rafmacalaba/datause-encoder-data huggingface dataset:
households and raise the quality of education in low-cost private schools. To these ends, it employs a variety of instruments such as providing vouchers to poor households in disadvantaged urban neighborhoods to attend selected low-cost private schools and supporting low-cost private schools with monthly per-student subsidies conditional on school quality standards, teacher training courses on subject content and pedagogical and classroom management techniques, and the insertion of competitively-hired subject specialist trainers in secondary schools for fixed terms.<sup>4</sup> The FAS program is PEF’s largest program. In the fiscal year 2009−10, PEF spent roughly 2.4 billion rupees (US$28.7 million, in current dollars) on the program—this amount accounts for 93% of total expenditures by the organization in that year. It was initiated in November 2005 on a pilot basis in 54 schools in seven districts in Punjab. Since then, PEF has rapidly expanded program coverage in phases in terms of additional districts as well as more schools within districts. The program has proceeded through six phases (the pilot phase is considered by PEF to be phase 1). Phase-6 program schools entered the program in April 2010. As of June 2010, the program covers 798 thousand students in 1,779 private schools in 29 out of the 36 districts in Punjab. As a result of explicitly targeting the program at high illiteracy districts over phases 3−5, the majority of program students and schools (70% and 68%, respectively) are in seven districts located in the southern part of the province (see Figure 1).

Table 1 provides summary statistics based on current administrative data on program schools separately by phase of entry. The sample consists of 1,776 primary (classes 1–5), middle (classes 1–8), and secondary schools (classes 1–10, 6–10).<sup>5</sup> Looking at the aggregate sample (pooled across phases), the mean program school size is 455 students; in schools with both girls and boys enrollment, the mean ratio is 1 (gender parity). Program schools have on average 17 teachers and 16 classrooms. The mean student-teacher and student classroom ratios are 27:1 and 29:1, respectively. The majority of program schools is middle level (64%), coeducational (97%), rural (59%), and registered (100%). The phase-wise statistics indicate that mean enrollment is positively associated with length of program participation, this relationship also holds for mean boys and girls enrollment. The number of teachers and classrooms are also positively associated

> 4 The organization is continuing to expand its set of instruments: for example, it is now considering supporting private organizations/individuals that adopt and manage non-functional public schools handed over by the government for this initiative.

> 5 The three higher secondary schools in the program are excluded.

8
# **Table C.25: Robustness to re-ranking effects**

**Panel A: Effects on women’s promotions and income (Pooled waves)**

||(1)<br>Supervisor<br>(Dummy)|(2)<br>Personal<br>income<br>(Taka)|(3)<br>Household<br>income<br>(Taka)|
|---|---|---|---|
|selected|0_._35***|_−_641_._04|_−_1513_._18|
||(0_._07)|(611_._34)|(2054_._15)|
|N|160|160|160|
|Nominated mean|0_._00|9932_._62|24384_._10|
|Nominated SD|0_._00|1893_._51|7292_._15|

**Panel B: Effects on women’s decision-making power**

||Decis|ion-making (In|dex)|
|---|---|---|---|
||Pooled waves|Wave 1|Wave 2|
|selected|_−_0_._22|_−_0_._03|_−_0_._39 **|
||(0_._27)|(0_._49)|(0_._18)|
|N|160|78|82|
|Nominated mean|0_._22|_−_0_._15|0_._56|
|Nominated SD|1_._14|1_._51|0_._51|

**Panel C: Effects on assignable goods expenditures and remittances (Pooled waves)**

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes as|% in househo|ld income)||
|selected|3_._20|2_._00***|_−_1_._02|_−_1_._11|4_._31*|
||(2_._18)|(0_._60)|(1_._09)|(1_._36)|(2_._31)|
|N|160|55|160|71|159|
|Nominated mean|6_._98|1_._52|5_._42|3_._50|5_._23|
|Nominated SD|5_._85|1_._19|5_._01|4_._83|10_._20|

Notes: Selected is a dummy variable indicating whether the female sewing-line operator is quasi-randomly selected for the promotion program. Nominated is a dummy variable indicating whether the female sewing-line operator is an eligible, shortlisted runner-up that was quasi-randomly not selected for the promotion program, and represents the comparison group for the selected group. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso from demographic controls and baseline bargaining measures, their squares, and factory, enumerator and month dummies. Where available, the baseline outcome is included in the amelioration set. The sample is limited to the half of the factories that were not assigned to the selection experiment discussed in Section 3.1. The sample in Panel C, Column 2 (Column 4) is further limited to those respondent with a daughter (son), or a female (male) child in the household.

