rafmacalaba/datause-encoder-data huggingface dataset:
how they correlate with any discrepancy between ESPS and PSF total household consumption aggregates.

Nevertheless, two conjectures can be made. First, if the PSF design is better able to apprehend food consumption than that of ESPS and if household or surveyor fatigue does not impact too much the ability to capture consumption of food commodities, then one can expect to find a higher level of food consumption in the PSF survey. Moreover, since in more than 80% of households all members take their meals together, the gap between surveys should not depend on the household structure. Second, for total household consumption, one cannot conjecture whether any difference should be observed on average between surveys, but we may expect the ratio of PSF to ESPS household consumption for a given household type to increase with the complexity of the household structure.

Testing these conjectures is not straightforward. Contrary to PSF, the ESPS survey has not been designed to capture the entire complexity of Senegalese households. One can only rely on the usual “relationship to head” question to recover, in as much as possible, the household structure and identify the number of budget decision units. The basic strategy is to count the number of household members of the different types, as defined by their relationship to the head: spouse, child, grand-child, parent, grand-parent, brother/sister, nephew/niece, other parent, unrelated etc. and then to compare that number to the household size. For instance, a simple two cell household can be constituted of a male head, his (unique) spouse and his children. The children can be those of the spouse, in which case they belong to her cell, or they can be born from another marriage, in which case they belong to the head’s cell. In any case, households with the head, one spouse and children and with no other members are two cell households. The difficulty is that these nuclear families are not the only kind of two cell households, but they are the only ones that ESPS can identify for sure. For instance, consider a household in which on top of the household head, his spouse and their children, one also 9
Another possibility is that some other shock affected the treatment area relative to the control area during the same time period. There may have been a policy change in each of the three treatment areas relative to the control areas that had a positive effect on fertility. The likelihood of a policy change in the three affected regions, within the time period in this study, is, however, reasonably low given there are three treatment groups across three countries.

Another possibility is that there is some factor that is changing differentially between the treatment areas and the control areas that affects fertility. I test for the differential trend between treatment and control groups in the included independent variables, but there may be omitted variables that change differentially between treatment and control groups over time. With this is mind, results should be interpreted with caution.

# **3. Data**

The data I use for this investigation is from the Demographic and Health Survey (DHS) (Demographic and Health Surveys 2005). The DHS is a nationally representative (state representative for the case of India) household survey that subsumed the World Fertility Survey. Data are available online free of charge on request to the distributors (Macro International). The survey has been conducted over 76 developing countries since 1985, and there are repeated surveys in most of the countries. The data however are not a panel, but rather a repeated cross section. Following a particular family is not possible, and clusters change between each wave, but we can observe changes in the average behavior at the regional and country level.

To conduct the analysis for this project, I chose to focus on India, Pakistan and Turkey. These three countries satisfied two key criteria for selection: they each had a natural disaster that in terms of the number of people killed were in the global top 20 since 1990 (see Table 1) according to EM-DAT (Centre for Research on the Epidemiology of Disasters (CRED) 2008). Secondly, DHS data were available before and after the earthquakes in each country. In this investigation, the fertility response in addition to the response to mortality is of particular interest. Thus, for there to be a mortality response, high mortality natural disasters must be the focus. The reason for requiring data before and after the natural disaster is to conduct difference13
4 Several trends are clear from the table. First, the value of the real minimum wage has eroded in Mexico over the period 1984 to 1990. With the exception of 1985, the trend decline in the value of the real minimum is continuous over this period. Second, the ratio of the minimum wage to the mean wages of both blue and white collar workers has eroded as well, although not as rapidly as the minimum given sizable reductions in real wages for all groups of workers over this period. In 1984, the minimum wage represented 22 percent of the value of the average Mexican wage and 42 percent of the value of the average Mexican blue collar wage. By 1990 these ratios had declined to 13 percent and 31 percent respectively. Panel B of the table shows that consistent with the decline in the real value of the minimum wage and its relationship to average wages paid in Mexico, the share of firms paying average wages near the minimum has declined as well, and quite dramatically over this period. The relationship between wages and the minimum wage suggested by the Mexican household data in Panel C is different however, suggesting that significantly greater numbers of _individuals_ as opposed _tofirms_ have been affected by minimum wage legislation. This is a potentially important difference to which I will return in the analysis that follows.

