rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

Using firm level data, this paper brings a new perspective to the question of who is vulnerable in volatile times. By focusing on gender differences in labor market impacts of crises, it illustrates how firm level data can benefit our understanding of who is at risk of losing their job and why. Existing studies that examine the distributional impact of crises almost exclusively rely on labor force and household level data, and consequently focus on labor supply. However, crisis induced changes in labor market outcomes are predominantly driven by changes in labor demand; labor supply responses are typically muted compared to the large contractions in labor demand that are a defining feature of crises (McKenzie, 2004; Thomas et al., 2002). In addition, micro-level studies of the distributional consequences of crises typically document significant heterogeneity in impact across broad sectors, yet fail to explore how such heterogeneity arises (see e.g. Fallon and Lucas, 2002). Moreover, they are not able to distinguish between different mechanisms that might explain why some workers are more vulnerable than others.

This paper helps redress these lacunae in the literature by examining how variations in firm responses during the East Asian crisis in Indonesia affected men‘s and women‘s relative employment vulnerability. It distinguishes between two previously unexplored mechanisms by which gender difference in impact might arise: differences in vulnerability could be the result of (i) _sorting_ by gender into firms and occupations that differ in their vulnerability to crises, and/or of (ii) of _differential treatment_ of men and women workers _within_ firms.

That employers often treat men and women differently in the workplace is widely documented (see Altonji and Blank, 1999 for a review of the literature), and there is no shortage of rhetoric asserting that women are disproportionately at risk during volatile times (Grown, 2009; ILO, 2009; World Bank, 2009).<sup>2</sup> A common concern is that women get dismissed first and hired last (
09|2.46|5.03<br>2.47|5.16<br>2.43|
|<br>Educ. Exp.|1101.84|1237.90|1145.16<br>1293.68|1051.22<br>1167.40|
|No. of Children|2.67|1.11|2.61<br>1.10|2.73<br>1.13|
|NSS1995 (6-18)|N=2|7819|N=15152|N=12667|
|Father's Sch.|5.24|2.44|5.16<br>2.46|5.33<br>2.42|
|Educ. Exp.|1338.27|1529.59|1382.00<br>1588.18|1285.96<br>1454.75|
|No. ofChildren|2.67|1.14|2.60<br>1.12|2.75<br>1.17|

Notes: CFPS stands for China Family Panel Survey, IHDS stands for Indian Human Development Survey, and NSS stands for National Sample Survey.
(EIA, Annual Industrial Survey), from INDEC. This is a panel of rms spanning the period 1994 to 2001, with information on investment, disinvestment, employment, revenue, production and materials. The second source of data is the Encuesta Permanente de Hogares (EPH, Permanent Household Survey), a standard labor force survey with a panel component, also from INDEC. It spans the period 1996 to 2006 and has information on sector of employment (or non-employment) and wages. We use the panel component of the survey to compute movements of workers across sectors. Firms and workers are aggregated into 6 sectors: food and beverages; textiles, apparel and leather; nonmetallic minerals; primary metals and fabricated metal products; other manufactures; and non-traded goods. Workers are further grouped into non-employment. We refer the reader to on-line Appendix A for more details 14
the unweighted average industrial growth rate over the period 1980-89 would have been 4.3% instead of 3.7%, if Brazil had achieved the average rank in contract enforceability of OECD countries, again a strong assumption. The effect is even stronger for industries that depend heavily on external finance. The plastic product industry, for example, with an external dependence of 114% would have grown by 0.1% instead of -2.9%, if Brazil had achieved the level of contract enforcement of OECD countries.

Table A4 reinforces the previous results using firm-level data. A recent study by Demirguc>-Kunt and Maksimovic (1998) shows that in countries with a more efficient legal system, a higher share of firms use long-term financing. They analyze the proportion of firms in countries that grow faster than predicted by internal cash flow and short-term credit availability using an indicator of the rule of law, and controlling for other firm-specific and country characteristics. The results indicate that the legal environment has a statistically significant and economically large impact on the proportion of firms whose growth rate exceeds the growth that could have been supported by internal cash flow and short-term debt only. If Brazil had achieved the average level of rule of law as the OECD countries, a very significant improvement, more long-term finance would have been available, and 50% instead of 37% of Brazilian firms would have been able to grow beyond the constraints imposed by internal cash flows and short-term debt.