89
|TABLE 7. Marginal Effects of CCTs on<br>Divorce|
|---|
|Pr(Divorce = 1)|
|CCTs<br>0.0024|
|(0.0082)|
|Time = 2<br>−0.0286|
|(0.0039)|
|N<br>5,796|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Marginal effects are presented from equation from a fixed-effects linear-probability model (equation (3)), pooling first and second follow-ups and controlling for child composition; dummies for crop losses, illness, and business shocks; and the inverse mills ratio, as computed in equation (2) (see table S3). N is the number of households that did not abandon the study by the first follow-up. Robust standard errors clustered at the municipality level are in parentheses.

38
one plausible explanation is that smaller transfers push more households across the poverty — threshold per unit of transfer scarcity poverty traps may therefore provide a justification for lower, rather than higher, intensity interventions. On the other hand, we find increasing intervention scope, through complementary interventions, increases long-run cost effectiveness; the contrast between this result and the effect of increasing intervention intensity is consistent with the presence of frictional poverty traps. The long-run cost effectiveness of complementary interventions only manifests at relatively distant time horizons; while frictional poverty traps may justify increasing intervention scope, high costs of complementary interventions can undermine cost effectiveness. While targeted larger interventions may be desirable from a policy standpoint, our results highlight that poverty traps are neither necessary nor sufficient to justify larger interventions.

This paper is organized as follows. Section 2 describes the metaänalysis sample and data extraction. Section 3 estimates average effects. Section 4 estimates the impacts of increasing transfer size, Section 5 compares our estimates to those from richer countries, and Section 6 estimates the impacts of complementary interventions. Section 7 concludes.

# **2 Data and Context**

In this section, we describe the construction of the tables of studies, interventions, and estimates, including impacts of UCT and TUP interventions on consumption and intervention characteristics.<sup>4</sup>

## **2.1 Study inclusion**

Our metaänalysis focuses on randomized control trials of temporary unconditional cash transfers (UCT) and multifaceted graduation programs that include unconditional cash and/or

> 4The full extracted data from the studies, including both the reported point estimates and the impacts on consumption in 2010 USD PPP that we use for analysis, are made available at `https://docs.google.com/spreadsheets/d/1txIOHFWQcLjmHllUv2b-AzmyhcnvxtOALLXdbG5KDjs` .

8
# **ENDNOTES**

> **1** The thinly researched subject of world/domestic price comovement reflects, perhaps, the un‐ availability of data. That, however, changed recently for agricultural products following the re‐ search project led by Kym Anderson whose methodology (Anderson et al. 2008) resulted in a consistent global database which includes prices received by farmers and paid by consumers in 75 countries (www.worldbank.org/agdistortions).

> **2** The rejection of the efficient market hypothesis in the presence of comovement argued by Granger (1986) corresponds to Pindyck and Rotemberg’s (1990) ‘bubbles’ or ‘market psycholo‐ gy’ explanation for excess comovement—provided that prices used in Granger’s sense have been adjusted accordingly by the fundamentals.

- **3** The early methodological setbacks of the literature of price comovement among different commodities may explain why the subject has not been researched more thoroughly.

> **4** The exogeneity of energy prices assumed here reflects the large size of energy markets com‐ pared to the size of other commodity markets as well as the fact that energy is a key input in the production process of most commodities. Hence,  _1_ is interpreted as transmission elasticity ra‐ ther than just a cointegration parameter, similar to the cases of domestic/world price links where domestic prices are typically assumed to be a function of world prices.

> **5** Commodity‐specific regressions show that the high elasticity estimate of beverages is driven by cocoa (  _1_ = 0.52, _t‐value_ = 5.35), a result with no obvious explanation. On the other hand, the similar elasticity size among food sub‐indices extends to most individual commodities especial‐ ly for the components of grain and edible oil indices.

**_— 11 —_**
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# **3 Research Design and Data**

## **3.1 Research Design**

In partnership with the government, we conducted a cluster randomized controlled trial to test different approaches to girls’ empowerment in northern Côte d’Ivoire. While the full research design allows to study both the effects of school-based interventions targeting girls in schools, and the effects of community-based interventions, this paper focuses specifically on the effects of the community-based interventions targeted to out-of-school girls.

The school-based component was included in the original design but faced significant implementation challenges, including reduced intervention duration and scheduling conflicts between school administration and the implementing NGO. These challenges resulted in low participation rates and no significant impacts on key outcomes. Complete details and analysis of the school-based intervention are available upon request.

