rafmacalaba/datause-encoder-data huggingface dataset:
**Table A3: Conflict in Irrigated Regions after the Arab Spring in the G5 Sahel and Rest of  Africa**

|Dependent Variable:|||D<br>|ummy=1 if anyB<br>|ATTLE in a cell-y<br>|ear<br>|||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)<br>G5 S|(3)<br>ahel|(4)|(5)<br>Res|(6)<br>t of West Afric|(7)<br>a(excluding|(8)<br>G5)|
|ln(Irrigated Area) X Post|0.0582***|0.0582***|0.0471***|0.0470***|0.0125|0.0125|-0.00087|-0.00078|
||(0.0108)|(0.0108)|(0.011)|(0.011)|(0.0122)|(0.0122)|(0.0129)|(0.0128)|
|Observations|34160|34160|34160|34160|22440|22440|22440|22440|
|Number of Clusters (Grid-cells)|1708|1708|1708|1708|1122|1122|1122|1122|
|R-sq|0.066|0.226|0.234|0.234|0.125|0.335|0.346|0.347|
|Country-Year FE|Y|Y|Y|Y|Y|Y|Y|Y|
|Cell FE||Y|Y|Y||Y|Y|Y|
|Controls X Time|||Y|Y|||Y|Y|
|Climate Controls||||Y||||Y|
|Mean Conflict|0.0181|0.0181|0.0181|0.0181|0.0856|0.0856|0.0856|0.0856|

Each observation corresponds to a 50km x 50km grid-cell observed in a given year between 1998 and 2017. The outcome variable is a dummy variable indicating that the grid-cell experienced a battle event in a given year, as reported in the ACLED dataset. Irrigated area is measured as the percentage share of an average plot of land that is equipped for irrigation, as documented by Siebert et al (2015). Controls include log of population at the start of the period as well as dummies to indicate the presence of wetlands, croplands, pasture lands, rivers and the homelands of ethnic groups that historically relied on agricultural livelihood strategies. Standard errors in parentheses clustered at the grid-cell level. Climate controls include mean temperature and log of total rainfall in the wet and dry seasons. * p<0.1, ** p<0.05, *** p<0.01.

25
Column 4 of table 4 reports the share of the variation in per capita expenditures explained by a set of household and cluster social and demographic characteristics that excludes the asset indicators. The approach follows that of the first stage of the “poverty mapping” methodology described in Elbers, Lanjouw and Lanjouw (2002, 2003) and Alderman et al. (2002) in which per capita expenditures is regressed, administrative region by administrative region in each country, on urban location, age and sex of the head of household, employment indicators of the head of the household; highest education attained by male and female household members; demographic composition of household, and cluster means of all these variables.<sup>24</sup> The share of the variation explained by these models is similar, or higher, than that in the models that simply include indicators. This share is highly correlated with the rank correlation between the asset index and per capita expenditures with a correlation of 0.694 across datasets.<sup>25</sup> The intuition for this result is somewhat less clear. One possible explanation is that a high explanatory power, either by the asset indices or the alternative set of household characteristics, indicates less measurement error in reported expenditures. By this logic, when measurement error is low, per capita expenditures and an asset index yield similar rankings and _vice versa_ . This is consistent with earlier studies that have argued that one advantage of an asset index is less measurement error in identifying the long-run “wealth” or “income” that is associated with inequalities in education (Filmer and Pritchett 2001) or child nutritional outcomes (Sahn and Stifel 2003). Those papers argue, on the basis of results consistent with attenuation bias as well as instrumental variables regressions, that the evidence is consistent with more measurement error in per capita expenditures than in an asset index. The results here are typically consistent with attenuation associated with measurement error: in most of the outcomes considered in figures 1 to 7 the rich-poor gap is larger when using an asset index than when using per capita expenditures (the exception being health care seeking behavior).

Per capita expenditures are certainly measured with error. The literature suggests that the level of aggregation at which consumption and expenditure data are collected affects accuracy (Hentshel and Lanjouw 1996; see Deaton and Grosh 2000 for an overview). It is also

> 24 The share reported in table 4 is the total sum of squares explained in these regressions divided by the overall sum of squares.  Clusters are the lowest sampling unit used in the survey: they are typically the primary sampling unit (PSU) from which on the order of 15-30 households are randomly selected.