Table 2 summarizes minimum wage and wage trends using Colombia's Annual Industrial Survey, a survey of large manufacturing firms that is similar to the Mexican Survey, over a partially overlapping period of time.<sup>4</sup> The data for Colombia show the opposite trend to that of Mexico over the 1980s, with rising real minimum wages, rising ratios of minimum to average wages, and greater shares of firms with wages in the range of the legislated minimum over the 1980s. In addition to differences in trend there are sizable gaps in magnitude in the two tables- whereas 27 percent of firms in Colombia reported paying unskilled workers average wages that were below 1.5 times the Colombian minimum wage in 1987, only 10 percent of Mexican firms reported paying unskilled Mexican workers average wages in this range. Indeed, at no time was the impact of the minimum wage on firm wages in Mexico as great as it was in Colombia over the common period analyzed here.<sup>5</sup> 4Beginning in 1984 the Survey was restricted to firms with greater than 10 employees.

5Although the Mexican Household Survey Data give estimates of affected workers that are closer to the Columbian figures at least in 1988, they include informal as well as formal sector workers, and are not strictly comparable for this reason.
**Figure 4: Second-order dominance test for average influence per publication**

<!-- Start of picture text --> 0 2000 4000 6000<br>citations<br>Harvard<br>Berkeley<br>LSE<br>Oxford<br>Princeton<br>WB<br>Yale<br>100<br>80<br>60<br>40<br>20<br>0<br>cumul. area under normalized inverted citation curve<br><!-- End of picture text --> 19
**Figure 7. Out-of-Pocket Costs after Tax**

<!-- Start of picture text --> Sweden<br>Netherlands<br>Finland<br>Belgium<br>Germany<br>Ireland<br>Austria<br>Belgium<br>United States<br>Canada<br>France<br>High Income<br>Australia<br>Italy<br>Mexico<br>United<br>Average<br>New Zealand<br>Japan<br>Colombia<br>LAC Average<br>Brazil<br>Peru<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Out-of-Pocket Cost after Tax/GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries. Note: Findings for Brazil and Peru are based upon tuition fees of a relatively small sample of higher education institutions. The findings for these countries should therefore be considered preliminary.

Average out-of-pocket cost of tertiary education in Latin America, 60 percent, is significantly higher compared to high-income, 19 percent.

Colombia is the only LAC country where student assistance implies a significant improvement in the affordability of tertiary reduction. Education and living costs reach 64 percent of GDP per capita, but grants and loans reduces the costs to 50 percent, a 14 percent reduction. Student assistance in Mexico improves affordability by 1 percent of GDP per capita. In Brazil, out-of pocket costs amount to 77 percent. Similarly, Peru’s out-of-pocket costs remain similar to total costs, which can be attributed to its low availability of grants and loans for tertiary education. Although availability of student assistance in Latin America is comparatively low, the impact can still be significant in segments of the population in particular if the student assistance is well targeted.

The inclusion of student assistance (grants and loans) in the equation also introduces a dramatic change to affordability in Anglo-Saxon countries (22 percent) and Northern European countries (15 percent). Actually, the high level of student assistance in AngloSaxon countries equals the large difference in total education costs that exist between Continental Europe and Anglo-Saxon countries. The out-of-pocket cost that the families from these two country groups have to shoulder during studies becomes more or less similar.

