rafmacalaba/datause-encoder-data huggingface dataset:
underreport absences to a central education agency. In contrast, work hours outside of the classroom to prepare lessons and provision of after-school tutoring are not mandated, so we argue that the incentive to misreport them is likely to be weaker, at least in the Lao context.

Furthermore, if teacher effort were regarded as a continuous and unobserved (latent) index variable that is manifested in terms of both absence and number of work hours (or tutoring activities), teacher work hours would approximate this variable better since work hours are a continuous variable with multiple values while absence is just a binary variable with two values.<sup>2</sup> The number of work hours or different aspects of tutoring activities as a measure of teacher effort is especially relevant in places where absenteeism is not excessively high. Incidentally, our new measures of teacher after-school tutoring activities make this paper a useful contribution to another emerging literature on private tutoring.<sup>3</sup> Most existing studies on after-school tutoring, however, focus on student demand and use of such services, and we add a little-explored angle by investigating instead the supply side of this phenomenon as part of a teacher’s work choice.

Second, we use reported delays in the payment of teacher salaries to capture pecuniary incentives,<sup>4</sup> and the extent of teacher autonomy, parent- teacher associations’ (PTA) activities, and a school principal’s authority to measure non-pecuniary incentives. While these incentives represent the current practice in the education system in Lao PDR and are relatively amenable to policy influence in this country as well in other developing countries’ context, most of them have

> 2 Models with unobserved latent index belong to a general group of models called limited dependent variable models. See, for example, Maddala (1983) for a general treatment of such models in econometrics.

> 3 Private tutoring (or supplementary education)  is widespread and can be found in countries as economically diverse as Cambodia, the Arab Republic of Egypt, Japan, Kenya, Morocco, Romania, Singapore, Turkey, the United States, and the United Kingdom. A survey of the prevalence of tutoring in 22 developed and developing countries finds that in most of these countries, 25–90 percent of students at various levels of education receive private tutoring, and private tutoring can have a positive impact on student performance (Dang and Rogers, 2008). In several UNESCO and ADB publications, Bray (2009) and Bray and Lykins (2012) argue that the phenomenon of private tutoring deserves far more attention from both policy makers and researchers than it has been given.

> 4 Salary delays may also be a non-pecuniary incentive if teachers interpret it as, say, lack of appreciation for their job; however, given the common occurrence of this phenomenon in Lao PDR alongside generally high teacher satisfaction, it is much more likely for salary delay to function as a pecuniary incentive.

3
Step one follows Table 1 of Borusyak et al. (2022) which applies their approach in the context of Autor et al. (2013) (hereby ADH). Table 11 in the Appendix reports summary statistics for the tariff shocks computed with the importance weights generated by applying the _ssaggregate_ Stata command. Given that service sectors do not have associated tariff shocks, we start with columns (1), (4) and (7) (one for each of our exposure variables) which includes the “missing” service shock of zero in each period. As in ADH, we see that the shock distribution is unusual as evidenced by a zero interquartile range, which reflects the large fraction of total employment accounted for by the service industry. We also see a high concentration of industry exposure as measured by the inverse Herfidahl index (HHI), which corresponds to the “effective sample size of our equivalent regression”, here only 39. This value is even lower (1.6) if we compute the HHI at the level of 4-digit NAICS codes (as opposed the 6-digits in the main regression), which suggests that even less industry-variation is available when shocks are allowed to be clustered by groups or serially correlated. Additionally, the mean of our shocks is significantly different from the zero shock of the missing service industry. As for ADH, these elements point to the need to exclude the service industry shocks from the identifying variation.

Columns (2), (5) and (8) therefore report the same statistics with the service industry excluded, which results in a more regular distribution of shocks and a relatively high inverse HHI of 2,536 in the 6-digit NAICS case and 44 in the 4-digit aggregation. The largest shock weights are 0.14% at the 6-digit and 6.4% at the 4-digit level which suggests a good degree of variation. Columns (3), (6) and (9) summarise the within-period distribution, confirming that there remains a sizable residual shock variation.<sup>23</sup> Table 12 in the appendix recreates the robustness check suggested by Borusyak et al. (2022) in their Table 4, where the coefficients are estimated using an equivalent industrylevel regression which obtains valid exposure-robust standard errors. Standard errors are clustered at the NAICS 4-digit level in column (1) and the 6-digit level in column (2). Our results remain robust and significant after undergoing this transformation.

Taken together, these checks, as well as others presented earlier, allay some of the key potential concerns surrounding our analysis and demonstrate that these findings continue 23Table 2 in Borusyak et al. (2022) analyses the correlation patterns of shocks across industries through intraclass coefficients. While their model does not converge for our specification, we explore different levels of clustering by sector and find no major differences in our results. Table 3 in Borusyak et al. (2022) looks at correlations between shocks and the controls included in ADH which are not included in our specification.

