rafmacalaba/datause-encoder-data huggingface dataset:
19. A recent study on domestic workers indicates<sup>thatthe incidenceof girls ages 10-16who</sup> work in this sector increased by 10 percent.<sup>7Thisis strikingconsideringthat overalllevels of child</sup> labor have declined substantially over<sup>time.Today,20 percentof all Brazilianwomen whowork are</sup> domestic employees. While most wotking<sup>girlsare white,inthe sectorof domesticservice,sixty</sup> percent of girls are non-white. Girls who<sup>workin domesticservice receiveonaverage 60 percentof</sup> minimum wage and some girls who do<sup>domesticwork(4 percent)dosofor nopayat all.Lastly,</sup> girls working in domestic service tend to<sup>drop out of school(30 percent).</sup> 20. Locality of work indicates the type of<sup>activitiesin whichchildrenare involved(seeTable5).</sup> As the majority of children are working<sup>intheagriculturalsector,particularlyin ruralareas,more</sup> than half of children (54 percent) are<sup>workingon afarm,ranch,poultryfarm, orsmallrural</sup> property. Gender differences in work locality<sup>areparticularlypronounced,againreflectingthe</sup> sectors that differentially engage child labor.<sup>About 20 percent of workinggirls are in their</sup> employer's home, and 12 percent are at home<sup>carryingout domesticchores,comparedto1.4</sup> percent and 2.7 percent respectively among<sup>theboys.In contrast, working boysareprominent in</sup> localities such as roadways or public<sup>areas.</sup>

**Table 5: Distribution of 10 to 14 Year**<sup>**Old Working Children byGender and Work Locality**</sup>

|W'orkLocahrv|-Boy's(°`)<br>|-Girls.(O/%)|TotaIo(%)<br>|
|---|---|---|---|
|Store,repairshop,factory,office,school|26.0|17.1|23.10|
|Farm,ranch,poultryfarm,smallrural<br>property|57.6|46.4|53.8|
|Home|2.7|11.9|5.8|
|Employer'shome|1.4|20.5|7.8|
|Sitedesignatedbyemployer|2.6|0.3|1.9|
|Automotivevehicle|0.3|0.1|0.2|
|Roadwayorpublicarea|7.9|3.0|6.2|
|Other|1.5|0.7|1.2|
|Total|100.0|100.0|100.0|

Source: Calculations based on PNAD, 1995

> 7 Lavinas, Lena (2000).

8
Spring; the post-Arab Spring civil wars have led to many casualties and massive destruction (Ianchovichina, 2016). The paper focuses on non-neutral and non-humanitarian external interventions because we expect that this particular type of intervention has the potential to disturb the status quo in a country by increasing the incentives of different groups to raise resources for fighting and thus altering the groups’ probability of winning. We also believe that this type of intervention has the highest potential to increase the intensity of fighting and the associated casualties, as external support decreases the rebels’ dependence on local support and therefore their incentives to protect the local population.

**Figure 1 Distribution of Military Intervention Frequency by Type and Region**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>MENA SAFRICA LAAM ASIAE<br>Neutral and humanitarian Non-neutral and non-humanitarian<br><!-- End of picture text --> Data source: IMI data (Pearson and Baumann, 1993).

The global International Military Intervention (IMI) data set, which provides information on events involving foreign military deployment in countries around the world, indicates that there are large differences in the incidence of external military interventions by type and region. Since 1965 the incidence of non-neutral and non-humanitarian interventions has been highest on average in MENA and lowest in Latin America (Figure 1). In Sub-Saharan Africa – the region with the 7
drawn from existing gold card customers. Could it be that the higher-income customers in our sample were individuals with a particularly low demand for status goods, who had previously declined the platinum card? In fact, our sample consists only of customers who had previously never received an offer of a platinum offer. In addition, we can focus on customers with income between Rp 300 million and Rp 500 million. These customers were not eligible for the platinum card when they opened their account (and therefore could not have turned down a platinum offer). When we exclude customers with income above Rp 500 million in Table 1, column (6), we find a very similar result, suggesting that our income-heterogeneity results are unlikely to be driven by selection. Instead, consistent with our prior, lower-income customers appear to value the status signal more at the margin.

Finally, while we focus on the interpretation that higher-income individuals may have alternative ways to signal that they are high income than lower-income individuals, there could be alternative reasons why the treatment effect is higher for lower-income customers. It could be that higherincome individuals simply care less about social status. However, our second experiment, in which we offer the highest-income customers an opportunity to further differentiate themselves from the premium cardholder population, helps rule out this alternative explanation (Section 5).

