rafmacalaba/data-use-mentions-extended huggingface dataset:
Migration has been a major feature in Bangladesh’s recent history, and several studies have _directly_ measured and documented trends on internal migration implied by decreasing wage differences for similar workers across regions.<sup>9</sup> In this regard, migration statistics published by BBS based on the 2011 census is a valuable source providing information on the movement of workers across districts between 2001 and 2011. This data source does not fully cover our period of primary interest, 2005 to 2015, but the information is nonetheless informative and the best available.

According to BBS (2015), Dhaka was the most popular district for internal migration between 2001 and 2011. When moving from one region to another employment was one of the primary reasons. Since Dhaka has a large presence of the export-oriented RMG sector, it is not unusual that it attracted most migrants. After Dhaka, people prefer to migrate to districts near Dhaka, such as Gazipur and Narayanganj for similar reasons. According to the last round of Economic Census for Bangladesh, districts of Dhaka and Gazipur reported the highest number of declared employees in the garment sector in Dhaka division in 2013. During the same period, a good proportion of migrants settled in Chittagong division districts: Chittagong, Rangamati, Bandarban, Khagrachari, and Coxes’ Bazar. Chittagong district, in particular, attracts migrants due to prevalence of job opportunities in the RMG sector.

In contrast, cross-district mobility in India has been historically low as documented in several studies.<sup>10</sup> Given the higher degree of internal migration in Bangladesh than India, it is possible that labor market effects due to export shocks are more likely to dissipate countrywide over time. In the next section, we describe how we evaluate this hypothesis.

# 4. Estimation Data Set and Methodology

Our main dependent variables are average wages and informality probability. Average wage variable is measured in real _takas_ (normalized with the consumer price index). The informality variable is measured as probabilities at the sub-district level (rates)
variation for some municipalities, notably Cáceres, Campo Verde, Pedra Preta and Primavera do Leste, where land in agricultural use is far more valuable than pasture land. In these cases, INCRA assigns two values. To adjust for further intra-municipal variation in land value, we regressed mean municipal land values on agricultural suitability category, biome and roaddistance category. With the objective to assess the role of land value in private deforestation decisions, only sample points of nonprotected areas of Mato Grosso were used (i.e. where deforestation can be authorized). Based on the SEPLAN land use information, municipalities with two land values became separate observations.<sup>14</sup> We then used the parameters of the municipal level regression to impute land values for each sample point.

In order to spatially impute land values, we assume that plot-level land value is a linear function of land characteristics. Then, by linear aggregation, the INCRA mean municipal land value is a linear function of the proportion of municipio land with each relevant characteristic. We estimated this function by linear regression. Six observations with mean land value above 1700 Reais/hectare presented outliers in our dataset. These were the agricultural land areas in the municipalities of Alto Garças, Alto Taquari, Campo Verde, Itiquira, Pedra Preta and Primavera do Leste, all situated in the old agricultural frontier, mainly in south-east Mato Grosso. The outliers were extracted and the sample used for estimation was limited to observations with mean land value below R$1700/hectare. Table 17 depicts the summary statistics and Table 18 the outcome of the robust regression model. A simple exploratory functional form (chosen for ease of imputation) found that land values reflect locational rents from road proximity and soil quality, confirming the first hypothesis. For subsequent imputation purposes, the omitted observations with INCRA land values greater than R$1700 hectares were left with their original land value; we do not believe that these values are in error but rather reflect characteristics we are unable to measure.

>
# **4.2. Unemployment claims**

As an indicator of the evolution of unemployment, we present data from OAED, the public employment agency, on the number of unemployment benefit recipients and the new claims for benefits covering the period from January 2017 to May 2020.

