rafmacalaba/datause-encoder-data huggingface dataset:
Appendix Table 5a: RDD estimates of gaps in teacher language test scores associated with teachers hired pre- and post-FPE (multiple <u>bandwidths).</u>

||Bandwidth:|1|2|3|4|5|6|7|8|9|10|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Pooled|Coef|0.091|0.036|-0.083|-0.041|-0.041|0.004|-0.020|-0.015|-0.018|0.015|
||SE|(0.164)|(0.117)|(0.100)|(0.090)|(0.082)|(0.075)|(0.070)|(0.065)|(0.062)|(0.060)|
||Pval|0.578|0.760|0.407|0.651|0.620|0.957|0.774|0.815|0.775|0.803|
|Kenya|Coef|0.288|0.220|-0.078|-0.030|-0.014|0.024|-0.029|-0.021|0.000|-0.009|
||SE|(0.397)|(0.296)|(0.237)|(0.201)|(0.184)|(0.170)|(0.158)|(0.149)|(0.137)|(0.137)|
||Pval|0.471|0.458|0.741|0.883|0.938|0.887|0.854|0.886|0.998|0.946|
|Madagascar|Coef|-0.150|-0.114|-0.001|0.064|0.142|0.137|0.114|0.069|0.092|0.046|
||SE|(0.267)|(0.193)|(0.163)|(0.138)|(0.120)|(0.113)|(0.109)|(0.106)|(0.096)|(0.093)|
||Pval|0.575|0.556|0.994|0.644|0.241|0.229|0.296|0.513|0.339|0.623|
|Mozambique|Coef|-0.308|0.100|0.199|0.211|0.294|0.221|0.155|0.039|-0.017|0.159|
||SE|(0.346)|(0.290)|(0.246)|(0.239)|(0.202)|(0.179)|(0.172)|(0.162)|(0.169)|(0.169)|
||Pval|0.382|0.733|0.421|0.381|0.149|0.219|0.370|0.812|0.918|0.347|
|Tanzania|Coef|-0.314|-0.456|-0.612|-0.499|-0.504|-0.266|-0.257|-0.172|-0.178|-0.076|
||SE|(0.435)|(0.295)|(0.241)|(0.215)|(0.193)|(0.174)|(0.153)|(0.137)|(0.137)|(0.124)|
||Pval|0.472|0.124|0.012|0.021|0.010|0.128|0.095|0.210|0.194|0.544|
|Togo|Coef|0.054|0.321|0.237|0.080|0.047|-0.001|0.158|0.157|0.099|0.081|
||SE|(0.464)|(0.344)|(0.305)|(0.260)|(0.259)|(0.265)|(0.245)|(0.232)|(0.241)|(0.222)|
||Pval|0.909|0.356|0.441|0.759|0.856|0.997|0.521|0.499|0.682|0.714|
|Uganda|Coef|0.626|0.213|0.053|0.135|0.038|0.017|-0.014|0.042|-0.016|0.005|
||SE|(0.392)|(0.297)|(0.269)|(0.231)|(0.219)|(0.199)|(0.187)|(0.173)|(0.163)|(0.161)|
||Pval|0.115|0.476|0.845|0.559|0.863|0.933|0.939|0.809|0.924|0.978|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-9
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE water services and reducing nonrevenue water. In the water sector, the burden of operational utility inefficiencies is considerably higher than for the benchmark countries (figure 32).

**Figure 32. The burden of inefficiency in Sudan’s power and water utilities**

a. Uncollected bills and unaccounted losses in the power sector, as a percentage of GDP b. Uncollected bills and unaccounted losses in the water sector, as a percentage of GDP <!-- Start of picture text --> 0.700 0.35<br>0.600 0.30<br>0.500 0.25<br>0.400 0.20<br>0.300 0.15<br>0.200 0.10<br>0.100 0.05<br>0.000 0.00<br>Sudan Resource rich Sudan Resource rich<br>Collection inefficiencies Unaccounted losses Collection inefficiencies Unaccounted losses<br>Percenatage of GDP Percenatage of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

# Annual funding gap

Sudan’s annual infrastructure funding gap amounts to $2.9 billion per year, or roughly 14 percent of GDP (table 22). The largest funding gap is in the water and sanitation sector, followed closely by transport. Given strong private investment, only a small funding gap remains in ICT. In the case of power, existing spending is sufficient. But if power tariffs could be raised to cost-recovery levels, much more of this expenditure could be funded internally, freeing up external finance for other critically needed areas.