# **4 Status Signaling in Credit Card Transaction Data**

The results of our first experiment show that customers exhibit substantial demand for the pure status component of the credit card, beyond any instrumental benefits that the card provides. We suspect that individuals use the card to signal their income in order to build social status. In this section, we use detailed historical transaction data for a large sample of credit card customers to examine whether the usage of platinum cards in everyday life is consistent with social signaling motivations. To do so, we proceed in two steps. We first identify certain transactions, such as spending in restaurants and bars, as _‘social expenditures’_ , in which the credit card is likely to be visible to one’s peers. We then examine whether platinum cardholders are more likely than gold cardholders to use their card in such social contexts.

## **4.1 Data and Empirical Strategy**

We use credit card transaction data for a sample of 2,492 customers with active credit cards who opened their accounts between January 2014 and August 2015. Within this sample, we focus on customers with credit limits of Rp 20 million, Rp 30 million, Rp 40 million, and Rp 50 million, respectively. The credit limit for each customer is assigned based on a combination of the customer’s income and credit history, and there are multiple credit limits within each tier of the card. With few exceptions, gold card holders have a credit limit of Rp 20 million or Rp 30 million, while the vast majority of platinum card holders have a credit limit of Rp 40 million or Rp 50 million.

14
Study (SISS) indicate that students from the Philippines scored least well on the science tests for both grades 5 and 8 (IEA, 1988).

# Section III: Data and Analytic Method

Sample The research reported in this paper was conducted in the Philippines during the 1983-84 school year as part of the Second IEA Science Study (SISS). The sample comprised 475 science teachers and their 16,851 fifth-grade students and was derived from a two-stage stratified 1 r....om sample of classrooms. The primary sampling units were schools, which were stratified according to national region and public or private status. This yielded 13 strata for public schools (the national regions) and two strata for private schools (Metro Manila and non-Metro Manila). A random sample of elementary schools was selected, with the probability of selection proportional to size, judged by the number of classes in the school. At the second stage, a random selection of one fifth grade class per school was selected from a list of all fifth-grade classes within the school. .(SISS called for assessment of 10-year-olds or fourth grade students. Since the test was to be administered in English, conforming to the Philippine medium of instruction for mathematics and science commencing in third grade, fifth grade students, who were more fluent in English, were tested instead.) 17
in scenarios at levels of substitution (Elobeid and Tokgoz 2006, Banse 2007). Thus, they could have negative impact on the balance of trade, and at the same time developing countries will experience a reduction in imports and an increase in exports.

# **3.4 Chapter summary**

While the models reviewed here are not exhaustive, they are surveys of representative works on the economic impacts of biofuels. The literature has two types of models. One type is budgeting models, which provide point estimates of average costs and profitability of biofuel production under static conditions of technology and market prices. But predicting the profitability based on point estimates of oil prices, cost of feedstock, etc., should be avoided since such parameters are uncertain. Moreover, these models use market prices as opposed to economic values, and so should be viewed with skepticism given the embodied distortions due to government policies. They also ignore feedback effects of large-scale production of biofuels such as rising land, input costs, etc.

The second type of models is macro or aggregate models that analyze the impacts at the sector or economywide level. These models are concerned with predicting the impact of biofuel policies like mandates on the price of food, farm income, trade, and overall welfare. Most of the models focus on the economies of the United States and the EU and have not considered in detail the conditions in developing countries. They also do not emphasize the distribution of the impacts within a given sector of the economy.

In terms of results the various models are hard to compare with each other because the predictions are sensitive to the modeling assumptions, which vary widely. Different models analyze different future scenarios. The economic driver is modeled differently across different models. In some cases oil prices are the explicit main economic driver while some use government dictates on biofuel production as the main driver. Models also differ in the representation of future technology, elasticity of supply and demand, and representation of agricultural and trade policies. The models are sometimes opaque 56
# **5 The impact of COVID-19 on electricity consumption in India**

## **5.1 Modeling daily electricity consumption**

To understand the magnitude of the decline of electricity consumption due to the lockdown, we need to control for factors affecting electricity consumption like the season and the weather. We hence estimate the following model of electricity consumption using daily (t) data:

