rafmacalaba/datause-encoder-data huggingface dataset:
Studies that use child growth measures to look at health related impacts for children include, in particular, Hoddinott and Kinsey (2001) and Foster (1995). Hoddinott and Kinsey (2001) show that very young children in Zimbabwe grew signi fi cantly slower during a period of drought than children in times of normal rainfall. Foster(1995) showed similar results for children after a period of severe fl oods in Bangladesh. Neither of these studies fi nds any gender differentials in child growth, however.

The main econometric challenge for this paper lies in dealing with the endogeneity of the migration decision in a context where community level unobservables are likely to be important determinants of the main outcome of interest: child health. We use two strategies to address this potential endogeneity problem. The fi rst is to use instrumental variables to address selection in the migration decision. We use the prevalence rates of migration in the population at the village level as our main instrument for migration and use census level information on landownership in the village to get within village variation in this instrument. We also use a feature of migration that is particular to the context we study to obtain household level variation in our instrument. Mobility and seclusion restrictions on women typically require the presence of an adult male in the household. Households with a single adult male are therefore much less likely to undertake migration. We also show that, conditional on appropriate demographic characteristics, the number of adult males exercises no in fl uence on any outcome of interest. We can therefore interact the village-land group migration network with the number of adult males in a household (males above age 20) to obtain an instrument which varies at the household level. The identi fi cation argument is that, conditional on household demographic characteristics and village fi xed effects, the incidence of migration at the census level, within speci fi c land ownership groups in a village, interacted with the number of adult males in the household should affect the household’s opportunity to send a migrant but is unlikely to be correlated with any unobservable household or child attributes that affect nutritional status.

A second strategy is to con fi ne attention to migrant households and to examine anthropometric outcomes for children born before and after fi rst migration within such households. The advantage of this approach is that it allows us to control for any time invariant unobserved household attributes that may affect both the migration decision and child nutrition.

After accounting for selection into migration, the results indicate that migration has a substantially larger positive impact on growth outcomes for young girls. Moreover, the inputs other than consumption and is a stock variable. Neither of these is a concern for us since we are not looking at changes in body size for the same child over time.

4
roof. When enumerators and manuals are different for different surveys, there can be systematic differences to the same question asked.

Table 10 presents the statistical tests of the common variables between the two data sets (SWIFT and MCNA). All the common variables between the surveys are significantly different. The average household size in the SWIFT survey for the north is about 7, which is significantly greater (by one member) compared to the MCNA data. Similarly, there is significant difference in housing type, source of drinking water and household employment rate between the two surveys. As a result, the PPMT score in the MCNA data is slightly higher than in the SWIFT data. This implies that the PPMT would suggest an average person from the MCNA data is richer compared to an average person from the SWIFT data, which would directly impact the eligibility overlap using a common cut-off threshold.

_Table 10: Summary statistics of key variables by MCNA and SWIFT survey_

||**Survey**|||
|---|---|---|---|
|Variables|**MCNA**|**SWIFT**|**Difference**|
|Home: house or flat|0.905|0.986|-0.082***|
||[0.294]|[0.117]|(0.012)|
|Household size|5.946|6.993|-1.048***|
||[2.801]|[3.274]|(0.273)|
|Employment rate (emp/hhsize)|0.180|0.209|-0.029***|
||[0.179]|[0.144]|(0.011)|
|Water: public network tap|0.809|0.949|-0.141***|
||[0.393]|[0.220]|(0.023)|
|Pseudo-PMT score (log)|5.176|5.118|0.058*|
||[0.363]|[0.374]|(0.030)|
|**Observations**|**4012**|**300**||

Note: Standard deviations are in brackets, standard errors are in parentheses and significance levels are denoted as follows: *<0.10, **<0.05, ***<0.01 Second, the underlying populations of the SWIFT and MCNA surveys are quite different. SWIFT was designed to be nationally representative, albeit stratified to represent IDPs, who represent around 10 percent of the sample. MCNA was a vulnerability assessment and covers a significantly more vulnerable population. This may also explain some of the differences observed between the common variables in Table 10. These key differences in selection may have resulted in non-comparable samples which is affecting model comparison.

