rafmacalaba/data-use-mentions-extended huggingface dataset:
Here, _V LS_ refers to the volume of local shallow groundwater in m<sup>3</sup> that is available in GAEZ grid cell _g_ . _q_ ranges from 1 to _N_ , representing the set of aquifer grids intersecting with GAEZ grid _g_ . _V GW_ is the total volume of groundwater across all aquifer types available in aquifer grid _q_ . We multiply _V GW_ by the share of the aquifer area that intersects with GAEZ grid _g_ and the share of groundwater that is attributable to local shallow aquifers, _PoLS_ . We then multiply the total volume of local shallow water by (1 - _SDU_ ), where _SDU_ is the share of groundwater needed for sanitation and drinking usage.

To estimate a crop’s potential share of local shallow groundwater, we multiply _V LS_ by the share of the crop’s harvest area relative to the grid’s total area.

## **5.2.2 Estimating Water Demand**

To estimate the water demand for each crop in each grid, we sum the evapotranspiration of the crop and the crop’s water deficit during the growing season. For grids where either of these variables is missing, we impute the crop water demand or crop’s water requirement based on the mean crop water demand of immediate neighbors, neighbors within 25 kms of the grid’s center, or the national average crop water requirement for that crop.

# **5.3 Measuring Income Gains**

Understanding the income potential of the simulated gains in production is important for agricultural policy engagements with governments in Africa. To estimate income gains, we simply apply producer prices to GAEZ grids with estimated production gains and then compare the total gains to the 1.90 USD poverty line. We employ a back-of-the-envelope approach as described below.

We first source geo-located food product price data (Development Data Group, 2021) via the World Bank Micro-data Library API. Our API query returns local currency (nominal) prices for all African countries with production gains for each crop and
source of variation has implications for thinking about the likely effectiveness of hiring new supervisory staff (some of whom may be less diligent). We discuss these in section 5.3.

# **4.3 Correlates of Changes in Student Outcomes**

While the focus of our analysis has been on teacher absence, we also briefly consider the correlations between improvements in school quality (seen in Table 1) and learning outcomes. Table 8 presents panel regressions of the form in equation (1), where changes in normalized mean math test scores at the village level are regressed on changes at the village-level, including teacher absence, school facilities, and monitoring. Changes in household-level inputs such as growth in mean parental education and increases in the fraction of students taking private tuition are positively correlated with test score gains (significant in most specifications). School-level variables are not consistently significant, though higher STRs and higher teacher absence are both correlated with lower test scores (and significant in some specifications). However, there is no positive correlation between the improvements in most of the standard measures of school quality and student learning outcomes - including school infrastructure, mid-day meals, and teacher qualifications and training.

We only treat these results as suggestive because the data (with a seven-year gap in mean village-level test scores) is not ideal for testing the impact of school characteristics on test scores. The ideal specifications would use annual panel data on student test scores matched to these characteristics and estimate value-added models of student learning. Nevertheless, the main findings here are consistent with those in studies set in low-income countries using data sets that are better suited to study student learning outcomes, which find that teacher absences significantly hurt student learning, and that school infrastructure, teacher qualifications, and teacher training are typically not correlated with improved student learning.<sup>25</sup>

> 25Duflo, Hanna and Ryan (2012) show experimentally that lower teacher absence raises test scores, while Muralidharan (2012) shows this in value-added estimates with five
but still statistically significantly different from zero. The pattern across levels of economic development is shown in greater detail in Figure 3, which plots all gaps (and highlights the most recent observations) against economic development, as measured by GNI per capita. The relationship between the size of the gap and level of income forms a clear u-shape, indicating that, on average, the gap increases (gets more negative) from low income to middle-income country range, but then is diminishes for highincome countries. The lines show the results of locally weighted regressions of the latest observation for each country.

> 22 Karshenas (2003) estimates the gap using mean difference in the logs of the measures, at -.05.

15
greater amount of evidence needs to be built, and ideally we would like to have the expected effectiveness of the alternative modalities offered through G.

# Hence, _m = G * A * E_

In the context of our global simulations, the parameter _m_ is used as a single parameter which combines all three elements described above:

- γ, families are losing income. The income loss is an exogenous parameter, as is determined by existing GDP projections, from the World Bank and IMF.

- _d_ , countries have age group specific income elasticities to schooling<sup>55</sup> , which will lead

- some children to drop out.

