rafmacalaba/datause-encoder-data huggingface dataset:
of 2.8 percent relative to national nonfarm productivity under the actual distribution of regional output wedges) and national aggregate productivity to 169.1 thousand cedis per worker (an increase of 2.9 percent relative to national aggregate productivity under the actual distribution of regional output wedges). Whereas, reducing regional output wedges by 90 percent increases national nonfarm productivity to 241.2 thousand cedis per worker (an increase of 13.8 percent) and national aggregate productivity to 184 thousand cedis per worker (an increase of 11.9 percent).

- C. _Effect of the disparity in regional output wedges on the contribution of structural transformation to national aggregate labor productivity_

Based on Equation 1, we calculate the contribution of a constant pace of subnational structural transformation to national aggregate productivity,<sup>∑</sup> ∅ (L −L . For the regional pace of structural transformation ( ), we uniformly apply the national average rate of rr rr rr<sup>NN</sup> rr<sup>AA</sup> rr<sup>NN</sup> )∆θθ increase in the nonfarm employment share of 1 percentage point, obtained from GGDC data. rr<sup>NN</sup> ∆θθ Table 3 reports the simulated effects of the disparity in regional output wedges on the contribution of structural transformation to national aggregate productivity. Under the actual distribution of regional output wedges, the contribution of structural transformation to national aggregate productivity is 1.38 thousand cedis per worker. Moving from the actual to the efficient allocation of labor across sectors increases the contribution of structural transformation to national aggregate productivity to 3.74 thousand cedis per worker (a 172.1 percent increase from the level of contribution of structural transformation to national aggregate productivity under the actual distribution of regional output wedges).

The results presented in Table 3 are broadly in line with those presented in Tables 1 and 2. They suggest a strong negative association between the disparity in regional output wedges and the contribution of structural transformation to national aggregate productivity. Reducing output wedges in all regions by 50 percent increases the contribution of structural transformation to national aggregate productivity to 1.41 thousand cedis per worker (or an increase of 2.7 percent relative to the contribution of structural transformation to national aggregate productivity under the actual distribution of regional output wedges), while reducing regional output wedges by 90 percent increases the contribution of structural transformation to national aggregate productivity to 1.65 thousand cedis per worker (an increase of 19.7 percent).

20
We use a number of individual level demographic characteristics as control variables. These include age, a squared term for age and household wealth quintiles. We also add a measure that denotes media exposure in terms of frequency of listening to the radio. Exposure to information is a way in which norms are broken down and the literature on acceptance of family planning is replete with the importance of the media in changing attitudes and behaviors. This is especially important when the population in question is not educated. Yet another explanatory variable in our analysis is a measure of gender equality in marriage. In South Asia, eating order signifies a patriarchal hierarchy with men and elders usually eating before the rest. We believe that if wives eat with their husbands they display a form of equality in marriage and that this would to some extent affect other attitudes about marriage. In our sample the proportion of wives who say they eat with their husbands has gone up from 57.7 percent among the older cohort to 60.4 percent in the younger sample (Table 2).

Finally, we include region of residence as an indicator of cultural norms as have other studies in the past (see, for example, Mason et al, 1976). In India, it is common to use region as a proxy for conservatism and the literature on regional differences is strong (see for instance Dyson and Moore, 1989). Bangladesh, however, is all too often viewed as a homogenous entity in the development literature. One reason for this is that national data sets have limited questions that can allow for the links between norms and outcomes. Surveys that do include this information are small in scale and do not allow for national generalizations to be made. That cultural norms are regionally determined and there are more or less conservative areas is well-known. For instance, Sylhet is a region fraught with poor indicators of women’s status and universally regarded as conservative. Yet, it 14
CROP PRODUCTION AND ROAD CONNECTIVITY IN SUB-SAHARAN AFRICA