# 5. Conclusions and Next Steps

With protracted displacement and delayed implementation of recovery and resilience activities, the humanitarian situation in Iraq is in transition. While 1.2 million Iraqis still remain displaced, more than 4.8 million of the 6.1 million that fled as a result of the ISIS conflict have returned to their areas of origin (IOM Iraq, 2021). Low prospect of employment/livelihood opportunities, lack of access to proper shelter/housing and other services, reduced social cohesion (community tensions and fears of population change) and security concerns (blocked return or perceived insecurity) form obstacles for safe and sustainable reintegration of some returnees (IOM Iraq, 2020a). While the Ministry of Migration and Displaced (MoMD) provides some support, access to social protection remains a challenge for both the displaced and returnees due to lack of civil documentation, inadequate assessment capacity of 20
# **C. Data**

Two datasets are used in this paper – a census level panel dataset of all public schools in the Philippines, and a survey sample of 150 schools that was chosen from the census data.

_BEIS-NAT matched panel database (2005-2010):_ The dataset contains approximately 38,000 public elementary schools and 7,200 public high schools. The dataset was generated by merging school level Basic Education Information System (BEIS) administrative data with National Achievement Test (NAT) data that includes school level mean scores on standardized student tests for grades 5 and 9. Data was matched across school years to generate a panel dataset. A separate set of working papers (Yamauchi, 2012; Yamauchi and Parandekar, 2013) present analysis of the BEIS-NAT data to examine the impact of SBM and to analyze patterns of inequality regarding results and resources. A detailed field survey of 150 schools was also conducted to investigate SBM related issues.

_Three division field survey:_ The three divisions of Pangasinan II, Bohol and Surigao Del Sur were chosen as the sample divisions for the field survey. These were chosen to represent the three island groups of Luzon, Visayas and Mindanao respectively. The divisions were also selected because they represent varied experiences with SBM implementation. Surigao Del Sur schools have a longer experience with school level planning and management as part of the Third Elementary Education Project (TEEP) which started in Surigao Del Sur, along with in 22 other divisions, in 2002. Bohol has had the benefit of experience from the AusAID financed Strengthening Implementation of Visayas Education (STRIVE) which introduced school level planning and SBM related data management systems in the division. Pangasinan II, on the other hand, has not had the experience of any externally aided project, and SBM tools such as School Improvement Plans were introduced in most schools as late as 2009/2010.

_Stratified random sampling:_ All schools in the three divisions were distributed across strata formed by three key variables and to select randomly within the strata. The variables were the level of implementation of SBM in a school as determined by an SBM index; the NAT score performance across the past 6 years computed as a NAT index and the school size (enrollment). The SBM index and NAT index was used to generate a matrix of schools were randomly selected from each of the four corner cells and the middle cell of the matrix. The purpose of this stratification was to ensure _a priori_ that the sample had a sufficient variation in SBM implementation and NAT score performance and that there was adequate representation of schools of different sizes.

_Respondent groups_ : Within each sampled school, detailed interviews were carried out with the school principal, the chairperson of the school governing council (usually a parent), and a teacher randomly selected from the roster of teachers in the school. The same set of questions regarding SBM was implemented to the three respondent groups.

12
53 What does access to finance mean? Broad access to financial services implies an absence of price and non-price barriers. It is difficult to define and measure because there are many dimensions of access, including availability, cost, and range and quality of services being offered. While there is much data on financial sector development more broadly, until recently there was very little data on usage and access to finance, both for households and firms. Hence, there is also very limited analysis on the impact of access to finance on economic development. Research using firm level survey data suggests that financing obstacles are the most constraining among different barriers to growth (Ayyagari, Demirguc-Kunt and Maksimovic, 2005).

