rafmacalaba/data-use-mentions-extended huggingface dataset:
use duration data to test proposition 2, but we can interpret any tenure security effect as arising largely from landlord commitment problems.

# 4.3 Main results: The tenure security effect

Among the 563 tenanted plots in our sample, the median number of years that the same tenant has cultivated the plot is 7; 8 years for share-tenants and 4 years for fi xedrent tenants. Results reported in rows (5) and (6) of Table 4 strongly support a tenure security effect. The negative impact of leasing on FYM use declines signi fi cantly with tenure on the plot, whether leasing itself is treated as exogenous (p-value = 0.0015) or as

> 32 Note, however, that part of the landlord fi xed effect includes landlord means of unobserved plot and tenant characteristics. Since these unobservables are likely to have high intra-village correlation, we also compared the fi t of a regression with village fi xed effects to that of one with landlord fi xed effects on a sample of 263 plots (95 landlords in 20 villages) for which there were at least two landlords per village. The R<sup>2</sup> ’s are, respectively, 0.351 and 0.784. This suggests that unobserved plot and tenant characteristics that vary across villages account for very little of the landlord effect.

18
area which is the subject of an ongoing debate with macroeconomic, fiscal, monetary and prudential policy implications that go well beyond the remit of public debt management.

A preview of this note’s central conclusions is as follows:

- The trend of rising foreign investor participation in EM domestic bond markets was in place before the global financial crisis and has since accelerated, with foreign ownership levels now at historic highs in several EMs.

- High-level categorizations of “foreign investors” and “EMs” risk being misleading. The class of “foreign investors” includes a broad range of investor types, while EMs encompass countries across a spectrum of economic and market size and development. Therefore, for the purposes of debt management, a more detailed categorization of foreign investor types is needed, and the impact of, and appropriate debt management responses to, foreign participation may differ significantly across countries.

- There is a lot that debt managers can do should they wish to encourage foreign investor demand. Many of the measures that might be considered are compatible with broader market development goals, including benchmark-building, the adoption of a consistent and transparent issuance strategy, and active management of investor relations and market communication. Benchmark bond index inclusion may motivate taking additional, specific measures.

- Improving market monitoring in order to maximize debt managers’ knowledge of foreign investors’ participation in their debt markets is a priority, given the significant odds that offshore demand for EM debt will prove to be sustained. In many countries which are witnessing more foreign investor participation, there is an urgent need for accurate, detailed and up to date holdings data.

> 1 For a good overview through 2005 see IMF, _Structural Changes in Emerging Sovereign Debt and Implications for Financial_ Stability, Ch. 3 of the _Global Financial Stability Report_ (April 2006). A more up to date treatment is Burger et al (2010).

**2**
/rivers, or coastal areas (McCallum et al., 2013). In addition, a decline in groundwater storage can lead to a decrease in the water table (Wada et al., 2010), rendering groundwater inaccessible to terrestrial vegetation (Barron et al., 2014).

To the authors’ knowledge, this is the first paper looking at the potential impacts of the extension of photovoltaic water pumping systems (PVWPS) on GDEs. Even though several articles studied the suitability of photovoltaic water pumping systems (PVWPS) in different regions of sub-Saharan Africa (e.g., Gebrezgabher et al., 2021; Schmitter et al., 2018; Soenen et al., 2021; Xie et al., 2021), no article has investigated the risk posed to GDEs by the uncontrolled access to groundwater through photovoltaic water pumping across the continent. The potential to scale up shallow groundwater irrigation in the SSA region is undeniable and could be a game changer for poverty, food security, and climate adaptation (Rodella, Zaveri and Bertone, 2023). However, the analysis seeks to illuminate the unintended consequences of unfettered access and highlight the need for preventive measures to be in place prior to scaling up photovoltaic pumping.

In this article, we use the Analytic Hierarchy Process (AHP) to map the risk posed to sub-Saharan GDEs by an uncontrolled expansion of access to groundwater through photovoltaic pumping. More specifically, we evaluate the risk of over-exploitation for GDEs using mapped data on groundwater, irradiance, population, and GDEs. Section 2 details the datasets used in our analysis.

