rafmacalaba/data-use-mentions-extended huggingface dataset:
Unobservable characteristics that are omitted from the analysis such as ability or wellconnectedness may be correlated with both the explanatory variables—migration and remittances—and the outcomes of interest. The direction of the bias is unclear a priori.

Ideally, an unbiased estimate of the causal impact would be the difference between outcomes of households with migrants (and/or remittances) and their outcomes in the counterfactual scenario when these same households do not have migrants. However, households with migrants tend to be ―selected‖ based on unobservable characteristics. Therefore, households without migrants will not be a good counterfactual for them.

One method to potentially account for such possible biases is the use of panel data to perform fixed-effects or first-difference estimation. The panel data structure allows us to control for unobserved fixed heterogeneity using household fixed effects. However, the identification assumption would be that there are no time-varying unobservable determinants of the outcome variables. We are concerned that there might be unobserved shocks, such as changes in the structure of the economy or weather shocks, which correlate with the migration decision as well as child outcomes and labor supply of the origin household.

Another method, the instrumental variable approach, will be used to separate the impact of migration from selection effects. Historical migration networks, defined as the percentage of households in the village with migrants in the past, will be used as instruments for migration and remittances in estimating their impact on the sending households. We expect that larger initial migration networks would lower the cost of subsequent migration, through information or through financing, and thus induce more migration. The first-stage regression reported in Table 3 confirms this relationship. The bottom row shows that 1993 and 1997 migration rates are very strong determinants of whether a household has a migrant in 2007 (as well as whether a household receives remittances). The F-statistic of joint significance of the instruments is reported at 38 or higher. By living in a community with high levels of migration in the 1990s, a household has a higher probability of having an international migrant than
labor income from January to November 2020 **.** Additionally, variations in the number of employees throughout the year, peaking in the summer and dropping in the winter, impact data matching. The characteristics of employees also undergo seasonal changes, making the evolving employee pool more significant in our context than fluctuations in median/mean labor income values. When estimating the gap between median "true" and median "reported income" based on tax data for February 2020, we observed an increase in average tax compliance to 96%, while tax compliance at the median decreased to 85%. Considering these factors, we assert that July provides the most representative proxy for annual income, and therefore, we maintain our main estimate based on this month as the baseline.

**Bendek & Lelkes (2011) compare the “true income” reported in the household budget survey and “reported income” from the sample of tax declarations in Hungary and find that the average rate of underreporting of income in tax data is 9%– 13%.** The degree of underreporting is higher among selfemployed than among employees. The underreporting is highest at the bottom and the top of the income distribution. The underreporting of true income in tax data reduces the tax system's progressivity. Paulus (2015) compares the "true income" reported in labor force survey data and income reported in tax declarations in Estonia. The study found that about 12% of total employment income is not reported to the tax authority, primarily due to the partial underreporting of income (envelope payments). Kiełczewska et al. (2021) compare the "true income" reported in the household budget survey and "reported income" from tax declarations in Poland. According to their results, 31% of microenterprises employees and 12% of employees in Poland receive envelope payments. Envelope payments equal 6% of Poland's labor income and 1.6% of the Polish GDP.

**Research done for other countries clearly shows that underreporting
|Diff(p.p.)|0.043|0.042|0.069|0.099|0.036|0.044|
|---|---|---|---|---|---|---|
|Diff(%)|0.071|0.065|0.095|0.146|0.067|0.076|
|t-stat|3.36|3.71|5.04|5.12|3.03|1.81|

_Source:_ Authors’ tabulations using SEDLAC (CEDLAS and the World Bank).

Could remittances be associated with more job creation by financing self-employment and entrepreneurship? In El Salvador and Guatemala, remittances are associated with increased likelihood of self-employment (Table 6). In 2014, self-employment rates were 25.7 percent (from 28.7 percent to 36.1 percent) higher for those who received remittances relative to those who did not in El Salvador, and 40.9 percent higher in Guatemala. As in the analysis on informality, there is no evidence of a relationship between self-employment and remittances in Honduras. The likelihood of being an employer was higher for individuals who received remittances in El Salvador and Honduras than those who did not receive remittances. The share of working individuals who report being an employer among remittance receivers was 2.4 (El Salvador) and 3.6 (Honduras) percentage points higher than the control group. This is the equivalent of more than doubling the propensity to be an employer in El Salvador. No statistical relationship is found between remittances and employer status in Guatemala.