19
38 TABLE 2 AVERAGE SHARE OF P2IVATE (ASPR) AND PUBLIC (ASPU) FIRMS IN TOTAL INDUSTRIAL OUTPUT, 1976-1985

|O5s|YEAR|ASPR|ASPU|
|---|---|---|---|
|1|1975|0.721674|0.278326|
|2|1976|0.740659|0.259341|
|3|1977|0.724289|0.275711|
|4|1978|0.752052|0.247948|
|5|1979|0.753372|0.246628|
|6|1980|0.754654|0.245346|
|7|1981|0.743426|0.256574|
|8|1982|0.766957|0.233043|
|9|1983|0.765165|0.234835|
|10|1984|0.7689a1|0.231069|
|11|1985|0.760505|0.239495|

TABLE 3 AVERAGE SHARE OF PRIVATE (ASPR) AND PUBLIC tASPU) FIRMS IN TOTAL OUTPUT OF EXPORTABLE INDUSTRIES

|OBS|YEAR|ASPR|ASPU|
|---|---|---|---|
|1<br>|1976|0.840750|0.159250|
|2<br>|1977<br>|0.830160<br>|0.169840|
|3|1978|0.862868|0.137132|
|4|1979|0.857863|0.142137|
|5|1980|0.836718|0.163282|
|6<br>|1981<br>|C.815149<br>|0.184851<br>|
|7|1982|0.8'4955|0.125045|
|8|1983|0.857638|0.142362|
|9|1984|0.870658|0.129342|
|10|1985|0.818399|0.181601|

TABLE 4 AVERAGE SHARE OF PRIVATE (ASPR) AND PUBLIC (ASPU) FIRMS IN TOTAL OUTPUT OF IMPORiABLE INDUSTRIES

|O0S|YEAR|ASPR|ASPU|
|---|---|---|---|
|1<br>|1976<br>|0.768374<br>|0.231626<br>|
|2<br>|1977<br>|0.696982<br>|0.303018<br>|
|3<br>|1978<br>|0.750455<br>|0.249545<br>|
|4<br>|1979<br>|0.775547<br>|0.224453<br>|
|5<br>|1980<br>|0.774497<br>|0.225503<br>|
|6<br>|**1981**<br>|0.766003<br>|0.233997<br>|
|7<br>|1982<br>|0.777076<br>|0.222924|
|**8**<br>|1983<br>|0.783287<br>|0.216713<br>|
|9<br>|1984<br>|0.788850<br>|0.211150<br>|
|10|1985|0.790460|0.209540|
# **3. Data and methodology**

The existing census (cross-sectional) dataset on schools and madrasas contains annual enrollment figures spanning the period 1999-2003<sup>10</sup> . The dataset includes information on all secondary educational institutions (25,795 in total) in the 64 districts of Bangladesh. We use the 2003 round of the Census dataset, which provides information on all registered religious and secular post-primary educational institutions. Having sex and grade-specific data on enrollment in each secondary school for the years 1999-2003, it is possible to study the supply-side determinants of growth in total number of girls enrolled at the regional as well as school/madrasa level. We further merge these data to two unique datasets which provide union-level information on the number of BRAC schools and thana-level information on the number of BRAC branches and female members. Both of the latter datasets provide information on all BRAC schools and branches in the country.

Our objective in this paper is to test whether (i) presence of madrasas in a region (i.e. union) bolsters enrollment of female students and if so, (ii) whether madrasas located in regions with more BRAC schools have seen a higher growth in enrollment of female students<sup>11</sup> . Given the availability of data on annual student enrollment for four years, we do so by analyzing the determinants of enrollment growth across Bangladesh regions or unions (which are on average a cluster of 15 villages) in a simple cross-section regression framework. We follow the empirical income growth literature and specify a regression model of female enrollment growth. To capture convergence effect, we allow growth during 1999-2003 to depend on enrollment at the beginning of the period. Since we also want to know school type effects, our growth equation includes the level of school availability by type in 1999 and changes therein by 2003. This yields the following reduced form model of enrollment growth at the region (i.e. union) level: Gti = β 0 + β 1 Et-4+ β 2 X t-4,i + β 3 Δ Xit + β 4Wti+ π t + υ ti (1) where, Gti = Regional growth rate of female enrollment during 1999-2003 Et-4 = Regional enrollment in 1999 X2 =Mean characteristics of educational institutions of the region in 1999 Δ Xti = change in # of schools and madrasas between 1999 and 2003 Wti = Other attributes of the union (e.g. whether an urban region; average age of school/madrasa in the region; presence of a government school in the region) π t = Sub-districts ( _Upazila_ ) fixed-effects υ it= iid random error t = time index i = union index