2
characteristics of the child’s mother are included. To avoid _ad hoc_ functional form assumptions, age and education variables as well as household size are broken into categories each of which is entered as a dummy variable. OLS is used to estimate each model, with standard errors clustered at the PSU. (The <u>Addendum gives the actual regressions.)</u> As discussed above, in the event that household wealth is simply a poor indicator of nutritional outcomes, we also use household per capita consumption. For the relevant subset of countries, we estimate the regressions using household consumption per person (with, as noted in Section 4, some slight variations in the variables included in ��� and ����). The results were similar; details are found in the <u>Addendum.</u> Tables 12 and 13 present the results for Models 2 and 3 for underweight women and undernourished children respectively. The tables give the proportion of undernourished individuals who fall into the poorest 20% and 40% of the distribution of the predicted values based on wealth and (unlike prior tables) the additional covariates. We find that, on average, 32% of underweight women are found in the poorest 20% based on the predicted values from Model 2 (Table 12), as compared to 28% using only the household wealth index (Table 8). Focusing instead on the poorest 40%, the proportion rises to 56% using Model 2, as compared to 51% using wealth alone. Adding the individual variables (Model 3) we now find that (on average) 37% of underweight women are found in the poorest 20% in terms of the predicted values, rising to 61% for the poorest 40%. Similar improvements are evident for both stunting and wasting in children (comparing Tables 13 and 8).

However, it is clear that these augmented regressions still do a poor job at identifying undernourished individuals within households. While the predictive power
**Consumption** The consumption module of the TUP survey captured information on household food consumption for the week prior to the interview and non-food expenditures for the month or year prior to the interview. These are used to construct monthly _per capita_ consumption values, as detailed in Bedoya et al. (2019). Based on these data, we measure the logarithm of _per capita_ monthly consumption, using the same approach that the Afghan government used to determine the national poverty line. This monthly consumption aggregate thus captures a short-term (weekly) measure of food consumption during one of the planting seasons, as well as a medium-term (monthly and annual) measure of non-food expenditures (Deaton, 1997; Ravallion, 1998).

**Asset Index** We use survey data on household assets to construct a wealth index for each household, which provides an indication of each household’s wealth relative to others in the survey. Specifically, we calculate the first principal component of variation in household asset ownership based on the sixteen items listed in Table S1, across the 2,814 households with complete asset data, after standardizing each asset variable to zero mean and unit variance. This wealth index explains 25.3% of the variation in asset ownership. Figure S1 shows the distribution of the underlying asset index components and Table S1 shows the direction of the first principal component. Broadly, we expect that the asset index will provide an indication of each household’s long-term economic status, relative to other households in the survey.

**Other Variables** The TUP surveys collected several other covariates that we use in subsequent analysis. These include a food security index (composed of variables relating to the skipping and downsizing of meals, separately for adults and children), a financial inclusion index (composed of access to banking and credit, knowledge of banking and credit, and savings), and a psychological well-being index for the primary woman (standardized weighted scores on the Center for Epidemiological Studies Depression scale, the World Values
Voicu, 2010) as well as the availability of qualified labor and good infrastructure have all been identified as strong determinants of MNE spatial concentration or diversification in various contexts.

The Enterprise Surveys confirm that foreign multinational activity tends to be less widespread than domestic activity, either concentrated in industrial or economic hubs within the tertiary sector of host countries (see Fig. 10 using the share of the top subnational region in MNE output and aggregate economy output as a proxy for concentration). The concentration can be in stark contrast with domestic activity such as in the cases of Angola, Russia, India, and China, or in line with domestic economic activity, such as is the case in smaller African countries, Mexico or the Arab Republic of Egypt.

The positive relationship between FDI agglomeration and local economic activity leads to a multiplier effect that can benefit countries in terms of competitiveness: the more the latter grows, the greater the likelihood of that region attracting further FDI inflows and vice-versa. This partly explains why large cities such as those in China are foreign investment hubs, continually attracting FDI (Huang and Wei, 2011). The effect of such agglomeration on inclusiveness can be ambiguous to the extent that supply linkages extend to more remote areas and activities, and redistribution policies are in place.
NSS region are part of the _direct_ effect and the lights from the part of a city that has spilled over the border are part of the _indirect_ effect that comes from neighbors.<sup>22</sup>

# **V. Types of Urban Growth and Rural Poverty**

# _5.1 Effects of Unmasked Regional Lights on Rural Poverty_

We start by examining whether night lights have any effect at all, before we compare effects of lights coming from big cities with those from smaller towns. We refer to these results as ‘unmasked’ since we consider the entire area of each NSS region without first masking the pixels lit by the big cities.

