rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 3: Industry, occupati**|**on, and se**<br>Northern|**ctor of w**<br>Province|**orkers**<br>Eastern|**by gende**<br>Province|**r (Perce**<br>Western|**nt)**<br>Province|Other pr|ovinces|
|---|---|---|---|---|---|---|---|---|
||Men|Women|Men|Women|Men|Women|Men|Women|
|Industry|||||||||
|Industry self‐employed|3.0|6.1|5.0|17.1|5.3|12.2|5.4|8.3|
|Industry wage‐employed|17.7|6.0|16.4|7.8|23.8|22.9|17.2|13.1|
|Service self‐employed|12.3|9.1|14.1|19.8|19.7|14.6|14.4|10.7|
|Service wage‐employed|30.3|45.3|29.9|37.5|39.0|41.2|24.5|23.3|
|Agriculture self‐employed|21.1|19.4|18.8|15.1|3.5|4.6|24.9|30.0|
|Agriculture wage‐employed|11.4|13.5|13.7|2.7|2.1|2.5|10.3|13.8|
|Other sectors|4.2|0.7|2.1|0.0|6.6|2.1|3.2|0.8|
|Total|100|100|100|100|100|100|100|100|
|Occupation|||||||||
|Professional|14.8|32.4|12.3|29.4|25.6|31.5|12.9|17.6|
|Clerks/sales|12.3|14.8|18.1|21.1|17.6|20.6|12.6|13.3|
|Craft/machine workers|20.2|12.1|25.9|24.2|33.4|23.6|25.7|17.6|
|Elementary|52.0|40.3|42.8|24.8|22.7|24.2|48.1|51.3|
|Others|0.7|0.4|1.0|0.5|0.7|0.1|0.7|0.2|
|Total|100|100|100|100|100|100|100|100|
|Sector|||||||||
|Public sector|34.3|64.5|40.0|61.6|20.0|28.8|31.0|40.3|
|Private sector|65.8|35.6|60.0|38.4|80.0|71.2|69.0|59.7|
|Total|100|100|100|100|100|100|100|100|

Source: Labor Force Survey 2015 (Sample: Employed adults 15 years and older) Table 4 shows the geographic disparity in the wages of wage workers by the type of industry. In Industrial sectors and agriculture, wages in the North and East are slightly less than those in other provinces. Meanwhile, wages in the private sector and in elementary occupations, where women are also greatly underrepresented, are also moderately lower than in other provinces. Wages in the public sector are broadly similar across the provinces, although we see much more variation in public sector wages. The variation in private sector jobs is driven by the fact that distribution of employment across industries differs geographically as do their wages.<sup>3</sup> Overall, we see that wage disparities are much smaller than employment disparities between the North and the East provinces and provinces other than Western. RR

> 3 Median private sector wages in other provinces are marginally higher (about SL Rp 10,000 vs. SL Rp 8,000) for women. But women in the private sector in the North and the East are less likely to work in agriculture (with lower wages) compared to women in provinces other than Western. These two counteracting factors result in average wages that are approximately equal between North and the East and provinces other than Western.

12
**Table S10.** Impacts of Varying Within-Village Saturation of Treatment (Personal or Interpersonal Initiative interventions) on intrapersonal and relational outcomes at endline in the Mechanism Experiment

