rafmacalaba/datause-encoder-data huggingface dataset:
together and share a common source of food”. Most LSMS surveys include an individual as a household member only if she/he lived in the household for more than 3 months in the last 12 months (Glewwe (2000)), which would exclude the children in colleges away from home, as the college students in developing countries do not get 3 months break to go back home.<sup>5</sup> Some surveys such as IFLS in Indonesia and IHDS in India (the second round in 2012) include two randomly chosen non-resident children, which implies that the poorer households are more likely to face truncation, because fertility rate usually declines as household income increases. Most household surveys done by national statistical agencies also use similar coresidency restrictions to define a household.<sup>6</sup> The coresidency restrictions used in the surveys result in truncation of the sample as there is no information on the nonresident children, and truncation is likely to be nonrandom.<sup>7</sup> In developing countries such as India and Bangladesh, many girls move out of parental household when they drop out of school because of marriage, and the sons leave the household for jobs. In addition, the rural households are likely to miss more educated children systematically because most of the villages do not have a college (or even a high school), and children have to migrate to pursue higher education.

Figure 1 provides estimates of conditional probability of non-residence of children at the time of the survey at various schooling levels of children. Both in India and Bangladesh the probability of non-residence is much lower at the middle of the schooling distribution. Truncation thus is more prominent at the tails of the schooling distribution. There are also significant differences across countries and gender. The incidence of non-residency

> 5There are exceptions such as infants less than 3 months old are included as household members. For an excellent discussion on the issues involved in defining a household, please see Glewwe (2000). Some surveys use a much higher cut-off; for example, MxFLS in Mexico considers someone a household member if he/she lived in the household for a year or more.

> 6Some panel surveys carefully track the households over the years, and thus take care of the sample selection arising from attrition. However, most of them still use coresidency criteria to define household membership at the baseline. Thus even with no attrition, the data still suffer from truncation bias.

> 7This implies that it is not possible to implement selection correction using the Heckman procedure, as it is not possible to estimate a selection equation without knowing which households are missing children from the survey. The maximum likelihood procedure developed by Bloom and Killingsworth (1985) can be used, but it relies on strong distributional assumptions.

5
Implementing the full portfolio of water infrastructure projects would require a US$3.08 billion investment –an amount that exceeds the budget for new water infrastructure projects in the next four years. The estimated available budget over the next four years, as detailed in Table 9 below, is approximately US$ 0.35 billion. This figure is based on a conservative budget estimate that accounts only for the amounts allocated to 'new' water infrastructure projects and does not consider funds budgeted to maintain ongoing projects.

_Table 9: Budget allocation for new projects_

|Year|Budget for New Water Supply Projects<br>(LKR Billion)<br>(USD Million)|
|---|---|
|2017|10.4<br>76.3|
|2018|11.4<br>84.0|
|2019|12.6<br>92.4|
|2020|13.8<br>101.6|
|Total|48.2<br>354.3|

Source: National Water Supply and Drainage Board Galle Road, Ratmalana Vote Ledger Statement: May 31<sup>st</sup> , 2017. Notes: The allocations from 2018 up to 2020 are based on 10 % increase per year from allocations for year 2017.

Figures 5 and 6 show the ordering/ranking of the projects based on their SEI and FEI scores. These figures also display the estimated budget limit to show which projects could be implemented, given the budget constraint, if projects were selected according to one or the other index score only.