While the evidence presented so far relies on cross-country studies, the results from a recent Brazil study by Pinheiro and Cabral (1999) confirm these findings. Their analysis relies on the variance in private credit and the efficiency of the judicial system across Brazilian states. They show that states with more efficient judicial systems have a higher share of private credit to their GDP. To assess the economic significance of these results, consider the state of Sergipe in North East Brazil, which had the lowest level of judicial efficiency and a share of credit to GDP of
Policy Research Working Paper 6329

# **Abstract**

The paper provides the first large-scale survey-based evidence on the impact of the global food crisis of 2007–08 using an indicator of self-assessed food security from the Gallup World Poll. For the sampled countries as a whole, this subjective indicator of food security remained the same or even improved, seemingly owing to a combination of strong economic growth and limited food inflation in some of the most populous countries, particularly India. However, these favorable global trends mask divergent trends at the national and regional levels, with a number of countries reporting substantial deterioration in food security. The impacts of the global crisis therefore appear to be highly context specific.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The author may be contacted at D.Headey@cgiar.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
Here, _Yb_ is the fraction of observations in bin _b_ , _Xb_ is the incumbent’s victory/loss margin (in percentage points) at the bin’s midpoint, and _Db_ is an indicator for whether the bin is above the incumbent’s victory threshold (all bins have to be either entirely above or entirely below zero). If the density of the incumbent’s vote margin is smooth across the threshold of zero, the bins just above the threshold should contain as many observations as those just below the threshold and the parameter _γ_ 1 will not be statistically distinguishable from zero.

The election data are publicly available and were obtained from the Republic of the Philippines Commission on Elections (COMELEC). Mayoral election results from 2007 were used to identify winners/losers in each municipality and to calculate margins of victories/losses; returns from the 2004 election were used to identify incumbents.<sup>14</sup> The sample is limited to municipalities in which the incumbent mayor’s vote margin was within a 5 percentagepoint bandwidth of the zero threshold. This resulted in a sample of 153 municipalities. The incumbent won reelection in 81 of these municipalities and lost in the remaining 72.

> 14The majority of mayors elected in 2004 were matched with the 2007 election results using an automated process. Unmatched observations were mostly due to the fact that not all mayors elected in 2004 ran for re-election and to spelling errors. In the latter case, incumbent mayors were matched manually.

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ule comprising of over 345 items, compared to 114 unique items captured in the CPHS. Expenditures on household appliances, personal transport equipment, other durables are notably not covered in the CPHS consumption survey. Both surveys contain information on household asset ownership. Additionally, the NSS’ expenditure based on uniform recall period captures household consumption over the past thirty days, whereas the CPHS collects consumption based on the past four calendar months. Differences in recall periods across surveys can have large impacts on estimates of poverty (Deaton, 2003; Deaton and Dreze, 2002; Tarozzi, 2007).

Seventh, the CPHS household consumption aggregate includes expenditures on insurance premiums and loan repayments, which are excluded in NSS’ consumption expenditure aggregate.

