rafmacalaba/datause-encoder-data huggingface dataset:
following the intervention (follow-up). The baseline survey collected information on appliance ownership and use, lightbulb ownership (type, wattage, etc) and use (room of use, hours used in a typical day, etc.), electricity-related behaviors, and various household demographics. Importantly, we asked households to report the number of days in the past month that they did not have electricity due to outages.<sup>37</sup> Implementation of both survey rounds began in the month of March, specifically so that, when asking about the outages in the month prior, we were capturing winter months.<sup>38</sup> The number of CFLs distributed to each treatment household was tracked. The follow-up survey repeated questions from the baseline and also asked new questions on perception and understanding of CFLs.

**Spatial data:** GPS data were collected on the location of each participating house during the baseline survey. These data points permit various calculations relevant to potential spillovers in adoption, including the distance to nearest treated household and the number of treated households within certain radii. In addition, we use GIS data from OpenStreetMap on locations of all buildings in the study villages. These data permit calculations of the total number of households located within various radii (e.g., 100 meters, 200 meters, and so on) of each participating household.<sup>39</sup>

# **4.2 Household characteristics**

Households in the study sample are small, with an average of just under 4 members. Household monthly income per capita is on average 76 USD per month (2.45 USD per person per day) and household heads are educated, with 84 percent having finished secondary school. Most houses (91 percent) are owner-occupied, the majority of which were constructed during the period of the Soviet Union. They comprise an average of 4.3 rooms, and are typically constructed of brick (54 percent) or a hay/adobe mix (38 percent). Houses are served by formal connections to the electrical grid and metered individually (i.e., houses do not share meters).

Households receive a monthly electricity bill based on the meter readings of their consumption. At baseline, they use an average of 232 kWh per month in the summer (June to

> 37These questions were piloted extensively prior to the baseline survey. We asked about the number of days due to heterogeneity in outage length. The transformer repair required and availability of replacement parts determines the outage length after a transformer overload. According to the utility, transformer outages last between a few hours and a few days.

> 38Winter is when electricity demand is the greatest, stressing the transformers. For this reason, outages are most prevalent in the winter months.

> 39The calculations of the total number of buildings are further described in Appendix Calculation 1.

14
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relative to the baseline land use path is about 30 million hectares, which is about 2 percent of the total crop land in 2004. The difference declines rapidly as the likelihood of climate and technology shocks diminishes.

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foreign wholesale price index) rose over the period, perhaps partly due to the depreciation of various currencies vis-a-vis the dollar. These increases in costs may have offset the fall in import prices expected under the dollar depreciation. The combined cost index, w*, shows that the net impact of exchange rate and foreign cost changes was an overall fall in dollar-denominated foreign costs.

Table 2: Sample Means

|Variable|Mean|
|---|---|
|Import Price|97.14<br>(8.68)|
|Exchange Rate e|84.29<br>(9.83)|
|Foreign Cost Index c*|111.90<br>(9.67)|
|Wz*|93.69<br>(8.27)|
|_INV_|0.12<br>(0.33)|
|DUTY|0.15<br>(0.36)|
|w/w*|1.12<br>(.11)|
|N-<br>984||

The means for investigations INV and duties DUTY show the average proportion of sectors subject to investigations and duties over 1981-86. Investigations for subsidies or dumping were imposed for 13 percent of the sample. Duties were applied over 14 % of the sample.

15
From the evidence given in Table 8, it is not possible to conclude that earnings disparity is less for women relative to that for men. This conclusion holds for workers up to the age of 39. The opposite conclusion emerges for the workers aged 40 or more. Similarly, the lower disparity for women is observed only among the workers with education levels equal to or higher than the secondary school.

An interesting question that arises is whether earning differences between men and women are widening or narrowing during the recession. This aspect will not be pursued here due to lack of data.

# 7. Trends in inequality in Ukraine

Table 9 presents the inequality measures for Ulraine calculated from the family budget surveys covering the period from 1980 to 1992. The table gives the quintile shares, Gini index and decile distribution ratio (DDR). The DDR is defined as the share of the bottom 40 percent in relation to the share of the top 20 percent. An increase in this measure will imply a reduction in income inequality. All these measures have been computed by assigning eadh individual in a household an income level equal to the per capita income of that household. Thus, these measures indicate inequality in individual income distribution.