Financing obstacles are also found to be highest and most constraining for the growth of smaller firms (Beck, Demirguc-Kunt and Maksimovic, 2004a). At the household level, lack of access to credit is shown to perpetuate poverty because poor households reduce their children’s education (Jacoby, 1994; Jacoby and Skoufias, 1997). Similarly, Dehejia and Gatti (2005) find that child labor rates are higher in countries with under-developed financial systems, while Beegle et al. (2007) show that transitory income shocks lead to greater increases in child labor in countries with poorly functioning financial systems. A better understanding of what the chief obstacles to improving access are, and access to which type of financial services has the greater impact on reducing poverty and promoting growth, will need to wait for availability of better data and analysis in this area.

There are many different reasons why the poor do not have access to finance –loans, savings accounts, insurance services. Social and physical distance from the formal financial system may matter. The poor may not have anybody in their social network who knows the various services that are available to them. Lack of education may make it difficult for them to overcome problems with filling out loan applications, and the small number of transactions they
the survey data. Second, _market income_ , is computed as _disposable income_ plus direct taxes and contributions minus direct transfers. It includes direct personal income taxes, social security contributions (health and injury), and direct- and near-cash transfers.<sup>14</sup> _Market income_ , which is the primary income concept for the CEQ, is used to rank households into income deciles or quintiles as it represents income before state actions through taxes and transfers. Third, _consumable income_ is computed as _disposable income_ plus indirect transfers/subsidies minus indirect taxes. Finally, _final income_ is _consumable income_ plus imputed values of education and health care services. The monetarized value of education and health care services is conditional on the composition of households, declared use of public services and calculated values of these benefits in specific circumstances.

where h is income before taxes and transfers, are the taxes paid by the households for range of taxes analyzed, are the transfers received by households for range of transfers analyzed. and are ii II TT ii the amounts of tax and transfer paid and received by households, respectively, and H is the number of jj iih jjh BB jj SS SS individuals in the household. ii jj

# **Data sources**

The primary data providing individual and household-level information necessary to allocate the fiscal policy elements is the 2019/20 Cambodia Socio-Economic Survey (CSES).<sup>15</sup> This survey is collected by the National Institute of Statistics every two years. CSES 2019/20 includes modules covering education, health, labor market activity, income sources, and household consumption expenditure. CSES also provides a roster of individuals in each household that provides individual and demographic characteristics. The 2019/20 CSES uses the 2019 General Population Census of Cambodia as the sampling frame and is representative at the national level; by Phnom Penh, other urban, and rural areas; and by four geographic zones. The survey was administered to 10,075 households (44,549 individuals).

Administrative data for the survey year provides relevant information on the fiscal system. This paper focuses solely on the 2019 fiscal year because it overlaps with the 2019/20 CSES and it is the last year before the fiscal data is affected by the coronavirus disease 2019 pandemic. The Ministry of Economy and Finance is the source for total revenues collected by the government from households (from different taxes) and spending (on subsidies and social programs). Details of Government revenues and spending are also needed for validating the precision of imputations of the fiscal elements to the household sector.

> 14 Cash and near-cash transfers are transfer income from the government to individuals and families, and include Social Security, unemployment compensation, workers compensation, all means-tested cash transfers, and food and housing benefits.

> 15  See National Institute of Statistics (2020).

11
**Figure 1.1: INDIA, Patterns of missing values in PF variables**

**Figure 1.2: TURKEY, Patterns of missing values in PF variables**

|**Sales**|**Materials**|**Capital**|**Labor**|**# of**<br>**m.v**|**# of**<br>**obs.**|**% of**<br>**obs.**|**Sales**|**Materials**|**Capital**|**Labor**|**# of**<br>**m.v**<br>|**# of**<br>**obs.**<br>|**% of**<br>**obs.**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||0|4631|67.6|||||0<br>3|818<br>737|30.9<br>279|
|||||1|1113|16.3|||||1|345|.<br>13.0|
|||||3|913|13.3|||||2|189|7.1|
|||||2|89|1.3|||||1|185|7.0|
|||||2|47|0.7|||||2|133|5.0|
|||||2|28|0.4|||||4<br>|96<br>|3.6<br>|
|||||1|18|03|||||1|87|3.3|
|||||||.|||||2|35|1.3|
|||||1|10|0.1|||||3|6|0.2|
||||||||||||2|5|0.2|
||||||||||||3|5|0.2|
||||||||||||1|3|0.1|
||||||||||||2|2|0.1|