# **2.4 Addressing differences in instrument design**

In this section we document the necessary adjustments we applied to the CPHS datasets in order to address the differences in instrument design between the two surveys. First, we pool the CPHS interviews conducted during the second and third wave of a calendar year and the first wave of the following year to match (as closely as we can) the NSS-2011 reference period of July 2011 to June 2012. The second wave of CPHS starts in May and the first wave ends in April, with households reporting consumption for the past calendar month. Accordingly, CPHS consumption reference period will correspond to April (the month prior to May, when interviews begin) through March of the following year(the month prior to April, the last month of interview). The 2019-20 round of the CPHS consumption overlaps with the first week of the covid induced lockdowns (as the lockdowns in India were imposed on March 24<sup>th</sup> , 2020), and as such may provide limited evidence on how household consumption, poverty and inequality were impacted at the start of the lockdowns<sup>12</sup> Second, we exclude districts that are covered by the NSS consumption survey but not by the CPHS to obtain geographical consistency in
<!-- Start of picture text --> 50.0 Wave 2 posterior regret in IVR experiment<br>37.5<br>25.0<br>12.5<br>0.0<br>0.000 0.001 0.002 0.003<br>Posterior regret associated with the treatment arm selected<br>Count<br><!-- End of picture text --> Figure 5: Distribution of the posterior expected regret from wave 1 data and 200 simulated samples for wave 2, based on _β_<sup>_E_</sup> , _κ_<sup>_E_</sup> and _η_<sup>_E_</sup> drawn from their posterior distributions after wave 1.

1. The posterior expected regret at _t_ = 1 would be the basis for decision making if no other wave was conducted, and T1B was the arm with the lowest expected regret at that point. Panels B and C show the results of the simulations of wave 2. Panel B shows the average and median posterior policy regret and probability optimal of the chosen arm. On average, the simulation predicts an improvement in expected regret from continued experimentation of 0.04%, and an increase in the probability optimal for the chosen arm from 74.14% to 77.86%. Using the median of the distribution, the improvement would be 0.07% and to a probability optimal of 83.01%. Note that the average posterior expected regret has a heavily skewed distribution. The actual value of 0.02% observed after the second wave in the IVR experiment is at the 38th percentile of that distribution, as seen in Figure 5.

Panel C shows the average realized policy regret and the percentage of times the best arm is identified, both after wave 1 (where the experimenter would have chosen arm T1B) and after wave 2. Comparing the numbers for wave 1 with Panel A shows that the distribution
# Developed countries

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data than for the labor market changes measured by the ILO data, although the two measures are strongly correlated (0.74).<sup>10</sup>

> 9The data come from the ILO collection STLFS Short-term labor force indicators, updated December 3, 2020.

> 10Looking at measure of stop work for specific broad sectors, industry and services, a very similar picture emerges as for the general stop work measure; however, the country subset is even smaller.

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EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE still somewhat slower than southern Africa. The trade density (in US$ per km) is less than one-fifth of the density in southern Africa. Road freight tariffs average $0.07 per tonne-kilometer, in the middle of the range for African regions but still exceedingly high relative to global standards (Teravaninthorn and Raballand 2009).

**Table 2.1 . Relative performance of transport corridors in Africa**

|**Corridor**|**Length**<br>**(kms)**|**Road in good**<br>**condition (%)**|**Trade density (US$**<br>**millions per km)**|**Implicit velocity**<br>**(km per hour)**|**Freight tariff**<br>**(US$ per ton-km)**|
|---|---|---|---|---|---|
|Western|2,050|72|8.2|6.0|0.08|
|Central|3,280|49|4.2|6.1|0.13|
|**Eastern**|**2,845**|**82**|**5.7**|**8.1**|**0.07**|
|Southern|5,000|100|27.9|11.6|0.05|

Note: Implicit velocity is the total distance divided by the total time taken to make the trip, including time spent stationary at ports, border crossings, and other stops Source **:** Teravaninthorn and Raballand 2009.

According to the 2010 International Logistics Performance Index (LPI),<sup>1</
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# **_II. DATA AND EMPIRICAL SPECIFICATION_**

We test our predictions on annual observations from a sample of approximately 70 developing and developed countries from all continents. Overall, we employ a panel of more than 2,000 observations, but the panel structure is unbalanced. Specifically, for a few developed countries, the starting year is approximately 1960; for the majority of the sample, the starting year is approximately 1970; and for transition countries, it is approximately 1992. The last year of observations is 2004. Table S.1 (in the supplemental appendix, available at http://wber.oxfordjournals.org/) reports the full list of countries with data and the 1970 and 2002 values of key policy and media variables.