### log _Electricityt_ = _τt_ + _DWt_ + _WYt_ + _Holidayt_ + _β_ 1 _Coolingt_ + _β_ 2 _Heatingt_ + _βtTrend_ + _εt_ (2)

The explanatory variables are a set of fixed effects that control for the day of the week, _DWt_ , the week of the year, _WYt_ , and holidays, _Ht_ . We include two variables that control for the daily temperature. We control for the temperature degrees above and below of 22 _._ 8<sup>_◦_</sup> _C_ , such that _Coolingt_ = _max{tempt −_ 22 _._ 8<sup>_◦_</sup> _C,_ 0 _}_ and _Heatingt_ = _max{_ 22 _._ 8<sup>_◦_</sup> _C − tempt,_ 0 _}_ , respectively. This temperature is associated with the minimum electricity consumption in India, which we confirmed by regressing log electricity consumption on temperature and a quadratic term of temperature suggesting that electricity consumption increases above and below this temperature (see Appendix B). The variables of interest are the set of dummy variables _τt_ that indicate each day in 2020 until the last day of the sample. These variables capture how daily consumption in 2020 differs from consumption in previous years, conditional on the temperature and holidays.<sup>15</sup> As discussed above, electricity consumption in India grew over time and hence we include a linear time trend (trend). This model is estimated separately for India and for all Indian states and Union territories.

Table 2 presents the estimation results for different versions of the electricity consumption model. Until December 2019, the upward trend in electricity consumption alone explains over 70 percent of the variation (column 1). Since electricity consumption dropped below the trend in 2020, an estimation until the end of May 2020 results in a less steep trend that explains somewhat less of the variation (column 2). When we add the daily fixed effects for 2020 as described above, only the observations until December 2019 determine the coefficient on the trend (column 3).<sup>16</sup> Figure 2 shows that there is some seasonality in electricity consumption, which we control for by including the week of the year. Taking care of the trend, the deviation

> 15As an alternative, one can also estimate the model until the end of December 2019 and compute the (out-ofsample) prediction errors. These are identical to the daily fixed effects.

> 16Note that the explanatory power is now slightly above the one for the model only estimated until December 2019 because the daily fixed effects take out the errors in 2020.

11
where is the reintegration outcome of interest (education, house ownership, etc.) and indicates whether the household received a reduction in assistance ( = 1 is the reduction of assistance from $350 ii ii yy DD to $150). The variable is the number of days before or after the change in reintegration assistance ii DD (bandwidth). Selection of was conducted using the data-driven mean-squared error-reducing strategy, ii zz proposed by Calonico, <mark>Cattaneo, and Titiunik</mark> (2014). ii zz

# **_Baseline Differences in Treatment and Control Groups_**

We observe some differences in observable characteristics of returnees according to the time of return and the amount of reintegration assistance received. Individuals who returned after the reduction of reintegration assistance to $150 per individual earned, on average, about 200 Pakistani rupees (about $1.92) less per person per month than those who were given $350. This difference is miniscule compared with the size of the reintegration assistance.

We performed balance tests to examine differences in baseline characteristics between the potential treatment and control groups. Table 1 presents the results of a balance test to examine differences between the treatment and control groups around the 55.3-day average bandwidth.<sup>6</sup> The results shows that the differences in baseline characteristics are largest for household size, age of household head upon return, and probability of uneducated household head, which we control for in the regression model. Differences in baseline per capita income are insignificant.

**Table 1 Balance test of baseline characteristics of control and treatment groups within bandwidth of 55.3 days**

||**_(1)_**<br>_$350_|**_(2)_**<br>_$150_||_(1) versus_|||
|---|---|---|---|---|---|---|
|**_Variable_**|_allowance_|_allowance_|_Overall_|_(2)_|_p-value_|_N_|
|Transport**allowance**per<br>capita (US dollars)|48.889|45.820|46.575|3.069***|0.000|2,522|
||(0.571)|(0.295)|(0.265)|(0.612)|||
|Household per capita income<br>(PRe)|1,114.420|1,026.595|1,048.186|87.825|0.389|2,522|
||(88.985)|(50.443)|(43.881)|(101.917)|||
|Household size (indicated on<br>**voluntary repatriation form)**|5.481|4.585|4.805|0.896***|0.000|2,522|
||(0.126)|(0.068)|(0.060)|(0.139)|||
|Household head gender (0:<br>Female, 1: Male)|0.785|0.721|0.737|0.065***|0.002|2,519|
||(0.016)|(0.010)|(0.009)|(0.020)|||
|Age of household head|39.376|36.781|37.420|2.595***|0.001|2,519|
||(0.662)|(0.372)|(0.325)|(0.752)|||
|Uneducated|0.785|0.657|0.688|0.129***|0.000|2,519|
||(0.016)|(0.011)|(0.009)|(0.021)|||

> 6 The selection of bandwidth was conducted using the mean-squared error-reducing approach of the rdrobust package on Stata.