**Figure 1.3: SOUTH AFRICA, Patterns of missing values in PF variables**

**Figure 1.4: TANZANIA, Patterns of missing values in PF variables**

|**Sales**|**Materials**|**Capital**|**Labor**|**# of**<br>**m.v**|**# of**<br>**obs.**|**% of**<br>**obs.**|**Sales**|**Materials**|**Capital**|**Labor**|**# of**<br>**m.v**|**# of**<br>**obs.**|**% of**<br>**obs.**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||0|1265|69.9|||||0|313|37.8|
|||||1|220|122|||||3|130|15.7|
|||||4|123|.<br>68|||||1|81|9.8|
|||||||.|||||1|74|8.9|
|||||3|99|5.5|||||1|51|6.2|
|||||1|47|2.6|||||1|38|4.6|
|||||2|24|1.3|||||4|37|4.5|
|||||1|17|0.9|||||2|30|3.6|
|||||2|7|0.4|||||2|26|3.1|
|||||2|4|0.2|||||2|25|3.0|
|||||1|1|01|||||3|9|1.1|
|||||2|1|.<br>|||||2|5|0.6|
|||||||0.1|||||2|3|04|
|||||3|1|0.1|||||3|3|.<br>0.4|
||||||||||||2|2|0.2|
||||||||||||3|1|0.1|

# Notes:

Yellow means information available on the corresponding variable. White means information is missing. Source: Authors’ calculations with ICS data.

Tables from 2.1 to 2.4 of the appendix show the distribution of the number of observations available in the original sampling frame, in the complete case and in the sample with replacement, along with the percentage of observations lost with respect to the original sampling frame. From Table 2.1 the percentage of observations lost in India in the complete case varies when we move industry by industry and size by size. Flagrant cases of loss of observations are small firms operating in the non-metallic products sector (61.9%) or the medium-sized firms of the food sector (55.37%). The replacement process allows retrieving for the analysis a considerable percentage of observations. After the replacement we only lost 28.6% and 22.6% in the two cells mentioned previously. In Turkey the percentages of observations lost by size and industry (see Table 2.2) range from 40% (medium-sized firms in the transport equipment sector) to 87.3% (small firms in textiles and apparels industry). South Africa lost 50% of small firms in textiles and apparel and chemical, rubber and plastics sectors (see Table 2.3). Lastly, Tanzania lost more than 70% of small firms in paper, edition and publishing and machinery and equipment and 73% of large firms in textiles and apparels (Table 2.4).

9
Table 8 presents the regression results for different regions. When the results in Table 5 and Table 8 are compared to each other, it can be seen that the results are consistent and robust, but still some regional differences are observed. The estimated coefficients of the two tax simplification variables are statistically significant and have the expected positive sign. The control variables also have the expected sign and are statically significant determinants of tax