- Learning, measured in terms of Harmonized Learning Outcomes (HLO); PISA score; and PISA Level.

- Schooling, measured in Expected Years of Schooling (EYS).

- LAYS,  Learning Adjusted Years of Schooling.

_Simulation 1: Effect on LAYS (years)_ This analysis examines the impacts of school closures on the stock of earning Adjusted Years of Schooling (LAYS) as well as on Harmonized Learning Outcomes across country income groups. Additionally, we combine data on the projected GDP per capita change provided by the MPO (Macro Poverty Outlook, WBG) with the Global Monitoring Database (collection of globally harmonized household survey data, WBG) to estimate how much dropout is likely to occur as a result of COVID-19. The HCI 2017 database is used as the baseline for these calculations.

∆ _LAYSc = f(_ ∆ _HLOc ,_ ∆ _EYSc)_ changes in the LAYS of country _c_ is a function of changes in both the HLO and EYS of country _c_ , where,

**HLO** , Harmonized Learning Outcomes of country _c_

**EYS** , Expected Years of Schooling of country _c_

Hence, we simulate the impact of COVID-19, both in terms of school closures and household income, on both the HLO and EYS as per the equations below: where,

**sc** , school closure (as a share
# **3. Results**

# **Price Changes**

Consistent with the CPI, the HPI shows that inflation exploded between 2015 and 2017. The mean price index increased substantially from 1.31 in 2015 to 8.07 in 2016 and exploded to 30.75 in 2017 (Figure 1). The results show that non-food price inflation is drastically higher than food price inflation during this period. We use an adjusted Wald test to test the significance of the differences in the means across the three years. The test confirms that the differences are statistically significant (p<0.01), meaning that inflation has increased significantly over time.

Figure 1: Recent trends in price index <!-- Start of picture text --> 60.00<br>40.00<br>20.00<br>0.00<br>Food price index Non Food price index Total price index<br>2015 2016 2017<br><!-- End of picture text --> Source: Authors’ calculations based on HFS market prices data The increase in prices varies across states and have been much higher in the Eastern Equatorial, Central Equatorial, and Northern Bahr El Ghazal states than in other states (Figure 2). This is mainly driven by the fact that food price inflation increased much more in these states. Since the price developments have been so different across the country, it is important to consider this significant geographic variation when analyzing the impact of inflation on livelihoods.

Figure 2: Inflation by state - 2017 (Base year 2015=100) <!-- Start of picture text --> 500%<br>400%<br>300%<br>200%<br>100%<br>0%<br>Northern Bahr El Western Bahr El Lakes Western Equatoria Central Equatoria Eastern Equatoria<br>Ghazal Ghazal<br>Food inflation Non Food inflation Total inflation<br><!-
seats in the US House of Representatives are held by women), there has been little discussion on reforming the aspects of the 1991 MPSL that subordinate women to men. Tonnessen and El Nagar (2013) posit several reasons for this. Namely, the 2008 National Elections Act was passed because women who represented a diversity of geographic regions, socioeconomic strata, and political leanings came together to form one broad coalition. Since the passing of the 2008 National Elections Act, however, the great majority of the seats in the National Assembly have been filled by women who represent a narrow spectrum of that coalition. Tonnessen and El Nagar classify these women as the ‘gender equity’ bloc. This bloc supports the existing 1991 MPSL. Disagreeing with these women are women who are not in the government but in civil society who Tonnessen and El Nagar classify as the ‘gender equality’ movement. This movement’s platform is synonymous with Western human rights platforms that oppose gender differentiation and the gender subordination that the 1991 MPSL condones (Tonnessen and El Nagar 2013). In a recent article about the role that international law might play in removing gender disparities in Sudanese law, Halim (2019, 41) suggests that, “The state may give token recognition to gender equality by placing some women in governmental offices and appointing some to the legislative body. Lip service may be paid to the important role of women in the development of the country, but women are only permitted to perform the role designed for them by the ruling patriarch.”

