rafmacalaba/datause-encoder-data huggingface dataset:
There is substantial evidence supporting the conclusions that Chinese women have achieved higher levels of well-being compared to their counterparts in other developing countries. A comparison of a range of development indicators illustrates this point strikingly. Based on statistics from the _World Development Indicators_ published by the World Bank, female life expectancy at birth in 19922 was 71 for China, 59 for India, 59 for Pakistan and 62 for Indonesia. Female literacy rates in the same year were 68% for China, 39% for India, 22% for Pakistan and 77% for Indonesia. In 1992, the birth rate per thousand in China was 19, while India's birth rate was as high as 29 and the average figure for poor countries excluding China and India was 37. It is widely believed that, to large extent, much of this achievement in improving women's welfare can be attributed to the implementation and enforcement of a wide range of gender-focused public policies over the past few decades (Das Gupta _et al,_ 2001).

All the above statistics illustrate China's success in empowering women and in combating deep-rooted feudal values, which would otherwise have largely excluded Chinese women both socially and economically. However, despite China's attractive record in this area, there exist surprisingly few empirical studies evaluating gender-focused public polices and programs. Recent work includes a case study by the All-China Woman's Federation (1993) and the World Bank study by Coletta and Sutton (1989), but none of these is based on rigorous empirical analysis of survey data. This may be due partly to the lack of attention paid inside China tc collecting quantitative survey data on this topic.

Recently, however, several national surveys have been conducted in China focusing on assessing women's social and economic status. These include: (1) the 1991 Survey of Women's Status in Contemporary China, (2) the 1991 Survey of Female Science and Technology Employees, (3) the 1992 Survey of the Situation of Children, and (4) the 1993 Survey of Female

> 2 We chose 1992 so as to coincide with the implementation of the Chinese rural community program under evaluation in this paper. Detailed discussion of the project is presented in Section 2 below.

4
# 1 Introduction

Business entry regulation varies widely across the world. Djankov et al (2002), ...nd that the number of procedures for registering a business ranged from 2 in Canada to 21 in the Dominican Republic in 1999. Given these large di¤erences, an important question to study is the e¤ect of entry regulation on economic outcomes. Most work on this question has been based on cross-country studies<sup>1</sup> , yet such an approach su¤ers from endogeneity issues. In particular, cross-country studies cannot rule out that causality goes the other way, such that increases in entry or output lead to reforms. It is also possible that both simpler regulation and higher entry or growth are caused by a third variable. Recent work has also looked at cross-country, cross-industry variation to try to account for endogeneity<sup>2</sup> . However, this approach cannot quantify the overall e¤ect of di¤erences in entry regulation, since all estimates are relative to a benchmark value of the “natural”rates of entry within industries.

This paper uses within country micro-level data to examine the e¤ect of entry regulation on economic outcomes. Speci...cally, it exploits cross-municipality and cross-time variation in a recent business registration reform in Mexico to measure the e¤ect of this reform on registration, employment, and income, which allows for establishing causality more convincingly than crosscountry studies (See Pande and Udry, 2005, for a call to move to research of this type). From a policy perspective, it is also important to study the impact of reforms, since some policy institutions, such as the World Bank, promote simpli...cation of business registration.

The use of micro data also makes it possible to trace out the e¤ects of the reform on the functioning of the product and labor markets. Many economists have argued that barriers to entry harm consumers by raising prices and thwarting employment growth. This paper ...rst examines whether the reform led to the creation of new ...rms or merely to the registration of existing informal businesses<sup>3</sup> . Having shown that it led to the creation of new businesses, the paper traces out the impact of this increase in competition on consumer prices, incumbents’ income, and employment.

The paper starts by building a simple model describing the expected e¤ects on the product and labor markets when the cost of registration drops. In the model, high cost of registration prevents individuals with medium range ability from opening a formal business. Therefore,

> 1 For example, Loayza, Oviedo, and Servén (2005) and Djankov, McLiesh, and Ramalho (2006) provide evidence that countries with less regulation grow faster.

> 2 Klapper, Laeven, and Rajan (2006), as well as Fisman and Sarria-Allende (2004), show that countries with heavier entry regulation have lower ...rm entry and lower growth in value added in naturally high-entry industries.

