rafmacalaba/data-use-mentions-extended huggingface dataset:
three years is non-zero, with the maximum of these two amounts determining the level of advance payments.

Firms selling manufactured goods and certain service businesses, such as hotels and restaurants, are also liable for the monthly sales tax. The sales tax is eectively a VAT, with a recrediting mechanism for taxed inputs, but has a relatively narrow base that excludes most services (see Brockmeyer and Hernandez (2016) for details). Approximately 20% of income tax-liable rms are liable for the sales tax.

Instead of paying the regular income tax (and, where applicable, the sales tax), retailers in certain sectors and below certain size thresholds (i.e., those with annual purchases below 150 base salaries, net assets below 350 base salaries, or with less than six employees) can opt into a simplied regime. Under the simplied regime, a tax is levied on inputs at sector-specic rates that vary from 3% to 9.8%. Firms in this regime declare on a quarterly basis, and can claim credit for withholding by state institutions for the income tax, but not for withholding by credit card institutions for the sales tax. During the period we study, approximately 30,000 rms led their taxes under the simplied regime, while 360,000 rms led their income taxes under the regular regime. Firms can opt into or out of the simplied regime by submitting a D140 modication form, or deregister completely by submitting a D141 deregistration form.

Figure 1 shows the percentage of tax-registered rms that did not le a tax declaration in 2014 by rm and tax type. For the income tax, this share was 19% of corporations and 25% of the self-employed.<sup>13</sup> It is lower but still substantial for the sales tax19% for corporations and 14% for the self-employed. This proportion has been stable over the last ve years, and is based on a tax register that is updated on a regular
20 We measure financial development with the ratio of private credit to GDP: again we prefer to work with a dummy variable (FD) that takes on a value equal to 1 if this ratio is larger than its yearly median value. When we add in our model the interactions between the financial development dummy variable and international reserves and external debt over GDP, the results on workers’ remittances are by large confirmed (Table 7, columns [1] and [3]). As to exchange arrangements, we use the measure constructed by Reinhart and Rogoff (2002) which is meant to be alternative to the standard classification published in the IMF’s _Annual Report on Exchange Rate Arrangements and Exchange Restrictions_ . More precisely, we start from Reinhart and Rogoff’ coarser classification that group countries according to 5 categories from “no separate legal tender” to “freely falling” and we further group them into 3 categories<sup>27</sup> . As shown in columns [2] and [4] of Table 7, the introduction of the interaction between the exchange rate arrangement dummy (ARR) on one side and international reserves and external debt on the other do not affect the results on remittances.

Table 8 proposes three other robustness exercises. The first one relates to country size: here we build a dummy variable (SMALL) that is equal to 1 for countries with less than 500,000 resident as of 2002. The second one splits countries according to their literacy rate, again below and above the median (LIT). The third one aims at separately identifying the effects of civil wars on financial instability. For this, we use the Armed Conflict Dataset which is a comprehensive new database of civil conflicts developed by the International Peace Research Institute of Oslo (Norway) and the University of Uppsala (Sweden)<sup>28</sup> . The database, which focuses only on politically motivated violence, allows us to define a dummy variable (WAR) which is equal to
br>(prelim.)|Based on 1987 OHS|1980-85,<br>U.N. 1984<br>assessment;<br>1985-90,<br>Bank projection|
|Tunisia|Mar 84|6966 F|Bank projection from census|Bank est.|Based on U.N. 1984<br>assessment|
|Turkey|Oct 85|50664 F|U.N. 1988 revision (prelim.)|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Turks and Caico<br>Islands|s Nay 80|7 F|UNPVSR<br>1/88 assessment|--|--|
|Tuvalu|may 79|7 F|UNP;3R 4/87 (official<br>est.)|Bank est.|Bankest.|
|Uganda|Jan 80|12637 F|Official<br>est.|U.N. 1988 revision (prelim.)|Bankest.|
|United<br>Arab<br>Emirates|Dec 80|<br>1043 F|U.N. 1988 revision<br>(prelim.)|Based on U.N. 1988 revision<br>(prelim.)|<br>Based on U.N. 1984<br>assessment|
|United Kingdom|Apr 81|55678 F|Eurostat1987|Eurostat 1987|Eurostat 1987|
|United States<br>of America|Apr 80|226546 F|UNPVSR<br>1/87 (official<br>est.)|Based on 1981-84TFR, USBOC<br>1985,<br>and 1985 CBR, UNPVSR<br>4/87|Based on UNPVSR 4/87|
|Uru%ay|Oct 85|<br>2931 F|Bank projection<br>from<br>official<br>1986 est.|1980-85,<
Table 4: Estimation of Rural and Urban Log-Normal Parameters