|||**Reference group: 25%**<br>**Saturation of**<br>**Program Beneficiaries**<br>**Treated**|**50% Saturation of**<br>**Program Beneficiaries**<br>**Treated vs 25%**|**75% Saturation of**<br>**Program Beneficiaries**<br>**Treated vs 25%**|
|---|---|---|---|---|
|**Outcome**|**df**|**Mean**<br>**(SD)**|**Coefficient**<br>**(SE)**<br>**Cluster robust****_p-_value**|**Coefficient**<br>**(SE)**<br>**Cluster robust****_p-_value**|
|**Psychosocial**<br>**Composite Index**|**2487**|**-0.02**<br>**(1.02)**|**0.08**<br>**(0.09)**<br>**0.382**|**0.15**<br>**(0.09)**<br>**0.087**|
|**Intrapersonal**<br>**Composite Index**|**2487**|**-0.03**<br>**(1.07)**|**0.13**<br>**(0.09)**<br>**0.145**|**0.14**<br>**(0.08)**<br>**0.075**|
|Well-being|2487|-0.03<br>(1.04)|0.07<br>(0.08)<br>0.390|0.14<br>(0.08)<br>0.086|
|Self-Efficacy|2473|-0.02<br>(1.00)|0.05<br>(0.09)<br>0.570|0.04<br>(0.08)<br>0.636|
|Future<br>Expectations|2473|-0.03<br>(1.04)|0.15<br>(0.08)<br>0.079|0.13<br>(0.08)<br>0.079|
|**Relational**<br>**Composite Index**|**2487**|**0.00**<br>**(0.99)**|**0.03**<br>**(0.09)**<br>**0.749**|**0.11**<br>**(0.08)**<br>**0.176**|
|Partner Dynamics|2226|0.06<br>(1.01)|-0.03<br>(0.06)<br>0.674|0.01<br>(0.07)<br>0.906|
|Household<br>Dynamics|2487|-0.01<br>(0.99)|0.04<br>(0.08)<br>0.648|0.14<br>(0.08)<br>0.087|
|Decision-Making|2473|-0.02<br>(1.03)|-0.03<br>(0.08)<br>0.693|0.05<br>(0.07)<br>0.430|
|Social Standing|2473|-0.02<br>(1.04)|0.06<br>(0.09)<br>0.522|0.10<br>(0.09)<br>0.270|

52
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# **Tables**

**Table 1: Consumption of Alcohol and Tobacco as a Share of Total Consumption**

||**Househol**|**d living on le**<br>|**ss than...**<br>||
|---|---|---|---|---|
|||**$1 per**<br>**day**|**$2 per**<br>**day**|**Ratio**<br>**($2/$1)**|
|Rural|Côte d'Ivoire|2.7%|2.2%|81.5%|
||Guatemala|0.4%|0.5%|125.0%|
||India - UP/Bihar|3.1%|3.0%|96.8%|
||Indonesia|6.0%|6.8%|113.3%|
||Mexico|8.1%|6.5%|80.2%|
||Nicaragua|0.1%|0.6%|600.0%|
||Pakistan|3.1%|2.9%|93.5%|
||Papua New Guinea|4.1%|5.1%|124.4%|
||Peru|1.0%|1.3%|130.0%|
||South Africa|2.5%|3.4%|136.0%|
||Timor-Leste|0.0%|0.0%|100.0%|
|Urban|Côte d'Ivoire|3.5%|3.3%|94.3%|
||India - Hyderabad|2.5%|2.7%|108.0%|
||Indonesia|5.5%|6.3%|114.5%|
||Mexico|3.6%|4.2%|116.7%|
||Nicaragua|1.0%|0.7%|70.0%|
||Pakistan|3.0%|2.9%|96.7%|
||Papua New Guinea|0.6%|4.4%|733.3%|
||Peru|0.2%|0.8%|400.0%|
||South Africa|5.0%|5.1%|102.0%|
||Timor-Leste|0.0%|0.0%|100.0%|

Note: Adapted from Banerjee & Duflo (2006).

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<!-- Start of picture text --> Comparison of non-linear models<br>10%<br>5% -<br>an —~<br>0 ~~ 40 60 80 100 ‘ies<br>see a |<br><!-- End of picture text -->

<!-- Start of picture text -->
-15% ———o —<br>-20%<br>-25% -<br><!-- End of picture text -->
> **Figure 11 Total fiscal risks from early termination of public-private partnership portfolio in sample countries as percentage of annual government revenue**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>Low Medium less low High less medium<br>AlbaniaArgentina Bhutan BrazilBulgariaBurundiEthiopiaGeorgia GhanaIndonesia Kenya Kosovo PeruRomaniaSolomon IslandsSouth AfricaUkraine<br>Percent of annual government revenue<br><!-- End of picture text --> _Source:_ Authors’ calculations.

_Note:_ Total fiscal risks are the maximum expected loss at the 99 percent confidence over the entire contract period, expressed as percentage of GDP of a single year.