> 3 Registration of existing informal businesses may still result in an increase in production and e¢ ciency even if no new businesses are created due to the bene...ts of formality.

1
||**Env**<br>|**ironmenta**<br>|**l**<br>|||**Ec**<br>|**onomic/**<br>|**Social**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**A.1.1**|**A.1.2**|**A.1.3**|**A2.1**|**A2.2**|**A2.3**|**A2.4**|**A2.5**|**A2.6**|**A2.7**|**A2.8**|
|**M2**|0.011|0.065|4.000|24.211|27.645|4.000|2.000|3.000|4.000|3.000|4.000|
|**M3**|0.015|0.063|4.000|5.504|9.819|4.000|4.000|5.000|4.000|3.000|4.000|
|**M4**|0.014|0.085|3.000|42.417|45.326|3.000|3.000|3.000|3.000|4.000|4.000|
|**M5**|1.087|4.412|3.000|42.225|45.326|3.000|3.000|3.000|4.000|4.000|4.000|
|**M6**|0.272|1.103|3.000|8.083|14.601|3.000|4.000|3.000|3.000|4.000|3.000|
|**M7**|0.027|0.110|2.000|8.083|14.601|3.000|4.000|3.000|3.000|4.000|3.000|
|**M8**|0.307|0.581|4.000|29.061|2.065|4.000|2.000|3.000|3.000|5.000|3.000|
|**M9**|0.000|0.000|4.000|100.000|100.000|4.000|1.000|4.000|3.000|5.000|3.000|
|**M10**|0.022|0.037|3.000|6.967|0.000|2.000|4.000|2.000|5.000|3.000|4.000|
|**M11**|0.077|0.451|4.000|2.771|13.007|2.000|3.000|3.000|5.000|4.000|4.000|
|**M12**|0.000|0.043|2.000|8.083|14.601|2.000|2.000|3.000|3.000|4.000|5.000|
|**M13**|100.000|100.000|3.000|8.083|14.601|3.000|2.000|2.000|3.000|3.000|3.000|

_Table E.4. Application of PROMETHEE II: Evaluation scores for long-term criteria by DM1_

|||**Environm**<br>|**ental**<br>||||**Ec**<br>|**onomic/**<br>|**Social**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**B.1.1**|**B.1.2**|**B.1.3**|**B.1.4**|**B2.1**|**B2.2**|**B2.3**|**B2.4**|**B2.5**|**B2.6**|**B2.7**|**B2.8**|
|**M1**|0.044|0.260|4.000|4.000|30.072|92.328|4.000|3.000|2.000|3.000|3.000|4.000|
|**M2**|0.019|0.105|5.000|5.000|9.420|87.621|5.000|4.000|4.000|4.000|4.000|5.000|
|**M3**|0.005|0.007|3.000|3.000|30.072|88.781|3.000|2.000|2.000|2.000|2.000|2.000|
|**M4**|0.011|0.049|4.000|4.000|30.072|89.555|3.000|4.000|2.000|2.000|2.000|2.000|
|**M5**|0.333|1.584|4.000|4.000|100.000|100.000|3.000|4.000|2.000|2.000|2.000|2.000|
|**M6**|0.083|0.384|3.000|3.000|26.812|88.072|2.000|3.000|4.000|3.000|3.000|3.000|
|**M7**|0.009|0.024|3.000|3.000|26.812|88.072|2.000|3.000|4.000|3.000|3.000|3.000|
|**M8**|0.194|0.344|5.000|5.000|21.739|0.000|4.000|5.000|3.000|3.000|5.000|5.000|
|**M9**|0.022|0.028|5.000|5.000|0.000|66.925|4.000|5.000|3.000|3.000|5.000|4.000|
|**M10**|0.008|0.000|3.000|3.000|21.739|79.884|2.000|2.000|2.000|2.000|2.000|2.000|
|**M11**|0.037|0.230|3.000|3.000|31.159|90.135|2.000|3.000|2.000|2.000|3.000|3.000|
|**M12**|0.000|0.078|5.000|5.000|26.812|88.072|4.000|4.000|2.000|2.000|4.000|5.000|
|**M13**|100.000|100.000|4.000|4.000|26.812|88.072|4.000|3.000|4.000|2.000|3.000|4.000|

58
7 (2) lutl - a + Pt + et Is fitted and the slope coefficlent Is tested for slnificant difference from zero.<sup>2</sup> This residual trend method Is preferable to the moving CV test because It Is less Influenced by extreme price outcomes.