**Table 8 –Regional Differences in Tax Corruption (2002-2012)**

|**_Dependent variable: Tax corruption_**|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**SSA**|**EAP and**<br>**SASIA**|**LAC**|**MENA**<br>**and ECA**|
|**Constant term**|-21.308|46.893|53.717|13.975|
||(-2.342)**|(1.384)|(7.391)***|(0.845)|
|**_Tax simplification_**|||||
|**log(Tax payments)**|4.574|2.855|6.357|3.254|
||(3.222)***|(1.624)|(6.165)***|(2.065)**|
|**log(Tax time)**|3.802|2.106|4.857|2.017|
||(2.674)***|(1.747)*|(1.85)*|(1.837)*|
|**_Political and Political Institution Determinants of Corruption_**|||||
|**Bureaucracy Quality (higher better quality)**|-0.846|-3.101|-0.338|-1.411|
||(-1.707)*|(-1.682)*|(-1.524)|(-0.733)|
|**Democratic Accountability (higher better)**|-1.45|-1.2|-2.023|-1.157|
||(-1.733)*|(-1.816)*|(-3.807)***|(-1.754)*|
|**_Judicial and Bureaucratic Determinants of Corruption_**|-11.184|-20.703|-1.864|-9.483|
|**Rule of Law (higher better)**|(-8.511)***|(-5.619)***|(-2.509)**|(-3.929)***|
|No. of observations|340|126|124|257|
|J-statistics|2.485|2.556|2.649|2.487|
|Arellano-Bond serial correlation test AR(1)|0.460|0.414|0.474|0.461|
|Arellano-Bond serial correlation test AR(2)|0.888|0.908|0.878|0.889|
|Jarque-Bera normalitytest|1.264|1.297|1.246|1.267|

Note: The estimation method is a panel - GMM. Annual data are used. t-statistics are given in parenthesis. * indicates 10% significance level, ** indicates 5% significance level, and *** indicates 1% significance level. These significance levels are equal to one minus the probability of rejecting the null hypothesis of zero coefficients. J-test is for overidentification problem where H0: there is no overidentification problem. For serial correlation z-tests, H0 is "there is no serial correlation"; and for normality test, H0 is "normal distribution".

corruption. The exception is the SASIA and EAP region. The statistical significance level of the 32
fixed effects). The coefficient for _Hindu Non-Dalit_ was negative and significant in the case without control variables (columns 1 and 2) but the variable lost significance once other controls and fixed effects were included, indicating that they were statistically speaking no different from their Dalit counterparts once we include measures of socio-economic status. The Muslim effect however was robust and significant, suggesting that the effect may not be driven by socio-economic status, at least to the same extent as the Dalits.

As an additional robustness check, we perform a similar analysis using village-level data.<sup>18</sup> Our sample now consists of the 3644 villages in the NFHS-3, and our left-hand side variable of interest will be average fertility of women in a village (defined as the total number of children divided by the number of women who were interviewed in a village). The dependent variables are as follows: the fraction of ever-born boys who had died before the age of 5 and the fraction of ever-born girls who had died before the age of 5. The set of our control variables is similar to that in the previous section, except that they are constructed as village-level averages. We control for the fraction of a village’s population that is Muslim, as well as upper-caste Hindus. We also control for the average female age, age at marriage, primary school completion and the fraction of the population that has never attended school. Similar variables for men – average age, fraction of men who completed primary school and fraction of men who never attended school – are also included as controls. We also include a set of control variables that focus on wealth. Measures of average landholdings, the average household wealth index and the fraction of households that are rural, are included in this group of control variables. Finally, we also include a set of community level averages for vaccination rates for measles, polio, DPT and BCG, nutritional intakes and female labor force participation rates.

The results for male mortality are presented in columns 4–6 of Table 6 and the results for female mortality are presented in columns 5–9. Note that the coefficient for _Muslim population_ is not significant in the male mortality regressions, and is negative and significant in the female mortality regression that includes fixed effects (column 9). On the contrary, the coefficient for _Hindu Non-Dalit Population_ is negative and significant in the male mortality regressions without controls (column 4) and positive and significant in the female mortality regression with controls (column 8). We interpret this as evidence that Muslim girls exhibit lower mortality rates than the Dalits, but upper-caste Hindu girls display higher mortality

> <u>18</u> Ideally, we would like to have performed the analysis at the level of a district, but this was not possible with the NFHS-3 dataset. District information was not included in the public release of the data.

13