# **4.3. Labor market flows**

Administered by the Ministry of Labor and Social Affairs (MoLSA), ERGANI is the national employment registry in Greece and covers all registered employers who contribute to the Social Security System. The ERGANI monthly reports provide daily information on labor market flows in the private sector, and we use the reports from January 2018 through June 2020. More specifically, the data we analyze covers new hires, overall and by type (full-time, part-time and shift work), and separations (layoffs, quits and contract terminations). On a monthly frequency, labor market flows are disaggregated by gender, age and region. In addition, we disaggregate these flows by occupation (2-digit) and sector of economic activity (2-digit) for the periods JanuaryApril 2019, and January-April 2020.<sup>12</sup> Using ERGANI daily data, the change in daily flows since the onset of the pandemic and the government restriction on layoffs can be analyzed through a simple regression framework. More specifically, we adopt a single group interrupted time series analysis, in order to compare how outcomes change between the pre-pandemic period and two post-pandemic sub-periods, i.e., one since the onset of the pandemic and one since the government intervention to protect jobs: yt = a + b1(ct×St) + b2(ct×Rt) + Wt<sup>d</sup> + ct + ut where y is the daily number of hires (or separations), S is dummy for the period after the onset of the pandemic (26 February 2020), R is a dummy switched on after the implementation of layoff restrictions (18 March 2020), c a linear daily
> As pointed out by (Kalemli-Ozcan, Sorensen, Villegas-Sanchez, Volosovych, & Yesiltas, 2015), ORBIS is a good starting point to construct a firm-level data set because it provides administrative data for over 130 million entities worldwide covering all sectors

> 4 For the alternatives of SOE reform and also different approaches based on the type of sector, please also refer to the CPSD SOE Knowledge note (Sanchez-Navarro, Goodwin, & Kikeri, SOE CPSD Knowledge note, 2021) and Private sector toolkit (WB, forthcoming). 5 Also known as AMADEUS product for the ECA region.

4
and adult Total Energy Expenditure (TEE) estimates. Columns 8 through 10 are based on the reported coefci<br>slope in (Duh and Spears 2017), where disease is proxied by infant mortality. Column 9 reports infant mortality rate (I<br>live births) from the World Development Indicators and the mid-level estimate based on a slope coefcient of one. Colum<br>by 1.74 sand 0.426, the high and low end estimates reported in (Duh and Spears 2017) Table 8. Column 11 “Surplus” <br>consistent with average weight gain using the National Family Health Survey (NFHS) data (see text and Appendix Table|

32
# _5.4 Impacts on Learning Outcomes_

Table 9 measures impacts of the two different types of cash transfers on learning. We examine scores on a standardized mathematics and French test (French is the official language in Burkina Faso and the language used in all primary schools), which were designed by the survey team in collaboration with education specialists at the Burkina Faso Ministry of Education. The tests were given to all children ages 7 to 15 in the surveyed households. We examine the impacts separately for enrolled children (as indicated by the school data) and for all children. We also examine impacts on final end-of-year school grades (column 1). Unlike the tests we designed for French and mathematics, final grades are only available for enrolled children. They are also not standardized and can thus vary across schools or even within schools. In columns 2 and 5, the dependent variable is the age-standardized z-score for the number correct on the mathematics test. In columns 3, 4, 6, and 7, the dependent variables are age-standardized z-scores for the number correct on the overall French test and the French reading sub-section, respectively.

We find no significant impact of the interventions on grades or achievement tests, except for a positive impact of CCTs on the reading section of the French test for the sample of all children.<sup>30</sup> However, it is important to stress that even though there is no differential learning across treatment and control groups, this does not mean there is no learning going on for these children. For children in the treatment groups who get enrolled between baseline and round 3, their mean test scores increase and they improve at the same rate as for children in the control group who get enrolled across rounds. Our findings imply that transfers increase enrollment, and these children (who would not have enrolled absent the intervention) are learning as much as their peers in the control group. This can further be seen by comparing the results for all children with only enrolled children. For
administrative level (i.e. states, provinces, or departments). Most studies find evidence of absolute convergence at the first administrative level, albeit at a speed below the one observed in developed countries, in Brazil (Azzoni, 2001; Serra et al., 2006), Chile (Serra et al., 2006), Peru (Serra et al., 2006; Iacovone, SanchezBayardo and Sharma, 2015), and Colombia (Serra et al., 2006), but not in Argentina (Serra et al., 2006) and Mexico (Rodriguez-Pose and Sanchez-Reaza 2002; Chiquiar 2005; Serra et al., 2006). In the latter, convergence was observed up to the mid-1980s, but not afterwards. In spatially polarized countries, such as Brazil, Chile and Peru, convergence is found to be stronger within regional clubs.

