rafmacalaba/datause-encoder-data huggingface dataset:
**Table S10:** Long Difference, OLS and IV Results - RSDP I - III

||NT|L|Urb|an|Cropl|and|N|TL|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._04<sup>_∗∗∗_</sup>|0_._02|0_._002<sup>_∗_</sup>|0_._0000|0_._01<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._08<sup>_∗∗_</sup>|0_._05|0_._01|_−_0_._01|0_._04|0_._05|
||(0_._01)|(0_._01)|(0_._001)|(0_._0004)|(0_._004)|(0_._01)|(0_._04)|(0_._03)|(0_._02)|(0_._02)|(0_._04)|(0_._04)|
|Imp Rd._×NTL_96 Lit||0_._11<sup>_∗∗∗_</sup>||0_._01<sup>_∗∗∗_</sup>||_−_0_._01<sup>_∗∗_</sup>||0_._12<sup>_∗∗∗_</sup>||0_._08<sup>_∗∗∗_</sup>||_−_0_._03|
|||(0_._03)||(0_._003)||(0_._01)||(0_._04)||(0_._03)||(0_._03)|
|Constant|0_._02|0_._02|0_._0005|0_._0001|_−_0_._01<sup>_∗_</sup>|_−_0_._01|_−_0_._18|_−_0_._17|_−_0_._09|_−_0_._08|_−_0_._15|_−_0_._16|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._13)|(0_._13)|(0_._08)|(0_._07)|(0_._15)|(0_._15)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|208_._63|104_._26|208_._63|104_._26|208_._63|104_._26|84_._51|42_._55|84_._51|42_._55|84_._51|42_._55|
|Observations<br>|1,271,082|1,271,082<br>|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.003|0.005|0.0001|-0.001|-0.001|-0.001|-0.01|0.01|0.001|0.01|-0.003|-0.004|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Lit indicates that the Woredas had some positive nighttime lights at baseline. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S17
# Annex Table 2: The effect of orphanhood, all children at baseline, regardless of orphan status at baseline (Dependent variable: _ever married)_

||(1)<br>All|(2)<br>Women<br>(17-23 in 2004)|(3)<br>Men<br>(17-28 in 2004)|
|---|---|---|---|
|Male prime-age adult died|0.016|-0.070|0.120|
||(0.070)|(0.098)|(0.096)|
|Female prime-age adult died|-0.070|-0.029|-0.128|
||(0.062)|(0.088)|(0.088)|
|Father died|0.072*|0.083|0.066|
||(0.040)|(0.058)|(0.057)|
|Mother died|0.043|0.024|0.057|
||(0.045)|(0.068)|(0.063)|
|_Baseline characteristics_||||
|Head has at least primary schooling|-0.089**|-0.163**|-0.044|
||(0.043)|(0.065)|(0.058)|
|Head male|0.029|0.105|-0.031|
||(0.050)|(0.076)|(0.067)|
|Age of household head|-0.001|-0.002|-0.001|
||(0.001)|(0.002)|(0.002)|
|Log of expenditure per capita|-0.007|-0.034|0.004|
||(0.036)|(0.055)|(0.048)|
|Permanent flooring in dwelling|-0.138***|-0.176**|-0.111*|
||(0.050)|(0.079)|(0.065)|
|Non-farm household|-0.044|0.048|-0.088|
||(0.044)|(0.069)|(0.055)|
|Father lives in household|0.043|0.002|0.080|
||(0.048)|(0.075)|(0.064)|
|Mother lives in household|-0.057|-0.060|-0.064|
||(0.044)|(0.067)|(0.060)|
|Observations|1,064|473|591|
|R-squared|0.176|0.190|0.182|

Notes: Results reported are marginal effects estimated using a Probit. Robust standard errors in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. Includes controls for age and age squared, district fixed effects and whether child was in school at baseline. Sample includes children who were already orphaned at baseline.