||Ru|20<br>ral|00<br>Urb|an|Ru|20<br>ral|07<br>Ur|ban|
|---|---|---|---|---|---|---|---|---|
||Dir.|Emp.|Dir.|Emp.|Dir.|Emp.|Dir.|Emp.|
||7.265|7.265|7.880|7.827|7.518|7.521|8.001|7.907|
|meanlog|(0.007)|(0.007)|(0.008)|(0.010)|(0.114)|(0.011)|(0.010)|(0.015)|
||0.595|0.604|0.696|0.753|0.615|0.608|0.716|0.928|
|sdlog|(0.006)|(0.006)|(0.007)|(0.006)|(0.009)|(0.008)|(0.009)|(0.013)|

_Source_ : Authors’ calculations based on Moroccan HBS.

Applying eq. [10], we obtain the figures shown in table [5], which reports the entire time series of inequality indicators between 2000 and 2007; 2000 and 2007 are calculated based on the sample data while the rest is imputed.

During the period considered, inequality slightly increases in urban areas while remaining relatively stable in rural ones. Growth in the early 2000s is less volatile than in the previous decade and posts substantially higher annual rates (Clementi et al., 2022). This growth
tribution of transit migrants can now be expressed as where _k_ = _U_ means the destination _k_ is any country other than USA.

Our next dataset, the 2010 DIOC provides data on the number of people who migrated within the last 10 years to OECD destination countries. This allows us to calculate the number of natives who moved from _j_ to _k_ within the last 10 years if _k_ is an OECD country. We denote this matrix _M_<sup>_s,j,j,k_</sup> . Next, we define _M_<sup>_s,j,j,N_</sup> as the number of people who move to non-OECD destinations (labelled by _N_ collectively) and _M_<sup>_s,j,j,j_</sup> as the number of people who stayed in their birth country _j_ and did not migrate to any country. Both of these are 40 _×_ 1 vectors for each skill group _s_ . In summary, natives (who were born in _j_ and living in _j_ ) are divided into the following groups: Staying at home, _M_<sup>_s,j,j,j_</sup> , moving to a specific OECD destination _k_ , _M_<sup>_s,j,j,k_</sup> and moving to any non-OECD destination, _M_<sup>_s,j,j,N_</sup> .

The log-likelihood contribution of direct migrants is equal to where _k ∈/ OECD, k_ = _j_ means _k_ is different from birthplace and it is not an OECD country. _k ∈ OECD, k_ = _j_ means destination _k_ is different from birthplace _j_ and is an OECD country. We merge all non-OECD destinations when we calculate the likelihood contribution since we do not have individual flow data for those. Finally the log-likelihood contribution of stayers is equal to Based on log-likelihood contributions from equations (10), (11) and (12), the parameter vector 19
|Training for innovation||0.343***||0.348***||0.337**||0.292*|
|---|---|---|---|---|---|---|---|---|
|||(0.106)||(0.109)||(0.155)||(0.157)|
|Equipment for innovation||0.884***||0.911***||1.055***||1.063***|
|||(0.092)||(0.096)||(0.134)||(0.134)|
|Purchase of knowledge||0.127||0.178||‐0.300||‐0.230|
|||(0.168)||(0.175)||(0.283)||(0.286)|
|Constant|‐0.621*|‐1.014***|‐0.633*|‐1.337***|‐0.284|‐0.878***|‐0.409|‐1.195***|
||(0.332)|(0.213)|(0.358)|(0.224)|(0.522)|(0.313)|(0.513)|(0.315)|
|Observations|8,550|8,550|7,854|7,854|1,868|1,868|1,747|1,747|

Standard errors in parentheses; *** p<0.01, ** p
0.012|0.052|0.000|0.023|0.032|No|
|% HHs in extreme poverty|0.076|<0.001|0.067|0.085|0.002|No|

# _A high resolution map of cholera risk in Lusaka_

Figure 3 shows the 100m X 100m predicted map of cholera risk across the study area, along with the prediction uncertainty per pixel. The geographical pattern of risk is heterogeneous and predicted annual incidence rates vary from zero to in excess of five cases per 1,000 people per annum. The greatest concentration of elevated risk lies in the western Wards of Kanyama and Harry Nkumbula, which both
rented from relatives, the duration of the contract and whether a written contract is present. Considering the 2002-2012 period, we note an increase in formalization, with longer, written contracts.

