rafmacalaba/datause-encoder-data huggingface dataset:
|P000568|1996|1997|4 Closed|Congo,Republic of|
|---|---|---|---|---|
|P000568|1996|1998|5 Closed|Congo,Republic of|
|P000568|1996|1999|6 Closed|Congo,Republic of|
|P000568|1996|2019|5/7/2019<br>7 Closed|Congo,Republic of|
|P000569|1988|1988|0 Closed|Congo,Republic of|
|P000569|1988|1989|6 Closed|Congo,Republic of|
|P000569|1988|1990|8 Closed|Congo,Republic of|
|P000569|1988|1991|10 Closed|Congo,Republic of|
|P000569|1988|1992|12 Closed|Congo,Republic of|
|P000569|1988|1993|14 Closed|Congo,Republic of|
|P000569|1988|1994|16 Closed|Congo,Republic of|
|P000569|1988|1995|17 Closed|Congo,Republic of|
|P000569|1988|1996|18 Closed|Congo,Republic of|
|P000569|1988|1997|18 Closed|Congo,Republic of|
|P000583|1980|1985|0 Closed|Comoros|
|P000583|1980|1986|0 Closed|Comoros|
|P000583|1980|1987|0 Closed|Comoros|
|P000583|1980|1988|0 Closed|Comoros|
|P000583|1980|1989|6 Closed|Comoros|
|P000584|1982|1985|0 Closed|Comoros|
|P000584|1982|1986|0 Closed|Comoros|
|P000584|1982|1987|0 Closed|Comoros|
|P000584|1982|1988|0 Closed|Comoros|
|P000585|1982|1985|0 Closed|Comoros|
|P000585|1982|1986|0 Closed|Comoros|
|P000585|1982|1987|0 Closed|Comoros|
|P000586|1983|1985|0 Closed|Comoros|
|P000586|1983|1986|0 Closed|Comoros|
|P000586|1983|1987|0 Closed|Comoros|
|P000586|1983|1988|0 Closed|Comoros|
|P000586|1983|1989|4 Closed|Comoros|
|P000587|1984|1985|0 Closed|Comoros|
|P000587|1984|1986|0 Closed|Comoros|
|P000587|1984|1987|0 Closed|Comoros|
|P000587|1984|1988|0 Closed|Comoros|
|P000587|1984|1989|5 Closed|Comoros|
|P000587|1984|1990|7 Closed|Comoros|
|P000587|1984|1991|9 Closed|Comoros|
|P000587|1984|1992|10 Closed|Comoros|
|P000588|1984|1985|0 Closed|Comoros|
|P000588|1984|1986|0 Closed|Comoros|
|P000588|1984|1987|0 Closed|Comoros|
|P000588|1984|1988|0 Closed|Comoros|
|P000588|1984|1989|6 Closed|Comoros|
|P000588|1984|1990|8 Closed|Comoros|
|P000588|1984|1991|10 Closed|Comoros|
|P000590|1987|1988|0 Closed|Comoros|
# **Table C.20: Placebo test—Comparing top and bottom half of selected group**

**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)|
|---|---|---|---|
|placebo|0_._03|216_._24|427_._10|
||(0_._06)|(532_._25)|(1165_._31)|
|N|296|296|296|
|Nominated mean|0_._30|9799_._68|23183_._01|
|Nominated SD|0_._46|4291_._32|8744_._07|

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

||Decisio|n-making (I|ndex)|
|---|---|---|---|
||Pooled waves|Wave 1|Wave 2|
|placebo|0_._07|0_._17|_−_0_._03|
||(0_._10)|(0_._18)|(0_._09)|
|N|296|145|151|
|Nominated mean|_−_0_._14|_−_0_._23|_−_0_._06|
|Nominated SD|1_._02|1_._25|0_._72|

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

||Women|Girls|Men|Boys|Remittances|
|---|---|---|---|---|---|
|||(All outcomes a|s % in househ|old income|)|
|placebo|0_._31|0_._85|_−_0_._47|0_._56|0_._60|
||(1_._93)|(0_._71)|(0_._55)|(0_._51)|(1_._62)|
|N|296|116|296|129|295|
|Nominated mean|8_._13|2_._29|4_._46|2_._00|9_._25|
|Nominated SD|14_._83|3_._00|5_._14|2_._28|12_._91|