Table 9 Inequality of Per Capita Aggregate Family Income: Ukraine 1980 to 1992

|Ouintles|||Yea|_|||
|---|---|---|---|---|---|---|
||1980|1985|**1989**|**1890**|1991|1992|
|**I1St**|**8.13**|**7.09**|**9.41**|**10.35**|**11.01**|**10.56**|
|2nd|13.04|14.83|13.99|14.31|14.72|15.53|
|**3rd**|**16.04**|16.37|17.37|17.51|17.82|16.07|
|4th|19.83|20.51|21.33|22.83|23.99|23.69|
|5th|42.96|41.2|37.9|35|32.46|34.15|
|Gini Index|34.64|32.16|26.5|25.13|21.8|23.4|
|Decile Destribudion<br>Ratio|49.28|53.2|61.74|70.46|79.27|76.4|

21
and Thomas (1990) to the Malawian context may produce fruitful insights into the household expenditure decision-making process. The next round of the Malawian Integrated Household Survey will provide additional information on production and expenditure decisions, allowing for better comprehension of cash cropping choices in the face of food price increases.

The cash crop adoption results demonstrate an important vulnerability in developing world agriculture, which is particularly pertinent with the increasing global commercialization of agriculture. Identifying the individuals most affected by the volatility in food prices is the first step toward remedying their situation. As food markets continue to generally increase, acknowledgment of previous disparities in nutritional outcomes allows for increased effectiveness in targeting the neediest for food aid during times of future food price shocks.

Increasing international trade provides overwhelming benefits to many sectors of the global economy. Understanding and accounting for the potential pitfalls associated with agricultural commercialization will only strengthen the movement toward decreasing hunger. This paper helps to pinpoint the determinants behind negative health outcomes for commercialization, and continues the research on the effects of food price shocks on the developing world. Future work, with panel datasets, international food price spikes and more specified price data will allow for an even greater understanding of how food price increases influence the health outcomes of children around the world.

20
more labor to market activities (as opposed to home production and/or leisure) in response to agricultural productivity shock and that observed e¤ects of agricultural productivity increase on hired labor and wages are not simply due to reallocation of labor within market oriented activities due to heterogeneity in production technology utilized by households in food (rice) production.

The results presented so far con...rms that agricultural daily wage increases with a positive rainfall shock. But there is a decline in hired labor in response to positive productivity shock. From the theoretical analysis a higher wage and a positive response in labor supply to market activities imply that we would expect a signi...cant positive e¤ect on household income. While data on wages are available in the HIES, these surveys unfortunately do not provide any reliable information on income from self-employment in agriculture and non-farm activities. Thus we cannot directly estimate the impact of positive agricultural productivity shock on village income. The surveys, however, provide good information on household consumption expenditure. In the absence of information on income from self-employment, we take per capita household expenditure as an indicator of household income and welfare. The regression result with log of per capita expenditure as the dependent variable is reported in column 5 of Table 3. The results show statistically signi...cant and numerically large positive impact of rainfall shock on per capita expenditure. The coe¢ cient estimate implies that a one percent increase in rainfall increases per capita expenditure by 0.25 percent.

The regression results suggest a decline in hired labor in response to rainfall shock. Since the poor are more likely to be dependent on hired labor, we check how rainfall shock a¤ects their consumption poverty. The average per capita expenditure level of households 28
Demographic trends, which have lowered the dependency ratio, help explain the reduction in headcount poverty (see Section 4).<sup>3</sup> This trend is likely to deepen further in the future as Brazil’s richer regions, such as the Southeast, experience far lower fertility rates by age group than the Northeast.