_Figure 5: SEI ranking for water infrastructure projects in Sri Lanka_ <!-- Start of picture text --> Social and Environmental Indicator (SEI)<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>P14 P15 P2 P9 P18 P3 P24 P7 P17 P26 P6 P20 P10 P5 P4 P16 P22 P8 P23 P19 P13 P11 P28 P27 P1 P12 P25 P21<br><!-- End of picture text --> ‐‐‐‐ Budget constraint of US$ 0.35b Source: Authors' calculations 25
war (henceforth, war-cohort). The findings from the analysis of education are then used to motivate the analysis of employment outcomes of the war-cohort.<sup>11</sup> The analysis of the effect of conflict on employment of the war-cohort is based on a sample of 10,583 prime-age men and women (age 22-49 in 2007). The definition of employment in the last 14 days is based on the questions 1, 2, 3, 5 and 6 from Module 5: Labor Market Section. The definition is consistent with the ILO definition of employment (see Appendix B). The analysis of wages is based on monetary and in-kind wages received by an individual from the occupation in which an individual was employed the longest number of hours. The choice which occupation was the main and which was secondary was made by the 2007 TLSS survey personnel based on the answers to questions 5 and 7 of Module 5: Labor, Part B. Question 5: “For how many hours a week in the last 14 days did you do this work?” Question 7 (to be filled by survey personnel: “Check for first and second highest answers to Q5 (hours worked per week) for this individual.” Based on the answers individual’s wages and in-kind payments from the main and secondary occupations were calculated. The average total income earned in the primary (287 somoni per month) and secondary (7 somoni per month) occupations are drastically different, indicating that secondary jobs provide only a minimal supplementary income.

# **4.2 Conflict exposure variable**

The geographical exposure to the conflict differed significantly in Tajikistan. The southern and eastern regions, such as Khatlon and the Raions of Republican Subordination (RRS) and the country capital Dushanbe were severely affected by the conflict over a long period of time. These regions suffered from repeated clashes between the government and the opposition, or were subjected to the occupation by various factions participating in the conflict over several years. To evaluate the impact of the conflict on the population, this study employs a conflict variable based on a compilation of some of the events related

> 11 The summary statistics for the samples used in the analysis of education and employment are reported in Appendix Tables 1.1 and 1.2. Appendix Table 2 tests for the equality of means for variables used in the analysis of employment with the conflict affected area defined as inclusive and exclusive of Dushanbe. The t-tests indicate that based on the observable characteristics the sub-samples from the affected and lesser affected areas are significantly different from each other, and therefore it is important to control for these characteristics in the regression analysis.

11
# **Table of Contents**

## I.    Introduction

- II.     Trade Reform and the Rationale for Aid-for-Trade Programs

1.  Aid for Trade as a Compensatory Scheme

- 1.1 Mitigating Revenue Losses

- 1.2 Mitigating Cuts in Productive Expenditure

- 1.3 Mitigating Adjustment Costs and Implementation Costs

2. Aid for Trade as a Promotion Scheme

- 2.1 Facilitating Domestic and Regional Investment in Core Infrastructure

- 2.2  Supporting Capacity Building and Institutional Reform

- 2.3  Financing Complementary Structural Reforms

- III.    Implications for Trade Reform in the Caribbean

1.  Relevance of Aid-for-Trade Arguments

2. Some Specific Issues

- IV.     Trade Reform, Aid for Trade, and Growth: Policy Experiments

1.  The Quantitative Framework

2. Baseline Scenario 3. Simulation Results, 2009-20

- VI.     Summary and Concluding Remarks

References Box 1—The Gains from Trade and Access to Infrastructure 2
> <sup>6</sup> The prosperity gap—our measure of inclusive growth—also declined by about half, to below 2, when tax collections rose from 7 and 15 percent of GDP. There is some uncertainty, however, in the size of the improvements in future growth as tax revenue increases as the regression is subject large standard errors for lower tax revenue. The relationship between taxes and (inclusive) growth is also complex and these regressions are still subject to endogeneity issues and the results cannot be treated as causal. Therefore, raising tax revenue cannot be seen as a panacea for boosting economic growth. Moreover, achieving such an increase in tax revenue is challenging requiring a mammoth effort from policy makers. The average increase in tax over a tenyear period in our sample was about 3 percent of GDP (looking only at those who increased taxes). An 8 percent of GDP increase, from 7 to 15 percent, falls above the 90<sup>th</sup> percentile.