8
**Table A3.6. Real GDP at factor cost by sector: level in 2012 (constant US$mn) and base growth by** **<u>period (% per year)</u>**

||**2012**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|
|**Mining**|137.2|20.7|2.9|7.6|
|**Non-mining**|1,292.9|5.4|6.7|6.3|
|**Agriculture**|546.5|4.8|7.4|6.7|
|**Non-mining industry**|82.6|5.5|4.7|5.0|
|**Private services**|486.8|4.3|6.5|5.9|
|**Government services**|177.1|9.7|5.8|6.9|
|**Human development services**|83.3|9.1|6.1|6.9|
|**Infrastructure services**|27.1|11.6|7.0|8.3|
|**Othergovernment services**|66.7|9.8|4.9|6.3|
|**Total**|1,430.2|7.3|6.1|6.5|

**Note: Constant US$ are at 2009 prices.**

**Table A3.7. Sectoral structure for base scenario in 2012 and 2030 (%)**

||**Shares(%) i**|**n nationa**|**l ....**||||**Shares(%) fo**<br>**exports in**<br>**sector**|**r ...**<br>**imports**<br>**in sector**|
|---|---|---|---|---|---|---|---|---|
|**Year Sector**|**VA**|**Output **|**Emp**|**loyment**|**Exports**|**Imports**|**output**|**demand**|
|**2012 Agriculture**|38.2|25.1||49.6|32.5|0.5|22.3|1.4|
|**Industry**|15.4|26.4||7.5|53.7|85.0|34.1|73.6|
|**Mining**|9.6|8.8||1.6|50.6||99.5||
|**Manufacturing**|2.0|5.4||1.3|3.0|46.2|5.0|82.3|
|**Other**|3.8|12.2||4.6||38.8||63.3|
|**Services**|46.4|48.5||42.9|13.8|14.5|5.0|13.3|
|**Private**|34.0|35.9||33.3|13.8|12.6|6.7|15.4|
|**Government**|12.4|12.6||9.6||1.9||7.0|
|**Total**|100.0|100.0||100.0|100.0|100.0|17.0|41.3|
|**2030 Agriculture**|44.1|30.8||53.8|41.2|0.5|46.4|1.3|
|**Industry**|22.6|26.7||7.2|42.3|80.9|53.4|75.2|
|**Mining**|19.9|13.6||3.6|39.0||99.6||
|**Manufacturing**|1.2|4.9||1.1|3.3|55.0|14.9|84.0|
|**Other**|1.6|8.2||2.4||25.8||60.0|
|**Services**|33.3|42.4||39.0|16.5|18.7|13.6|16.9|
|**Private**|21.9|29.7||28.6|16.5|16.1|19.4|21.3|
|**Government**|11.4|12.8||10.4||2.5||7.4|
|**Total**|100.0|100.0||100.0|100.0|100.0|34.4|42.2|

36
- Not input subsidy/price intervention or programs

- Not quantitative study (eg. meta narrative)

- Meta-analysis or review article

- Non-causal method

- No impact on productivity/yield/income

# **A3. Full-text screening criteria for exclusion**

- Not impact on output, yield, income, or productivity

- Simulation/calibration

- Non-causal method

- Non-original research/review paper/meta-analysis

- Not related to agriculture

- No confidence interval/standard error

- Number of observations not included

- No baseline/comparison group mean

- Not input subsidy/price intervention or programs

# **A4. Articles included in the full text analysis**

Abman, R, and C Carney. 2020. “Agricultural Productivity and Deforestation: Evidence from Input Subsidies and Ethnic Favoritism in Malawi” 103. https://doi.org/10.1016/j.jeem.2020.102342. Abate, GT, T Bernard, A e Brauw, and N Minot. 2018. “The Impact of the Use of New Technologies on Farmers’ Wheat Yield in Ethiopia: Evidence from a Randomized Control Trial” 49 (4): 421. https://doi.org/10.1111/agec.12425.

Beaman, Lori, Dean Karlan, Bram Thuysbaert, and Christopher Udry. 2013. “Profitability of Fertilizer: Experimental Evidence from Female Rice Farmers in Mali.” The American Economic Review 103 (3): 381–86.

