as well as changes in what constitutes higher quality capital. The reforms also included surcharges for institutions deemed

> 17 However, the changes between wave 4 and wave 5 were not statistically significant.

23
- <mark>a. Author doesn't report attrition, but reports sample of 800 with only 479 observations on survival status.</mark>

- <mark>b. Death is measured from filings at register of companies, so authors cannot distinguish death from becoming informal/not reporting.</mark>

- <mark>c. Death is measured from manufacturing census, cannot distinguish between dropping below size threshold from death.</mark>

- <mark>d. Attrition rate does not include firms in households that were re-interviewed, but where authors were not able to match the firm over time. e. Cited in Mead and Liedholm (1998)</mark>

- f. Cited in Liedholm and Mead (1995). Death rates are based on tracer surveys which are subject to severe selectivity biases that potentially understate these <mark>death rates.</mark>

70
(2013) , we define 9 age bins as follows : <5, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, and 40+. **Establishment Size** is the total number of workers which includes workers employed directly, workers employed through contractors, supervisory and managerial staff, other employees, working proprietors, unpaid family workers, and unpaid working members if the establishment is a cooperative factory. As a measure of growth rates, we construct

**Establishment Size Ratio** which is the ratio of each firm’s establishment size scaled by the average establishment size of all firms in its birth cohort. Thus, we scale the employment of each firm in youngest age bin (<5) in 2004 by the average size of that age bin in 2004, of the firms in the age bin (5-9) in 2004 by the average size in the youngest age bin (<5) in 1999, of the firms in the age bin (10-14) in 2004 by the average size in the youngest age bin (<5) in 1994, of the firms in the age bin (15-19) in 2004 by the average size in the youngest age bin (<5) in 1989, of the firms in the age bin (20-24) in 2004 by the average size in the youngest age bin (<5) in 1983, and so on. Since our sample period extends from 1983/84 to 2004/05 we are able to construct this ratio for only the age bins <5, 5-9, 10-14, 15-19, and 20-24.

To deal with outliers, we winsorize the bottom and top 1% of all plant-level variables and drop firms that stated initial year of production to be before 1800 and clear data errors where the year of initial production is given to be after the year of the survey. The data also provide National Industry Classification (NIC) codes that map onto different revisions of the International Standard Industry Classification (ISIC) codes.<sup>13</sup> Using this we construct three digit

> 13 <mark>The census years 1983/84 uses NIC-70 which maps onto ISIC-Revision 2 at the 3-digit level. The 1989/90 and 1994/95 censuses use NIC-87 which maps onto ISIC-Revision 3 at the 3-digit level. The 1999/00 and 2000/01 censuses use NIC-98 which maps onto ISIC-Revision 3 at the 3-digit level. The 2004/05 census uses NIC-04 which maps onto ISIC-Revision 3.1 at the 3-digit level.</mark>

13
- 15 -

<!-- Start of picture text --> Figure 8. Milk export price and quantity, 2001-05<br>1000<br>100<br>10<br>1<br>0<br>0.000001 0.0001 0.01 1 100 10000<br>ln (Export volume in thousands of tons)<br>ln (Export price in constant 2000 euro)<br><!-- End of picture text --> <!-- Start of picture text --> Source: Author's calculation based on WITS COMTRADE database and IFS .<br><!-- End of picture text -->

# **III. METHODOLOGY AND DATA**

**Production.** Public infrastructure investments are necessary to improve production and export efficiency in any industry. Consider a representative farmer _j_ , who produces (and exports) agricultural commodity _k_ and maximizes the following profit function:

For each commodity _k_ , _p_ * _jk_ and _sjk_ are the unit price and market share of country _j_ , respectively. _FCjk_ is the fixed cost of production. _M k_ denotes the potential market size of product _k_ . The marginal cost ( _MCjk_ ) is assumed to be shifted depending on the level of public infrastructure ( _INFj_ ), weather conditions ( _Wj_ ) and the volume of sales. _ER_ is the exchange rate of the foreign against the local currency. Under some reasonable assumptions, we consider the following supply function:<sup>14</sup> 14 Essentially, it is assumed that the derivative of demand quantity with respective to price is constant.