# **Figure 5 Impact of taxes on growth and inclusive growth**

<!-- Start of picture text --> A. Predicted 10-year cumulative growth B. Predicted prosperity gap<br>Percent Unit<br>40 5<br>35<br>4<br>30<br>3<br>25<br>2<br>20<br>1<br>15<br>10 0<br>7% tax to GDP 15% tax to GDP 7% tax to GDP 15% tax to GDP<br>Source: Authors’ calculations.<br>Note: Orange whiskers reflect 90 percent confidence intervals.<br><!-- End of picture text -->

# **6. Potential channels through which taxes affect growth**

Once the threshold for tax collection that affects future growth has been identified, it is worth analyzing the possible channels through which higher taxes may impact growth. In this section we look at several possible explanations of tax and growth relation focusing on the possible ways of using tax revenue by governments. Our analysis is motivated by two main questions. First, do governments spend more on health and education as they have more resources available? Second, are higher taxes associated with lower government consumption and real GDP growth volaility?

> 6 The negative slope on future growth above the threshold (table 3, column 3) suggests that as tax to GDP increases, future growth slows. This is not the case in the prosperity gap; that is, future prosperity gap does not decrease as tax revenue increases above the identified threshold.

10
end up paying the minimum balance fee, unaware of it. Sophisticated customers will also be attracted to the 2 percent deposit rate but will never pay the minimum balance fee as they will take action to avoid it. Banks will therefore make enough money from naïve customers to cover the losses from offering the account to sophisticated consumers: naïve customers will cross subsidize the sophisticated ones.

Note that if a bank decided to price the savings account more transparently, offering savings accounts at 1 percent without the minimum balance fee, no customer would be attracted to the 1 percent account because it offers a lower interest rate. All customers would still demand the 2 percent savings account, naïve customers failing to realize that they will end up paying the minimum balance fee and sophisticated ones realizing that they are better off earning 2 percent and avoiding the minimum balance fee altogether.<sup>14</sup> The equilibrium is thus one in which financial products offered have hidden fees that are only taken into account by sophisticated consumers. More formally, the following testable implications can be derived:

1. The staff of financial institutions are knowledgeable about the financial product but will only provide customers with information to contract it, without disclosing its cost voluntarily. As a result, both naïve and sophisticated customers are aware of all requirements to open a savings account or contract a loan. In addition, sophisticated customers ask about fees such as the minimum balance fee on deposit accounts or the late payment fee in credit products and thus learn about such costs while naïve customers do not ask about them and thus remain uninformed.

2. Banks will not engage in informative marketing. Marketing campaigns, if any, will advertise the deposit rate but not the minimum balance fee for savings. Similarly, marketing campaigns for credit products will not advertise the total cost of the credit nor detailed fees and charges.

3. Naïve and sophisticated customers are offered the same product, but naïve customers will earn less net interest on savings and pay more in total for credit as they also pay fees.

In Section 6 we test these predictions more formally.

# **4. Experimental Design**

The audit study was conducted in collaboration with the regulator and the consumer protection agency in each country. In Mexico four towns near the greater metropolitan region of Mexico City were selected with populations ranging from 30,000 to 1,600,000: Amecameca, Cuernavaca, Ecatepec, and Los Reyes de la Paz. These towns were selected for their high penetration of financial institutions targeting

> 14 Alternatively, naïve customers could understand the fee structure but be overoptimistic about the chances of their savings balances falling below the minimum. In either case, they would prefer the 2 percent account over the 1 percent account offer.

9
programs, tariffs, and quotas are examples of the tirst set of measures, while deficiency payments comprised the second group. Indirect income supports and other assistance include input subsidies, capital grants, research etc. To estimate the tariff equivalent of border protection based on the PSE estimates, I consider only the effects of market price support measures (price support, tariffs, quotas, and other NTBs) and exclude the direct and indirect income transfers and other assistance. The tariff equivalents based on data about market price support measures from the OECD and USDA databases are calculated as follows:

|(1)||=<br>I(VPIP/VPWP)-I] *100|
|---|---|---|
||wlhere|X<br>=<br>tariff equivalent|
|||VPIP<br>=<br>value of production at internal prices|
|||VPWP<br>=<br>value of production at world prices|