> 25 When asset indicators are included in the set of household characteristics the share of variance in each country increases by a small amount, but the association with the rank correlation between the assets index and expenditures is similar: the correlation coefficient 0.710.

25
Table A8: Time use does not differ by treatment

||(1)<br>Hours Idle|(2)<br>Time Chores|(3)<br>Time Social|(4)<br>Time Ration|
|---|---|---|---|---|
|Work|0.033|-0.115|0.108|-0.108|
||(0.094)|(0.174)|(0.140)|(0.091)|
|Cash|0.079|-0.229|-0.002|-0.119|
||(0.114)|(0.176)|(0.180)|(0.105)|
|Shrp. q-val: Work|1.000|1.000|1.000|1.000|
|Test Work=Cash|0.613|0.440|0.497|0.861|
|Shrp. q-val Work=Cash|1.000|1.000|1.000|1.000|
|Control Mean|2.284|3.154|2.870|0.321|
|Observations|726|726|726|726|

**Notes:** This table reports the treatment effect of receiving employment or cash relative to the control group on time use. The columns show OLS estimates of a regression of the outcome on a dummy for assignment to the work treatment, as well as the baseline measure of the dependent variable, camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014).

All outcomes are measured in number of hours spent on the activity per day. ‘Idle’ is the average number of hours respondent reports being idle per day. ‘Chores’ is the amount of time reported spend on household chores such as cooking or fetching water. ‘Social’ is the amount of time spend socializing with others. ‘Ration’ is the amount of time spend getting household rations. ‘Market’ is the amount of time spend at the market. While not an exhaustive list of all reported activities, these activities make up the bulk of the waking (daytime) hours of the average refugee respondent. Effects on all activities available upon request.

Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” reflects p-values, referred to as ‘sharpened q-values,’ adjusted for multiple hypothesis testing using Anderson (2008) for all outcomes in this table.

52
14 It must be noted that a binary variable such as the SW indicator is necessarily restricted in how much it can measure. While we think of the SW indicator as proxy for the above list of factors, it is interesting to ask which of those which might be the most important ones in determining natural resource discoveries. Given that no other meaningful variable is available for most of the time period we are studying, we approach this question in section IV.D by investigating with which alternative measures of openness and institutions the SW index is most closely correlated.

# **A first look at the data: effect of opening markets on discoveries**

As a first look at the data, we conduct an event-study type of analysis where we calculate the average number of discoveries prior and after liberalization for all such episodes in the updated Wacziarg and Welch dataset.<sup>16</sup> Figure 4 shows that the number of discoveries significantly increases once economic liberalization has led to a more market-oriented economy. The average number of discoveries per year and country rises from 0.2 prior to liberalization to 0.36 afterwards.<sup>17</sup> The pattern linking liberalization and discoveries seems to hold across geographical regions and time periods. Looking at all episodes of market opening in our dataset (82 cases), we observe that in 54 percent of cases countries discovered more natural resources in the 10 years following a shift to a market-oriented economy than in the ten years before, in 24 percent of cases there was no difference, and in 22 percent of cases they discovered less.

> difficult to reverse and thus more credible than say sectoral regulations that govern rather narrower aspects of a bilateral relationship between firms and national authorities.

> 16 In practice, we regress the number of discoveries on a set of period fixed effects while controlling for event fixed effects. We then retrieve the coefficients of the period fixed effects and plot them. 17 Discoveries seem to start increasing about 1-2 years prior to opening, perhaps due to an anticipation effect.
- _region_ : a partition of _survey_ into the sub-national region level at which population density is matched, for example, the Indian state of Bihar in 2005

Note that adding fixed effects means our identification is derived from heterogeneity within these regions. Depending on the question we seek to answer, this may be _over_ controlling. For example, in the case of the region fixed effects, the difference in population density between regions within the same country may be of policy relevance.