18
## **Spatial Matching of the Data**

Using collected data on the geographical coordinates, the analysis merges the DHS dataset with SPEI from 1980 to 2018. The merging consists of assigning to each individual the weather conditions of the grid cell in which their DHS cluster coordinates are located. This paper also uses similar spatial matching to identify the administrative region in which individuals resided at the time (year and month of interview) of the survey.

Our identification strategy, as described in greater detail in the next section, exploits within region variation in the exposure of households to (wet or dry) water shocks. The estimated impacts of water shocks on labor outcomes compare "treated" households (those that experienced a wet or dry water shock) in clusters B and C with those in the "control" group (those that did not experience a water shock) in cluster D, averaged across the full sample of the dataset. The SPEI varies at the grid cell level, and is already normalized by the long-term average within a grid cell. Treatment thus represents exposure of households to unusually wet or dry conditions compared to their local historical climate.

# **3.2 Summary Statistics**

Our main sample consists of 580,592 individuals. The sample covers period from 2005 to 2018 for 16 countries of Sub-Sahara Africa. Table 1 summarizes the key variables of interest. In our sample, 64 % of individuals have a job. Of those who are employed, 17% have a high-skilled jobs, and about 23% are farming employees. The average age of individuals in the sample is 29 years, with relatively highest share in age group 15-24 (39%), following by people in 25-34 (29%). Individuals aged 45 years and above account only for 10 % of the sample. Regarding education, the average individual has 4.3 years of education, with the majority (roughly 60%) completing the secondary and about 31% with no education. Most individuals (61%) live in rural areas. Individuals who were surveyed experienced, on average, weather conditions resulting in an SPEI equal to _−_ 0.120, which reflects that on average they experienced more dry shocks than wet shocks.

Table 2 reports the sample distribution regarding the ancestral mode of subsistence. Agriculture ranks as the predominant mode of food production with 59% of the historical activities. Then husbandry represents 22%; and herding 19%. It is worth noting that communities used to rely on different activities in the same period. The paper uses this information to examine the adaptation over time in local activities and how these determine responses to short-run 9
Thus, following the differenced-out method outlined earlier and introducing the initial conditions of 1991/92 as additional controls for time-varying heterogeneity associated with borrowing from microcredit programs, we get the following differenced model as per equation (3): We have so far considered _Y_ as a categorical (0/1) variable indicating whether a household is indebted or not, based on the cut-off point at 20 percent of the debt-income or debt-asset ratio. The concern with estimating impacts on a categorical variable is that the changes are observed only when the underlying continuous variable (debt-asset ratio, for example) changes across the cut-off point. That is, the categorical variable is insensitive to any changes in the continuous variable as long as they are confined to above or below the cut-off point. For example, the categorical indicator of indebtedness for a household does not change when its debt-asset ratio rises from 0 to 0.19 or falls from 0.70 to 0.21, even though these changes are quite significant to understanding a household’s indebtedness. Even worse, in certain cases changes in the categorical variable may be misleading regarding the overall indebtedness situation.<sup>15</sup> Therefore, the categorical indicator of indebtedness may not be the variable of choice when we are interested in changes in indebtedness over the whole spectrum of the debt-asset ratio. That is what we are interested in when we want to assess microcredit participation impacts on household indebtedness. The concern for policymaking purposes is whether an increase in the debt due to borrowing is accompanied by a relatively higher increase in the income or assets induced by the borrowing, rather than whether the debt is less than 20 percent of the income or asset.

> 15 This is possible when, for example, the debt-asset ratio gets worse by going up from a value little below 0.20 to one little over 0.20 for a small number of households, and at the same time gets much better by dropping from 0.70 to 0.25 for a large number of households. In this case, the categorical indicator would report a rising indebtedness when in fact the debt-asset ratio would show a large improvement.

23
the asset index as compared to expenditures. They conclude that “the asset index, as a proxy for economic status for use in predicting enrollments, is at least as reliable as conventionally measured expenditures”.