Correlation coefficients for detrended data ar' used to measure the patterns of association between different prices. To test for changes In covariance patterns over time, however, the residual trend approach can be generalized as follows. With ut and vt denoting the residuals from the Initial trend equations (1) for two prices, one regresses the product of the two against time, (3) utvt , a + Pt + et, and tests to see If _.3_ is significantly greater or less than 3 zero.

# Data Sources

World price data, available for 1949-87, were obtained from the World Bank's International Economics Department, International Commodity Markets Division. These are the International prices of standard types of commodities In major 2 Hereafter, the t statistic for the P from the regression of the absolute value of the price trend residuals against time (equation E2]) Is referred to as the t statistic from the residual trend.

3 The t statistic for the p from the regression of ~'Ie cross product of price trend residuals for two prices (equation [33) Is referred to as the t statistic from the cross-product trend.
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# **1. Introduction**

According to the International Labor Organization (ILO), child labor has declined worldwide in the past 15 years. But the numbers are still alarming. In 2012, 168 million children ages 5 to 17 years were in child labor. More than half of them, 85 million, were in hazardous work.

Labor laws and regulation are the first-order policy instruments advocated by international organizations such as the ILO and used by many national governments worldwide to fight child labor. Indeed, ILO Convention 138 on the Minimum Age for Admission to Employment of 1973 and ILO Convention 182 on the Prohibition and Immediate Action of the Worst Forms of Child Labor of 1999 have been ratified by 168 and 180 countries, respectively, at the time of writing. Convention 138 recommends that national laws should set the minimum employment age above 15. Convention 182 defines an individual under age 18 as a child. It lists the types of occupations that should be restricted until age 18 and defines the worst forms of child labor that should be prohibited in all circumstances.

There is a consensus that very early exposure to work is harmful to a child’s human development. In fact, most countries have put in place laws prohibiting work for children under a certain age. However, there is not much agreement on the minimum age at which a child should be allowed to work.

For instance, of the 62 mostly developing countries with publicly available data on child labor from the second and third rounds of UNICEF ́s Multiple Indicator Cluster Surveys (MICS) for the 2000s, two countries set the minimum employment age at 12, 23 at age 14, 21 at age 15, 11 at age 16, and five countries at age 18 (Edmonds and Shrestha, 2012).

In the United States, the _Fair Labor Standard Act_ (FLSA) uses age 14 as a general rule for the minimum employment age, but that does not prevent significant variation in regulations across states. According to the Department of Labor, 18 states require employment certificates from employers who hire individuals under age 18; one state requires a certificate for individuals under age 17; 21 for under age 16, and one for under age 14. Twelve states either do not require or do not issue employment certificates.<sup>1</sup>

> 1 See http://www.dol.gov/whd/state/certification.htm.

2
# Table 6.7 (Conet.)

||Based<br>in D|on Value-Ad<br>omestic Price|d<br>s|Based o<br>in|n Value-Add<br>World Prices|ed|
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|In (upper Management)||-01E-4<br>_(0-35E-3)_|||0.12E-3<br>(038B-3)||
|In (Wage BiU of<br>Temporary Workers)<br>InK|-0.11E-3<br>_(0.15E-3)_<br>0.661*<br>(0.126)|-0.94W-4<br>(0.14E-3)<br>0.748<br>(0.101)|-0.34EW4<br>(0.14E-3)<br>0.53*<br>(0.118)|0.E-4<br>(0.17E-3<br>0.77*<br>(0.151)|-0.16E4<br>(0.17E-3)<br>0.789*<br>_(0.115)_|0.89E-5<br>(0.16E-3)<br>0.688e<br>(0.132)|
|rime|-0.016<br>(0.032)|-0.031<br>(0.032)|-0.59E-3<br>(0.031)|-0.949<br>(0.036)|40.037<br>(0.035)|-0.029<br>(0.034)|
|R2|0.915|0.900|0.900|0.907|0.892|0.894|
|_N_|91|91|91|86|86|86|