Our regression specification is: where _i_ indexes individual children, _p_ is the region for which population density is matched, _s_ indicates a DHS survey, and _c_ is a country. _X_ is an extensive set of controls which we use throughout the analysis of the international dataset. It includes six indicators for the child’s household owning the six common DHS assets (electricity, radio, TV, motorcycle, car, refrigerator); indicators for sex, birth calendar month, and multiple births; year of birth entered linearly; indicators for first, second, or third birth order; an indicator for household education; and the mother’s age entered linearly.<sup>8</sup> We also control for whether or not the child’s own household defecates in the open. When child height is the dependent variable, we always add a vector of 120 age-in-months by sex indicators. Standard errors are conservatively clustered at the level of 172 DHS surveys (thus, India’s entire 2005 DHS is one cluster), except in specifications with country fixed effects, where standard errors are even more conservatively clustered at the country level.

# **3.3.2 Results**

Table 3 reports estimates of equation 2, for infant mortality in panel A and for height-forage in panel B. It reports results using several combinations of fixed effects and controls.

> 8We are constrained to use variables that are available in all of the DHS surveys.

13
development. Below we also discuss the necessary control variables for our test of the market failure hypothesis.

# _Export Discovery and Diversification_

Imbs and Wacziarg (2003) analyze how productive diversification behaves across income levels, and found that although there are theoretical arguments supporting both a positive and a negative monotonic relationship between diversification and growth, the evidence shows that neither view is correct. There is, in fact, a robust pattern whereby as countries develop, production is diversified until reaching a relatively high level of GDP per capita, between $13,000 and $14,600 1996 US dollars, after which point economies become increasingly specialized.

Although Imbs and Wacziarg (2003) used domestic production and labor data in their analysis of the stages of diversification, the same result can be found in export data. We construct a Herfindahl index ( _H_ ) of exports for each country in every year with reported exports between 1992 and 2003, and estimate the following equation using a fixed-effects estimator:<sup>3</sup> The results, summarized in Table 1, indicate that, similar to the pattern in domestic production data uncovered by Imbs and Wacziarg (2003), a country’s export basket becomes more diversified as income rises until a relatively high level, at which products.

7
- 14 -

Project-Financed PHN Research

|Region/<br>Country|Project|Fiscal<br>Year|Total<br>Amount<br>of Project<br>(US$ million)|Amount<br>of Loan/<br>Credit<br>(USS million)|
|---|---|---|---|---|
|Senegal<br>|Rural Health<br>|1983<br>|16.7<br>|_15.0_<br>|
|Senegal|Human Resources Davelopment|1991|37.8|35.0<br>|
|Sierra Leone<br>|Health and Popuation Sector Support<br>|1986<br>|_5.7_<br>|5.3<br>|
|Tanzania<br>|Health and Nutrition<br>|1990|70.0|47.6|
|Togo|Population and Health Sector Adjust-||||
||<br>ment|1991|52.7|14.2|
|Uganda|First Health|1988|_65.5_|42.5|
|Uganda<br>|Social Costs of Adjustment<br>|1990|37.0|28.0|
|Zaire|National AIDS Control Program||||
||Assistance|1989|21.9|8.1|
|Zaire<br>|Social Sector<br>|1991<br>|37.0|30.4<br>|
|Zambia|Social Recovery|1991||20.0|
|Zimbabwe|Family Health|1987|52.6|10.0|
|Zimbabwe|Second Family Health|1991|116.9|25.0|
|EAST ASIA AN<br>|D PACIFIC<br>||||
|China<br>|Rural Health and Medical Education<br>|1984|322.8|85.0|
|China|Rural Health and Preventive Medi-||||
||cine<br>|1986|177.4|80.0|
|China|Integrated Regional Health Develop-||||
||ment|1989|113.0|52.0|
|Indonesia|Third Populadon|1980|72.6|35.0|
|Indonesia|Provincial Health|1983|54.4|27.0|
|Indonesia|Fourth Population|1985|94.4|46.0|
|Indonesia|Second Health (Manpower Develop-||||
||ment)|198S|65.9|39.0|
|Indonesia|Second Nutrition and Community||||
||Health|1986|_57.5_|33.4|
|Indonesia|Third Health|1989|103.5|43.5|
|Indonesia|Fifth Populadon|1991|148.4|104.0|
|Korea|Population|1980|93.4|30.0|
|Korea<br>|Health Technology<br>|1991<br>|81.2<br>|60.0|
|Philippines|Health Development|1989|108.4|70.1|
|SOUTH ASIA|||||
|Bangladesh|Third Populaton and Family Health||||
|||1986|213.8|78.0|
|Bangladesh|Fourth Population and Health|1991|601.4|180.0|
|<br>India|<br>Second Population|1980|96.0|46.0|
|lidia|Tamil Nadu Nutrition|1980|66.4|32.0|
|Ildia|Third Population|1984|123.5|70.0|
|India<br>|Fourth Population<br>|1986|89.9|51.0|
|India|Fifth (Bombay and Madras) Popula-||||
||<br>tion|1988|182.0|57.0|
Department Environmental Energy Technologies Division Lawrence Berkeley National Laboratory. Available for download at http://eetd.lbl.gov/ea/ies/ieua/Pubs.html. [Accessed May 2011].