The value of production at world prices, VPWP, is defined as:

||VPWP||VPIP - MPS where|
|---|---|---|---|
||MPS|=|Q *(Pd - P) or the value of market price support|
||Q|=|volume of production|
|Therefore,|C=<br>(Pd|- Pw)/Pow|or (Pd / Pwo) -1 *100|

where Pd is the domestic price and Pco is the reference (border) price at which purchases from the world market could have taken place. MPS is the value of all market price support -- an estimate ol aggregate transf'ers to producers as a result of measures such as tariffs, import quotas, and other trade barriers. The dif'l'erence between world prices and internal prices, as a percentage of world prices is the tariff' equivalent of' border measures. This is measured either as an import tariff (subsidy) or an export subsidy (tax), or both. If domestic prices are above (below) world prices, the price wedge reflects an export subsidy (tax) equivalent for exportables and an import tariff (subsidy) equivalent for importables.

17
# **Table S2.6 Estimating the effects of percentage changes in domestic food prices on changes in the $1.25/day poverty headcount for different gaps between successive poverty observations**

||**R**<br>|**obust regression result**<br>|**s**<br>|
|---|---|---|---|
|Regression Number|**1**|**2**|**3**|
|Gap between successive poverty observations|**1-4 years**|**1-3 years**|**1-2 years**|
|Change in log of domestic food prices|-22.36***|-21.59***|-10.85|
||(5.95)|(6.45)|(8.04)|
|Number of observations|273|248|198|
|Observations from low income countries (%)|15.23%|11.27%|8.65%|
|||**IV-GMM results**||
|Regression Number|**4**|**5**|**6**|
|Gap between successive poverty observations|**1-4 years**|**1-3 years**|**1-2 years**|
|Change in log of domestic food prices|-50.02|-63.79|6.70|
||(37.84)|(45.76)|(40.20)|
|Number of observations|273|248|198|
|Observations from low income countries (%)|15.23%|11.27%|8.65%|

Source: Author’s estimates.

Notes: See text for definitions of the variables. *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Standard errors are reported in parentheses. The robust regressor corresponds to the _rreg_ command in STATA<sup>TM</sup> . For the IV GMM, errors are clustered at the country level. Specification tests for the IV-GMM results are omitted for the sake of brevity, but are similar to those reported in Table 2, with weak instruments being a potential problem.

30
When non-response bias can be satisfactorily accounted for, nationally representative surveys that test for HIV prevalence are clearly preferred among the available survey tools; they are generally expected to provide more accurate information on HIV prevalence than sentinel survey data. However, most countries are not yet in the position of being able to afford such surveys, or of repeating them frequently. At the same time, urgent calls for better and more timely response to the HIV epidemic raises demand for sub-national estimates of HIV prevalence. This demand arises from the recognition that the geographic distribution of HIV prevalence is likely to reflect “pockets” of high prevalence in some areas and low prevalence in others. At the aggregate level such heterogeneity may not be apparent, constraining policy makers from tailoring their responses to the specific geographic, and other, circumstances of different localities. There is thus heightened awareness of a need to better understand the correlates of HIV status, including the association between HIV and geographical characteristics. A question that arises in this context is whether it is possible to obtain reliable sub-national estimates of HIV prevalence using existing data sources, such as sentinel data coupled with other sources of information.

This paper examines the case of Malawi, where the adult prevalence is estimated to be the 8<sup>th</sup> highest in the World (UNAIDS, 2006), and demonstrates how one can utilize small area estimation (SAE) methods to obtain a sub-national (district) level profile of HIV prevalence in a setting where national population-based surveys of prevalence (such as the 2004 DHS) are absent. The approach considered here draws from the small area estimation approach applied to welfare measurement (Elbers et al., 2003). It represents the first time that small area estimation methods have been applied to HIV prevalence data.