**Table 22. Funding gaps by sector**

|US$ million|ICT|Irrigation|Power|Transport|WSS|Total|Total as<br>share of<br>GDP|
|---|---|---|---|---|---|---|---|
|Spending needs|(310)|—|(424)|(1,785)|(1,701)|(4,220)|(19.8)|
|Existing spending*|253||424|509|32|1,218|5.7|
|Reallocation potential within sectors|0||0|0|0|0||
|Efficiency gains|0||484|20|79|584|2.7|
|Funding gap|**(57)**||**—**|**(1,255)**|**(1,590)**|(2,902)|**(13.6)**|
|Reallocation potential across<br>sectors|0||267|0|0|267|1.2|

_Source_ : Derived from Foster and Briceño-Garmendia (2009). _Note:_ Estimates use the base scenario of spending needs. Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. WSS = water supply and sanitation; ICT = information and communication technology; GDP = gross domestic product. *traced to needs **Assuming complete fungibility across sectors — = Not available.

> <sup>44</sup>
calculation, showing the predicted cause-specific probabilities and the changes in the probabilities induced by the hypothetical policy of universal access to safe water.

The first point to note in Table 6 is that the predicted changes in cause-specific mortality probabilities do indeed differ in relative terms compared to the corresponding hazard rate coefficient estimates. Nonetheless, it is still true that safe water has a negligible impact on the risk of dying from tetanus and birth-related causes, which is encouraging from the point of view of model validation. Universal access to safe water reduces the odds of dying from diarrhea, acute respiratory infection (ARI), and other illness by about the same absolute amount. In relative terms, however, improved access to safe water reduces the death rate from diarrhea the most, followed by ARI and other illnesses.

It should be noted that the prevalence of fever/ARI is particularly high in China. Using the 1992 CHS, the estimated ARI incidence among children U5 is 17.5% compared to the LDC average of 9.4% (using all low-income countries with DHS data excluding China). Although epidemiological studies often suggest that fever/ARI is caused mainly by exposure to air pollution resulting from using solid fuels for cooking and heating, or combustion of fossil fuels for transport and power generation<sup>18</sup> , other pathways through which the ARI virus can be transmitted are also possible. The recent outbreak of severe acute respiratory syndromes (SARS) in China and Hong Kong, has shown that such a virus can be transmitted through piped water. While perhaps surprising, in China at least, some ARIs may be due to water-borne pathogens.

To sum up, what have we learned by estimating a competing risk model versus a simpler all-cause model of child mortality? First, how reliable is the cause of death information? Establishing causes of child death accurately is difficult, especially when children died at home as is often the case in most LDCs. We assess the reliability of the China survey by comparing the distribution of cause of death with that in a Pakistan survey which uses the much more thorough (and expensive) methodology of verbal autopsy. The overall distribution of cause of death in rural China resembles that in rural

> 18 Rudan and Aambell (2002) reviewed 39 community-based studies to estimate the incidence of ARI in children U5 in LDCs. They conclude cautions should be taken on the empirical findings, and only studies with explicit diagnostics criteria for ARI, and diagnoses were made by specially trained field workers who were involved surveillance over at least one year, cab be regarded as methodologically credible.

11
<!-- Start of picture text --> 1970 1980 1990 2000 2010 2020<br>Year<br>Ln(Gold Price) Ln(African Price)<br>Ln(China Price)<br>2<br>1<br>0<br>Ln(Price)<br>−1<br>−2<br><!-- End of picture text --> Figure 1: Gold and Ivory Prices Note: Gold prices come from the London Bullion Market (morning prices), quoted in US dollars and deflated by US CPI published by BLS. The gold price is normalized so that the mean is zero. The ivory prices are the median of the partialled-out price.

