rafmacalaba/datause-encoder-data huggingface dataset:
a given rainfall a higher soil moisture capacity means that a crop can withstand a longer dry spell. In addition, where soil moisture capacity is very high, a full moisture reservoir in the soil may be able to support several months of a crop cycle without additional rainfall or irrigation. For given annual rainfall, payoffs to irrigation investments are, therefore, more limited where soil moisture capacity is higher. Icdgatian pgential is defined as the percentage of a district's area inside any type of irrigadon command area, that is, the sum of proposed command area, command area under construction and already existing command area. This variable has been measured using the Irrigation Atlas of India. Planned command areas are a good indicator of the remaining potential for canal irrigation in India as they reflect long-range plans. Any area not yet included in these plans has virtually no potential. Flood pQtential is the proportion of the district liable to flooding. The number of cool months are those with mean temperatures less than 18 degrees centigrade. Wheat does not grow in areas without cool months and this variable proxies the ability to grow wheat.

# 4. ECQNOMETRIC REULTS

Table 4 shows the first stage credit equation that is used to predict credit supply in the second stage equations. It was estimated with the random effects technique.

The table shows that better agroclimate, as measured by a long rainy season, high irrigation potential and high soil moisture capacity, leads to higher credit use. Conversely, lending is lower in areas with high flood risk. As expected, areas with better 21
potential spatial error, we apply a Markov Chain Monte Carlo simulation method using the Gibbs sampler.<sup>6</sup> Although our approach uses relatively sparse matrices, our ability to correct for spatial autocorrelation is limited by computer memory and operating system capacity<sup>7</sup> Our application (Windows 32-bit operating system, MatLab software) cannot handle matrices larger than 16,000 square elements – far smaller than the 993,401 observation cells in our estimation dataset. We adjust to this limit by switching from 2.5 arc-minutes to 50 arc-minutes (93 km at the equator). We classify the data in 400 groups, with the weights matrix counting points within 150-kilometers of a given point as neighbors.<sup>8</sup> For each of the 400 groups, we assume a burn-in of 500 iterations, and save the simulated parameters from the following 500 iterations. This generates 200,000 parameter sets (for the 400 groups), which we use to compute average coefficients and their standard errors. We present these estimates in column 3 of Table 7.

Since reduced _t_ -statistics are generally expected in smaller datasets, we also employ a third variant to aid interpretation of our results. We estimate a standard probit model for the same reduced data set of 400 groups, using Markov Chain Monte Carlo (MCMC) simulation methods, without controlling for spatial autocorrelation. Computed average coefficients and standard errors are presented in Table 7, column 4.

> 6 The Gibbs Sampler allows us to simulate a joint distribution of parameters by simulating each set of parameters, one set at a time, conditional on all other parameters. The special advantage of the Gibbs Sampler for use in spatial probit stems from the fact that it also simulates the continuous latent variable (Albert and Chib, 1993). See Thomas (2007) or LeSage (1999) for detailed discussion of the methodology.

> 7  The parametric approach to spatial probit requires a weights matrix to quantify the relationship of each observation to the rest. Since only observations in geographic proximity generally have non-zero influence, it is reasonable to use sparse matrices for computation.

> 8  In practice, this approach specifies horizontal, vertical, and diagonal points within 150 km as neighbors (assuming the points exist on land (neither in the ocean nor an inland body of water))..

9
<!-- Start of picture text --> Output ( XP )<br>σ p<br>Aggregate intermediate demand ( ND ) Value added ( VA )<br>σ=0 σ v<br>Intermediate demand ( XAp ) Capital-labor ( KL ) Aggregate land ( TT )<br>σ m , σ w σ kl σ t<br>Intermediate demand by region Unskilled Capital-skilled Land demand<br>of origin ( XD & XM ) labor ( UL ) labor ( KSK ) by type ( T d )<br>σ u σ ks<br>Unskilled labor Skilled Aggregate<br>demand by type ( L d ) labor ( SKL ) capital ( KT )<br>σ s σ k<br>Skilled labor Capital demand<br>demand by type ( L d ) by type ( K d )<br><!-- End of picture text --> Figure 1: Nested Structure of Production _Source:_ van der Mensbrugghe (2005c).