**Table 4.7 Impacts of road connectivity on crop production: West Africa**

|Equations|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Dependent variable|Ln (tot|al crop prod<br>high-input)|uction,|Ln (tot<br>low-i|al crop produ<br>nput/subsiste|ction,<br>nce)|Ln (tot|al crop prod<br>irrigated)|uction,|
|Estimation method|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|IV–Tobit|
|Ln (travel time to nearest 25K cit)|0.700***|||–0.921***|||–0.693***|||
|y|(0.182)|||(0.116)|||(0.219)|||
|Ln (travel time to nearest 100K cit)||0.463**|||–1.102***|||–0.573**||
|y||(0.213)|||(0.136)|||(0.278)||
|L l i 500K i|||4.056***|||–1.033***|||–1.952***|
|n (trave tme to nearest cty)|||(0.351)|||(0.199)|||(0.422)|
|Ln (otential roduction hih-inut)|1.725***|1.729***|1.578***|||||||
|p p gp|(0.032)|(0.032)|(0.038)|||||||
|L ttil dti lit||||0.420***|0.406***|0.440***||||
|n (poena proucon ow-npu)||||(0.018)|(0.018)|(0.019)||||
|Ln (otential roduction irriated)|||||||0.069***|0.068***|0.068***|
|p p g|||||||(0.021)|(0.021)|(0.022)|
|Ln (oulation)|0.271***|0.242***|0.217***|–0.009|0.002|0.063***|–0.158***|–0.132***|–0.107***|
|pp|(0.029)|(0.028)|(0.028)|(0.018)|(0.018)|(0.016)|(0.035)|(0.035)|(0.028)|
|Ln nihbr ltin rt|0.340***|0.338***|1.846***|0.793***|0.645***|0.566***|0.263***|0.231**|–0.375**|
|(ego popuao aggegae)|(0.061)|(0.080)|(0.152)|(0.039)|(0.052)|(0.085)|(0.065)|(0.096)|(0.175)|
|Country dummies|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Constant|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|Wald test of exogeneity|18.21|1.90|171.60|44.17|47.90|14.26|9.61|4.64|23.81|
|(p-value)|0.000|0.168|0.000|0.000|0.000|0.000|0.002|0.031|0.000|
|Left-censored observations|1,831|1,831|1,831|104|104|104|3,427|3,427|3,427|
|Uncensored observations|13,730|13,730|13,730|15,446|15,446|15,446|9,709|9,709|9,709|
|Total observations|15,561|15,561|15,561|15,550|15,550|15,550|13,136|13,136|13,136|

_Source:_ Own calculations.

_Note:_ West Africa includes five countries: Nigeria, Benin, Togo, Ghana, and Côte d’Ivoire. Total crop production (value) is 20-crop aggregate using the medians of African country-level crop prices. Standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

22
country has a higher Gini than the most unequal of developed countries (Portugal), where the Gini coefficient is below .4.

True, it could be that there are important biases in these statistics. Most of the figures from Latin America come from income data while those in other part of the world are consumption based<sup>6</sup> and this is important because gini indices based on consumption data tend to be substantially lower than gini indices based on income data. In fact, the analysis in the World Development Report (2006) on Equity and Development, suggests that gini coefficients can differ by 10 percentage points depending on whether one uses income or consumption. For example, the income based gini coefficient for Nicaragua is around .54 but that based on consumption data is about .42. Similarly in Peru which has an incomebased gini slightly below .55 and a consumption-based gini that is below .45.

Yet, even if these differences held for all the countries, it is still worth noting that the only region that would have inequality levels above those found in Latin America is SubSaharan Africa.

**<u>Figure 1. Inequality in Latin America. Gini indices (%)</u>**

**Panel A. Inequality in Latin America**

<!-- Start of picture text --> 65<br>60<br>55<br>50<br>45<br>40<br>Panel B. Latin America in the global context<br>80<br>70<br>LAC<br>60 SSA MENA<br>SA<br>EAP<br>50 ECA<br>40<br>30<br>20<br>10<br>0<br>Gini index<br>BOL HTI JAM GTM BRA PRY PAN ECU COL PER CHL NIC HND DOM MEX BLZ SLV ARG CRI LCA URY VEN GUY TTO<br>Gini index<br><!-- End of picture text --> Source: Own calculations based on Gasparini et al. (2007) and povcal data.

> 6 The exceptions are the developed countries and a handful (about 10 percent) of developing countries.