Notes: Table only includes women selected for the promotion program, where the upper half of the ranked list in each factory is assigned to a placebo treatment which is zero for the lower half. Standard errors clustered at the individual level in parentheses. Controls chosen using pdslasso (ivlasso for Panel A, Column 4) 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 in Panel C, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

**Table C.21: Lee (2009) bounds**

|**Outcome**||**Pooled waves**|||**Wave 1**|||**Wave 2**||
|---|---|---|---|---|---|---|---|---|---|
||OLS estimate|Lower bound|Upper bound|OLS estimate|Lower bound|Upper bound|OLS estimate|Lower bound|Upper bound|
|Supervisor|0_._29|0_._26|0_._29|0_._34|0_._30|0_._33|0_._24|0_._21|0_._24|
|Decision-making (Index)|0_._13|0_._04|0_._23|0_._44|0_._34|0_._55|_−_0_._23|_−_0_._27|_−_0_._15|
|Women’s expenditures|2_._58|0_._59|2_._82|2_._18|0_._54|2_._36|3_._02|0_._62|3_._32|
|Girls’ expenditures|0_._82|0_._42|0_._88|0_._54|0_._58|0_._72|1_._17|0_._62|1_._29|
|Remittances|2_._82|1_._55|3_._05|2_._78|1_._61|3_._09|2_._62|1_._39|2_._86|

86
# **Remittance Stability, Cyclicality and Stabilizing Impact in Developing Countries**

## **Ileana C. Neagu and Maurice Schiff***

JEL codes: F24, F22 Keywords: _remittances, FDI, ODA, stability, cyclicality, stabilizing impact_

> * Development Economics Research Group (DECRG), The World Bank, MC3-303, 1818 H Street, NW, Washington, DC 20433; E-mail: ineagu@worldbank.org and mschiff@worldbank.org respectively. The views expressed in the paper are those of the authors and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
# **TABLES BASED ON THE RICS-AgSS DATA**

**Table 1: Participation Rates & Contributions to Household Income, by Region**

||**Tigray**|**Amhara**|**Oromia**|**SNNP**|**Total**|
|---|---|---|---|---|---|
|% of households owning a nonfarm<br>enterprise|22%|20%|23%|37%|25%|
|Average ratio of total enterprise<br>profits to household income<br>(enterprise owners only)|0.40|0.44|0.46|0.38|0.42|
|% of households relying exclusively<br>on non farm enterprise activity|1.6%|2.4%|3.4%|2.0%|2.7%|
|% of male headed households owning<br>a nonfarm enterprise|18%|12%|13%|25%|15%|
|% of female headed households<br>owning anonfarmenterprise|29%|35%|42%|52%|41%|

**Table 2: Sector Composition of the Nonfarm Enterprise Sector**

||**Tigray**|**Amhara**|**Oromia**|**SNNP**|**Total**|
|---|---|---|---|---|---|
|**_Manufacturing_**|**_30%_**|**_43_%**|**_35_%**|**_32_%**|**_36_%**|
|Manufacturing (excl: grain milling, food and|13%|22%|12%|14%|15%|
|beverages, distilling, wearing apparel)||||||
|Food and beverages, brewing/distilling|13%|20%|23%|17%|19%|
|Grain milling|3%|1%|1%|1%|1%|
|**_Trade_**|**_56%_**|**_41_%**|**_52_%**|**_58_%**|**_51_%**|
|Whole sale trade|10%|4%|4%|8%|6%|
|Retail trade via stalls and markets|19%|22%|25%|31%|26%|
|Retail (not stalls/mkts)|28%|15%|23%|19%|20%|
|**_Services_**|14%|16%|13%|11%|13%|
|Services (services, m|7%|9%|8%|4%|7%|
|Transport services|0%|1%|1%|0%|1%|
|Hotels and restaurants|7%|6%|5%|6%|6%|
|Others (specialized services)|0%|0%|0%|0%|0%|

_Note:_ The table shows percentages of enterprises belonging to each sector.