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Schneider, M., Schmidt, T.S. and Hoffman, V.H. (2010). “Performance of Renewable Energy Technologies under the CDM”. Climate Policy 10 (2010): 17–37.

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Shelanski, H. A. and P. G. Klein (1995). ”Empirical Research in Transaction Cost Economics”. Journal of Law, Economics and Organization 11 (2):335-361.

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Skytte, K., Meibom P., Uyterlinde M.A, Lescot, D., Hoffmann, T. and del Rio P. (2003). “Challenges for Investment in Renewable Electricity in the European Union”. Background report in the ADMIRE REBUS project supported by the European Commission. November 2003.

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- 39 -
and the household's income. I also include the number of girls in the family who are of marriageable age. If their presence raises festival expenditures it is consistent with the notion that they are being put on "display" to find good spouses indicating that festivals provide an opportunity to communicate information for private gain. Finally, in order to see if festival expenditures are driven by demonstration effects, I include the festival expenditure of the neighboring household as an explanatory variable, as well as a set of village dummy variables.

In addition to analyzing the determinants of festival expenditures I also examine whether festival expenditures generate social and economic returns. I examine two types of returns - the family's social status, and a household specific index of the unit prices for food faced by the family. The social status variable is constructed by asking each family in the village about their sense of the respect and social standing accorded to _other_ families in the village. This is coded into a measure with four levels, a score of 1 denoting the lowest level of social standing and 4 denoting the highest'<sup>5</sup> .

The unit food price index requires some explanation'<sup>6</sup> . In the process of doing fieldwork I noticed that there was a large variation in the unit prices charged for food to different members of the village. The primary reason for this were quantity discounts since many of the families were severely liquidity constrained and were compelled to purchase food once a week on payday. The fieldwork revealed that households which had good social relationships were able to get around this by forming shopping clubs that would buy food in bulk and then distribute it among the members. Some well-regarded individuals were able to get food on credit from the shopkeepers that again permitted them to get around liquidity constraints. Thus, socially well-connected households seemed to suffer less from high prices. On the other hand, households who did not have good social relationships faced markups of between 15-40 per cent on their food purchases. To get a summary measure of the variation in unit prices across households, I construct a Laspeyres price index which used the prices and quantities purchased by the median household as a base to calculate price variations, for a basket of goods, for everyone in the sample. With this method, the median household's index number is 100; and a household with an index of 200 faces prices that are double the sample median.

The social status and price index variables will be regressed against festival expenditures", the household head's age, the number of adults and children in the family, the head's education,

**_15_** The social status regressions will be estimated with an ordered Probit regression. 1<sup>6</sup> A detailed analysis of this can be found in Rao (2000).

17 Festival expenditures will be treated as an endogenous variable using instrumental variables (IV) with the number of marriageable daughters and the expenditures of neighbors as excluded variables.