In Bahia, the fall in the dependency ratio contributed to poverty reduction. Bahia is in the middle of a baby bust. After expanding at 2.1 percent per year between 1980 and 1990, Bahia’s population increased by only 1.1 percent per year during 19912000 and reached 13.1 million in 2000.<sup>4</sup> The main explanation is the sharp drop in the birth rate and some out-migration. The typical poor person lives in a larger household with more children than the non-poor. In Bahia, poor households contain on average 4.2 individuals in 2001, down 0.4 individuals since 1993 (Table 2.1). Poor households have 1.2 individuals more than non-poor households. The average number of household members below age 15 is much higher in poor households than in non-poor. Poor households have on average 1.8 children below age 15, triple those of the non-poor. Each worker in a poor household supports four family members. For the non-poor the number falls to three.

|**Table 2.1:**|**Averag**<br>**Avg.**|**e Hous**<br>**House**<br>**Size**<br>**P**|**ehold Si**<br>**hold**<br>**oor**|**ze, 199**<br>**A**<br>**Househ**<br>**< 1**|**3-2001**<br>**vg. # o**<br>**old M**<br>**5 years**|**f**<br>**embers**<br>**old**|
|---|---|---|---|---|---|---|
||Bahia|NE|Brazil|Bahia|NE|Brazil|
|**1993**|4.6|4.5|4.4|2.1|2.0|2.1|
|**2001**|4.2|4.2|4.1|1.8|1.8|1.9|
|||**Non**<br>|**-Poor**<br>||||
|**1993**|3.3|3.3|3.3|0.9|0.8|0.9|
|**2001**|3.0|3.1|3.1|0.6|0.6|0.7|
|_Source:_Author’s ca|lculation bas|ed on PNA|D 1993- 20|01.|||

3 Demographic factors have direct and indirect impacts on prices and poverty. As the size and age composition of the population changes, the relative size of the labor force and the number of dependents also change, modifying the dependency ratio of families, and therefore their level of poverty. This is the direct effect of demographic changes. It captures the effect that demographic changes have on quantities: number of children, size of the labor force, and the number of elderly people. These changes in quantities, however, will, in general, influence prices in the economy. In particular, changes in the rate of growth of the population and in the age structure may have important impacts on both labor supplies and savings. As a consequence, demographic changes may have considerable impact on the level of wages and on interest rates. Since these prices are important determinants of family income, they are bound to have a profound influence on the level of poverty. These are the indirect impacts of demographic changes on poverty, which occur through the effects of demographic changes on savings, wages, and interest rates. 4 Data source: Demographic census from 1991 and 2000.

5
the NK model for floating ER countries but more procyclical than predicted for fixed ER countries. In SSA countries, the empirical MPS is more procyclical than optimal for both types of ER.

In closing, it is worth mentioning several caveats to our results. Empirically, a key challenge is estimating the persistence of commodity price shocks when they are close to a random walk. Some of the results for specific commodities, or the countries producing them, may be estimated less precisely, which is why we focus on the cross-country results. Theoretically, our model abstracts from three important real-world features: political economy, finite reserves, and nonlinearities. First, raising expenditure in booms is politically easier than cutting it in busts, which can lead to a suboptimal “ratcheting-up” of expenditure. Second, our results also apply most directly to countries with many years of commodity production ahead of them. We show that over the past 20 years, stable or increasing reserves and production are actually very common. But in countries whose reserves will soon expire, intergenerational equity is a primary concern, which we abstract from. Third, non-linearities can introduce precautionary savings motives that undermine the simple predictions of the PIH. Relaxing these assumptions provides interesting areas for future research.

# **REFERENCES**

- Alesina, A. and G. Tabellini, 2005. "Why is Fiscal Policy often Procyclical?" CESifo Working Paper Series 1556, CESifo Group Munich.

- Alesina, A., F. Campante and G. Tabellini, 2008. "Why is Fiscal Policy Often Procyclical?" _Journal of the European Economic Association_ ,6(5), 1006-1036

- Allaoua, L. and M. Achouche, 2018. “Fear of Floating and Exchange Rate Pass Through to    Inflation in Algeria”, _Les Cahiers du Cread, vol. 33 – n 122._

- Dale S. and B Fattouh. 2018. “Peak oil demand and long-run oil prices” https://www.bp.com/content/dam/bp/businesssites/en/global/corporate/pdfs/energy-economics/bp-peak-oil-demand-and-longrun-oil-prices.pdf

- Borensztein, E., O. Jeanne, and D. Sandri, 2013. “Macro-hedging for Commodity Exporters.” _Journal of Development Economics_ 101 (1): 105–116.