> 10 The Census has been conducted by the Ministry of Education, Government of Bangladesh. 11 As many as 24% (N=1179) of the unions in Bangladesh do not have a secondary madrasa.

8
the new standard will have a negative impact on African exports of cereals, dried fruits and nuts to Europe. Wilson et al. (2003) used the gravity model to examine the impact of drug residue standards on trade in beef and found that Tetracycline standard in beef has a negative and significant impact on world trade in beef. The study predicts that if international standards set by CODEX were followed in antibiotics, global trade in beef would rise by over US$3.2 billion. Using the gravity model to examine Japan’s stricter pesticide residue limit on vegetables exports from China, Sun et al. (2005) found that Japan’s stricter Chlorpyrifos standard has a negative impact on China’s vegetables export to Japan. Most recently, Chen et al. (2008) examines the impact of Chlorpyrifos MRLs standards on China’s export of vegetables and the impact of Oxytetracycline MRLs on aquatic products. Their results show that food safety standards imposed by importing countries have a negative and statistically significant effect on China’s export of agricultural products. Furthermore, the authors find that the trade effect of food safety standards is much larger than that of the import tariff. Finally, Xiong and Beghin (2013) apply the score indices constructed by Li and Beghin (2012) to study the effect of standards relative to CODEX standards in Canada and the US. The authors find that imports by US and Canada are not affect but the stringency of this type of regulation; however, the authors find that exports from Canada see a positive effect in foreign markets.

We specifically apply the Helpman, Melitz and Rubinstein (2008; henceforth HMR) gravity model for our analysis. Based on the Melitz (2003) model, HMR extended the gravity equation of Anderson and van Wincoop (2003) and developed an estimation procedure to obtain the effects of trade barriers on the intensive and extensive margins of trade. The model takes account of the empirical facts that firms in a typical industry are heterogeneous in terms of efficiency, that only a fraction of them export and that exporters tend to be more productive than non-exporters (see i.e., Eaton et al , 2004). Without control for heterogeneity, estimates of the effects of trade barriers on firm level exports will be confounded with their effects on the number of firms that export, and without control for zero bilateral trade flows, estimates will be affected by selection bias. Bilateral trade data at the firm level would optimally be used to empirically estimate the effects on the two margins; however, the authors solve this problem by exploiting the presence of zero trade flows in aggregate bilateral trade data. Nearly half of the potential bilateral trade flows in their data have a value of zero. In our case, nearly 90% of potential trade flows have a value of zero making it imperative to use a methodology that deals with zero trade.

8
- Annen, K., and S. Knack (2018): “On the delegation of aid implementation to multilateral agencies,” _Journal of Development Economics_ , 133, 295–305.

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- Bueno de Mesquita, B., A. Smith, R. M. Siverson, and J. D. Morrow (2003): _The Logic of Political Survival_ . MIT Press, Cambridge.

- Burnside, C., and D. Dollar (1997): “Aid, Policies and Growth,” Policy Research working paper; no. WPS 1777. Washington, D.C.: World Bank Group.

- (2000): “Aid, Policies, and Growth,” _American Economic Review_ , 90(4), 847–868.

- Custer, S., Z. Rice, T. Masaki, R. Latourell, and B. Parks (2015): “Listening to leaders: which development partners do they prefer and why?,” AidData, Willamsburg VA.

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- (2017): “Aid, China, and Growth: Evidence from a New Global Development Finance Dataset,”

- AidData Working Paper 46, Williamsburg, VA: AidData.