A strand of this literature explores regional income dynamics by sector. Using sectoral value added data, Iacovone et al. (2015) find absolute convergence in the Peruvian manufacturing and mining sectors, but not in services and agriculture. Iacovone et al. (2015) attribute the lack of convergence in poverty rates across departments to limited reallocation of labor toward the converging sectors, while Sotelo (2020) suggests that this could also be due to substantial regional differences in farm income. He links these differences to spatial variations in trade costs and land quality and the practice of farmers to allocate land to many crops, which differ in land intensity. In Mexico, growth in tourism has also had uneven spatial effects. Faber and Gaubert (2019) show that the local economic effects of tourism are in part driven by significant positive spillovers from manufacturing. However, these local spillovers are mostly offset by reduced agglomeration econonomies in less touristic regions.

Another strand of this literature explores the effects of trade liberalization during the 1990s and early 2000s and increased demand for commodities on regional income dynamics. Chiquiar (2005) shows that NAFTA did not reverse the pattern of divergence in Mexico’
# **4. DATA AND RESULTS**

The analysis is based on the India Annual Survey of Industry (ASI) which was available to us from 1983/84 through 1997/98 disaggregated by state and 3 digit (NIC) industry.<sup>16</sup> This is a survey of registered manufacturing firms, reporting employment in two skill categories (nonmanual and manual workers), which we interpret as representing skilled and unskilled labor, respectively. The data also contain wage-bills for each labor category, output, value added, capital stock and a number of other variables.<sup>17</sup> The public-use ASI data are of uneven quality. We discovered numerous internal and external inconsistencies in these data and believe that we have resolved most of them. In keeping with the Griliches’ tradition of attention to measurement issues (Griliches, 1986) we discuss what we’ve learned here. (A separate appendix on data construction is available upon request.) We examined time-series of employment for sudden changes in each 3 digit industry by state, and then compared these figures with totals available from the Central Statistical Organization with data from other sources when any inconsistencies remained unexplained. We found two types of data problems: 1) Possible coding mistakes - The data indicate a very sharp decline in employment in West Bengal between 1984 and 1985, from 1.1 million manufacturing workers to 800 thousand. 180 thousand of that decline is reported in one 3 digit industry, aluminum manufacturing (NIC 335)<sup>18</sup> . Similar declines are reported for value added, output and other statistics. That sharp reduction in employment is inconsistent with the slight but steady employment _increase_ reported in official statistics by the Bureau of Economics and Statistics in the Government of West Bengal.<sup>19</sup> Compared to national totals for employment, the 1984 figures from our ASI data seem to be about 160,000 too high, indicating that this one industry is probably
2 percent, 89 percent and 74 percent of GDP respectively. Even 40 years after a pandemic shock, high-income countries remain 4 _._ 2 percent below steady state GDP, compared to 2 _._ 6 percent for middle-income countries and 0 _._ 8 percent for low-income countries. This is because the effect of human capital lost through disruptions to school and work persists until all the affected students and workers have left the labor force.

There are several reasons for the _asymmetric_ impact of a _symmetric_ shock on countries of different income levels. One is that returns to education do not vary much across income levels but there is significantly less schooling to begin with in middle and low income economies: the average years of schooling in each group is 13, 8 and 6 years, respectively. The negative effect of the employment shock is also higher in high-income economies, due to the presence of higher returns to experience there.