Second, focusing on the China ivory price series, Figure 2 suggests that the price of ivory has experienced a stable average growth rate over the periods 1970-1992 at 5.03 percent per year and 1993-2014 at 5.52 percent; these two numbers are not statistically different from each other. The price of ivory experienced a sudden drop around 1992-1993, probably due to the enforcement of the 1989 CITES ban on international trade of ivory products coming into effect, at least as far as markets were concerned. The trend break is statistically significant with a _p_ -value close to zero, and quantitatively large. The drop in the price between 1992 and 1993 is 1.3 log points, which corresponds to approximately 17 years’ worth of price growth if compared to the pre-break price growth of 0.0503 per annum.<sup>6</sup> The trend break does not affect our estimation of equation (11), as our estimation sample period only starts from 2002 due to the availability of poaching data as explained below.

12
# **1 Introduction**

Can minimum wages reduce gender wage gaps? Using firm-level census data from Indonesia, this paper examines the impact of minimum wage changes on gender pay gaps among production workers in the manufacturing sector in Indonesia (1995-2006), as well as their impact on men’s and women’s relative employment prospects.<sup>i</sup> It examines heterogeneity in impact by level of education and position in the wage distribution.

While a large literature has documented gender pay discrimination (see Altonji and Blank, 1999; Blau, Ferber and Winkler, 2010; and Cain, 1986 for reviews) and examined the effects of minimum wages (see Neumark and Hellerstein, 2005 for a review), very few studies have focused on the impact of minimum wages on gender wage gaps and men’s and women’s relative employment prospects. The bulk of the literature on gender pay discrimination and minimum wages has relied on individual data, as they offer the valuable ability to control for individual characteristics that determine earnings.

This paper presents a complementary approach, examining how gender differences in wages and employment across employers respond to minimum wage changes using firm-level data. Such data enable one to control for employer characteristics and firm productivity, determinants of wages which are not usually measured well in worker-level data. Firm data also enable analysis of gender differences in employment impacts of changes in minimum wages _within_ firms over time.<sup>ii</sup> Focusing on impacts on production workers, a relatively homogenous group of workers engaging in fairly similar (and rather routine) tasks, in a single sector, manufacturing, helps mitigate concerns that the documented impacts are driven by heterogeneity in impact across sectors and occupations.

2
kernel-based propensity score matching method to ensure that the profiles of women in BH areas and non-BH areas are similar based on observables.

The kernel-based propensity score matching (PSM) method estimates each woman’s propensity for residing in the BH-affected area during the period of the BH insurgency using a kernel density function. Each of the three control groups (women who reside in the BH area in the period before the BH insurgency, women who reside in the non-BH area before the insurgency and women who reside in the non-BH areas after the BH insurgency) are then matched to the sample of women who reside in the BH area in the period during the BH insurgency. The common support is composed of the sample of women who reside in the BH area to whom counterfactuals are found in each of the three control samples. The method weights each of the individual observations in the three control groups based on propensity scores, creating a balanced control group that does not differ systematically from the treated group on observables. The balanced sample is then used to estimate the average treatment effect of residing in the BH area during the BH insurgency on IPV outcomes using the DD model specified in equation (1) and applying the weights from the PSM.<sup>22</sup> One advantage of the kernel-based PSM is that it does not uniquely pair observations and as such minimizes the risk of data imbalance and model dependence (Jann, 2017). However, a key assumption is that only variables that are fixed over time (or pre-treatment variables) should be included in estimating the propensity scores. Caliendo et al. (2005) argue that previous research and information about the institutional settings should be employed in building the model. Hence, we rely on evidence from Ceriani and Verme (2018) who find that age; education; employment status: and, ownership of household assets are associated with the decision to stay or flee from BH affected areas.<sup>23</sup> The study sample is thus matched on individual characteristics of the women and their partners using the following variables: age; education; and employment status; household size, type of settlement (urban versus rural); and household wealth status<sup>24</sup> (see appendix section <u>A3</u> for kernel-based matching results). Equation (1) is then estimated on the common support of the matched sample.

> 22 This is implemented with the _“diff”_ command in Stata by Villa 2016.

> 23 Larger household size, being male, being older and being unemployed were associated with not migrating during conflict while more years of education was associated with migrating during conflict.

> 24 We use household wealth as a proxy for ownership of household assets.

16
permits. The evidence in Table 7 and Figure 17 presents a consistent story that a lack of reliable and affordable finance is a constraint that households and firms face in the Northern and Eastern provinces.