In Figure 1 we display maps of selected areas, East, North and West, and South, in India, with district boundaries following the 2001 Census. Panel A displays the maps representing the situation in 2002. Panel B maps represent the situation in 2012. The blue colour represents high levels of fixed rent, while the yellow represents low levels (with the red an in between stage). Note that most of the populous states in the south of India, such as Tamil Nadu, Andhra Pradesh, Telangana and Kerala, have witnessed a substantial increase in fixed rent arrangements.

# **Migration**

We do not have any direct measure of landlord migrants, as there is no information on land ownership in the Census. We proxy for landlord migrants using high-skilled migrants, a group defined by a cut-off of at least 10 years of education.<sup>15</sup> This assumption is supported, at least to some degree, by the data. Using the NSS Land and Livestock Survey round 70 (conducted in 2013), we note a significant, positive correlation between acreage of land owned and the education (in years of education) of the household head (a correlation coefficient of 10%). In Appendix Figure A3 we present the histogram of the education level of the head of the household, by type of landholding. On the left-hand side are the landless, defined as possessing less than 2 hectares, and on the right-hand side, the landed, defined as owning more than 2 hectares. While there is an overlap between the educational distributions of the landless and the landed, we note that very few of the landless have more than ten years of education. We notice a similar pattern using the national ARIS-REDS panel survey of rural households from 2005. Using the sample of all individuals who engaged
# 1. Introduction

Recently, Uganda has made impressive progress in terms of reducing poverty. The poverty headcount rate under the national poverty line declined from 31.1 in 2005/06 percent to 19.7 2012/13.<sup>2</sup> The majority of this poverty reduction can be attributed to the high growth in household agricultural income, at approximately 6 percent per year (World Bank, 2016). Key policies advanced by the government, mainly the stabilization of the conflict in the Northern region and infrastructure investments, contributed to agricultural income growth. There was also a component of good fortune: favorable rainfall and positive trends in commodity prices. However, only a small proportion of growth in household agricultural income can be explained by changes in the nature of production or an increase in the adoption of better technology in agricultural production (Barrett et al. 2017, World Bank 2016). Only around 40 percent of agricultural households _have ever_ used improved agricultural inputs and there has been little progress in adoption over the 2005/16 to 2012/13 period. The low use of improved inputs by farmers remains a constraint for agricultural productivity and, therefore, for sustained poverty reduction in Uganda.

The objective of this paper is twofold: to explore the financial returns of using agricultural inputs and to examine what factors are constraining input use among Ugandan households. First, the returns to two key inputs ‐inorganic fertilizer and hybrid seeds‐ are examined to determine whether the inputs that are available to farmers in Uganda are profitable. The analysis explicitly examines net returns, not just increases in gross income. It takes into account the volatility of crop prices (Hill and Mejia‐Mantilla 2017) and the problem farmers face in securing quality inputs (Bold et al. 2017). For the second objective, we test if some of the key constraints to input use identified in previous studies are present in the Ugandan context using a relatively long panel data set. This allows us to account for long‐run
of additional eligibility conditions that were not included in our modeling due to a lack of available data (i.e., asset test, as explained below). These unmodeled conditions further restrict the number of households eligible to apply for the program, leading to differences in the number of beneficiaries between the administrative and survey data.

**To estimate the take-up rate, we exploit the difference between the simulated number of potentially eligible households, as estimated from the Survey on Income and Living Conditions (SILC) data, and the total number of beneficiaries, as reported in the administrative data.** In this process, we randomly assign beneficiaries among potential eligible households to match the administrative records. This approach helps us account for the complexities associated with eligibility criteria and take-up rates, allowing for a more comprehensive and nuanced assessment of the MSA and heating allowance programs.