Bollen et al. (2002) compare an asset index to per capita expenditures in a study of fertility using data from two countries and find that the estimated impact of economic status is larger when using the asset index. They also find that estimates of the impact of other variables on fertility differ somewhat depending on the measure of economic status used but conclude that “most of the changes in coefficients are not large.” Sahn and Stifel (2003) compare an asset index to reported expenditures using 12 different household survey datasets from 10 countries: the correlations between the household rankings using the two measures range between 0.39 and 0.50 in six of the datasets, between 0.51 and 0.70 in four of the datasets, and greater than 0.7 in two of the datasets. They also compare the two measures in terms of how they predict child height-for-age and draw two main conclusions. First, they cannot reject the null hypothesis that the coefficient on economic status is different across multivariate models that use different measures of welfare. Second, they find that the asset index does an “equal or better job at stratifying the population into well and poorly nourished individuals.” Wagstaff and Watanabe (2003) compare inequalities in child malnutrition using adultequivalent expenditures and an asset index. They compare the concentration index (a summary measure of unequal distribution) based on these two measures using household survey data from 19 developing countries. The difference between the concentration indices is statistically significantly different from zero in only four countries for low weight-for-age, and in only four countries for low height-for-age. They conclude that “[...] it seems for the most part to make little difference to the measured degree of socioeconomic inequalities in malnutrition among under-five children whether one measures [socioeconomic status] by expenditures or by an assetbased wealth index.” In his study of the performance of asset indices using rural National Sample Survey data from India, Mukherjee (2006) analyzes the gradients in the fraction of five to 16 years olds who had ever attended school as well as in the fraction of the adult population who are literate. He finds that per capita expenditures and an asset index yield very similar gaps between those in the richest quintile and those in the poorest quintiles. For example, he reports that the fraction who 5
grossly inadequate. The recent history of telephone services shows how quickly decentralized services can develop in SSA under the right conditions. From 1960 to 2000, telephone landlines grew so slowly (3.2% per year) that coverage in 2000 was limited to 1.4 lines per 100 inhabitants. In contrast, mobile phone connections grew so quickly after 1993 (55% per year) that coverage had reached 22.5 per 100 inhabitants by 2007 (Figure 1).<sup>1</sup> The rapid expansion of mobile phones has made telephone service affordable for many poor households, through a variety of local expense-sharing arrangements.

In this paper, we assess the feasibility of a similar expansion of decentralized energy services in Sub-Saharan Africa. Using Ethiopia, Ghana and Kenya as case studies2, we ask where decentralized service appears currently to be lower-cost than centralized network provision, and how this could be altered by likely changes in technologies and fossil energy prices. Our assessment employs a spatially-disaggregated model that estimates the comparative costs of network and decentralized electricity provision across each country. Among the decentralized power options, we focus particularly on renewable technologies such as solar, wind and biodiesel.

The remainder of the paper is organized as follows. Section 2 briefly reviews the rural electricity supply problem in Africa. In Section 3, we describe the energy options to be considered and the model we use to explore those options. Section 4 presents our comparative estimates for network and decentralized electricity provision under current and possible future conditions. Section 5 provides a summary and discusses the implications of our results.

> 1 For comparison: Telephone landline coverage per 100 inhabitants is 40 in high-income countries, 22 in the East Asia-Pacific region, 17 in Latin America and the Caribbean (LAC) and 3.2 in South Asia (SAS). Mobile phone coverage per 100 inhabitants is 85 in high-income countries, 65 in LAC and 23.7 in SAS. 2 We chose these three countries because high resolution data on renewable power potential (solar and wind) are available from SWERA (2001 and 2004). Renewable energy potential in these countries has long been recognized (see e.g., Edjekumhene et al. 2001). The analysis could be extended to other African countries using available data at somewhat lower spatial resolution.

3
ments; the third may suffer from simultaneity concerns, and is only used in conjunction with our other instruments.