-24-
## **_Box 1 Is reported ownership of HEs biased by social norms?_**

Social norms in highly patriarchal (or matriarchal) societies could lead to a tendency for men (or women) being reported as the owner of HEs. For instance, despite a woman being the main person responsible for a HE, her husband could be reported as the owner while the woman is reported to be a family helper in the enterprise. Is this a problem in our household survey data? Is there a bias toward one gender?

The data do not allow an analysis of this question, but it does indicate that if such a bias exists, it does not affect our analysis very much. While it is true that the majority of those who report their employment status as family helpers are females, there simply are not very many contributing family workers in HEs. Depending on the country, between 77 and 93 percent of HEs are single person operated, hence only between 7 and 23 percent of the observations at most could have some misreporting (assuming that all people working in the HE were reported). Even if half of the observations that could have misreporting did so, only 7 percent of observations on average would be biased, leaving little room for a significant impact on the averages shown here.

# **3. Recent employment trends in a growing Sub-Saharan Africa – Why is employment in household enterprises on the rise?**

The past decade has seen a resurgence of economic growth in low-income SSA countries, including the countries in our study. Mozambique, Ghana, Rwanda, Tanzania, and Uganda all reported GDP per capita growth greater than 3 percent per capita per annum 2000-2008, and the others reported per capita growth between 1-3 percent per annum (World Development Indicators, 2012). During this growth period, in all of the study countries non-farm private wage and salary employment grew faster than the labor force, as did employment in non-farm enterprise activity, including owners and family members in both HEs and MEs (figure 1).

7
border cities creating congestion, pollution and large delays that impact both local citizens and international transport companies (OCDE, 2019). Various types of investments can help. Border delays, especially in densely populated areas, has led countries to plan new infrastructure in place with a view to facilitating trade and reducing corruption at the borders, and one-stop border posts (OSBPs) have been prioritized. At the moment, few OSBP border posts are currently operational in West Africa, meaning high delays and border frictions around the borders (Figures A2 and A3 in the Appendix).

This paper considers the gains from better road and border infrastructures.

# **3 Quantifying the wider economic benefits of corridors**

## **3.1 Theoretical framework**

We use the theoretical framework from Redding (2016) applied to 662 locations in 16 countries in West Africa: Benin (76 districts), Nigeria (37 provinces), The Gambia (6 districts), Senegal (46 districts), Mauritania (44 districts), Guinea-Bissau (9 districts), Mali (50 districts), Togo (21 districts), Ghana (137 districts), Cameroon (58 districts), Niger (36 districts), Burkina Faso (45 districts), Côte d’Ivoire (34 districts), Liberia (15 districts), Sierra Leone (14 districts), and Guinea (34 districts). Each location is indexed by _i_ . Locations differ from one another in terms of land supply, productivity, amenities and their geographical location relative to one another. Bilateral trade costs for goods are assumed to take the iceberg form, such that _dni_ units of a good must be shipped from location _i_ for one unit to arrive in location _n_ , where _dni_ > 1 for _n_ ≠ _i_ and _dnn_ = 1. Land and labor are the two factors of production. Workers are mobile across locations within country but not across countries. They have idiosyncratic draws for preferences for each location within their own country.

**Consumer preferences** Preferences for a worker in location _n_ depend on goods consumption ( _Cn_ ), residential land use ( _Hn_ ) and an idiosyncratic amenity shock to the utility from residing in that location:

The goods consumption index ( _Cn_ ) is defined over consumption of a fixed continuum of goods _j_ ∈ [0,1]: The price index for goods consumption ( _Pn_ ) is: The idiosyncratic amenity shocks capture the idea that workers have heterogeneous prefer6
scenario that assume a percent increase in producer prices twice as large as the percent increase in consumer prices, the welfare incidence remains negative (-5%) for the country as a whole.

The welfare incidence analysis across the income distribution shows negative effects across all income groups. Yet, there is heterogeneity across quintiles. The largest losses are not experienced by the poorest, but mostly by rural households in the middle of the distribution (quintiles 2 and 3), which are below but closer to the poverty line. However, the poorest households (two lowest quintiles) tend to experience the largest welfare reductions in the urban areas. These welfare losses imply an overall increase in the poverty headcount ratio of nearly 6 percentage points (from 46.7% to 52.6%).