12
**Table 2** : **Regressions of Social Welfare Growth on Average Income Growth**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|---|---|---|---|
||||||**Atki**|**Atki**|**Atki**||
|**Social Welfare Function**|**Bottom 10%**|**Bottom 20%**|**Bottom 40%**|**Bottom 90%**|**nson**<br>**Index (1)**|**nson**<br>**Index (2)**|**nson**<br>**Index (3)**|**Sen Index**|
||||**Panel A: S**|**ample with spells**|<br><br>**of minimum 5**|<br><br>**year length**|<br>||
|Avg. growth-Min 5 year spells|1.151***|1.075***|1.021***|0.991***|1.008***|1.043***|1.083***|1.003***|
||(0.0842)|(0.0626)|(0.0467)|(0.0223)|(0.0238)|(0.0468)|(0.0640)|(0.0273)|
|Number of Observations|285|285|285|285|285|285|285|285|
|R-squared|0.476|0.650|0.783|0.944|0.925|0.717|0.571|0.921|
|Variance decomposition|0.413|0.605|0.767|0.952|0.918|0.687|0.527|0.918|
|P-val. wald test,slope=1|0.0765|0.234|0.654|0.687|0.749|0.362|0.197|0.903|
||||**Panel B**|**: Sample with lo**|**ngest spell per**|**country**|||
|Avg. growth-Long spells|0.961***|0.953***|0.955***|0.975***|0.970***|0.943***|0.932***|0.974***|
||(0.162)|(0.119)|(0.0785)|(0.0249)|(0.0382)|(0.0835)|(0.118)|(0.0376)|
|Number of Observations|117|117|117|117|117|117|117|117|
|R-squared|0.403|0.535|0.685|0.927|0.897|0.688|0.538|0.887|
|Share of variance due to growth|0.420|0.561|0.718|0.951|0.924|0.730|0.578|0.911|
|P-val. wald test,slope=1|0.810|0.696|0.569|0.324|0.435|0.496|0.565|0.497|

Notes: This table reports the results from a regression of growth in the indicated social welfare function (in each column) on growth in average incomes, in the sample of nonoverlapping spells lasting at least five years (Panel A), and in the sample of long spells with one spell per country (Panel B). Heteroskedasticity-consistent standard errors reported in parentheses. * (**) (***) indicate significant differences from zero at the 10 (5) (1) percent level. The last row in each panel reports the p-value associated with the test of the null hypothesis that the estimated slope coefficient is equal to one.

42
|Change in share of provincial<br>commerce workers between 2002 and<br>1998|(0.015)<br>0.013<br>(0.075)||||
|---|---|---|---|---|
|Share of transport-working hours-1||-0.446<sup>***</sup><br>(0.030)<br>|||
|Change in share of provincial transport<br>workers between 2002 and 1998||0.191<sup>**</sup><br>(0.083)|||
|Share of finance-working hours-1|||-0.468<sup>***</sup><br>(0.085)||
|Change in share of provincial finance<br>workers between 2002 and 1998|||0.074<br>(0.106)||
|Share of other-services-working hours-1||||-0.416<sup>***</sup><br>(0.021)|
|Change in share of provincial other<br>service workers between 2002 and<br>1998||||-0.048<br>(0.039)|
|Constant|-0.062<sup>**</sup>|-0.013|0.003|-0.006|
||(0.029)|(0.010)|(0.002)|(0.010)|
|N|18957|18957|18957|18957|
|adj. R2|0.178|0.221|0.229|0.193|

Notes: - Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01 - Control variables are Household head (HH)’s ethnicity, HH age, HH gender, HH vocational education dummy, HH high school dummy, HH middle school dummy, HH primary school dummy, spouse vocational education dummy, spouse high school dummy, spouse middle school dummy, spouse primary school dummy, household size, and number of working age members. All of these household characteristics are reported in 2002. Standard errors are clustered at province level.

This section considers the impact of export to the United States on service sub-sectors, namely, “commerce” (which consists of sales of vehicles and vehicle parts, retails, wholesales, hotels and restaurants), “transport, storage and communication”, “finance” and “other remaining services” (codes 65 to 99 in Appendix A1). The results are presented in Table 5.2. We find that in provinces with larger tariff declines, the shares of working members and working hours increase in “commerce”, and to a lesser extent, in “transport, storage and communication” and “other remaining services”. A 10% decrease in provincial tariffs causes an average increase of 0.05 in the share of employed members in commerce (column 1 of Panel A). In other words, employment in commerce increases by 5 percentage points. The same tariff cut causes an average increase of 0.08 21