# _5.5.2 Adverse Macro-Financial Shock_

Multiple types of macroeconomic shocks have significant effects on the probability of early termination (see table 2). The fiscal risks presented so far assume no macro-financial shock, such as depreciation of local currency or a stress in the banking sector.

This section simulates the results of a macro-financial shock. The Systematic Banking Crises dataset of Laeven and Valencia (2020) identifies 104 banking crisis episodes among the countries included in the PPI dataset, 13 of which also involved both sovereign debt and currency crises. During these 13 episodes, the maximum yearly deviation in depreciation rate from its long-run average ranged from 15.1 to 116.0 percentage points, with an average of 48.3 percentage points.<sup>16</sup> The simulation assumes a 48.3 percentage-point depreciation shock and the occurrence of both a banking and a debt crisis in year 0.<sup>17</sup> The PPP portfolio of each country is assumed to

> 16 The statistics exclude two episodes in Ecuador, because the adjusted currency conversion factor remained stable even though the official rate of depreciation was 56.8 percent in the banking crisis of the early 1980s and 77.2 percent in the crisis of the late 1990s.

> 17 The scenario abstracts from the sequencing of different macro events, as all events are assumed to occur in the same year.

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- Fasani, S. and L. Rossi (2018). Are uncertainty shocks aggregate demand shocks? _Economics Letters 167_ , 142–146. 16

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- Fern ́andez-Villaverde, J., P. Guerr ́on-Quintana, J. F. Rubio-Ramirez, and M. Uribe (2011). Risk matters: The real effects of volatility shocks. _American Economic Review 101_ (6), 2530–61. 6, 17

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- Friedman, M. (1968). The role of monetary policy. _The American Economic Review 58_ (1). 6

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- .

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25
- intervention may also change (resource effects) and outcomes may change, such as due to lags in outcome responses, market responses (partial equilibrium assumptions are fine for a pilot but not when scaled up), social effects/political economy effects; early vs. late capture.  The “scaled-up” program may be fundamentally different to the pilot. There has been too little work on external validity and scaling up.

- 8. _Understand what determines impact._ Replication across differing contexts can help, though the feasibility of spanning the relevant range of contexts is unclear. Intermediate indicators can help understand impacts as can qualitative research/mixed methods. It can be used to test the assumptions made in rationalizing an intervention. In understanding impact Step 9 is key:

- 9. _Don’t reject theory and structural modeling._ Standard evaluations are “black boxes”: they give policy effects in specific settings but not structural parameters (as relevant to other settings). Structural methods allow us to simulate changes in program design or setting. However, assumptions are needed; that is the role of theory.

- 10. _Develop capabilities for evaluation within developing countries_ . Strive for a culture of evidencebased evaluation practice. Evaluation is often a natural addition to the roles of the government’s sample survey unit. Independence should already be in place; connectivity to other public agencies may be a bigger problem. Sometimes a private-sector evaluation capability will be required.

**_Questions for evaluative research._** Evaluative research should not take the intervention as pre-determined, but must begin by probing the problem that a policy or project is addressing. Why is the intervention needed?  How does it relate to overall development goals, such as poverty reduction?  What are the market, or governmental, failures it addresses? What are its distributional goals? What are the trade-offs with alternative (including existing) policies or programs?  Researchers can often play an important role in addressing these questions. This involves more precise identification of the policy objectives (properly weighing gains across different sub-groups of a population, and different generations), the relevant constraints, which include resources, information, incentives and political economy constraints, and the causal links through which the specific intervention yields its expected outcomes.

This role for evaluative research in conceptualizing the case for intervention can be especially important when the capacity for development policy making is weak, or when it is captured by lobby groups, advocating narrow sectoral interests. The _ex ante_ evaluative role for research can also be crucial when practitioners have overly strong priors about what needs to be done. Over time, some practitioners become experts at specific types of interventions, and some may even lobby for those interventions. The key questions about whether the intervention is appropriate in the specific setting may not even get asked.

11
including HIV co-infection. Estimates excluding the benefits of lowering TB–HIV coinfections may underestimate the benefits of DOTS coverage but avoid the risk of double-counting the costs of TB and HIV. To a large extent, the benefits of TB-control programs depend on the availability of antiretroviral treatments, and the benefits of TB control and treatment are likely to be greatest in countries where the life expectancy of co-infected patients is not curtailed by AIDS.