Our first, and primary, instrument is government effectiveness.<sup>4</sup> Although there are potentially many channels by which an effective government bureaucracy can affect economic outcomes, we contend that the primary means by which this occurs is through service delivery, and in particular the delivery of educational services. In many countries, especially developing ones, educational expenditure is one of—if not the—largest components of total public expenditure, and education at the primary and secondary level is largely publicly-provided.<sup>5</sup> If government effectiveness does matter to per capita incomes, there is a strong likelihood that it does so mainly through its mediating effect on the delivery of education. Although we make no claims of causality at this stage, the strong relationship between governance and human capital is nevertheless visually captured in Figure 1.

<!-- Start of picture text --> Relationship Between Human Capital and Governance<br>CANSWEUSANORNZL<br>KOR AUS<br>CH<br>BGR ROMRUS SVKHUNPOLCZE HKGISRJPNCYP IRLBELDEUGBRFINDNKNLD<br>PAN FJIPHL ARG GRC TWNISL AUT<br>COGVENECUNICHNDDOMPRYIRNZMBZWELKACUBDZASWZSYRSLVBRAJAMINDTURGUYPERURYHRVMEXCRIEGYBOLTTOIDNJORCOLCHNBWAKWTZAFBHRTHAMUSTUN SVNMYSCHLITAPRT ESPFRA<br>IRQ PAKGHAKEN LSO<br>LBRZARSDNMMRRWACMRHTICAFTZATGOMWIBGDSLEU G TMAGMBPNGNPLSENBEN<br>NERGIN MLI MOZ<br>−2 −1 0 1 2<br>Quality of institutional governance<br>Source: Authors’ calculations, using Barro & Lee (2001) and Kaufmann, Kraay & Mastruzzi (2007)<br>15<br>10<br>5<br>Mean years of schooling<br>0<br><!-- End of picture text --> Figure 1: Positive relationship between quality of institutional governance and mean years of schooling, 2000, with fitted regression line. The (bivariate) regression is significant at conventional levels.

There are two other main channels by which effective government may affect economic out4 We are not the only authors to recognize that the institutional setting can be an important instrument for education. Hanushek & W ̈oßmann (2012) exploit the institutional structure of the educational _system_ —specifically, the use of external exit exams, extent of school choice, and degree of local school autonomy—as instruments for cognitive skills.

> 5Education expenditures typically account for about 14% of government expenditures, which is typically (though not always) the largest single budget item (with the exception of social security in a small minority of countries).

7
spent on the consumption of the good produced in the non-traded sector of the concerned economy. The rest of the result follows from proposition 4.

Note that in course of the above argument, the prices of the commodities (which depend on the productivity parameters only) remain unaltered. Under the altered assumption where the relatively poor consume the nontraded good proportionately less than the relatively rich, the previous propositions get completely reversed.

# **3. The Evidence in Favor of Non-Homothetic Preferences**

In order for the income distribution to affect aggregate demand and, hence, the ‘reference basket’, thereby opening up the link between consumption pattern, inequality and PPP that is central to this exercise, preferences need to be non-homothetic. In other words, the item-wise budget shares must vary with aggregate household expenditure. To provide evidence on this issue, we ran cross-country regressions of mean budget shares on aggregate per capita expenditure, inflation (as measured by the GDP deflator), and country dummies reflecting the country’s status as a ‘low’, ‘middle’ or ‘rich’ income country. Taking the high income OECD and non OECD countries as the default category, the country dummies 2, 3, and 4 refer to, respectively, ‘middle high income’ countries, ‘middle low income’ countries and ‘low income’ countries.<sup>10</sup> These cross-country regressions were run on the pooled cross-country consumption data collected in the ICP 2005 and ICP 2011 exercises [World Bank (2008, 2015)]. The local currency–denominated aggregate expenditures were converted to US $ using the PPP for that year.