:.Signriicant at the S % statistical test level. * Significant at the 10 % statistical test level. Our sample in these industries consists of 18 rtms
# **3.3 Summary statistics**

The first two panels in Table 2 present pre-intervention sample means, by treatment group, for child and household characteristics respectively. The sample of children does not include those born after the pre-intervention survey (they do not have baseline data), and the sample of households reflects one observation per child that we observe in the followup survey even if they were born after the baseline survey. Consistent with the random assignment, we find that characteristics are balanced across the three groups, with three exceptions: in cash localities, the share of boys is lower than in both in-kind and control localities; households in control localities are more likely to have an unmarried head than those in cash localities; and control households are more likely to have running water at home than those in cash villages.

Individual consumption data was only collected for children aged 1 through 4 years. As the 24-hour food recall module could overstate or understate actual consumption, it is difficult to draw definitive conclusions about child health, however, comparing caloric and micronutrient intake to Recommended Daily Allowances (RDAs) suggests that most children consume too few calories and that for many, those calories do not contain enough essential micronutrients. In particular, 89 percent of children consume fewer than the RDA of calories, and 32, 46, and 41 percent of children are not consuming the RDA of iron, vitamin C, and zinc, respectively. Households in our sample are large and poor, with a total per-capita expenditure of about 360 pesos per month, or $36 US dollars, and about 5.5. household members. Approximately 30 percent of households have at least one member who speaks an indigenous language. The household head has on average 7 years of education, about 40 percent of households have a dirt floor, and around 50 percent do not have running water.

The bottom panel in Table 2 presents the characteristics of the closest school to each PAL community. The closest school is typically a general school and on average it is located 1km from the center of the community. The average distance remains the same when the closest school is a community or an indigenous school. The student-teacher ratio is around 29, and repetition rates are around 9 percent in general schools and 18 percent in community and indigenous schools. On average, the cost of the closest school is 4,200 pesos, including contributions, materials’ and uniforms’ costs. As mentioned above, this cost is entirely driven by general schools, as indigenous and community schools are free of charge.

11
# **4.4.2 Decomposition results**

Table 8 presents the decomposition results. Panel A reports the change in the OLS coefficient on a dummy variable for India when a linear control for village-level open defecation is included, as in table 5. Panel B reports the change in the unexplained difference when open defecation is added to a weighted Blinder-Oaxaca decomposition. Panel C presents counterfactual differences from non-parametrically weighting the Indian sample to match the distribution of village and household open defecation in the African sample.

Columns 1 and 2 present the basic result: the simple sample mean with no controls, with and without adjustment for sanitation. Village-level open defecation linearly explains 99 percent of the India-Africa gap.<sup>27</sup> In the Blinder-Oaxaca and non-parametric decompositions, sanitation explains more than 100 percent of the gap; this “overshooting” is plausible because, in addition to having worse sanitation, Indian households are richer, on average, than African households.

The next four pairs of columns similarly find that open defecation explains much of the India-Africa gap after controls are added. Specifically, columns 3 and 4 control for demographic variables before decomposing the remaining gap: sex-specific birth order indicators and an indicator for single or multiple birth.<sup>28</sup> Columns 5 and 6 first control for a vector of socioeconomic controls, the same controls used earlier in equation 3. Columns 7 and 8 control for a village-level estimate of infant mortality, computed from mothers’ reported birth

> 27Household-level open defecation, used instead of village-level, explains 68 percent of the gap, a reminder of the importance of disease externalities.

> 28Jayachandran and Pande (2012) note that birth order is a predictor of child height in India (see footnote 10). For example, in the sample of rural children under 5 used here, I find that first children are 0.063 ( _p_ = 0 _._ 044) standard deviations taller than second children. However, this gap falls to 0.034 ( _p_ = 0 _._ 275) if controls for village and household open defecation are included (jointly significant with _F_ 2 _≈_ 95), suggesting that children born into larger households may be more likely to be exposed to environmental fecal pathogens, although various forms of intra-household discrimination surely exist, as well ( _cf._ Jeffery et al., 1989).