# **Incorporating health gains in GDP measures: full-income approach**

GDP measures are the most widely used measures of economic activity in countries, but they have well-known, serious shortcomings, such as not measuring nonmarket goods (health, for instance) and home production. Two countries could have the same per capita GDP, but life in one country could be long and healthy while it is short and unhealthy in the other. GDP calculations fail to take account of health because they are based on what an economy produces rather than on the aggregate utility (or happiness) of the country’s population.<sup>3</sup> A growing literature on the economic value of health improvements has focused on expanding the idea of GDP to include improvements in health (Usher 1973; Nordhaus 2002; Becker, Philipson et al. 2003; Murphy and Topel 2005).<sup>4</sup> This concept, known as full-income GDP, incorporates both annual income and the number of years over which this income is enjoyed. In short, full-income approaches impute the value of increased life expectancy on economic well-being using revealed-preference approaches to value each year of longer life. Longevity gains can be quantitatively important when measuring welfare. Becker and colleagues find that when longevity gains are taken into consideration, average yearly “full income” grew 4.1 percent between 1960 and 2000 for the poorest 50 percent of countries, of which 1.7 percentage points were due to health (Becker, Philipson et al. 2003). The implication is that much of the welfare improvement

> 3 GDP measures do take into account what a country spends on health but do not measure or value the output of the health sector.

> 4 A recent WHO report incorporated these methods to measure the economic effect of chronic disease (WHO 2005) and determine changes in full-income attributable to reductions in chronic disease in regions where there have been significant improvements.

9
Table 4 Decomposition of Trade Effects

||**Percent Change**<br>**I**|**s in Factor Prices**<br>**II**|**Change in Total I**<br>**(US$ Millions)**<br>**III**|**ncome by Factor**<br>**IV**|
|---|---|---|---|---|
||**Import**<br>**Supply Effect**|**Export Demand**<br>**Effect**|**Import**<br>**Supply Effect**|**Export Demand**<br>**Effect**|
|**Skilled Labor**|10.14|29.33|4,866.73|14,072.39|
|**Unskilled Labor**|0.58|6.37|876.90|9,614.60|
|**Capital**<br>**(Agriculture)**|‐0.54|‐11.60|‐360.60|‐7,780.91|
|**Capital**<br>**(Manufacturing)**|0.00|‐29.00|‐1.03|‐14,134.24|
|**Capital(Services)**|1.44|35.55|1,783.20|43,972.40|

Source: Author’s Simulations Another contributing reason for the differences in effects can be traced back to how they affected inputs demands in the services sector. Mode 1 service imports into India are complements to skilled labor. So, when Mode 1 imports into India increase, they also increase the demand for skilled labor, especially in the services sector where they are used the most intensively. So, there is a small increase in the demand for skilled workers, pushing up their wages, and that of the other inputs – unskilled labor and sector specific capital. With labor being pulled away from non‐service industries, the demand for specific capital in those industries declines, although the rental rate of capital declines by only a small amount in agriculture.

The increase in the demand for labor caused by the increase in Mode 1 services supply, however, is much smaller than the increase in the demand for labor caused by the increased export demand. The greater demand for Indian Mode 1 services (the export demand effect) pulls up the demand for labor and capital much more than the increase in Mode 1 import supply (the import supply effect), leading to larger quantities of skilled and unskilled labor leaving the non‐services industries. With larger numbers of labor leaving those industries, demand for and income from sector‐specific capital decline, leaving owners of those types of capital worse off.

This decomposition thus shows that the imports of Mode 1 services were not responsible for most of the decline in the income of capital owners in agriculture and manufacturing. In contrast, the higher demand for labor caused by the services sector trying to meet the higher Mode 1 services export demand is responsible for the income declines.

22
percent state mosque donations, remittances, and government and social services payments as main source of income.