24
between the two.<sup>15</sup> We present estimates including and excluding provincial dummies, and with or without Aceh. A range of factors, including propensity to report, may vary at the provincial level.

# **_Hypothesis: Poverty is associated with increased conflict._** Our estimates focused on

both absolute consumption poverty levels at the district level (from the SUSENAS), how poor a locality was relative to its respective district (PODES), and recent trends in locality poverty.<sup>16</sup> District poverty levels appeared to increase the likelihood of conflict (Table 8, (3) & (4)), although this was no longer the case when provincial dummies were added for the provinces (Eq., 1-2) or when we added Aceh to the sample. This suggests the province specific dynamics of district poverty incidence may matter. In one of our qualitative case study provinces, East Java, poorer districts actually seemed to be associated with less conflict (Eq. 7). Although relative poverty did not appear to be significantly associated with conflict, _changes_ in poverty were significant in rural areas.

**_Hypothesis: Inequality is associated with increased conflict._** Although the SUSENAS allows us to calculate a measure of household consumption inequality, the indicator is only

> 15 The qualitative work in the previous section focused on rural areas ( _desa)_ in East Java and NTT.  In contrast, for example, the work of Varshney on communal conflict in India focuses on cities.

16 We created the Poverty Gap index of poverty (Foster Greer Thorbecke) using per capita consumption as a welfare measure. The poverty gap is based on the average distance that the poor’s consumption falls below the poverty line. We defined village-based poverty from the PODES, based on the Indonesian family planning board’s (BKKBN) census of poor households. The indicators are whether a household has an earth floor, whether household members have separate clothing for work and leisure, whether they are able to perform their religious duties, and if _rel_ households are able to seek medical care when needed. Relative poverty is defined as _pij_ = _pij_ / _~~p~~ j_ where _pij_

> <sup>_~~p~~_</sup> _j_ the fraction of poor households in village<sup>_i_</sup> in region j. is the mean poverty rate in all villages in district j.  Our relative measure was based on a question which asked whether there had been a recent increase in these “poverty letters.”

23
# Table A1 Statistical Description

The sample consists of all financial listed firms in the CSMAR database from 2008-2017.

||N|Mean|St. Dev.|Min|Max|
|---|---|---|---|---|---|
|∆(Investment/Total Fixed Asset)|1250|0.03|0.43|-.832|1.25|
|Titling Firm dummy|1250|0.04|0.19|0|1|
|ROA in 2012|1250|6.16|17.12|-13.29|40.18|
|Tobin Q in 2012|1250|2.04|8.69|.11|232.59|
|Δ Tobin's Q|1250|0.83|6.71|-89.14|101.35|
|The relative size of the acquirees in the M&A deals|1250|0.05|1.27|0|44|
|Shenzhen dummy|1250|0.06|0.23|0|1|
|Tenure Unexpired|1250|0.02|0.16|0|1|
|Rights Uncontended|1250|0.02|0.15|0|1|
|SOE dummy|1250|0.69|0.46|0|1|
|Historical Annual Stock Market Return|1250|-0.36|0.12|-0.54|-0.07|
|Δ Historical Annual Stock Market Return|1250|-0.01|0.19|-0.37|0.40|
|Dividend / Total sales|1250|0.02|0.07|0|1.94|
|Incentive Package|1250|2.53|8.48|0|78.04|
|Chairman-CEO duality dummy|1250|0.71|0.46|0|1.00|
|% of Independent board|1250|0.36|0.05|0.09|0.57|
|Size-Age Measure|1250|0.83|3.91|-0.68|23.92|
|External Finance Reliance|1250|0.27|0.26|-0.14|1.14|
|CAR in QE announcement|1250|0.19|0.09|-0.50|0.50|
|Bureaucrats on boards|1250|0.11|0.38|0|3|
|Party Member|1250|0.14|0.88|0|1|
|All Disputes|1250|0.01|0.11|0|1|
|Disputes with government|1250|0.01|0.09|0|1|
|Disputes with private firms|1250|0.01|0.10|0|1|

59