# Microsimulation Techniques

## **Finally, we use microsimulation techniques to evaluate policy scenarios and identify the most significant**

**impact on poverty and inequality reduction.** The simulation of policy reforms involves modeling the impact of policy changes on poverty and inequality indicators by comparing pre-reform vs post-reform income distributions.  This analysis helps policymakers understand the effects of different policy packages on poverty and inequality and identify the most effective interventions to reduce them **.** This approach can help inform the design of more effective and equitable social policies and ensure that resources are targeted to those most need them.

**Changes in social assistance programs, such as the Monthly Social Allowance (MSA) scheme and heating allowance, can impact household and individual welfare through several transmission channels** . These channels can be categorized into three main components: eligibility, transfer size, and program take-up.

- _Eligibility:_ Changes in the eligibility criteria of social assistance programs can directly influence which households or individuals qualify for the benefits. If, for example, the reference value for the MSA is increased, a more significant portion of the population would meet the new
fundamentally—and national accounts data from the World Development Indicators in local currencies. Each time we consider GDP per capita and GDP. We prefer GDP per capita throughout the paper since it is closer to a welfare relevant metric but expect to obtain similar results with GDP.

– **Table 5** Robustness: different data sets for GDP and GDP per capita

||_GD_|_P per capita s_|_eries_|_G_|_DP series_||
|---|---|---|---|---|---|---|
||PWT7<br>_rgdpch_<br>(1)|PWT9<br>_rgdpna/pop_<br>(2)|WDI<br>_gdplcu/pop_<br>(3)|PWT7<br>_rgdpch × pop_<br>(4)|PWT9<br>_rgdpna_<br>(5)|WDI<br>_gdplcu_<br>(6)|
|||_Panel (a) Str_|_uctural breaks _|_estimated with s_|_ize = 0.10_||
|Executive Constraints ( <sup>�</sup><br>_INS_0)<br>|-0.209***<br>(0.064)|-0.255***<br>(0.077)|-0.170**<br>(0.069)|-0.181***<br>(0.046)|-0.163***<br>(0.045)|-0.161***<br>(0.051)|
|Fractionalization ( <sup>�</sup><br>_ELF_15)<br><br>|0.018***<br>(0.004)|0.020***<br>(0
Policy Research Working Paper 10869

# **Abstract**

This paper uses remotely sensed and farm-level data to assess the micro-level impacts of the war in Ukraine. Remotely sensed, high-resolution data on areas of war-induced agricultural field damage in different periods are combined with crop cover data for a 2019–23 panel of about 10,000 village councils. Estimates suggest that there were significant negative effects of field damage on crop area, with persistent, direct impacts, the size of which increased over time. However, the economic losses due to conflict-induced increased transport costs reduced profitability by more than 60 percent, far surpassing the losses from direct crop damage in conflict areas. The lack of diversification into less transport cost sensitive, higher value crops—even in areas far from the conflict zone—points to constraints to adaptation and diversification. By increasing the resilience of farmers in non-conflict areas, removing such constraints could accelerate post-conflict recovery and complement efforts toward reconstruction in directly affected areas.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org, dali1@worldbank.org, guido.lemoine@ec.europa.eu, Nataliia.kussul@gmail.com, and Andrii.shelestov@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank
from estimates of the capital stock per customer for an efficient firm plus information on operation and maintenance costs and efficiency levels (in terms of losses and employees) for the period 2010–15, derived from the IBNET data set. A total cost-reflective tariff that covers both operating expenditure (OPEX) and capital expenditure (CAPEX) (defined as CRT) and a costreflective tariff that covers only OPEX (defined as CRTopex) are estimated.<sup>5</sup>