# [Table 1 about here]

The results of the regressions on a five broad item breakdown of aggregate expenditure have been reported in Table 1. Relative to the default country group consisting of the rich countries, the ‘middle low’ and ‘low income’ countries spend a greater share of the household budget on items of necessity such as food and clothing, and significantly less on education and health. Four of the five item groups reject homotheticity quite convincingly. Consistent with Engel’s law, the budget share of the first group of items, which is heavily dominated by the necessities

> 10 These are defined in Appendix A.

18
few non-migrant households in these remote, agricultural communities relying predominantly on subsistence agriculture would have on net benefited from the crisis.<sup>xii</sup> Consistent with these possibilities, real total and per capita expenditures for non-migrant households of children under three years old declined by 15 percent between 2008 and 2012 (and by even more for migrant households). Moreover, the 2009 qualitative research pointed to negative spillover effects of the crisis on non-migrant households in the four communities studied (Section 4.2). Therefore, the direct and indirect channels of the crisis likely would have negatively affected non-migrant households as well, leading to attenuation of the DD estimates of the full effect of the crisis on migrant households alone.

The second principal assumption for identification relates to parallel or common trends, which for the cross-sectional cohorts are that in the absence of the economic shock, the ageadjusted nutritional status measures of children under three years old in migrant and non-migrant households would have trended similarly between 2008 and 2012. As in most non-randomized DD analyses, we are unable to conclusively demonstrate common trends.<sup>xiii</sup> Extensive empirical and theoretical literatures make clear that migration is selective, however, and therefore both observable and unobservable differences between migrant and nonmigrant households potentially threaten the common trends assumption. In Section 4.1, we document in this context that migrant households in 2008 are better off on a range of indicators of economic well-being and the nutritional status of their children under three is substantially higher. Although we do not directly assess the impact of migration (and remittances) on child nutritional status or other outcomes in this paper, previous literature suggests the migrant advantage is likely the combination of beneficial impacts of the migration as well as of selectivity—migrant households are different and some outcomes for their children might have
Simple OLS regressions using panel data by country and sector on trade and robots point to a positive relationship between robot intensity in own production and imports sourced from less developed countries. They also reveal an even stronger association with exports to these countries. The direction of causality is further supported by an instrumental variable strategy motivated by our theory, exploiting variation in exposure to robotization across countries and sectors. The positive impact of Northern robotization on imports from the South is mainly driven by exchanges of parts and components, which attests to the importance of explicitly modeling input-output linkages. Models that only consider final goods trade would fail to anticipate the sizable surge in imports of intermediates from the South that Northern robotization induces.

We then turned back to our theoretical model to study quantitatively the effects of robot price reductions on wages and welfare, both in the North and in the South. Increased robot adoption in the North initially depresses labor demand, and therefore leads to lower wages. Nevertheless, robotization induces welfare gains, as the income losses associated with lower labor income are more than offset by the gains associated with increased production, lower prices, and increased income from the rental rate of robots. Interestingly, the model points to an important nonlinearity in the relationship between wages and robot prices. As robot adoption proceeds in the North, production in the robotized sector continues to expand, thereby raising demand for the tasks in which robotization is technologically unfeasible. Eventually, the share of labor in the robotized sector starts to expand, thereby raising overall labor demand and leading to higher wages in the North.

Even if production in the South is not subject to robotization, Southern wages and welfare are impacted by robot-induced shifts in international specialization. While robotization makes Northern producers more competitive, it also raises demand for Southern inputs. Real wages and welfare are likely to increase in the South, reflecting also lower consumer prices. Welfare gains in the South are likely to be even more pronounced if robotization also becomes feasible there. Given that trade in intermediate and final goods is the key channel whereby Northern robotization impacts wages and welfare in the South, the importance of this channel is magnified if trade frictions are removed.

Although our model relies on static mechanisms, the main channels we emphasize may have important implications for the link between openness and economic development. Buera and Oberfield (2017) emphasize that international trade is an important channel behind the transmission and diffusion of best practices across countries, and has therefore been an important driver of 26