Produced goods are transformed into consumed goods by means of a transition matrix, which has a simple one-to-one mapping due to data limitations. The household demand structure is determined according to the linear expenditure system (LES), in which consumers maximize StoneGeary utility functions subject to the disposable income constraint.<sup>17</sup> Other final demand agents— government and investment—use the CES expenditure system. Import demand is modeled using the nested Armington specification, in which consumer products are differentiated by region of origin and combined using CES functions.<sup>18</sup> On the supply side, producers allocate output to domestic and export markets the model according to a constant elasticity of transformation (CET) specification. World prices are exogenous, which means that any increase in import demand or export supply can be satisfied without affecting global prices (small country assumption).<sup>19</sup> The aggregate stock of capital is allocated across sectors in a CET fashion, with capital being

> 17See Deaton and Muellbauer (1980, Chapter 3) for a detailed discussion of the LES demand system, and Stone (1954) for the Stone-Geary utility function.

> 18See Armington (1969).

> 19The export elasticity in the sugar sector is finite to reflect the US sugar quota.

14
so on ⎯ it remains rare for evaluations to establish that an intervention actually funded what it intended and was supplemental to local spending. It is our conclusion from this analysis that this should be ascertained as a first step in evaluating the impacts of development projects.

# **References**

Alberto, Abadie and Guido Imbens (2006a), “Large Sample Properties of Matching Estimators for Average Treatment Effects,” _Econometrica_ , 74(1), 235-267.

- Alberto, Abadie and Guido Imbens (2006b), “On the Failure of the Bootstrap for Matching Estimators,” mimeo, University of California, Berkeley.

- Feyzioglu, Tarhan, Vinaya Swaroop and Min Zhu (1998), “A Panel Data Analysis of the Fungibility of Foreign Aid,” _World Bank Economic Review_ , 12(1), 29-58.

- Heckman, James and Petra Todd (1995), “Adapting Propensity Score Matching and Selection Models to Choice-Based Samples,” Working Paper, Department of Economics, University of Chicago.

- Hines, James and Richard Thaler (1995), “The Flypaper Effect,” _Journal of Economic Perspectives_ , 9, 217-26

- Hirano, Keisuke and Guido Imbens, 2002, “Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization,” _Health Services and Outcomes Research Methodology_ , 2, 259-278.

- Hirano, Keisuke, Guido Imbens and Geert Ridder (2003), “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” _Econometrica_ , 71, 1161-1189.

Imbens, Guido, 2004, “Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review,” _Review of Economics and Statistics_ , 86(1), 4-29.

Jacoby, Hanan (2002), “Is There an Intrahousehold ‘Flypaper Effect’? Evidence from a School Feeding Programme,” _The Economic Journal_ , 112, 196-221.

Khilji, Nasir and Ernest Zampelli (1994), “The Fungibility of U.S. Military and Nonmilitary Assistance and the Impacts on Expenditures of Major Aid Recipients,” _Journal of Development Economics_ , 43, 345-362.

Pack, Howard and Janet Rothenberg Pack (1990), “Is Foreign Aid Fungible? The Case of Indonesia,” _The Economic Journal_ , 100, 188-194.

21
where Y, denotes net revenue from farm crop and livestock production, i.e. the difference between total output and variable cash costs including spending on hired labor, animals, machinery rental and fixed factors, but excluding family labor. Q, is a vector of inputs and fixed factors (i.e. land cultivated and the land and labor endowments) and **Z,** is a vector of household characteristics including education and whether the household has a non-farm wage job. Finally, **_I,._** denotes the predicted value of investment j as derived from a linear probability model, as is commonly applied in the literature.

# **3. Data and descriptive statistics**

Our empirical estimation is based on a nationally representative survey of 8,540 farm households conducted in 2001 by the Ethiopian Economic Association's Economic Policy Research Institute (EEA/EEPRI). The sample was chosen to represent the country's main agro-ecological regions and to account for differences in population density, farming systems, and access to markets. We use them to provide descriptive statistics, for the country as a whole and its major regions, Tigray, Amhara, Oromia, SNNPR, and the mainly pastoral areas of Benshangul-Gumz, Afar and Somali which we classify under "others". Descriptive evidence demonstrates the importance of tenure security but also suggest that changes in these arrangements could have an important impact on economic outcomes.