**Figure 17: Employers in the Northern and Eastern provinces see access to finance as the biggest constraint**

<!-- Start of picture text --> 47% Biggest constraint faced by firms<br>Northern Province<br>Eastern Province<br>32%<br>Sri Lanka<br>27%<br>21%<br>17%<br>15%<br>13% 14%<br>11% 11%<br>9% 10%9%<br>6% 7%6% 7%6% 7%<br>1% 0% 0% 3% 1%4%1% 0% 3% 0% 0% 2% 0%4%<br>Business permits Electricity educated workforce Political instability Corruption disorder<br>Access to finance Practices of competitors in informal sector Labor regulations licensing and Inadequately Customs/trade regulations Access to land Crime, theft,<br><!-- End of picture text --> Source: Sri Lanka Enterprise Survey 2011. The survey sampled 610 firms nationally, of which 26 and 28 firms were in the Northern and Eastern provinces, respectively.

In short, the investigation of factors underlying the low level of employment rates in the North and East revealed several suggestive findings. First, the source of the gender employment gap is not different endowments in easily observable characteristics such as age, ethnicity, or educational attainment. Second, since gender employment disparities are greater than wage disparities, both demand and supply factors are likely contributing to the gender gap in employment. Third, women in the North and East are particularly disadvantaged with respect to skills, which is likely to adversely affect their employment prospects. Finally, access to formal finance also appears to be an issue in the North and East, which could also impinge upon employment opportunities.

# 4.1.4 Supply factors

Supply factors that could affect the labor force participation rate could include variables such as fertility rates, availability of child care, husband’s income, transfer income, substitutes for women’s time (such as household appliances), and preferences for work. Although the data sets available to us do not allow us to address all these issues, we examine some of them to the extent possible. In the most basic labor supply model, an increase in non‐labor income reduces labor supply (assuming leisure is a normal good) – resulting in a negative income effect (Borjas 2008). An increase in own wage, on the other hand, increases labor supply by making leisure more costly – resulting in a positive substitution effect. In Table 7, we present estimates of a model of the determinants of labor force participation of women to examine the 22
25 referred to as a "frequency" index, shows the share of all tariff line products covered by one or more nontariff restrictions.19 Although these measures have some limitations (see Laird and Yeats, 1990 for a discussion), they show the extent to which nontariff restrictions are available to reinforce the effects of the block preferences. The data refer to 1988 and have been subject to significant change since then. However, again appealing to the normal persistence in patterns of protection and noting that the Treaty of Asuncion provides for the removal of NTBs on intra-bloc trade, the data very likely identify the elements of discrimination. We recognize that this is all a bit tenuous, but we believe there is content in these statistics. Given the tariff data, however, our thesis that discrimination exists does not depend on these NTB measures.

Table 7 summarizes statistics relating to these barriers. The data strongly suggest that tariff preferences, and the protection they provide for intra-trade, have been a major factor behind the recent pattern of trade changes. For example, Mercosur tariffs on the most dynamic products -- the first decile 19The frequency index (F) for importing country j shows the percentage of tariff lines covered by some preselected group of nontariff measures and is defined by, _(5)_ Fj = (ED1N + 1 _Nt)_ 100 where Ni is tariff line item i, Di is a dummy variable that takes a value of unity if one or more NTBs is applied to the item or zero otherwise, and N, is the total number of lines in the product group. The above summation is made over all countries exporting to importing country j. Similarly, the trade coverage index (C1) is defined as,

- (6) _Cj_ = ((ZDi,t m **V;,t n)/TVi,t** .). 100

where Vi,tn is the value of imports in tariff line item i in year (t-n) and Di,t is a dummy that takes a value of unity if an NTB is applied to the item and zero otherwise. If n and m are zero the index is based on current trade values, otherwise base year weights are used.

The UNCTAD records contain information about the following types of nontariff restrictions which are included in the frequency and coverage ratio tabulations: tariff quotas, anti-dumping duties, restrictive import authorizations, total prohibitions, suspended import authorization licenses, anti-dumping investigations, state monopolies, differential health and safety regulations, differential prohibitions based on non-commercial considerations. The records may understate the current importance of some restrictions like anti-dumping actions since they do not incorporate data on the surge in new cases that occurred in 1992-93.
# **2.3 Preschool provision**

According to Brazilian law, in the period of analysis preschool services were provided to children aged between 4 and 6. There are two types of preschool providers:

- **(i)** Public preschool centers which are run either by the municipality or the state.