The results of this study indicate that the welfare effects of food price spikes in Mozambique are likely to be disproportionately felt by the households in specific regions and within certain bands of income. These households reside in relatively poorer regions of the country and may be vulnerable to other shocks as well. These findings underscore the importance of minimizing the welfare losses of increases in the prices of staples. Increasing productivity in agriculture and resilience to weather shocks and other risks (e.g. pests) will contribute to raising the supply of food to Mozambique’s own rapidly growing population and regional markets. The opposite, inhibiting trade within the country and across borders in periods of high prices, will undermine agricultural output and food availability. Similarly, improvements in the efficiency of local agricultural markets (better connectivity, information on prices, etc.), including the functioning of input markets, will reduce production and marketing costs, reducing the welfare impacts of food price volatility. Early warning systems such as food security, food price and rainfall monitoring systems can help identify hot spots and manage food surpluses and humanitarian assistance. Finally, there is a need for non-distortionary, rapid scalable safety nets to protect the well-being of those highly vulnerable to high food prices.

26
eligible for a second SAP payment (because they were living in areas under ECQ in May of 2020) received it by the end of June (DSWD, 2020), but almost none of the non-4Ps in our sample had received a second SAP payment by this time. By the end of July, about 22% of non-4P SAP recipients had received a second payment.<sup>5</sup> The 4Ps households continued to receive 4Ps transfers every other month (in May, July, and September 2020 during our study period), in addition to the two SAP top-ups. The government waived the program requirements during our entire study period and paid households their full transfer amount each pay period. These bimonthly cash transfers were much smaller than the SAP transfer amount, especially during summer vacation (April-May), when households do not receive the education component of the 4Ps grant.

# **3 Data**

We rely on a panel survey of low-income households across the Philippines. The baseline wave of this survey was conducted, in person, starting in November of 2019 for a separate project on cash transfers and domestic violence (Sahay et al., 2023). The sample consists of households with children and which, at the time of an impact evaluation of the program in 2014, had poverty scores just below and above the 4Ps eligibility cutoff. Cho et al. (2021a), which includes a more detailed description of the data, shows this sample is fairly representative of the poor and near-poor in the Philippines – specifically, the bottom 40% of the region-specific per capita income distribution.

The remaining survey waves were conducted during the pandemic and therefore over the phone. Wave 1 took place at the end of April, wave 2 at the end of June, wave 3 at the end of July, and wave 4 at the end of October. The timeline in Figure 1 depicts the events discussed in the previous section along with the timing of each survey wave. We also report the share of non-4Ps in our sample who had received a SAP payment by the end of wave 2 (70%) and wave 3 (73%). Of the non-4Ps who were going to receive a SAP payment, the majority had received it by the end of June.

The shaded areas in Appendix Figure A1 highlight the 26 provinces represented in our data. No single province or region dominates the sample. Importantly, as we show in Appendix Table A1, the geographic distribution of the 4Ps is very similar to that of the non-4Ps, which indicates that the groups were balanced in terms of the severity of the mobility restrictions they experienced, as these were largely determined at the province level.

We compare the baseline characteristics of 4Ps and non-4Ps households in Table 1. 4Ps households are significantly larger than non-4Ps households, but on many other important dimensions, the two groups are

> 5This is slightly smaller than but similar to the share of 4Ps beneficiaries who were eligible for a second SAP payment, according to DSWD (2020).

7
quality”. Identical analysis performed in the 1990s found that roughly 70 percent met the “acceptable” or “good” quality standard (IEG, 2010).

Even so, there are recent examples of influential projects that sought to integrate impact evaluation results with cost-effectiveness analysis to collectively set policy priorities, as with the Disease Control Priorities Project (Jamison et al. 2006). Arguably, as impact evaluation methods continue to mature in sectors like Education, Governance, Agriculture, and Health and Nutrition, evaluators may consider how to re-institutionalize VFM practices.