**Table 6.  Self-employment and employer rates, propensity score matching results**

|||<br>El Sa<br>|<br>lvador<br>|<br>Guat<br>|<br>emala<br>|<br>
# 1 Introduction

In recent years Brazil has achieved much success in reducing poverty and income inequality. According to World Bank data, relative poverty (based on a PPP US$2 per day metric) has fallen markedly, from 21 percent of the population in 2003 to 11 percent in 2009. Extreme poverty (based on a PPP US$1.25 per day metric) also dropped significantly, from 9.8 percent in 2004 to 6.1 percent in 2009. At the same time, income inequality fell significantly. Between 2001 and 2009, the income growth rate of the poorest decile of the population was 7 percent per year, while that of the richest decile was 1.7 percent; as a result, income inequality (as measured by the Gini index) fell markedly, from 0.594 in 2001 to 0.521 in 2011–a 50-year low. Key drivers of these achievements have been low inflation, sustained economic growth (at a yearly average of 4.0 percent during 2002-08 and 5.1 percent during 2010-11), well-focused social programs, and real increases in the statutory minimum wage.

Despite these achievements, however, inequality remains at relatively high levels, and there is still a large gap in access to education. Although universal coverage in primary education has been achieved, quality at both the basic and secondary levels remains a concern. Growth and private sector activity continue to be hampered by various barriers and regulations, as well as inadequate infrastructure and a weak business climate.<sup>1</sup> Gender inequality remains high, despite some significant improvements. In 2003, under his first presidency, Luiz Inácio Lula da Silva created a federal governmental body with the objective of dealing with gender equality issues. This also led to the creation of a National Plan for Women’s Policies (NPWP), which was adopted in 2004. The Plan reaffirmed the commitment by the Brazilian Federal Government and by the other governmental bodies to incorporate a gender perspective in public policies
In all, therefore, we come up with the following augmented baseline specification in which we interact our services linkage, ������� , with an institutional variable varying by country and time, such that: ������� ������������ ���������� ∗������ ��� � ���� ϛ�� ������ (4) In the process of selecting an appropriate proxy variable for domestic institutions, our experience shows that the variables from the World Bank governance indicators database are highly correlated with each other. Based on Van der Marel (2016) and Beverelli _et al_ . (2015) we select both level of corruption and rule of law, plus a third indicator called regulatory quality and perform a factor component analysis so as to take out the common variation between the three institutional measures. The selection of the third variable is based on the fact that regulatory quality is important proxy indicator for strong and independent regulatory bodies that oversee services markets as outlined in Sàez _et al_ . (2015).

Furthermore, we provide additional interaction effects with variables varying at different levels. One is that we also include our time‐invariant firm‐level foreign share variable to see whether foreign firms are significantly more hurt by regulations, and for which type, as opposed to pure domestic firms. Second, we also develop an industry‐specific variable of see where goods and services sectors are placed in terms of their productivity relative to the so‐called technology frontier. Through this way we have three different types of interaction strategies to see whether essential country, sector and firm characteristics have any differential influence on the relationship between services regulations and downstream economic performance.

# **4 Results**

The results of our baseline estimations are presented in Tables 2 and 3. These tables report the results using our baseline regression as specified in equation (3
Quality determinants explain much of the variance in academic outcomes (determined mostly within the institution) and of formal employment (since institutions may vary in their ability to place students). However, they explain little of the variance in wages, which are monetary outcomes and therefore sensitive to the national context. Field of study accounts for a substantive share of explained outcome variation (6 to 21 percent, depending on the outcome), consistent with the documented cross-field variation in dropout rates, net returns, and SCP value added (Ferreyra et al. 2017, Dinarte et al. 2022, Ferreyra et al. 2021).

Among quality determinant categories, T&C accounts for the largest share of explained variation of ETG (25%), formal employment (22%) and wages (8%), and LPS accounts for the largest share of explained variation of dropout rates (18 percent). Costs, in turn, explain a nonnegligible share (13 percent) of the explained variation of academic outcomes but nothing of labor market outcomes. Student and program characteristics account for very little of the explained outcome variation but HEI characteristics account for more, particularly in the case of the academic outcomes (10 percent for dropout rates and 5 percent for ETG).

# **Individual-level regressions**

**Brazil.** Despite having student and peer administrative data in addition to survey-level data, we explain relatively little (12-15 percent) of the variation in graduation and labor market outcomes (Table 4). Nonetheless, the role of student and peer administrative variables is considerable for labor market outcomes: together they account for 30-40 percent of their explained variation (Table 4 and Figure A2). In contrast, they explain little of the variation in graduation, much of which is explained by field of study (24 percent) and HEI characteristics (32 percent), consistent with program-level findings. As in the latter, field of study explains much variation in graduation, while the geographic unit (state, in this
significantly affected, we include lagged dependent variable in all regressions in the main text. Appendix 1 shows that our results are robust to the exclusion of Ycpt−1 . αpt are province-year fixed effects. City characteristics include average years of schooling, population, average age, population share of Han ethnicity in the structural transformation regression. We drop average years of schooling when the dependent variable is school enrollment rate. We add additional city characteristics, including GDP per capita and rural population share in robustness checks. These variables are not included in the baseline regression because controlling these variables will reduce our already small sample size.