4
# **6. References**

<mark>Allendorf, K. (2007). Couples' reports of women's autonomy and health‐care use in Nepal.</mark> _<mark>Studies</mark>_ _<mark>in family planning</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(1), 35-46.</mark> <mark>Adams, R. (1991).</mark> _<mark>Protests by pupils: empowerment, schooling, and the state</mark>_ <mark>. Falmer Pr.</mark> <mark>Ambler, K., Doss, C., Kieran, C., & Passarelli, S. (2017).</mark> _<mark>He says, she says: Exploring patterns of spousal agreement in Bangladesh</mark>_ <mark>(Vol. 1616). Intl Food Policy Res Inst.</mark> <mark>Anderson, C. L., Reynolds, T. W., & Gugerty, M. K. (2017). Husband and wife perspectives on farm householddecision-making authority and evidence on intra-household accord in rural Tanzania.</mark> _<mark>World development</mark>_ <mark>,</mark> _<mark>90</mark>_ <mark>, 169-183.</mark> Batliwala, S. (1994). The meaning of women's empowerment: new concepts form action. In Sen, G., Germain, A. and Chen, L. (eds) Population Policies Reconsidered: Health, Empowerment and Rights.

<mark>Becker, G. S. (1973). A theory of marriage: Part I.</mark> _<mark>Journal of Political economy</mark>_ <mark>,</mark> _<mark>81</mark>_ <mark>(4), 813-846.</mark> <mark>Bloom, S. S., Wypij, D., & Gupta, M. D. (2001). Dimensions of women’s autonomy and the influence on maternal health care utilization in a north Indian city.</mark> _<mark>Demography</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(1), 67-78.</mark> 31
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE in ICT accounts for almost 65 percent of all private participation in the country’s infrastructure. Water and sanitation received almost 35 percent of private investment in the country, and power a conspicuously small percentage.

**Table 18. Financial flows to Burkina Faso infrastructure, average 2001–06**

$ million per year averaged over 5-year period

|||||Capital investment<br>||||
|---|---|---|---|---|---|---|---|
||O&M<br>(public)|Public|ODA|Non-OECD<br>Financiers|PPI|Total, capital<br>investment|Total<br>spending|
|ICT|10|2|6|1|64|73|83|
|Power|59|10|33|1|0|44|103|
|Transport|0|35|45|21|0|100|100|
|WSS|46|3|37|0|33|74|119|
|Total|115|50|121|22|97|291|406|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology.

Burkina Faso’s current spending on infrastructure is also a relatively small share of its GDP when compared to its peers. Burkina Faso spends 7.5 percent of its GDP on infrastructure, the level of other nonfragile LICs (figure 26), but the pattern of its investments is different. Burkina Faso’s public sector invests substantially more in power but less in ICT and transport than its African peers.

**Figure 26. Spending allocated to address infrastructure needs**

<!-- Start of picture text --> SSA<br>LIC-Fragile<br>LIC-NoFragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Senegal<br>Niger<br>Mali<br>Ghana<br>Benin<br>Cote d'Ivoire<br>Nigeria<br>Burkina Faso<br>Cape Verde<br>0 5 10 15<br>Capital Investment O&M Percentage of GDP<br><!-- End of picture text --> _Source:_ Foster and Briceño-Garmendia 2009.

_Note:_ LIC = low-income country; MIC = middle-income country; ECOWAS = Economic Community of West African States; SSA = SubSaharan Africa; O&M = operations and maintenance.

For power, transport, and water supply and sanitation, the pattern of finance is similar to the peer group, with a mix of public and donor finance. Burkina Faso’s state-owned enterprises finance most of the operations and maintenance, particularly in the power sector, whereas the government finances most 37
# **4. Empirical Results**

# _4.1 Baseline Difference in Differences Estimation_

Table 3 presents the baseline regression results for the difference-in-differences estimation strategy using years of education as the dependent variable and including fixed effects for province and children’s age.<sup>7</sup> All specifications also control for child gender, poverty status, an indicator for the dataset (2000 DHS data), age of the household head, the highest level of education among all adults in the household, the number of children under five in the household, and the rural/urban status of the household.<sup>8</sup> The difference-in-differences estimate is the coefficient on the interaction between the young cohort (age 6 to 15) and the variable indicating the DHS 2000 survey. Results suggest a negative impact of the genocide of -0.421 years of education, similar to the corresponding estimate in Table 2, Panel A that was obtained without any controls, and the coefficient is statistically significant at the 1 percent level.<sup>9</sup> Within a given province and for a given aged child, being in the young cohort of children in the 2000 data and exposed to the genocide during typical schooling ages implies significantly lower educational attainment for these children.<sup>10</sup> The regressions in columns 2 and 3 explore the heterogeneity of this conflict impact. In column 2, we interact the difference-in-differences variable with a female indicator. Results suggest the negative impact of the genocide is stronger for males. A potential reason might be