A major goal of IE studies is to provide scientific evidence on the policies and programs that do and do not work to improve development outcomes. However, if IEs do not include program or policy cost information, resource-constrained policymakers will have limited evidence to guide their selection of efficient programs and policies, or to consider the cost implications of scaling, replicating or reproducing programs and policies found to be effective.

Cost-benefit analysis (CBA) and cost-effectiveness analysis (CEA) would be useful for (i) identifying whether social benefits exceed social costs of a single intervention; (ii) comparing worth of interventions with different (monetizable) outcomes; and (iii) comparing the relative worth of interventions that share common outcome(s). CEA and CBA help identify which intervention produces a given amount of outcomes for the least cost. Including VFM analysis may help policymakers understand and use evidence from the growing number of IE field studies in their program decisions by identifying which interventions produce the most outcome for a given cost (White 2014; Dhaliwal et al. 2013; McEwan 2012). Such evidence can provide insight that is “counter to common sense, popular appeal, and traditional ideas” (Levin 2001).

Including VFM directly in IEs yields at least two advantages over producing them separately. First, collecting cost data at the time of the intervention and data collection on the IE outcomes is likely to be less onerous in terms of expense and time than reconstructing program costs ex post. Second, evaluators, policymakers, and other stakeholders can use the evidence to make more efficient decisions when high-quality, rigorous estimates of impact also include an estimate of their Cost.

3
analysts, and policymakers a greater wealth of information. Tax records contain extensive information on firms and individuals that is often not captured by traditional data collection mechanisms such as household surveys, and, as such, they can provide valuable input to statistical systems. However, many Latin American countries have yet to make public their income tax data. Colombia is the first country to share the disaggregated micro-data from personal income tax records. This section applies the methodology described above to compute the Gini coefficient for Colombia in 2010 using data from both household surveys and administrative tax records.

# **3.1 Data**

Around 1.4 percent<sup>4</sup> of Colombia’s working population pays income taxes. While 1.1 million individuals (out of a population of 4.5 million) filed an annual tax return in 2010, the rest of the population (3.4 million) paid taxes through withholding. Unlike other countries like the United States, Colombia’s income tax system does not allow for joint – returns for married couples. This allows us to compare incomes across data sources – household survey and tax records using the same unit of analysis (individuals).

Colombians file their income tax returns differently depending on whether they are small or large taxpayers. This analysis considers both types of taxpayers: those who are not required to keep accounting books (small taxpayers) and those who are required to do so (large taxpayers). The small taxpayer data (Form 210) is composed of a balanced microdata panel and tabulations made by the tax agency in Colombia (Dirección de Impuestos y Aduanas Nacionales, DIAN). In the case of large taxpayers (Form 110), the data set is only composed of a balanced micro-data panel.<sup>5</sup> The data provides annual information about labor income, capital gains, other incomes, deductions, exemptions, and taxes paid.

The Great Integrated Household Survey (GEIH) collects information about labor force conditions, socio-demographic characteristics, and different sources of income. The bottom

> 4Dirección de Impuestos y Aduanas Nacionales (DIAN), Exposición de Motivos.

> 5Since we are only considering a balanced panel in which individuals are observed if they are present for all the years, the results could be potentially biased.

11
**<mark>Table 5.4. Non-Tradable Production (NTP) Output</mark>**

||Status-Quo|Complete<br>Convergence|
|---|---|---|
|Total NTP (in USD)|$780,487,162|$880,138,323|
|As share of GDP, if added (in %)|16.16%|14.93%|
|GDP, if NTP added (in USD)|$4,830,231,798|$5,887,610,953|
|Growth (in %)|19.27%|45.38%|
|Female NTP (in USD)|$530,209,334|$597,905,484|
|Female share of NTP (in %)|67.93%|67.93%|
|Male NTP (in USD)|$250,277,828|$282,232,839|
|Male share of NTP(in %)|32.07%|32.07%|

_Source:_ Authors’ simulation based on expanded version of CKW 2017 model. _Notes:_ NTP stands for Non-Tradable Production. Complete Convergence refer to the value of the nontradable production if the Model 7.3 would be applied.

40