34
index shows that a significantly higher proportion of voucher respondents compared with nonvoucher respondents were from the poorest 40 percent of households (52 percent vs. 37 percent; _P_ <0.01; see table 2). No analysis was done to place voucher beneficiaries into a national poverty scale. The wealth quintiles reported here refer only to the study population.

Table 2 Percentage Distribution of HealthyBaby Voucher and Nonvoucher Respondents, by Household Wealth Index

|Household wealth index|Voucher<br>respondents|Nonvoucher<br>respondents|All women|
|---|---|---|---|
|Poorest quintile|28.3|18.8|20.8|
|Poorer quintile|23.3|17.9|19.1|
|Middle quintile|14.5|20.8|19.4|
|Richer quintile|18.1|20.7|20.1|
|Richest quintile|15.7|21.9|20.5|
|Number of respondents|502|1,811|2,313|

_Source:_ Population Council, Household Survey 2010–11.

_Note:_ Percentages may not sum to 100 in some cases because of rounding.

**<<B>>** Knowledge of Maternal Health Services and Conditions

Awareness of the HealthyBaby voucher increased over time. In particular, among women who were interviewed in the 2008 household survey, 21 percent had heard about the HealthyBaby voucher, which was promoted on radio in late 2008 before voucher sales commenced and well before the first delivery using the voucher in late January 2009. This more than tripled to 76 percent among women interviewed in the 2010–11 survey. The proportion of women who were from villages included in both the 2008 and 2010–11 surveys and who had heard about the HealthyBaby voucher also increased from 22 percent in 2008 to 75 percent in 2010–11. In addition, 78 percent of those living in villages with a voucher user in 2010–11 had heard about the HealthyBaby voucher, compared with 69 percent of those from villages with no voucher user. Results from the household surveys further show that women who had heard about the HealthyBaby voucher were more likely to be aware of possible dangers during pregnancy, childbirth, or immediately after 21
Policy Research Working Paper 6488

# **Abstract**

: Estimates of the extent of corruption rely largely on self-reports of individuals, business managers, and government officials. Yet it is well known that survey respondents are reticent to tell the truth about activities to which social and legal stigma are attached, implying a downward bias in survey-based estimates of corruption. This paper develops a method to estimate the prevalence of reticent behavior, in order to isolate rates of corruption that fully reflect respondent reticence in answering sensitive questions. The method is based on a statistical model of how respondents behave when answering a combination of conventional and random-response survey questions. The responses to these different types of questions reflect three probabilities—that the respondent has done the sensitive act in question, that the respondent exhibits reticence in answering sensitive questions, and that a reticent respondent is not candid in answering any specific sensitive question. These probabilities can be estimated using a method-of-moments estimator. Evidence from the 2010 World Bank Enterprise survey in Peru suggests reticence-adjusted estimates of corruption that are roughly twice as large as indicated by responses to standard questions. Reticence-adjusted estimates of corruption are also substantially higher in a set of ten Asian countries covered in the Gallup World Poll.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at akraay@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 3. Summary of child poverty and inequality effects**

|**Fiscal intervention**|**Effect**|**Marginal**<br>**contribution to child**<br>**poverty reduction**|**Marginal**<br>**contribution to**<br>**redistributive**<br>**effect**|**Kakwani**|
|---|---|---|---|---|
|Direct Transfer: Family Other (means-tested)|Direct|5.4|1.1|1.0|
|Direct Transfer: Monthly child allowance (means-tested)|Direct|2.5|0.5|1.0|
|Direct Transfer: Pregnancy & childbirth benefit|Direct|2.2|0.3|0.7|
|Other Family Contributory transfers|Direct|1.7|0.3|0.6|
|Direct Transfer: Benefit for bringing up a child up of 2|Direct|1.2|0.2|0.8|
|Direct Transfer: Family Other (non-means tested)|Direct|0.8|0.2|0.8|
|Direct Transfer: Family Birth child grant (non-means tested)|Direct|0.2||0.9|
|Education Upper Secondary*|Direct||1.0|0.7|
|Education Lower Secondary*|Direct||0.6|0.6|
|Education Primary Level*|Direct||0.4|0.6|
|Education Pre-School*|Direct||0.3|0.6|
|Direct Transfer: Family child benefit for twins (non-means tested)|Direct|||1.2|
|Direct Transfer: Benefit for raising a child under 1 (means-tested)|Direct|||1.2|
|Direct Transfer: School children enrolled in 1st grade (means-tested)|Direct|||1.0|
|Direct Transfer: Familymothers in tertiaryeducation(non-means tested)|Direct|||0.1|
|Indirect subsidies: Electricity direct effects|Indirect|2.1|0.5|0.2|
|Direct transfers: disability|Indirect|1.5|1.1|0.5|
|Direct transfers: unemployment|Indirect|1.4|0.6|0.5|
|Direct transfers: other|Indirect|0.2|0.3|0.7|
|Direct transfer: Heating allowance|Indirect|0.1|0.3|1.3|
|Indirect subsidies: Gas indirect effects|Indirect|0.3||0.1|
|Health Social Security*|Indirect||2.2|0.4|
|Health Public Health*|Indirect||0.8|0.5|
|Direct transfer: Social Monthly Assistance GMI|Indirect||0.1|1.4|
|Indirect subsidies: Gas direct effects|Indirect|0.1|0.0|-0.1|
|Indirect subsidies: Electricity's indirect effects|Indirect|0.9|-0.1|0.1|
|Indirect taxes: Excise Alcoholic Beverages|Indirect|-0.1|||
|Indirect taxes: Oil derivates direct effects|Indirect|-0.5|0.2||
|Indirect taxes: Oil derivates indirect effects|Indirect|-1.2|0.2||
|Indirect taxes: VAT|Indirect|-6.7|1.4|0.0|
|Indirect taxes: Excise Tobacco|Indirect|-1.2|-0.1|-0.1|
|Direct transfers: non-contributory pensions|Indirect|||1.2|
|Education Tertiary*|No||0.2|0.3|

_Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

Note: * Poverty effects not estimated.

|1|Childpovertyand inequalityreduction|
|---|---|
|2|Child poverty reduction|
|3|Inequalityreduction|
|4|Childpovertyreduction but unequalizing|
|5|Child poverty increasing|
|6|No child poverty or inequality effects|

**The first group comprises fiscal interventions targeting children that significantly reduce child poverty and inequality.** This includes the monthly child allowance, pregnancy and childbirth benefits, benefits for raising a 2-year-old child, and various means-tested and non-means-tested family and children's benefits. Additionally, indirect child benefits like social protection transfers for disability, unemployment, health allowance, and electricity subsidy (indirect effects) are part of this group.

40
# **1. Introduction**

Foreign direct investment (FDI) can reduce income gaps between developing and advanced economies. In the neoclassical world with perfect capital mobility and technology transfer, capital readily flows from rich to poor countries, seeking higher returns in capital-scarce economies. We do not see this in the data. Even though the southern African countries (the South African Development Community, SADC hereafter) are poor on average, per capital FDI inflows for the SADC region are only 36.6 U.S. dollars (in 2000 value), which is about 18 percent of the average level for the non-SADC sample (202.8 dollars), and 58 percent of the average level for the similar-income sample (63.2 dollars). Moreover, within the SADC region, country differences are huge: FDI per capita (in 2000 U.S. value) ranges from single digits (Malawi, Zimbabwe, Madagascar, Democratic Republic of Congo, and Tanzania) to 10 to 30 dollars (Mozambique, Zambia, Mauritius, and Swaziland), to 50 to 100 dollars (Lesotho, South Africa, and Angola), and to 167 dollars in the outlier in this region, the middle-income Botswana. Even within this region we detect a positive relationship between average income and FDI per capita. This pattern holds in the world sample as well. Thus, the hope of relying on FDI inflows as a “supply side remedy” to catapult poor countries into the fast track of development, as prescribed by the United Nations (1999), is not likely to materialize soon.

Perhaps with these low FDI numbers in mind, policy makers and development agencies are concerned about FDI performance in SADCs.<sup>2</sup> One hopes that FDI can play a positive role in local economic development, but this is not obvious. It is possible that foreign entrants merely capture rents, that the new jobs created may merely crowd out domestic jobs, and that there is no technological spillover, but rather “market stealing”, effects on domestic firms. In addition, it is possible that the benefits of FDI differ across economic contexts. For example, the benefits may depend on market size and the timehorizon of foreign capital, which itself is shaped by the institutional environment. This combination of high hopes and unclear evidence puts empirical research in center stage. Thus, it is useful to investigate empirically whether FDI plays a positive role in SADC,

> 2 An ongoing World Bank report on SADC regional integration also emphasizes the role of FDI on SADC development.

2
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46
study, to our knowledge, to both probe the underlying mechanisms driving impacts on wellbeing and offer a benchmark against a standard cash transfer program.