- Caballero, R., and A. Krishnamurthy, 2004. “Fiscal Policy and Financial Depth.” NBER WP 10532,

46
<!-- Start of picture text --> 120<br>70<br>50 50<br>45 F- 45<br>vos 2010s<br>106 —— 1015<br>No ExistingRoad<br>1996 2002 2007 2010 2016<br>Phase | Phase II Phase III Phase IV<br>Note: Width of lines corresponds to road segment length.<br><!-- End of picture text -->

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_19_ by this correction.

# **IV. Results**

In this section, we present results on the primary outcomes of interest, investigate potential channels of impact, and discuss the implications and potential welfare impact of our findings.

# _A._ **_Main Results_**

# Expenditures and Revenues

We first present evidence that the grant increased school expenditures; this is of independent interest as school and household finances are fungible and school owners had considerable leeway in how the grant could be spent. Table 2, column 1, shows that school fixed expenditures increased for _L_<sup>_t_</sup> and _H_ schools relative to control in the first year after treatment; the magnitudes as a fraction of the grant amount in the first year were 61% for _L_<sup>_t_</sup> and 70% for the _H_ schools. Fixed costs primarily include infrastructure-related investments, such as upgrading rooms or new furniture and fixtures; spending on these items is consistent with self-reported investment priorities in our baseline data.

The fact that schools increase their overall expenditures despite the grant being (e↵ectively) unconditional suggests that school investments o↵er better returns relative to other investment options. While consistent with the presence of credit constraints, investing in the school could also reflect the lower (zero) cost of financing through a grant. In this context, Banerjee and Duflo (2012) suggest a test to directly establish the presence of credit constraints. Suppose that firms borrow from multiple sources. When cheaper credit (i.e. a grant) becomes available, if firms are not credit constrained, they should always use the cheaper credit to pay o↵more expensive loans. In fact, they should draw down the expensive loans to zero if credit is freely available. In Appendix Table E1, we examine data on borrowing for school and household accounts of school owner households. While there is limited borrowing for investing in the school, over 20% of school owner households do borrow (presumably for personal reasons). Yet, we find no statistically significant declines in borrowing at the school or household level as a result of our intervention.

We now consider whether these expenditure changes a↵ected school revenues. Since schools may not always be able to fully collect fees from students, we use two revenue measures: (i) posted revenues based on posted fees and enrollment (cols 2-4), calculated as the sum of revenues expected from each grade as given by the grade-specific monthly tuition fee multiplied by the grade-level enrollment; and (ii) collected revenues as reported by the school (cols 5-7).<sup>26</sup> To obtain the latter measure, we inspected the school account books and computed revenues actually collected in the month prior to the survey.<sup>27</sup> While this measure captures revenue

> 26Posted revenues are available for rounds 1,2, and 4, and collected revenues are available from rounds

> 2-5. We use baseline posted revenues as the control variable in all revenue regressions.

> 27Over 90% of schools have registers for fee payment collection, and for the remainder, we record
(2012) find that progress between 1995 and 2007 on MDGs 1, 4 and 5 outcomes was generally pro-rich. On interventions, both Suzuki et al. (2012) and Victora et al. (2012) mostly find pro-poor progress, except on skilled birth attendance (SBA) where Suzuki et al. (2012) find progress has been pro-rich.

# **Data and indicators**

# _Data_

Like Suzuki et al. (2012), we take our data from both the DHS and the MICS surveys. These global household survey programs are the only regular source of comparable household-level data on the health MDG monitoring indicators. We analyzed the raw data from 164 DHS surveys and 71 MICS surveys, covering a total of 91 countries between 1990 and 2011. After eliminating countries with only one period of data, and one dataset with some implausible values, we were left with 64 countries with at least two periods of data. Depending on the MDG indicator (see below for selection criteria and definitions), we have data for between 9 and 63 countries, with an average of 40 countries per indicator (see Table 3). There are some indicators with up to six surveys per country, but the average is 2.7.

We thus cover more countries than previous studies (64 compared to the 22 analyzed by Moser et al. (2005), the 40 analyzed by Suzuki et al. (2012), and the 35 analyzed by Victora et al. (2012)), and unlike previous studies whose trends have been estimated from just two surveys per country per indicator, ours are estimated from several surveys per country per indicator, for an average of 2.7.

4