# **2.2 Descriptive statistics**

Table 1 presents some descriptive statistics of the rates of households connected to piped water in each country analyzed; countries are listed in descending order by gross domestic product (GDP) per capita. These statistics are country-wide, and outline the areas covered by each household survey and not only those served by utility companies.<sup>6</sup> Column 1 shows the proportion of all households (regardless of whether they have access or not) that report using piped water as their main source of drinking water.<sup>7</sup> Column 2 shows the same figure for rural households, column 3 for urban households, and column 4 for poor households. Despite wide variations in coverage rates across countries, connection levels are consistently higher among urban households than both rural and poor ones.<sup>8</sup> The African countries in our sample with the lowest GDP per capita (Ethiopia, Mali, Niger, and Uganda; column 5) also have the lowest connection rate, at below 5 percent among rural and poor households. Bangladesh, a relatively poor country, presents a similar pattern. Nigeria’s connection rates are quite low given the country’s income per capita, but disparities among different population segments are smaller. The three Latin American countries have substantially higher connection rates, between 70 and 95 percent for the general population, and around 50 percent or over for rural and poor households. Vietnam falls in the middle, with a connection
# **Appendix A: Sources for the 1998 Corruption Perceptions Index**

|No|Source|Year|Who was Surveyed?|Subject Asked|Number<br>of Replies|Number of Countries|
|---|---|---|---|---|---|---|
|1|Political & Economic Risk<br>Consultancy|1997<br>1998|Expatriate Business<br>Executives|Extent of Corruption in a way that<br>detracts from the business environment<br>for foreign companies|280|12 Asian countries|
|2|Gallup International|1997|General Public|Cases of corruption for the following<br>group of people: politicians, public<br>officials, policeman, and judges|over<br>34000|44 mostly developed<br>countries|
|3|Institute for Management<br>Development<br>(World Competitiveness<br>Yearbook)|1996<br>1997<br>1998|Business Executives<br>in Top and Middle<br>Management|Improper practices (such as bribing or<br>corruption) in the public sphere|3102<br>2515<br>4314|46 mostly developed<br>countries|
|||1996||Irregular, additional payments<br>connected with imort and exort|1537|40 developed and<br>developing countries|
||World Economic Forum (Global|||p p<br>||56 developed and|
|4|<br>Competitiveness Survey)|1997|Business Executives|permits, business licenses, exchange<br>controls tax assessments police|2778|<br>developing countries|
|||1998||, ,<br>protection, or loan application|3500|68 developed and<br>developing countries|
|5|Political Risk Services<br>(International Country Risk
is extracted from Lesotho’s 2017/18 Continuous Multipurpose Household Survey and listed in Annex 6.

The availability of the three common modules containing a group of consecutive questions in household questionnaires is summarized by Table 3. The details of the subcategories of transport expenditure vary dramatically by country, which can be found in Annex 1 and Annex 2.

**Table 3. Availability of three common sections in household questionnaires**

|**Country**|**Expenditure**<br>(recallperiods if available)|**Durable goods**|**Access to facilities**|
|---|---|---|---|
|Albania|1 month|X|X|
|Burkina Faso|7 days,3 months|X|X|
|Cambodia|1 month, 3 months,<br>6 months,1year|X|X|
|Ethiopia|7 days|X|X|
|Iraq|1 month,1year|X|X|
|Malawi|7 days,1 month|X|X|
|Mali|Not available|X||
|Nepal|1 month,1year|X|X|
|Niger|7 days, 1 month,<br>1year|X|X|
|Nigeria|7 days,1 month|X|X|
|Tanzania|7 days,1 month|X|X|
|Uganda|1 month,1year|X|X|
|Afghanistan|1 month,1year|X|X|
|Armenia|1year||X|
|Benin|3 months,6 months|X||
|Bhutan|1 month,1year|X|X|
|Burundi|3 months,1year|X|X|
|Cameroon|3 months,6 months|X|X|
|Colombia|Not available|X||
|Gambia|1 month,1year|X|X|
|Ghana|1
c born in town j (Nc,j) from the 1991 population census with that obtained from the individual death data. For each death, we observe the date of the event and a variety of individual characteristics including town and year of birth. We recover the total number of deaths occurred over the relevant period in cell

> 13Proxying the place of residence at a young age with the place of birth is fairly customary, for example in the large literature started by Angrist and Krueger (1991) exploring the effects of changes in US compulsory schooling state laws. For the sake of comparability we should have performed also the analysis on 1981 data on the basis of town of birth. However, the 1981 census provides information on the place of birth only at the larger province level. Thus by using the place of birth instead of residence we would have included in the sample also people likely to live in towns more strongly exposed to the quake and to other compensatory interventions, invalidating the spirit of the research design.