- **(ii)** Private preschool centers which are run independently.

Public centers are financed by the public budget, predominately at the municipal-level, though a number of states have a small number of state public preschool centers as well. Legal provisions mandate that parents seeking to enroll their children in public preschool must do so in a center that is located near their home. Enrollment in public centers is not subject to tuition fees and these centers cannot reject children unless demand exceeds capacity.

Private preschool providers are generally for-profit and have full discretion over tuition fees. While there is no systematic information on prices charged by private centers, data from _Pesquisa de Padr ̃oes de Vida_ for 1996/1997 reveal that households whose children were enrolled in private child care spent about 1,000 Brazilian _reais_ on tuition fees per year, on average. Table 2 reports summary statistics on the household income of families whose children are enrolled in public and private preschool. In line with the price differences described above, we observe that the monthly income of households where children are enrolled in private preschool tends to be considerably higher than that of families that enroll their children in public preschool: mean income is almost twice as large, and marked differences are also observed across several other moments of the distribution of monthly household income.

The market for private preschool services remains highly independent and unregulated. Although education authorities set minimum recommended standards on teacher qualifications and group size, centers were not bound by strict legal constraints on these variables. By contrast, quality standards in the public sector are under direct control of local and central authorities.

9
As for developing countries, three stylized facts emerge. First, compared with the sharp rise in inflows, FDI outflows remain relatively modest. Although they have risen over the last decade, in 2000-01 outflows amounted to less than a third of inflows in developing countries as a whole, and even less (about 10 percent) in Latin America. Thus, for developing countries FDI inflows and net flows have moved in close tandem, in contrast with industrial economies, where large increases in inflows have translated into little change in net flows.

Second, a considerable portion of the rise in FDI inflows to developing countries over the last decade took the form of increased cross-border M&A. By the early 2000s, these had grown to account for nearly half (and even more in the case of Latin America) of FDI inflows, up from about 10 percent in the late 1980s. Unlike in industrial countries, however, in developing economies greenfield FDI still accounts for a large portion of FDI inflows.

Third, much of this M&A increase was due to privatization of public assets. The latter accounted for roughly one-third of the increase in M&A inflows to developing countries in general, and half for Latin America in particular, over the period during which comprehensive privatization data are available.

# **3. Econometric analysis**

_Objective._ Our empirical objective is to analyze the dynamic relationship between greenfield FDI, cross-border M&A, domestic investment, and GDP growth. Specifically, we want to examine how the behavior of a given variable is related to the future behavior of the rest. There are two aspects to this analysis: effect and predictability. The first deals with whether changes in a variable have a lasting impact on another. The second examines whether the behavior of a given variable helps predict the behavior of the rest.

8
- 16 -

airline transport, and telecommunications. The final section will draw on the various strands of the empirical analysis to present a framework for efficient regulatory policy in developing countries. In developing this framework, particular attention will be given to the likelihood that developing countries are likely both to be more vulnerable to problems of monopoly or oligopoly than their industrialized country counterparts, and more constrained in the availability of the resources and personnel needed to gather and process the information required for the efficient operation of a regulatory system.

# **II:** ANTITRUST REGULATION IN INDUSTRIALIZED COUNTRIES

This section consists of two parts. The first is a general discussion of antitrust regulation and the forms it can take. We discuss the relative advantages of orienting antitrust toward influencing market structure or toward preventing abusive conduct. We then discuss the primary methods available for influencing market structure and what forms of conduct might be proscribed. Following this, we consider enforcement issues: whether to use the general judiciary or a specialized agency to enforce antitrust statutes; whether or not the "reasonableness" of specific business practices (or their effects) should affect their permissibility; and what should be the penalties for antitrust infractions.

The second part of this section provides an assessment of industrialized countries' experience with antitrust (focusing on the U.S.) and the lessons and recommendations for LDC regulatory policy that can be culled from that experience. Our discussion of antitrust issues relies heavily on Scherer and Ross (1990) and Martin (1988).