# **_Land and property rights_**

Despite Sudan’s significant landmass, communal access to and legal ownership of land have become sources of conflict in recent decades, as customary rights have clashed with the state’s rights over, among other things, foreign companies’ rights to drill and mine (Calkins et al. 2015). According to a UN Food and Agriculture Organization (FAO) Gender and Land Rights Database, there are two different systems of 27
to estimate the total counts of mobile phones pertaining to Venezuelans and to the overall population in the canton across all companies. We adjusted these figures further in order to account for the share of the population owning mobile phones on the basis of the overall number of households in the 2010 Population Census.<sup>16</sup>

> 15More specifically, each of the events listed earlier (phone calls, text messages, data transfer events) makes uses of one or several of the cell phone towers owned by _Telefonica Ecuador_ . The cluster of geographic locations of the cell phone towers used more frequently during night time by each mobile phone was used to define the likely residence of the owner of the mobile phone device. In particular, the location of the residence was estimated to be the average of the centroids of the cell phone towers of the cluster, using as weights the number of events channeled through each of the towers. The coordinates of the residence are then mapped into the corresponding Census tract.

> 16Besides providing crucial information for our analysis here, these data were used to design a large survey known as EPEC. This has been designed to be representative of the Venezuelan migrant population in Ecuador as well as the Ecuadoran population residing in the host communities.

10
and Round 2 surveys. Support columns show the respective statistics of firms that have received government support. No-Support columns show the respective statistics of firms that have not received any government support. N denotes the observation number. Means are weighted using the sampling weights from the ES. Asterisks in the No-Support columns are for unconditional t-tests comparing the respective statistic with the same statistic for the firms that received support. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 4 for more details.

37
<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Strongly Disagree Slightly Neither Slightly Agree Strongly<br>disagree disagree agree nor agree Agree<br>disgree<br>2015 2016 2017<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A15: Fear for the future of South Sudan

# Biggest fear, future of South Sudan

<!-- Start of picture text --> 100 Other reasons<br>80<br>Bad economy<br>60<br>Health and disease problems<br>40<br>20 Poverty<br>0<br>Lack of economic opportunities<br>2015 2016 2017<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017

## **Table A1: Regression results for poverty and consumption**

|VARIABLES|(1)<br>Poor<br>(below<br>USD 1.90<br>PPP)|(2)<br>Poor<br>(below<br>USD 1.90<br>PPP)|(3)<br>Log(real<br>consumption)|(4)<br>Log(real<br>consumption)|
|---|---|---|---|---|
|Survey year: 2016|-0.286|0.031|-0.524|-0.934|
||(0.268)|(0.259)|(0.449)|(0.611)|
|Survey year: 2017|-0.051|0.
quantity, safety, and service options, but not hours of supply. However, the poor have lower ability to tradeoff income for services (i.e., higher marginal utility of income). Section 4 presents the policy implications of our findings – findings that have serious equity implications for allocating scarce public services and achieving universal water access. We conclude with some suggestions for future uses of conjoint analysis in the design and evaluation of water and sanitation programs.

# **2. Conjoint Surveys as Field Experiments**

Conjoint analysis has long been applied in market research to assist in the design of new products (Cattin et al., 1982). Many environmental economists have turned to conjoint analysis methods because of validity concerns identified in some contingent valuation studies (Adamowicz et al., 1999). Similarly, health economists also have become interested in the use of conjoint analysis as an alternative to contingent valuation (Ryan et al., 2001). The conjoint methodology is based on rigorous utility-theoretic principles (Louviere, Hensher, and Swait, 2000). Using conjoint analysis, goods are described by a series of attributes with varying levels. Using an experimental design, the attribute levels are combined to produce a collection different goods, or WSS alternatives in the case of our study. In the survey, respondents are presented with two or more options for WSS service levels and associated prices. Respondents select the option they prefer. Each respondent answers a set of choice questions (the optimal number, also determined by an experimental design, depends on the number of attributes and levels) to provide the level of variation needed for estimating the marginal utilities of each attribute. By asking respondents to make choices about different attribute combinations, researchers can determine the importance of different attributes and levels, the probability that individuals would select, for example, a particular WSS alternative compared to their current WSS situation, and to estimate willingness to pay for changes in attribute levels or for one WSS alternative versus another (Hensher et al.
# **1. Introduction**

Intensification of peasant agriculture, by increasing the use of modern inputs such as fertilizer and high yielding varieties, through centralized extension services that focuses on technological packages and government-guaranteed credits has been a key policy emphasized by Ethiopia’s overall development strategy of agricultural-led growth (Dercon and Christiaensen 2011; World Bank 2007). In a setting where high exposure to risk and meager surpluses from subsistence agriculture limit opportunities for self-insurance and savings, provision of credit is often seen as a key element to increase productivity through more intensive use of fertilizer and seed and to facilitate consumption smoothing. However, the potential impact of such a strategy and the extent to which it should be complemented with other efforts to improve access to technology have not been subject to rigorous analysis.