# **3.1 Household characteristics**

General household characteristics point towards a high level of poverty, dependence on agricultural income, and considerable rationing in labor-markets. Table 1 illustrates that the average household is composed of 5.11 persons (in adult equivalents), primarily male-headed (only 8% are headed by females), and has a head who is 45.3 years old. Illiteracy among the older generation is pervasive; in fact only 28% of the household heads in the sample are literate although the much higher level of formal education among the younger generation testifies to the rapid recent expansion of educational opportumities. In fact, the most educated person in a household has, on average, 5.18 years of schooling, varying between 3.9 years in pastoral areas and 5.78 in Oromia.

Income data, although less than ideally measured, suggest that 54% of the households have income below the national poverty line, with considerable regional variation; 6 26% of the households reported to have received food aid during the survey year and 12% got remittances or help from others. The fact that, with 75% of the total (out of which 72% cam from crop farming), agriculture made a very important contribution to household income, highlights not only the potentially large impact of increasing

> **6** It is admitable that this highest share of populaton below poverty line in Southem regions may be due to underestimation of the value of _Enset_ production which is a dominant crop produced in the region mainly for home consumption

II
8 pr is the ith element of the vector p(sr), Note that COL will now be increasing (decreasing) in yr if _f(pi/pr)Pi_ is greater than (less than) unity.

The task of the empirical work to follow is to estimate the demand functions in the budget share form (6) under the (testable) demand theory restrictions, and use the estimated parameters to retrieve the underlying cost function, so as to estimate the true cost-of-living index (4).

# 1II. Hethods of Demand Analysi

The modelling of consumer demand .cr housing raises two problemst i) housing is a heterogeneous good and so a single scalar measure of the quantity consumed is not readily available, and (thus) ii) observed market "priceso are more properly interpreted as expenditures on a bundle of (potentially) diverse housing attributes.

The approach adopted here for modelling housing demands treats housing as a composite good, the price of which is measured by a location specific hedonic price index.<sup>12</sup> The price index facing each househo'ld for its housing is thus the predicted cost for a fixed reference bundle of housing attributes (generally mean points), where the prediction is based on the estimated implicit prices of housing attributes for the district in which that household resides.

An advantage of the 1981 SUSENAS for this purpose is that it included quite detailed questions on dwelling characteristics in both urban and rural areas. For each of the 20 randomly selected districts (Kabupatens and Kota) in our SUSENAS sub-sample, implicit prices are estimated for the following dwelling and location attributes:
**Inclusive Refugee-Hosting in Uganda Improves Local Development and Prevents Public Backlash**<sup>∗†</sup>

**Keywords** : refugees, forced migration, public goods provision, development, Uganda, GIS **JEL Codes** : D74, F22, H51, H52, O15

> ∗This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts. The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership”. The program is funded by UK aid from the United Kingdom’s Foreign, Commonwealth and Development Office (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. This work does not necessarily reflect the views of FCDO, the WBG or UNHCR.

> †We are incredibly grateful to the UNHCR Uganda and World Bank Uganda staff for data and insights on refugee-hosting in Uganda; Herbert Musoke for help with obtaining education data; Paul Heysch de La Borde, Ia Mantecon Garcia, Isabella Preite, and Bayley Tuch for excellent research assistance. We thank Christopher Blair, Benjamin Fifield, Vasiliki Fouka, Margarita Puerto Gomez, John Ilukor, Apoorva Lal, Benjamin Laughlin, Carl Müller-Crepon, Melina R. Platas, Hannah Postel, Benjamin Reese, Audrey Sacks, Jeremy Springman, Stephen Joseph Winkler, Alexander Yarkin, Stephanie Zonszein, and participants from the 2021 PDRI Barriers and Bridges to Immigrants’ Integration Conference for helpful comments. Zhou acknowledges funding from SSHRC and CIFAR.

‡Assistant Professor, Department of Political Science, University of British Columbia, Harvard Academy Scholar, and CIFAR Azrieli Global Scholar, yangyang.zhou@ubc.ca, www.yangyangzhou.com

> §Professor, Department of Political Science, University of Pennsylvania, ggros@upenn.edu, www.web.sas.upenn.edu/ggros

> ¶Senior Data Research Coordinator, Penn Development Research Initiative, University of Pennsylvania, shuningg@sas.upenn.edu, www.pdri.upenn.edu/bio/shuning-ge/
# **Suggested Readings**

For a general treatment of the economics of both renewable and non-renewable natural resources see: Hartwick, John M. and Olewiler, Nancy D., _The Economics of Natural Resource Use_ (New York: Harper and Row, 1986).