As our dependent variable is either change in structural transformation variables or change in school enrollment, it automatically removes all time-invariant factors that affect structural transformation and educational outcomes at the city level. We further control province-year fixed effects to account for province-specific time trends. Essentially, we examine how increased local FDI presence affects the pace of structural transformation and school enrollment among cities in the same province. In all regressions, standard errors are clustered at the city level. Observations are weighted by initial population in each city.

A natural concern in Bartik IVs is that city-specific pre-trends are correlated with initial industry composition. As FDI deregulation is not assigned randomly across industries, it can introduce omitted variable bias if they are correlated with underlying trends that are not controlled for in the econometric model. McCaig (2011) discussed options to control for such underlying trends, one option is to include relevant initial conditions at the observation level. Therefore, we include initial sectoral employment share, school enrollment rate, foreign firms’ employment share, average years of schooling, population, average age, population share of Han ethnicity, GDP per capita so any underlying trends associated with these variables are controlled for, and different trajectories across cities are allowed.

# 2.3 Data

This paper combines various data sources to explore whether FDI catalyzes local structural transformation and human capital accumulation.

## **Population census**

This paper uses population census data
where _yit_ refers to the outcome of interest for principal _i_ in time _t_ ; _αborder_ is the border fixed-effects as defined for Eq. (1); _αt_ is a time trend; and _αct_ is a municipality-specific time trend. _Ceara_ is a dummy for schools at _Ceará_ ’s borders and zero for schools on the other side of the border (Figure 1); therefore, _β_ 0 measures the DD in the baseline period. _αyear_ is a set of exclusive dummies per year, i.e., from 2007 to 2017. The variable _DiDt_ interacts _αyear_ and _Ceara_ ; _γXi_ is a set of controls for principals’ characteristics (five age categories, race, and gender); and _εit_ is the error term. Because principals manage schools of different sizes, Eq. (2) weights the coefficients by school size, according to the school census. As before, the parameters of interest are the yearly differences in outcomes ( _βt_ ) at the border _relative_ to the difference during the baseline year of 2007.

Figure 4 plots the DD coefficients of interest for principal selection. Selection types are merged in four categories: "Election", "Designated", "Formal Selection", and "Hybrid".<sup>17</sup> Thus, Figures 4 _.A_ and 4 _.B_ present the _βt_ , and their respective 95% confidence intervals, relative to the difference in the baseline, in this case 2007.

The first result from Figure 4 is the opposite behavior shown by designations and formal selection of principals. Figure 4 demonstrates that municipalities in _Ceará_ had around a 10 _pp_ decrease in the probability of school principals being designated during the RBF period (2009 _−_ 2011) and almost a 20 _pp_ decrease after the RBF was reformulated (2013 _−_ 2017). In contrast, there is an increase in formal selection: when the RBF was introduced, principals in Ceará were around 10 _pp −_ 15 _pp_ more likely to go through a formal selection process
their nominations. If they run an imbalance, they can avoid penalties by trading in the on-system market.

# _Stage 4: Processes performed on the day after the gas day_

- BGT collects data about the volume and calorific value of gas at entry and exit points and in storage. At 4:00 p.m. metering data become available for the allocation process.

> 2 BGT's choice of interruptible sites must depend entirely on operational necessity. But on average over the year, all shippers with interruptible sites must be treated equally.

> 3 Flow orders are orders issued by pipeline companies that require shippers to inject or withdraw natural gas at a specific point to ensure continued flow of natural gas through the system during an emergency.
a randomly chosen diary week, we have _E_ [ _N |N_<sup>_∗_</sup> = _n_<sup>_∗_</sup> _, Y_<sup>_∗_</sup> = _y_<sup>_∗_</sup> ] = _n_<sup>_∗_</sup> and:

The model also implies that distributional indicators obtained from diary acquisitions may be liable to provide incorrect policy conclusions. For example, calculations reported in the Online Appendix show that if acquisitions occur independently at the same rate _N_<sup>_∗_</sup> across households, we have: where _CV_ ( _Y_<sup>_∗_</sup> ) is the coefficient of variation of consumption. The last expression implies that for items usually acquired once a week ( _N_<sup>_∗_</sup> = 1), the variance of acquisition must be at least twice as large as the variance of consumption. When the coefficient of variation is equal to one, the variance of acquisition becomes three times the variance of consumption.

We conclude that acquisition diaries may yield a distorted idea of consumption _Y_<sup>_∗_</sup> even if all entries are recorded correctly. However, entries may not be exempt from errors. As the effects of fatigue on accuracy have long been known to researchers (Silberstein and Scott, 2004), it is common practice to assist households through frequent visits by enumerators during the recording process. It is generally presumed that this design yields the most accurate data (see Brzozowski et al., 2017, for discussion).