The disruption to schooling is also likely a greater influence on long-term outcomes than the disruption to experience – as the interruption to work affects mostly lowskilled workers, whereas the interruptions to schooling are more widespread, and as the returns to education are several times larger than the returns to experience.

We then use our framework to pinpoint carefully _which pandemic scenario most accurately reflects the COVID-19 shock_ . We achieve this through a thorough analysis of several variables that vary across income levels and that affect the shock’s severity – such as data on school closures, access to and effectiveness of distance learning, and evidence on the extent of disruption to employment to different types of workers.

While high-income countries are more vulnerable to a symmetrical shock due to the relative importance of human capital, the impact of COVID-19 on the high- and middle-income countries turns out to be similar. The reason is that middle-income countries are hit much harder both in terms of employment loss and school closures. This contrasts with the policy literature on COVID-induced schooling disruptions that argues that the impact disproportionately falls on low income economies.<sup>1
in Table 6 in Evans et al. (2023). All six coefficients of overlap lie in the range (1.5, 3.6).<sup>44</sup> We illustrate the proposed method’s potential to improve on cross-regions comparisons (Objective 2). We consider the multidimensional poverty indicator constructed by Decerf and Fonton (2023) to compare the 36 Nigerian states in 2019. This indicator considers four dimensions, namely consumption (monetary), health, housing and security.<sup>45</sup> At national level, Decerf and Fonton (2023) estimate H<sup>ˆ</sup> at 52 percent, H<sup>ˆ</sup> M at 40 percent and ˆ HOD at 28 percent. There are large differences in multidimensional poverty rates across states, with H<sup>ˆ</sup> being around 20 percent for some states and around 90 percent for other states. The coefficients of overlap of all 36 states lie in the range (0.8, 2.7), with median value 1.7.

Table 2 compares the cross-states comparisons of various indicators with those obtained with the “true” indicator H<sup>ˆ</sup> . Most indicators considered correspond to H<sup>ˆˆ</sup> (ˆc) when assuming the same (counterfactual) value cˆ for all Nigerian states. The deviations from ˆ H are quantified through a “bias” index, which sums over all 36 states the absolute difference in the state’s rank for the indicator considered and for H<sup>ˆ</sup> .<sup>46</sup> The larger the bias index, the less reliably the indicator considered yields cross-states comparisons in line with those of H<sup>ˆ</sup> .

**Table 2:** Assuming the joint distribution yields better comparisons than wasting the data on one partial distribution.

<!--
OPTIMAL SIMPLE FISCAL RULES 14 Algerian non-resource GDP and Trinidad & Tobago’s SWF is about 28% non-resource GDP (data from the SWF Institute), so we set _Ass_ = 0 _._ 3 as an intermediate value. We assume steady state transfers are set to pay out all steady state revenues. The persistence and volatility of non-resource GDP is taken from estimating an AR(1) process on HP-filtered log real per capita GDP (from the World Development Indicators). While it is true that HP filtering removes much of the persistence in log GDP by constructtion, this is necessary given log GDP per capita is close to a random walk, and this is a standard procedure in the literature.

# 4. Main Numerical Results

Table 2 summarizes the main results for optimal and classical rules.

**HtM Stabilization: smoothing vs insurance** In the first column of Table 2, the government follows a rule which provides full insurance for the HtM household against non-resource and commodity price shocks. As a result, HtM household consumption remains constant at its steady state level (a welfare loss of zero for that HH), which is the same result we would get if the household had access to state-contingent Arrow-Debreu securities. Given the HtM HHs lack access to financial markets, one might think this policy is similar to government borrowing/saving on behalf of the HtM household as it “fixes” market incompleteness.