# **_Dependent variables_**

Our dependent variables are measures of agricultural protection. As an indicator of agricultural taxation and subsidization, we use the _relative rate of assistance_ ( _RRA_ ) to agriculture, taken from the Agricultural Distortions Database from the World Bank (see Anderson and Valenzuela 2008 for details). The _RRA_ index is calculated as the ratio between the agricultural and nonagricultural nominal rates of assistance: where _NRAag_ is the nominal rate of assistance to agriculture and _NRAnonag_ is the nominal assistance to nonagricultural sectors. The _NRAag_ measures total transfers to agriculture as a percentage of the undistorted unit value. It is positive when agriculture is subsidized, negative when it is taxed, and zero when net transfers are zero. The _NRAag_ at the agricultural level is obtained as the weighted average of assistance at the product level using the undistorted value of production as a weight. It includes a wide range of policies, such as the assistance provided by all tariff and nontariff trade measures applied to 12
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school year when the baseline took place), so that we only have 18,796 students who reported their age in August 2010.

We drop any students who are not uniquely identified after matching by name and age, yielding 15,940 students for whom the algorithm found a CPF in the SRF data. Of these, 3,657 had reported a CPF during the short-run impact evaluation. Comparing these CPFs with the ones found by the algorithm shows that they are almost always the same (only 2.68 percent are different), suggesting that the algorithm does an accurate job of identifying CPFs.<sup>5</sup>

# **_3.c. Attrition and Balance of Baseline Characteristics_**

The percentage of students in the starting sample for whom the algorithm found a CPF in the SRF data is 44.27 percent in the treatment group and 46.02 percent in the control group. The difference in the percentage of students with a CPF across the treatment and control group is thus small, although it is statistically significant at the 10 percent level.

We follow Bruhn et al. (2016) in examining the balance of student and school characteristics across the treatment and control groups, using the baseline data from the short-term impact evaluation (Table 1). As in Bruhn et al. (2016), we find that the percentage of female students is statistically significantly higher in the treatment group than in the control group (56 percent vs 54 percent) and hence we control for gender in our regressions. The only other variable that shows a statistically significant difference across the treatment and control groups is the one indicating that the student “Receives income,” but this difference is only significant at the 10 percent level.

In Table A1, we check if the identified sample shows similar short-run effects of the intervention to the ones in the original sample. The effect of treatment on financial proficiency scores is positive and statistically significant in both samples. The point estimates are slightly higher in the
Thus, for each pair of Census years t=1980, 1990, 2000:  _predicted_  5'' * _CONFLICT_ ln _OPc c_  '' 6  '' *  _COMc_ * _predicted_   _c_ '', _c_ ln  _OPcountryinitial_ , _c_   2'' * _OPENc_   4'' * _DISTc_ (4) GDPpc and English are not included in specification (4) because they are correlated to a large degree with the initial predicted occupational placement.

> 32 From Andrew Rose’s datasets available at http://faculty.haas.berkeley.edu/arose

> 33 Constructed using  www. prio.no, “Armed Conflict Version 2.1” by Gledisch, Wallensteen, Eriksson, Sollenberg and Strand (2004)

> 34 Robert Barro’s  “Religion Adherence Data”

> http://www.economics.harvard.edu/faculty/barro/data_sets_barro

18
also higher for Afghan households, consistent with the anecdotal evidence that they have more labor productivity in unskilled jobs than Iranians.

The effect of economic shocks is negative on expenditure but positive on income and wage rate. Although the positive impact of economic shocks on income and wage is counter-intuitive, our price deflator (CPI index) can be a reason for the positive coefficients. Because the exchange rate shock does not instantly pass through CPI, it perhaps shows its effect on nominal income more immediately than on the CPI price deflator. For expenditure items, our deflator is the monthly price index of the item, and thus the

> 6 IOM data has no information about the movement from Afghanistan toward Iran nor information on exit before 2015. One reason for unavailability of entrance data is that many Afghan refugees enter Iran via human smuggling while there is no restriction for them to exit from the borders.

10