# **2 Research Context**

## **2.1 Recent Events**

The Rohingya are an ethnic group that, prior to the genocide of 2017, lived predominately in Rakhine State along the western coast of Myanmar (also known as Burma) (Blakemore, 2019). The community traces their origins back to the 15th century, when thousands of Muslims settled in the former Arakan Kingdom, which was conquered by the Burmese Empire in 1784 (Albert and Maizland, 2020). The Rohingya have since faced multiple waves of discrimination and suppression, with the first major campaign of ethnic cleansing occurring in 1978 when the Burmese military, tasked with performing a census of the border regions to determine citizenship, conducted indiscriminate attacks across Rohingya villages in Rakhine state. This led to an estimated quarter million people fleeing into neighboring Bangladesh. Subsequent ethnic cleansing campaigns in 1992 and 2012 sent additional waves of Rohingya into Bangladesh (Watch, 1996).

On August 25, 2017, the Rohingya insurgent group Arakan Rohingya Salvation Army launched an attack on security force outposts in northern Rakhine, killing twelve security personnel. Within hours, Myanmar security forces responded. Satellite imagery documented the destruction of at least 392 villages (40 percent of all settlements in northern Rakhine), with 80 percent burned within three weeks. By October 2018, over 750,000 Rohingya refugees found themselves in a veritable city of makeshift tents along the southern tip of Bangladesh, stretching from Teknaf to Cox’s Bazaar. The largest and most densely populated refugee camp on earth was constructed in a matter of weeks (Hussam, 2019).

There are currently 34 camps in Bangladesh, each subdivided into blocks ranging in population density from 60 to 130 households. Each block is represented by a local leader (a _majhi_ ) who is responsible for organizing distribution efforts and serving as a liaison between humanitarian organizations and the refugee community. While refugees receive humanitarian assistance, most are unable to cover their basic needs and look for ways to supplement their income by selling their assets and the rations they receive, and seeking informal work opportunities (as they do not possess a legal right to work in Bangladesh).

6
# Table 6.8. Poverty rate by area of residence transition matrix estimates by the unweighted, current UNPS, and calibrated GWSM base estimators

||||2009/15|Calibrated||2013/15|Calibrated|
|---|---|---|---|---|---|---|---|
|||Unweighted|UNPS|GWSM<br>base|Unweighted|UNPS|GWSM<br>base|
|In non-poor HH in 2009|Rural|58.1|59.4|59.9|59.4|61.4|61.0|
|(or 2013) and 2015|Urban|81.1|83.5|81.6|84.2|83.8|83.4|
|In non-poor HH in 2009<br>|Rural|13.8|13.7|14.0|9.2|8.8|9.1|
|(or 2013) and in poor<br>HH in 2015|Urban|7.0|6.5|7.7|5.5|5.6|5.0|
|In poor HH in 2009 (or|Rural|11.4|10.6|9.7|14.7|13.4|13.2|
|2013) and 2015|Urban|3.0|3.1|2.1|3.8|4.1|5.4|
|In poor HH in 2009 (or<br>|Rural|16.7|16.3|16.3|16.7|16.4|16.6|
|2013) and non-poor HH<br>in 2015|Urban|8.8|6.9|8.6|6.5|6.5|6.2|

# 6.3. Summary of the empirical results

The comparison of the estimators has been carried out upon two levels: their statistical properties and their practical implementation. As far their statistical properties, section 4 introduced the properties of the new calibrated estimator and section 6 provided concrete results on real data. Given the complexities involved in claiming the superiority of one estimator to another, we used benchmarking statistics to conduct this evaluation. The results are summarized as follows:

- The calibrated GWSM base estimator seems to produce more accurate individual-level statistics than the current UNPS estimator.

- The two estimators produce equally accurate statistics at the household level, suggesting that the GWSM base estimator can be further improved by considering household known totals.

- While the cross-sectional estimates based on the transition matrix generally do not reproduce the exact cross-sectional estimates based on the entire UNPS sample, the sample refresh operation mitigates this problem. This suggests that both estimators can only partially deal with the dynamic nature of the population, making it important to i) coherently implement a periodic sample refresh (i.e., rotate the panel) and ii) improve the tracking rules, as panel attrition affects specific subpopulations of interest.

- The calibrated GWSM base cross-sectional estimates on the transition matrix appear generally more stable when changing the sample (i.e., 2009/2015 or 2013/2015) than the current UPNS estimates.

- While the scope of the simulation has been to obtain general conclusions, as far as the practical implementation level is concerned, considering the Uganda case to be concrete, we can give the following indication, defined to change the current process as little as possible: - 1 3 5. The 2 is not explicitly stored, since a non-respondent in the original sample (wave

- 1) is not clearly distinguished from a new individual that was not included in the original According to Table 5.1, Uganda panel data stores the variables, vv , vv and  vv sample.  variable  vv

- - 6 , using other stored variables and making some accurate assumptions. The variable should be directly asked We artificially created the population membership variable, vv

32