To fill the gap, this study investigates the productivity impact of being credit constrained in the semiformal sector of two regions in Ethiopia, one of them, Gojam, surplus producing and another one, Wollo, with more limited agricultural potential. We use a survey-based direct elicitation approach to identify credit constrained and unconstrained households. As disequilibrium is likely in credit markets, an endogenous switching regression model is used to explore the returns on productive endowments for constrained and unconstrained households. Findings suggest that political connections matter in determining access to semi-formal sector loans. Being credit constrained affects productivity in more productive villages where loans were used to buy agricultural inputs, with an estimated productivity loss of 11.4 percentage points due to being credit constrained. While this supports the notion of credit access being important for productivity of land use in the villages with potential, we find no evidence of an appreciable effect of being credit constrained in less productive areas where loans were largely used for purposes of non-farm activities and consumption smoothing, implying that allocation of credit might be governed by non-economic criteria. Before putting in place efforts to improve credit access, careful analysis of reasons for credit rationing and the impact of alternative policies will be appropriate.

The paper is organized as follows. Section
|
|---|---|---|---|---|---|
|**_w_**<sup>**_U_**</sup>|**0.8739**|0.7639|0.7410|0.8988|0.8983|
|**_w_**<sup>**_R_**</sup>|0.7034|**0.9295**|0.7362|0.9499|0.9189|
|**_Y_**|0.5874|0.5421|**0.9212**|0.6602|0.6164|
|**_Y pw_**|0.4083|0.6684|0.4613|**0.7660**|0.6320|
|**_Y pc_**|0.5409|0.7237|0.5558|0.8351|**0.7776**|

## **Notes:**

The reported correlation co-efficients are Pearson product moment correlation coefficients. All correlations are based on the full sample of 331 prefectures. _Y pw_ denotes income per worker; _Y pc_ income per capita - the observed values of these variables correspond to GDP per worker and GDP per capita respectively. Definitions of <u>all other variables are provided in Appendix A.1.</u> Figure B1, meanwhile, compares the simulated distribution of urban-rural wage ratios associated with the "after" equilibrium solution,  _After_ , with the actual observed distribution for 2007. In general, the correspondence between the two distributions is good, although actual urban-rural wage ratios are more dispersed. This is a consequence of a slight tendency for the equilibrium solution to over-predict (under-predict) urban-rural wage ratios where they are low (high).

41
country groups, however. As shown in Figure 3, the proportional change was very similar for high-income and developing countries in the 1970s spike, albeit only half as large for wheat as for rice. In the more recent spike, the proportional change for high-income countries was somewhat smaller in the case of rice and very much smaller in the case of wheat than for developing countries.

Assuming that output was able to respond only to a limited degree in the first half of each spike, and that the national elasticities of demand (including stock demand) are similar across countries for each product, we set the γi’s to zero and use equation (2) to estimate the contribution to international price changes of priceinsulating behavior resulting from national price-insulating policy behavior is the (negative of the) consumption-weighted global average change in the national Ti’s. For rice the cumulative decline shown in the world row of Table 1 was 46 percent between 2005 and 2008, which is in the same order of magnitude as the decline between 1972 and 1974 of 58 percent. For wheat, the globally-weighted _T_<sup>ˆ</sup> was -28 percent over the 2005-08 period, compared with -30 percent in 1972-74.

According to World Bank price data, the world price of rice increased by 127 percent between 2005 and 2008, and the price of wheat increased by 114 percent. By taking the interactions between the proportional changes in trade policy and other factors into account, we can estimate the magnitude of the non-trade shocks. Comparing these with the estimated trade shocks suggest that in 2005-08 more than 45 percent of the explained change in the international price of rice is due to the changes in border restrictions that countries used in an attempt to insulate themselves from the initial increases in price. For wheat, the corresponding estimate was 29 percent. In 2008 alone, the change in protection on rice explains almost half of the 90 percent increase in rice prices observed for that year.

One important and encouraging difference between the 2008 price
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