A survey of the literature on taxes and other instruments for obtaining revenues from, and regulating, various types of natural resources (fisheries, forestry, mining, oil and gas, and hydro-electricity), see: Heaps, Terry and Helliwell, John F., "The Taxation of Natural Resources," in Alan J. Auerbach and Martin Feldstein (eOs.), _Handbook of Public Economics, Volume_ I (Amsterdam: North-Holland, 1985), 421-72.

A general outline of the special problems of taxing natural resources in developing countries may be fcund in: Gillis, Malcolm, "Evolution of Natural Resource Taxation in Developing Countries," Natural _Resources Journal_ **22,** July 1982 620-48.

A survey of the theory and calculation of marginal effective tax rates, including alternative approaches and applications, may be found in: Boadway, Robin W., "The Theory and Measurement of Effertive Tax Rates," in J.M. Mintz amd D.D. Purvis (eds), _The Impact of Taxation on Business Activity_ (Kingston, Canada: John Deutsch Institute), 60-98.

An application of the role of rent taxation and the concept of effective tax rates to non-renewable resources is developed in: Boadway, Robin W., Bruce. N., McKenzie. Kenneth. J., and Mintz, J.M., "Marginal Effective Tax Rates on Capital in the Canadian Mining Industry," Canadian _Jouyrnal of Economics_ **20.** February. 1987, 1-17.

Some general issues of taxation in developing countries arc surveyed in: Newbery, D.M.G. and Stern, N.H., _T'he Theory of Taxation for Developing Countries_ (Washington: The World Bank), 1987.

For a recent treatment of the effects of taxes on investment in developing countries see: Shah. Anwar (ed.), _Fiscal Incentives for Investment in Developing Countries_ (Washington: The World Bank). May 1992.

57
**Figure 4.** Change in distribution of private school enrollment across income quintiles

<!-- Start of picture text --> 6-10 year olds 11-15 year olds 16-18 year olds<br>Income quintiles (poorest to richest) for each country Income quintiles (poorest to richest) for each country Income quintiles (poorest to richest) for each country<br>Level, 2005, Bangladesh Change, 2005-2010, Bangladesh Level, 2004/05, India Change, 2004/05-2009/10, India<br>Level, 2003/04, Nepal Change, 2003/04-2009/10, Nepal Level, 2006/07, Pakistan Change, 2006/07-2010/11, Pakistan<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>Change in distribution of private enrollment by income quintile (percent)<br>0<br>Bangladesh India Nepal Pakistan Bangladesh India Nepal Pakistan Bangladesh India Nepal Pakistan<br><!-- End of picture text -->

# _Source:_ Authors, based on household survey data.

_Notes:_ For each country, the five arrow origin points add up to 100% of private school enrollment in the earlier period, and the five arrow head points add up to 100% of private sector enrollment in the later period.