> Jayachandran and Pande (2012) also find, in their pooled DHS sample, that Indian first-borns are _taller_ than African first-borns. In the individual-level sample studied here, Indian first-borns are 0.019 standard deviations shorter (a much smaller gap than in the full sample, but still negative); controlling for open defecation, they are 0.133 standard deviations taller, a 0.15 increase similar to those when sanitation is controlled for in table 8. If, for example, IMR is controlled for, Indian first-borns are 0.132 standard deviations shorter than African first-borns, which increases by 0.16 to Indians being 0.025 taller.

28
**TABLE 11.** Cointegration Vectors: Levels Regression

Debt **Portfolio Flows**

|Variable|LAC|LAC|
|---|---|---|
|Constant|210.18<br>(0.191|-548.08<br>10.00)|
|ASIA|-0.40<br>(0.16)|-0.98<br>(0.00)|
|Dummy|429.58<br>(0.03)|483.10<br>(0.001|
|Trend||91.73<br>(0.00)|
|DF statistic|-0.49|-2.08|
|R-squared|0.08|0.44|
|AdM R-squared|0.05|0.42|
|S.E.|676.86|531.56|
|DWstatistic|0.51|0.79|
|F-statistic|2.84|16.87|
|Prob(F-statistic)|0.07|0.00|
||Signif. level||
|MacKinnon|5%|-3.48|
|Critical<br>values:|<br>10%|-3.17|

_Afore: p-values for each coeffiaent are in paren7theses._ Similar results were obtained for the regional linkages between equity portfolio flows. However, although the inverse relationship between equity flows to Latin America and Asia was observed, it was not found to be statistically significant below a 17% level of significance. The trend variable in the levels regression on equity portfolio flows was significant at the 10 percent level, implying that other factors that are specific to the countries in question within the region over time are more important in determining equity flows to that region.

The pace of liberalization of the domestic equity markets is one such factor influencing the direction of equity portfolio flows. An important policy. implication of 27
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(Cameron and Trivedi, 2005). To account for these aspects of the sampling design and obtain nationally representative estimates, I weight each sample observation (round-household/roundindividual) by the inverse of its probability of inclusion in the sample in the ESS2. The main parameter of interest is . It captures the average impact of refugee intensity on the welfare of host households. To identify the impact, I leverage a large spatial difference in ββ within-village temporal variations in refugee intensity, i.e., the intensity of the net influx of refugees, accounting for household- and round-specific fixed effects and a rich set of time-varying covariates.

**4.2. Identification** The estimates of the main coefficient of interest will have causal interpretation if the concerns of reverse causality and omitted variable bias are properly addressed. To assuage the former (ββ) concern, the index capturing refugee intensity is time-lagged relative to the period over which the outcome variables are measured. Omitted variable bias could stem from two decisions: the decision to leave refugees’ home countries (selection into migration to Ethiopia) and the decision relating to where to settle in Ethiopia (selection into places of settlement). To the extent any of these decisions involve refugees’ self-selection may lead to a bias in the estimates. Self-selection into migration is less important in the present case as refugees are forced migrants suddenly fleeing mainly conflict and violence, and small others political persecution (mandatory national service such as military conscription, gross human rights violation, detention, and others) and natural calamities.

The overwhelming majority of refugees in Ethiopia are hosted in formal camps or semiformal settlement sites (transit centers and entry points), in the latter case only until they are relocated to formal camps, which are located close to the country’s borders with the neighboring countries whence the majority of the refugees come (Eritrea, Somalia, South Sudan, and Sudan). The government decides on the location of these settlement sites<sup>25</sup> and undertakes relocation of refugees from semi-formal settlement sites to (usually nearby) formal camps. Refugees being forcibly and suddenly displaced naturally cross the border to go to the nearest refugee settlement site, implying that refugees’ self-selection is less of an issue when it comes to the places of

> 25See article 21(2) of Refugee Proclamation No. 409/2004.

16