The methodology presented in this paper combines HIV status information available from sentinel surveillance data (the 2001 ANC sentinel survey) with representative population-based data from a household survey (DHS 2000) as well as Census and GIS data. The proposed method essentially involves 3 stages. In the first stage information on individual characteristics from the DHS 2000 (such as age and education of women aged 15-49) is used to weigh the respective individual information from the sentinel survey. This exercise is carried out in order to render the sentinel data as representative of the underlying population as possible. In the second stage, the 4
# **TABLE 1: SUMMARY STATISTICS OF KEY VARIABLES**

||Number of<br>Observations|Mean<br>All hou|Std. Dev.<br>seholds|Mean<br>Migrant H|Std. Dev.<br>ouseholds|Mean<br>Non-migran|Std. Dev.<br>t households|
|---|---|---|---|---|---|---|---|
|_Household Variables (for households with a child aged 12 to 18)_||||||||
|Proportion of Households with a migrant|12980|0.22||1||0||
|Proportion of Households with a migrant by census definition<br>|12980|0.13||0.60||0.002||
|Proportion receiving remittances<sup>1</sup><br>|12301|0.06||0.22||0.02||
|Percentage share of income from remittances<sup>1</sup>|12301|3.83|16.79|14.91|30.87|0.80|7.43|
|_Individual Variables_||||||||
|Years of Schooling of Mother for children aged 12 to 18|20388|3.32|3.20|3.47|2.98|3.28|3.27|
|Years of Schooling of Males 12 to 15|6537|5.79|2.02|5.94|1.82|5.74|2.08|
|Years of Schooling of Males 16 to 18|4159|7.10|2.85|7.10|2.73|7.10|2.88|
|Years of Schooling of Females 12 to 15|6196|5.92|2.05|6.23|1.79|5.83|2.11|
|Years of Schooling of Females 16 to 18|3489|7.32|2.87|7.52|2.58|7.26|2.96|
|_State level variables_||||||||
|State migration rate in 1924|20388|0.006|0.008|0.011|0.008|0.004|0.007|
|Percentage of rural households owning land in 1910|20388|2.283|1.705|2.698|1.514|2.159|1.739|
|Male School Attendance in 1930 (% of 6 to 10 year olds)|20388|39.39|9.00|39.902|8.153|39.242|9.226|
|Female School Attendance in 1930 (% of 6 to 10 year olds)|20388|36.84|10.00|38.944|9.264|36.211|10.121|
|Gini of Household Income in 1960|20388|0.783|0.069|0.778|0.068|0.784|0.069|
|Number of Schools per 1000 population in 1930|20388|1.100|0.319|0.993|0.305|1.131|0.315|
|Gini of Years of Schooling for Males 15-20 in 1960|20388|0.554|0.080|0.551|0.087|0.555|0.078|
|Gini of Years of Schooling for Females 15-20 in 1960|20388|0.573|0.099|0.556|0.100|0.578|0.098|
|Average Male Years of Schooling in 1960|20388|2.614|0.638|2.671|0.647|2.597|0.635|
|Average Female Years of Schoolingin 1960|20388|2.437|0.745|2.561|0.756|2.400|0.738|

Source: own calculation from ENADID 1997 communities with population <100,000 and 50 or more households sampled. Education Ginis are only reported for communities with 20 or more children in the given age category

# **TABLE 2: DOES THE HISTORIC NETWORK AFFECT EDUCATION LEVELS IN NON-MIGRANT HOUSEHOLDS?**

Dependent Variable: Years of education attained (non-migrant households only)

||Males|Males|Females|Females|
|---|---|---|---|---|
||12 to 15|16 to 18|12 to 15|16 to 18|
|Living in state with migration rate above median in 1924|0.14|-0.07|0.18|0.27|
||(1.35)|(0.33)|(2.24*)|(1.50)|

T-statistics are in parentheses with standard errors clustered at the state level.

* significant at the 5% level.

Coefficients from OLS regressions which also include age of child, age of child squared, maternal education, proportion of rural households owning land in 1910, school attendance in 1930, income gini in 1960, number of schools per 1000 population in 1930, gini of years of schooling in 1960, and mean years of schooling in 1960 as other controls.

34