> 7 Since 1994 in Rwanda, it is forbidden in household surveys to ask questions about ethnicity and therefore the 2000 data do not contain this information so it is not possible to control for this variable in the regressions. However, the 1992 data contain this variable, so in robustness checks, we estimate the difference-in-differences regression separately for provinces with low and high percentages of Tutsi in 1992 and results are not significantly different. 8 The poverty status measure is an indicator variable for whether the household has more assets than the population mean. Assets used to calculate this wealth measure include piped running water, refrigerator, radio, finished floor, bicycle, motorcycle, and car.

> 9 Results are also robust when household level controls are not included in the regressions.

> 10 The identification strategy is valid as long as changes over time in schooling attainment would be similar across provinces in the absence of the genocide. To address any potential differential time trends across provinces, we reestimate the regressions in Table 3 and include a province specific age trend in the regressions and results are consistent and still statistically significant.

11
particular country by computing the ratio of net total imports over total food consumption of the four food groups. Dependency ratios across country groupings are then aggregated using simple averages. The analysis looks at the most important food products for MENA countries which are grains, edible oils, sugar, beef, and poultry.

In all developing MENA countries, except Lebanon, grains account for the largest share in total food consumption, while in high-income GCC meat has the highest weight in the consumption basket (Table 1A). Dependence on food imports in general is high across MENA except in Iran, and nearly complete (around or above 90 percent) in all GCC economies, except Saudi Arabia where it is 80 percent. Thus, in the absence of policies to soften the impact of world food price fluctuations, the passthrough effects from world to domestic prices should be largest in the GCC economies, as well as in some other countries, such as Yemen, Iraq, and Jordan.

To gauge the first-round effects of a sharp increase in key food prices we also compute the corresponding increase in the import bill as a share of the 2010 GDP and as a share of international reserves, excluding gold.<sup>9</sup> The analysis relies on USDA data for supply and demand of individual food commodities, World Bank international commodity prices, International Monetary Fund (IMF) data on international reserves, and World Bank data for the 2010 GDP estimates by country.

The assessment of vulnerability considers increases in international grains, oils, meat, and sugar prices from June 2010 to July 2011 relative to the same period in the previous year. Over this period wheat prices surged nearly 30 percent, corn prices surged 53 percent, sorghum rose 32 percent, and barley prices rose 27 percent. The increase in rice prices was more modest, at 7 percent. The increase in edible oil prices was also significant, with sunflower seed oil prices up 54 percent, rapeseed oil prices up 50 percent, soybean oil prices up 40 percent, and palm oil prices up 46 percent. Sugar prices were up 39 percent, while beef prices rose 22 percent. Only the prices for olive oil and poultry declined during this period.

> 9 The assumption is that import prices are the prices prevailing in major international markets for each commodity, expressed in US$, and that demand and supply do not respond to changes in prices.

-8-
placed bonds with an original maturity of more than one year.<sup>13,14</sup> Given that SDC Platinum does not collect data on the debt issues with maturity shorter than one year, our data set does not include commercial paper. To classify security issuances as domestic or international, we consider the main exchange where the issues are listed and compare it to the issuing firm’s nationality. The issues taking place in Hong Kong SAR, China or Taiwan, China are considered foreign issues for both China and India. For offerings that take place in more than one market, we consider the issues in each market as separate issues.

The data on equity capital raisings in domestic and international markets cover the period from 1991 to 2011. The coverage for bond issuance in international markets starts in 1991, while the coverage for domestic market activity is more limited and starts in 2000. Therefore, for bond financing activity we restrict our sample to the period 2000-2011. Our data set includes 18,085 security issuances <mark>, out of which 6,929 are bond issues and 11,156 are equity issues. This data set covers i</mark> ssues by 3,884 firms from China and 6,483 firms from India.