12
_Table 1: Regression results_

||||**_Employed_**<br>||||**_Wo_**<br>|**_rk stoppage r_**<br>|**_ate_**<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||_Raw mean_<br>_nationals_<br>_[SE]_|_Raw mean_<br>_refugees_<br>_[SE]_|_Uncond._<br>_difference_<br>_[p-value]_|_Conditional_<br>_difference_<br>_[p-value]_|_Exp. mean_<br>_refugees_<br>_[CI]_|_Raw mean_<br>_nationals_<br>_[SE]_|_Raw mean_<br>_refugees_<br>_[SE]_|_Uncond._<br>_difference_<br>_[p-value]_|_Conditional_<br>_difference_<br>_[p-value]_|_Exp. mean_<br>_refugees_<br>_[CI]_|
||(1)|(2)|(2) _–_ (1)|(3)|(1)+(3)|(3)|(4)|(4) _–_ (3)|(5)|(3)+(5)|
|February 2020|0,626|0,200|-0,426***|-0,308***|0,318||||||
||[0.018]|[0.026]|[0.000]|[0.000]|[0.21; 0.42]||||||
|May-Jun 2020|0,395|0,092|-0,303***|-0,188***|0,207|0,530|0,675|0,145***|0,083|0,613|
||[0.017]|[0.008]|[0.000]|[0.000]|[0.12; 0.30]|[0.023]|[0.027]|[0.000]|[0.163]|[0.50; 0.73]|
|Jul-Sep 2020|0,456|0,108|-0,347***|-0,239***|0,217|0,457|0,550|0,092***|0,045|0,503|
||[0.020]|[0.006]|[0.000]|[0.000]|[0.12; 0.31]|[0.024]|[0.024]|[0.007]|[0.433]|[0.39; 0.62]|
|Oct-Nov 2020|0,515|0,095|-0,420***|-0,317***|0,198|0,364|0,657|0,293***|0,240***|0,604|
||[0.022]|[0.006]|[0.000]|[0.000]|[0.09; 0.30]|[0.024]|[0.022]|[0.000]|[0.000]|[0.48; 0.73]|
|Jan-Mar 2021|0,579|0,129|-0,450***|-0,339***|0,240|0,243|0,564|0,321***|0,245***|0,488|
||[0.021]|[0.007]|[0.000]|[0.000]|[0.15; 0.33]|[0.021]|[0.023]|[0.000]|[0.000]|[0.37; 0.61]|
|Apr-Jun 2021|0,695|0,280|-0,414***|-0,297***|0,398|0,228|0,482|0,254***|0,196***|0,424|
||[0.017]|[0.009]|[0.000]|[0.000]|[0.31; 0.49]|[0.020]|[0.023]|[0.000]|[0.001]|[0.31; 0.54]|
|Demographics|No|No|No|Yes|Yes|No|No|No|Yes|Yes|
|County FE|No|No|No|Yes|Yes|No|No|No|Yes|Yes|
|N(pooled waves)|33,826|21,703|55,529|52050|52,050|17,803|3161|20,964|20,482|20,482|

Note: All results are weighted using appropriate sampling weights. Column 4 for each variable conditions on demographic controls and county fixed effects. For employment, the work stoppage rate and hours worked, demographic controls include age, age squared, gender, educational attainment and whether the individual was the respondent. For income, demographic controls include age, gender and educational attainment of the household head as well as a dummy indicating poor flooring material.

15
37

**_Difference-in-difference approach._** Finally, we investigate whether our results are robust to the use of the difference-in-difference methodology as suggested by Rajan and Zingales (1998). So far, we use a multilevel model to capture both the between and within country effects of reforms on firms’ productivity. However, some might be concerned as to whether our findings are driven by the methodology used. To allay such concerns, we re-estimate the model using the difference-in-difference approach. The empirical model could be specified as follows:

������ � ��� ���� �� ����������� ��������� ∗��������,����,���� � ����� ����� (9) �� and �� are full sets of industry and country dummies which helps to control for unobserved heterogeneity across industries and across countries; �������� ����������� and ���� are the conditional factors and firms’ individual characteristics, respectively, as described previously. Equation (9) is estimated for each reform with a simple OLS procedure. Results are reported in Table 8. All interaction terms between reforms and exposure variables are statistically significant. As previously, firms with financial access benefit less from financial reforms while the latter benefit more from fiscal reforms; productivity gains from fiscal reforms are lower for firms facing significant distortions.

|**Ta**|**ble 8. Impacts of specifi**<br>|**c reforms on productivit**<br>|**y– Alternative methodo**<br>|**logy**<br>|
|---|---|---|---|---|
||Financial sector<br>reforms|Financial sector<br>reforms|Fiscal reforms|Fiscal sector reforms|
||Conditional factor:<br>Financial access|Conditional factor:<br>Small firms|Conditional factor:<br>Distortions|Conditional factor:<br>Financial access|
|Reforms*<br>Conditional factor|-0.103**<br>(0.0452)|0.0358<br>(0.0216)|-0.0450***<br>(0.0160)|0.831**<br>(0.0236)|
|Conditional factor|0.001<br>(0.0147)|-0.292***<br>(0.0347)|0.0656<br>(0.0147)|0.009<br>(0.0142)|
|Observations|11,807|11,807|11,807|11,807|
|R-squared|0.221|0.220|0.221|0.221|
|Firms Controls|YES|YES|YES|YES|
|Country FE|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|

The table presents standardized coefficients of the effects of reforms on firm-level labor productivity growth using an OLS model. The specifications include firm’s individual characteristics, sector and year fixed effects. Estimates include country fixed effects. All estimates use the weights and are standardized so that can be compared across structural reforms. Robust standard errors clustered at the country level are in parentheses. ***, **, * denote significance at the 1, 5, and 10 percent <u>level.</u>