Carter, M, R Laajaj, and DA Yang. 2021. “Subsidies and the African Green Revolution: Direct Effects and Social Network Spillovers of Randomized Input Subsidies in Mozambique” 13 (2): 229. https://doi.org/10.1257/app.20190396.

Funsani, W, M Rickaille, J Zhu, X Tian, V Chibomba, AD Avea, and T Balezentis. 2016. “FARMER INPUT SUPPORT PROGRAMME AND HOUSEHOLD INCOME: LESSONS FROM ZAMBIA’S SOUTHERN PROVINCE” 15 (3C): 412.

Karamba, R Wendy, and Paul C Winters. 2015. “Gender and Agricultural Productivity: Implications of the Farm Input Subsidy Program in Malawi.” Agricultural Economics 46 (3): 357–74.

Mason, NM, A Wineman, L Kirimi, and D Mather. 2017. “The Effects of Kenya’s Smarter’ Input Subsidy Programme on Smallholder Behaviour and Incomes: Do Different Quasi-Experimental Approaches Lead to the Same Conclusions?” 68 (1): 69. https://doi.org/10.1111/1477-9552.12159.

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33

# **7. Discussion and conclusions**

The health system reforms the European and Central Asian (ECA) countries implemented during their transition from socialist economies in the 1990s provide a unique opportunity to assess the impacts of social health insurance (SHI) on the health and labor sectors. We took advantage of this highly unusual “experiment” in which many ECA countries unequivocally switched from general tax-funded to SHI systems in a relatively short period of time, and on a staggered basis, so as to shed light on two broad sets of currently unanswered questions: firstly, how does SHI affect national health spending, the way such resources are spent, and population health outcomes? Secondly, how does SHI impact national employment levels and informalization of the economy? In order to obtain empirical evidence on these issues, we have used regression-based generalizations of the differences-in-differences approach and instrumental variables (IV) methods on panel data from 28 ECA countries for the period 19902004.

Overall, there is reliable evidence that our empirical methods have allowed us to successfully control for the possible endogeneity of SHI adoption, and therefore that we have identified causal relationships between SHI adoption and outcomes. This supporting evidence comes from a variety of sources. In the first place, our random trend specifications control for any time-invariant country-specific unobservables that may be simultaneously correlated with SHI adoption and outcomes, and also control for country-specific linear trends in these unobservables over time. Second, diagnostic checks show that we cannot rule out the validity of the “parallel trends” assumption for the majority of the outcomes analyzed in this paper, thus conferring credibility on the parameters obtained from our random trend models (which use the evolution of outcomes in non-SHI countries as the counterfactual for countries that adopted
# **1 Introduction**

In an effort to improve public service delivery in resource scarce settings, governments and development agencies may seek to involve existing local non-state capacity in program implementation. However the development literature has a limited understanding of the challenges of doing so, and many questions remain about when, where, and how to implement such a policy. This paper leverages a recent experimental evaluation of a supportive intervention to India’s malaria control program to examine whether involving local non-state capacity significantly influences the individual health outcomes targeted by the intervention.<sup>1</sup> We find that using local NGOs in program implementation can indeed lead to positive outcomes, although success likely depends on the quality of the local implementing agency. Hence, in low-resource areas with few high quality local institutions, there may be little alternative to intensified state investment or state-led incentives if the goal is improved public services.

The malaria-control intervention was conducted simultaneously by three NGOs in two endemic districts in the state of Orissa. The effect of NGO involvement on mosquito net usage and fever care-seeking patterns– the main targeted outcomes of interest– varies significantly by the district of implementation. Utilizing simple regression decomposition or propensity weighting, we examine three potential causes for such different outcomes: (1) differential population characteristics across the two districts, (2) differential health worker characteristics, and (3) differential implementer characteristics. We find that differences in observed outcomes can mostly be ascribed to the first and third factors, as well as their possible interaction. Not only do our results raise concerns about the efficacy of local non-state capacity utilization to enhance health sector performance, they also highlight questions of the generalizable policy relevance of small-scale experimental evaluations implemented by non-state actors.

The literature on involving local non-governmental capacity in public service delivery usually highlights the potential gains from non-state provision of basic services. Berman [1998], noting the low quality of government-provided care and the financial burden of unregulated fee-for-service

> 1See Das et al. [2014] for a comprehensive summary and evaluation of this program.

1