### **3.1.1 Study Setting and Sample Selection**

The study was implemented in 280 localities in the catchment area of 60 middle schools (or _collèges_ ) eligible for the program in the regions of Poro, Tchologo, Bagoué, Folon, and Kabadougou. In collaboration with the Ministry of Education and the Program Implementation Unit, 60 eligible schools were selected from a total of 83 across the five program regions. These schools were chosen based on having the largest populations of girls at the time, in order to meet the project’ s target number of beneficiaries. We then selected 280 localities (villages or neighborhoods of urban localities) in the catchment areas of these schools.<sup>4</sup>

### **3.1.2 Randomization Strategy**

We used a two-stage randomization process. First, we randomized the 60 secondary schools into three groups of equal size: a control group, a group to receive in-school safe spaces, and a group to receive in-school safe spaces and support classes in mathematics and French.<sup>5</sup> The randomization was stratified by the number of localities around the schools and the number of girls eligible to attend the selected schools.

In the second stage, we randomize the 280 localities selected around the schools into treatment and control groups. The 56 localities around the 20 control group schools (figure 1) were automatically considered as a pure control, i.e., with no intervention taking place either in selected schools or in the localities. The remaining 224 localities around treatment schools were randomly assigned to four groups: a group of 56 localities receiving community

> 4Using data from the 2014 General Population Census (Recensement Général de la Population et de l’Habitat), we first selected localities with an estimate of at least 50 eligible girls, i.e., out-of-school girls aged 8 to 24. We then restricted to localities that were close enough to secondary schools that girls residing in these localities could reasonably commute to one of the 60 schools.

> 5Due to delays and other implementation issues, the support classes were not implemented. Both treatment arms were therefore merged into one group of 40 schools receiving in-school safe spaces.

8
with information on the average level of health. Section III presents some empirical illustrations of these two measurement tools using data for 44 developing countries on socioeconomic inequalities in and average levels of three health indicators: under-five mortality, child malnutrition, and fertility.

# **2. Measurement issues**

The starting point is the measurement of health inequalities. To make the discussion more applicable to typical health indicators, it is assumed that the health variable measures _ill health._ It might be an index based on, say, a self-assessed health question (Wagstaff and Van Doorslaer 1994; Gerdtham and others 1999; Humphries and van Doorslaer 2000). Or it might be an anthropometric measure of malnutrition (Wagstaff and Watanabe 2000; Wagstaff, van Doorslaer, and Watanabe 2001). Or it might be a binary variable capturing death prior to a certain age (Wagstaff 2000). The approach is easily modified for health measures that are increasing in good health. This section summarizes the basics of the concentration curve and concentration index, and then shows how the concentration index has underlying it an implicit value judgement concerning the weights to be attached to people in different points in the income distribution. The section then shows how the index can be extended to make explicit differing attitudes to inequality. Finally, the section shows how information on the average and on the degree of inequality can be combined into a single summary measure of health achievement that is linked to extended concentration index.

## _2.]. The concentration curve and concentration index_

Suppose we want to measure inequalities in health by income, or some other measure of socioeconomic status (SES). (The case of pure inequality is easily handled, and is discussed briefly below.) We rank individuals by their household's income (or whatever measure of SES we are using), starting with the most disadvantaged. Let p be the cumulative proportion of people, so ranked. The curve labelled _L(p)_ in Figure 1 is an Evans and others (2001), Gakidou and others (2000), Le Grand (1987), Wagstaff (2001) and Whitehead (1992).

4
49 unemployment rates and women's unemployment shares. For the future, when registered unemployment is expected to rise substantially this would imply that women's unemployment share should approach their share of the labour force. Whether one can detect in household survey data for 1992 and 1993 problem groups will be briefly discussed below.

- Even with strongly downward biased registered unemployment and notified vacancies regional divergence in U/V ratios is impressive. For example, in the Central Region (Central'nyj Rajon) the City of Moscow has had throughout the transition period a UN ratio of well below one while the district of Ivanov (Ivanovskaja Oblast') had a UN ratio of 15 in June 1993, reaching the value of 75 in June 1994 (Centre for Economic Analysis, 1994). The overall figures certainly hide this kind of regional mismatch!

- The same data also show an inverse relationship between the UN ratio of an oblast' and the distance to Moscow. As official employment has substantially contracted in Moscow low _UN_ ratios in Moscow and surroundings seem to indicate that in agglomeration centres like Moscow the shadow economy provides substantial employment opportunities not captured by official data.

- At least until the middle of 1994, unemployed flowing back into employment had as their largest destination the state sector. Firms in the state sector seem to have increased on average their level of labour hoarding made possible by subsidies provided by the central budget. Commander et al. (1995) show why given a certain technology firms might in the light of negative output shocks prefer to increase labour hoarding instead of choosing employment reductions via attrition.