Despite low incomes and low per capita consumption (which nevertheless exceeds incomes), food consumption scores (FCS) on average, across the sample, are acceptable. The mean is 40.5 (>35 is acceptable), dropping to a just-acceptable 36.1 among female-headed households with high standard deviation of 21.8 (Annex C). However, in certain districts households more often have borderline scores (25.5 – 35), for example in Ana, Tikrit and Qa’im (34.6, 30.2, and 26.7, respectively). One explanation for generally acceptable FCS might be the frequent incurrence of debt, whether as cash loans or goods on credit, which smooths food consumption. However, this needs additional data to verify.

Most households in the sample (77 percent) resided in standard housing, with the remainder living in unfinished, abandoned, or public buildings, or informal tent settlements. Just under half (49 percent) share their housing with other families, with an average of 3 families per shared shelter. Over half (51 percent) of the sample have access to private hygiene and water facilities, with just under half (46 percent) saying they shared these with relatives; less than 2 percent said they had no access to water, sanitation, or hygiene facilities.

_Figure 5: Unemployment and per capita consumption (IQD) over the assessment period._ <!-- Start of picture text --> 100% IQD 80,000<br>90%<br>IQD 70,000<br>80%<br>IQD 60,000<br>70%<br>IQD 50,000<br>60%<br>50% IQD 40,000<br>40%<br>IQD 30,000<br>30%<br>IQD 20,000<br>20%<br>IQD 10,000<br>10%<br>0% IQD 0<br>Unemployment Per Capita Consumption (IQD)<br>working member)<br>Per capita consumption<br>Unemployment (% of households with zero<br>Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Nov-18 Dec-18 Jan-19<br><!-- End of picture text --> _Data Source_ : CLCI Legacy Database.

# 4.3 Likely Eligibility of Future Humanitarian Beneficiaries under MoLSA Targeting

Following the completion of the MCNA survey in 2018, the Cash Working Group (CWG) updated the vulnerability assessment criteria for their cash transfer programs. Since the new models were yet to be fielded, there was no database similar to the legacy database to perform similar analysis. However, we 18
which prevents them from lowering school fees.<sup>30</sup> Our theoretical results (Corollary 3.3), however, suggest that migrant households’ decision on child migration may be more affected by school fees during a crisis; thus our use of the 2009 round will be limited to supplementary analysis.

There are challenges with measuring school fees. First, the administrative measures of citylevel school fees are not available; second, even if these data were available, it is impossible to predict the counterfactual school fees that migrant households who left their children behind would have paid if they had taken them along. For these reasons, we compute the mean and the median of migrant households’ per child expenditures on all school-related fees as measures of the school fees faced by migrant households.<sup>31</sup> To reduce endogeneity concerns with school fees, we exclude each household before implementing this calculation (i.e., for each migrant household, the mean (median) is based on the expenditures of all the migrant households in the sample except theirs). For robustness checks, in addition to the mean (median), we also compute alternative measures such as the 25<sup>th</sup> percentile and the 75<sup>th</sup> percentile of the household education expenditures.

As another check, we also compute alternative measures of school fees based on urban residents’ school expenditures in the same cities. The school fees that they pay can be viewed as another measure of school fees in the city (e.g., because of a different sampling frame for the urban households in the same city).<sup>32</sup> Thus, while the fees obtained from the migrant household sample vary for each migrant household, the fees obtained from the urban household

> 30 We do not explore the panel feature of the data set between 2008 and 2009 since despite substantial efforts to track individuals over time, the panel data suffer from exceptionally heavy attrition (58.4 percent). This is due to the mobile nature of migrant workers and the consequences of the financial crisis that hit China in 2009 (Akgüç, Giulietti, and Zimmermann 2013). An option is to construct synthetic panel data that can allow dynamic analysis (Dang et al., 2014), but we leave this for future research.

> 31 These fees include tuition, food and accommodation, remedial classes, other fees (e.g. school uniforms and so on) and “sponsorship fees/boarding fees/selecting school fees”. Unless otherwise noted, all numbers are our estimates from the RUMiC survey.

> 32 Figure 1, however, reassuringly indicates that there is no systematic difference between school fees obtained from the rural household sample or the urban household sample. See also Carletto, Larrison, and Ozden (2014) for a detailed discussion on the construction of proper sampling frames for collecting migration data.

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