However, from an aggregate perspective this rule is very inefficient because it concentrates risk with the Ricardian HH rather than sharing risk across households. In fact, the HtM household full stabilization rule has the worst aggregate welfare performance among the 7 rules considered in this section (a 4.9% of SS consumption welfare loss, _each period_ ). The full HtM stabilization rule leads to a very large consumption variance for the Ricardian HH (SD of 31% annually). This — is the difference between smoothing vs insurance the Ricardian HH and government have the financial technology to _smooth_ out anticipated changes in the time
different<br>Asian countries (with and<br>without documented son<br>preference)|Female infant and child<br>survival improved after the<br>1980s in countries or sub-<br>populations with<br>documented son<br>preference|N/A|
|Hu and Schlosser<br>(2012)|Higher and<br>increasing SRB<br>(indicator of<br>higher and<br>increasing<br>prenatal sex<br>selection)|Girls’<br>nutritional<br>outcomes|Pooled individual<br>data from 1992-93,<br>1998-99, and<br>2005-06 Indian<br>National Family<br>Health Survey<br>(NFHS)|Quasi-experimental study:<br>Triple difference-in-difference<br>that exploits geographic<br>variation in the prevalence of<br>prenatal sex selection over<br>time, and changes in outcomes<br>for boys v/s girls across states<br>over time|N/A|Larger reduction<br>in % of<br>malnourished<br>(wasted, stunted,<br>underweight) girls<br>in states with<br>increasing<br>prenatal sex<br>selection. This<br>result is not<br>explained by<br>changes in<br>mortality|
|Lin et al. (2014)|Access to sex-<br>selective<br>abortion in 1985|SRB and<br>girls’<br>neonatal<br>mortality|Individual-level<br>data from national<br>birth and death<br>registries, Taiwan<br>(1980 – 1996)|Quasi-experimental study: Uses<br>timing of legalization of<br>abortion, and cross-sectional<br>variation in cost of having more<br>children (variation in cost<br
on credit from the shopkeepers that again permitted them to get around liquidity constraints. Thus, socially well-connected households seemed to suffer less from high prices. On the other hand, households who did not have good social relationships faced markups of between 15-40 per cent on their food purchases. To get a summary measure of the variation in unit prices across households, I construct a Laspeyres price index which used the prices and quantities purchased by the median household as a base to calculate price variations, for a basket of goods, for everyone in the sample. With this method, the median household's index number is 100; and a household with an index of 200 faces prices that are double the sample median.

The social status and price index variables will be regressed against festival expenditures", the household head's age, the number of adults and children in the family, the head's education,

**_15_** The social status regressions will be estimated with an ordered Probit regression. 1<sup>6</sup> A detailed analysis of this can be found in Rao (2000).

17 Festival expenditures will be treated as an endogenous variable using instrumental variables (IV) with the number of marriageable daughters and the expenditures of neighbors as excluded variables.

12
Table 2: Placebo Tests

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Baseline<br>Model|Travel Scaled<br>by Avg<br>Monthly Incid|Avg Travel<br>Scaled by<br>Monthly Incid|Travel Scaled<br>by Non-Malarial<br>Case Incid|Eect on<br>Non-Malarial<br>Case Incid|
|Imported Incidence|1.094***|0.0290|0.00118|0.323|27.14|
||(0.357)|(0.0566)|(0.0579)|(0.337)|(25.09)|
|Lag Incidence|0.563***|0.588***|0.588***|0.574***|0.202*|
||(0.129)|(0.130)|(0.130)|(0.144)|(0.116)|
|Rain in cm|-0.000654|-0.00530|-0.00538|0.0444|-0.605|
||(0.00790)|(0.00741)|(0.00733)|(0.0560)|(0.843)|
|Lag Rain in cm|0.0269|0.0290|0.0288|0.0595|-0.198|
||(0.0181)|(0.0179)|(0.0176)|(0.0564)|(0.640)|
|Lag 2 Rain in cm|0.0319**|0.0382**|0.0381*