While these presented facts have been established looking mainly at aggregate data additional insights about labour market experiences of various groups have been gained by analysing household survey data. The following discussion is based on Foley's (1995) estimates of annual transitions between 1992 and 1993. It neglects the issue of round-tripping which should be kept in mind especially for unemployment transitions given their magnitude in the Russian labour market during the reported period Comparing gross transition probabilities between the three states of the Russian labour market with those of eastern Germany during the first phase of transition (Bellmann et al., 1995) and Poland (G6ra and Lehmann, 1995), some interesting patterns can be seen. The probability of remaining in employment is clearly greater
List, 2012; Diaconu et al., 2020), but it is unclear whether the effect can be attributed to the incentive channel alone. Our experiment resolves the practical challenge that incentives always also convey information because PFP contracts must state what is incentivized.

Second, we provide a conceptual framework and empirical evidence on spillovers in incomplete PFP contracts. A long-standing concern with PFP schemes is that incomplete contracts, i.e. those that only incentivize a subset of actions, could lead agents to “multitask” by diverting effort toward actions associated with the highest net gain (i.e., incentive net of cost), possibly at the expense of actions that have a lower payoff or are unmeasured (Holmstrom and Milgrom, 1991; Prendergast, 1999; Mullen et al., 2010; Miller and Babiarz, 2013; Finan et al., 2015; Bulte et al., 2021). Alternatively, performance-based contracts may crowd in effort, even on actions that are unincentivized or have a relatively low gain, for example, because of complementarities in production (Mullen et al., 2010; Sherry, 2016). The limited available literature does not find evidence of spillovers of PFP in health care, even for large schemes such as the United Kingdom’s Quality and Outcomes Framework PFP scheme (Campbell et al., 2007) or national PFP schemes in LMICs (Sherry et al., 2017; Celhay et al., 2019). We show theoretically that the magnitude and direction of spillovers will depend on the degree of complementarity in production. Further, we address a practical challenge in detecting spillovers, which is that workers can adjust effort on many—possibly unobserved—margins by using well-defined and self-contained experimental tasks for which we can assess performance on the universe of relevant actions. Our findings suggest that providing incentives for some actions also increases effort on unlisted actions by 4 pp, or 14 percent, which is indicative of complementarities in production.

Third, our paper also contributes to research contrasting the effects of loss or gain framing in contracts. This distinction is scientifically and practically important, as penalties can be politically and logistically challenging to implement because they involve withholding or recovering payments. Some experimental evidence—including from LMICs—suggests that identical incentives can be more effective if cast as penalties for poor performance rather than as rewards for good performance because of loss aversion (Kahneman and Tversky, 1984; Kahneman et al., 1991; Fryer et al., 2012; Hossain and List, 2012; Imas et al., 2017; Bulte et al., 2020, 2021). In contrast, other research suggests that framing does not matter very much in general settings (de Quidt et al., 2017; de Quidt, 4
employment occurred in export-oriented sectors.<sup>20</sup> As labor reallocation appears to occur across broad economic sectors, analyses which focus only on the formal industrial sector seem to be incomplete in capturing the whole impact of trade shock. The VHLSS 2002 and 2004 data used for analyzing wage movements in this study have an advantage relative to firm-level data as they allow the researcher to extend the analysis to all of Vietnam’s economic sectors.

# 3. THE IMPACTS OF THE BTA ON WAGES AND THE SKILL PREMIUM

# (a) Estimation strategy

This section investigates empirically how the exogenous trade shock resulting from the BTA translated into changes in wages for skilled and unskilled workers. Whereas this paper is inspired by the basic insights from the H-O-S model, its analysis does not follow the strict version of the H-O-S theory. Instead, I relax some of its assumptions so that the conceptual framework is consistent with Vietnam’s “real world” data and my empirical design. First, whereas the StolperSamuelson theorem links changes in relative output prices to changes in relative wages, it is difficult to model this linkage directly since price data are not readily available. Thus, I take an indirect approach assuming that the impacts of changes in product prices induced by trade are “revealed” in changes in labor demand.

Second, this paper extends the idea of the H-O-S theory to Vietnam’s provinces; i.e., it is assumed that Vietnam’s provinces have different specialization patterns given heterogeneity in endowments and difference in initial industrial development, and that the impacts of trade shocks translate differently at the province level. The model is designed to capture general equilibrium impacts of the trade shock within a province: it is assumed that labor is mobile across sectors and that factor prices tend to be equalized within province. In regard to labor mobility across

> 20 Discussing a similar situation, Anderson and Dimon (1999) report evidence that export oriented production in _maquiladoras_ (across-border export processing plants) created formal sector job opportunities for single women in Mexico.

12