> 6The model can be easily generalized to allow for random spending around _Y ∗/N ∗_ .

6
# **1. Introduction**

In recent years, interest in early childhood development (ECD) in low- and middleincome countries has expanded considerably. High grade repetition in school, early dropout from school, and poor learning as well as poor health of youth and adults are being traced to malnutrition, disease, and neglect during early childhood. Accumulated evidence reveals associations between cognitive and psycho-social skills, nutrition and health status measured at young ages, on the one hand, and later educational attainment, earnings, and employment outcomes, on the other (e.g., Behrman and Rosenzweig 2004; Currie and Thomas 1999, Murnane, Willett and Levy 1995 and Neal and Johnson 1996 for the United States; Alderman, _et al._ 2001b, Alderman, Behrman and Hoddinott 2005, Alderman, Hoddinott and Kinsey 2006, Glewwe, Jacoby and King 2001, Glewwe and King 2001, Maluccio, _et al._ 2005, Martorell 1999 for developing countries). These life-cycle links suggest that a potentially effective way of alleviating poverty and engendering economic development is through policies that promote better development of cognitive, social, motor, and language skills and health and nutritional status among pre-school-age children.

Accordingly, governments in a number of countries, including the Philippines, have introduced ECD programs for enhancing the learning environments, improving nutrition and reducing disease for preschool children. To date, however, there are few rigorous evaluations of the effectiveness of ECD programs on child development that use longitudinal data and attempt to address possible bias in the estimates due to nonexperimental allocation of ECD services across a population. Most studies also do not examine many dimensions of development among young children or incorporate information on whether and how impacts differ depending on age and/or duration of exposure to the ECD intervention (Behrman, Cheng and Todd 2004, Gertler 2004, and Frankenberg, Suriastini and Thomas 2005 are recent exceptions).

In this paper, we estimate the impact of an important ECD initiative of the Philippine government.<sup>1<
**Table 3.6b: Transition Matrices for the Recession Period**

||||Secondqu<br>|arter 2009<br>||
|---|---|---|---|---|---|
|||Inactive|Employed|Unemployed|Total|
||Inactive or out|24,484,780|7,822,462|951,089|33,258,331|
|Sd t 2008|Employed|7,980,215|34,688,008|1,198,473|43,866,696|
|econ quarer|Unemployed|543,984|833,811|215,513|1,593,308|
||Total|**33,008,979**|**43,344,281**|**2,365,075**|**78,718,335**|

**Source: Own estimates using rotating panels from ENOE.**

During the recovery (table 3.6c), the pattern of gross flows changed in the sense that there was an increase in employed workers, a reduction in inactive workers, but also an increase in unemployed workers. The flow from inactive to employed was reduced by 1 million compared with the previous year, and the flow from inactive to unemployed was about the same figure. Unemployment under such condition in both periods increased, as well as those moving from unemployed to employed. Those employed in both periods also increased, while flows from employed to inactive decreased, and flows to unemployed remained about same as the previous year.

22
studies using single-value, average-type country-level indicators of time-related trade costs such as the Doing Business Indicators. Instead, we compute a novel measure of import uncertainty taking advantage of the time-varying within-country distribution of Customs clearance times across manufacturing firms sourced from the World Bank Enterprise Surveys (WBES), thereby accounting for the heterogeneity of border times at the country-sector-firm-year level (Volpe Martincus, 2016). Specifically, we consider the within-country-sector-year interquartile range of the days elapsed between the time of arrival of imported inputs at the destination port of entry and the time of release from Customs, for a sample of 48 developing countries over 2006-2014.

Second, by matching this dataset with information on firm export dynamics at the origin-destinationsector-year level obtained from the World Bank’s Exporter Dynamics Database (EDD), we are able to shed light on how uncertainty in the time to import affects the decision of (i) non-exporters to enter foreign markets; (ii) both new and incumbent exporters to exit foreign markets, and (iii) firms to keep on exporting one, two and three years after the date they started serving the foreign market. Therefore, our paper emphasizes the granular origins of aggregate trade performance by exploiting firm-level information to derive country-sector level indicators of export dynamics. Export outcomes at the macro-level are driven by the activities of individual firms operating in different sectors across the country, with sometimes only a handful of them - dubbed export superstars - accounting for the lion’s share of total exports (Freund and Pierola, 2015).

By constructing a new measure of uncertainty in border clearance times for imports, we quantify a new source of trade costs. Traditionally captured by a wide range of variables including standard gravity-type measures such as bilateral distance, tariffs, contiguity, common language and participation in trade agreements, trade costs also relate to a country’s legal and regulatory framework, as well as the availability of quality infrastructure and other trade facilitation measures aimed for instance at reducing administrative red tape. Abundant research highlights the sizeable impact of trade costs