To analyze the characteristics and performance of the firms that issue and of those that do not issue in domestic and foreign capital markets, we match the data on security issuances from SDC Platinum with firm-level balance sheet data from the Bureau van Dijk’s Orbis database over the 2003-2011 period. Our sample covers only publicly listed companies, which gives us a more homogeneous sample of firms relative to using all firms. By not analyzing non-listed firms in our sample, we exclude relatively small firms for which it is probably very costly to issue bonds and equity and which are likely to have different accounting standards and to be informal (thus less

> 13 SDC Platinum collects data on security issuances mostly from filings with local regulatory agencies and stock exchanges. These data are augmented with data from other sources such as offering circulars, prospectus, surveys of investment banks, brokers, and other financial advisors, news sources, trade publications, and wires.

> 14 Foreign subsidiaries of firms with headquarters in China or India are not included in our analysis. For example, Tata Steel UK Ltd. and Sinochem International (Overseas) Pte. Ltd. are both excluded from our sample. We also exclude firms with headquarters in Hong Kong SAR, China or Taiwan, China.

8
|**Table 2: Key rotation charact**<br>|**eristics for**<br>|**sunflower,**<br>|**maize, and s**<br>|**oybeans**<br>|||||
|---|---|---|---|---|---|---|---|---|
|**Crop**|**Cer**|**eals**<sup>**a**</sup>|**Sunfl**|**ower**|**M**|**aize**|**Soy**|**bean**|
||**2017**|**2018**|**2017**|**2018**|**2017**|**2018**|**2017**|**2018**|
|**Panel A: Remote sensing**|||||||||
|Share following itself|0.199|0.166|0.107|0.100|0.240|0.278|0.171|0.147|
|monocrop 2017/2018||0.120||0.074||0.192||0.112|
|monocrop 2016/17/18||0.046||0.025||0.086||0.035|
|same crop 2016/2018||0.337||0.262||0.152||0.129|
|Sunflower|0.344|0.331|||0.249|0.241|0.136|0.150|
|Maize|0.102|0.142|0.243|0.287|||0.280|0.284|
|Soybeans|0.128|0.095|0.073|0.075|0.086|0.088|||
|Cereals|||0.426|0.391|0.247|0.201|0.215|0.207|
|Others|0.227|0.265|0.151|0.148|0.178|0.191|0.197|0.212|
|Area (ha/VC)|1166|1156|1240|1081|844|715|356|425|
|**Panel B: Statistics**|||||||||
|Yield (dt/ha)|34.99|40.28|21.58|24.91|50.47|72.83|17.15|23.83|
|Area per farm (ha)|187.91|189.85|319.58|321.97|301.84|312.15|202.74|190.83|
|Area statistics(F29)|867|894|832|838|604|629|382|345|
|Number of VCs|6,540|6,610|5,356|5,495|4,767|4,785|3,811|3,654|

aYield for cereals is calculated as value of wheat and barley per unit of land divided by average price. Source: Own computation from remotely sensed data for 2016, 2017, and 2018, and form 29 of state statistics database for yield and mean farm size. See text for further description.

16
This surge in experiential food insecurity was accompanied by an increase in selfreported exposure to food price shocks. Interviewed households were asked to indicate the presence of food price shocks in the 12 months leading up to the survey. While only 25 percent of households in 2010 reported facing a significant increase in food prices during the previous 12 months, a staggering 83 percent of households reported facing this shock in 2013. In the urban South, approximately 93 percent of households reported experienced a food price shock, more than 6 times the percentage of households in 2010.

The descriptive statistics create a compelling puzzle, but they are an incomplete evidence base. One concern is that changes in means of calories and dietary diversity might be driven by skewness in these distributions and may not be reflective of what the majority of the people are experiencing. For example, the mean of per capita calories is equal to the caloric intake at the 68th percentile of the calorie distribution in wave 1; FCS mean is at the 56th percentile in both waves. Another concern is simply that these are bivariate comparisons and it is possible that there are important confounding factors outside of food prices that are resulting in more people experiencing food insecurity.

To address these concerns, we leverage the panel aspect of the IHS to examine change over time at the individual level (rather than changes in the overall distribution). This allows us to understand whether the inverse relationship between observational measures of food insecurity (i.e. calories and diversity) and the reported experiences of food insecurity at the national level holds at the level of the individual. To address the concern about potential confounding factors, we use multivariate regression to control for observable factors, and a fixed-effects estimator to control for all time-invariant unobservable individual-level factors with the fixed-effects estimator.<sup>13</sup> As dependent variables in our model specifications, we

> 13 We considered a random-effects estimator, but results from a Hausman test led us to reject an

12