Table 2 reports the estimates of the SDM for the headcount index, H and the poverty gap index, PG that consider four different ways to use the night lights data. In columns (1) and (5), the total lit area for each NSS region, for any clusters of lights exceeding 20 percent of maximum brightness (DN=13) is used as the proxy for urban development. Variables in these models are in logarithms for the convenience of using elasticities.<sup>23</sup> The results suggest an absolute elasticity of rural poverty with respect to own-region lit urban area of about 0.1 to 0.2, with a slightly more precisely estimated elasticity for H than for PG. The elasticities with respect to the spatially weighted average of lit area in neighboring regions are larger, at between 0.3 and 0.4, and the spatial lag of the poverty rate has an elasticity of 0.3. Allowing for the spillover to neighbors and the feedback effects shown in equation (7), the total effect of growth in lit area on H has an elasticity of -0.6 and on PG has an elasticity of almost -0.9. These indirect effects will have both local and global components, given that  2 _k_<sup></sup> 0 and 
arrest charge does not have a match in the court data) and charges for which the court has not issued a decision.<sup>8</sup> We also drop misdemeanor C cases as these are inconsistently reported in our data. This leaves us with 2.6 million arrests. We geocode the addresses provided with the address information and match each arrest to the county in which the individual lived when they were arrested. We then collapse the data to the count of arrests at the county-day level. This leaves us with a balanced panel of 742,188 county-day observations from 2010 through 2017. We maintain separate counts of crimes by category of the arresting charge (for instance, violent crimes, assaults, etc).

Arresting charges are defined in the raw TPDS data. When we conduct analyses on total crimes, we pool all of these charges together. We also aggregate some of these charges into violent and non-violent crimes and analyze these separately. We consider the following crimes to be violent: assault, aggravated assault, homicide, manslaughter, kidnapping, domestic assault, and weapons crimes. We define non-violent crimes as: larceny, burglary, stolen property, traffic (excluding those resulting in manslaughter charges), marijuana possession, and marijuana dealing.

Our definition of violent and non-violent crimes is not exhaustive. There are some crimes that we do not consider violent or non-violent. Robbery, for example, is generally defined as “the action of taking property unlawfully from a person or place by force or threat of force.” Thus, while clearly a property crime, it might also be considered a violent crime due to the threat or use of force. Not all robberies, however, involve the actual use of force or violence. These crimes are included in our analysis of heat’s impact on total crime and we analyze heat’s impact on some of these uncategorized crimes individually, but we exclude them from our analyses of violent and non-violent crimes.

> 8These are indicated as cases where the
55|78|
|Emerging Latin America|75|46|44|
|Emerging Asia|47|38|42|
|New Member States|32|25|35|
|Candidates and Potential Candidates|56|38|39|
|Eastern PartnershipCountries|49|19|35|

Source: Eurostat, Abbas et al (2010).

In relation to private-sector indebtedness, a useful aggregate indicator is the ratio of private credit to GDP.<sup>5</sup> Table 3.6 shows the evolution of this measure, using World Bank data. For the new member states, this ratio grew quickly between 2002 and 2007 and continued to climb between 2007 and 2009. The Baltic States, Bulgaria, Hungary and Slovenia have the highest ratios but these remain well below the levels in more financially-developed advanced economies. The private credit ratio is far lower for the other emerging European economies but the rate of expansion over 2002 to 2009 has been broadly at the same pace.

In comparative terms, Table 3.6a shows that private credit in the new member states exceeds the level of financial development in emerging Asia and emerging Latin America but the other regions in emerging Europe are broadly similar to these other regions.

> 5 In addition to obtaining credit from the domestic banking system, domestic entities can also borrow directly from overseas banks or by issuing bonds domestically or internationally.  Table 3.4b shows the extent of cross-border borrowing by non-banks, while Table 3.4k shows the extent of international bond issuance by non-banks. However, in the other direction, some domestic credit might be redirected to other countries. For instance, the local affiliate of a multinational might borrow locally and make an inter-company loan to other units in the global enterprise.

19
# **Figure 8. Consumer Protection and Financial Literacy under Responsibility of Financial Regulator**

<!-- Start of picture text --> Consumer Protection Financial Capability Consumer Protection Financial Capability<br>100<br>90 79 79<br>80<br>67 67 67 67<br>7060 60 50 52 54 62 50 58 58 62 50<br>% 50 45 42<br>36 36<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA High Low Income Middle Income High income: OECD<br>income:<br>OECD<br><!-- End of picture text --> Source: CGAP and World Bank Financial Access 2010

**Arab microfinance institutions have developed consumer protection and financial responsibility codes on a voluntary basis** at a MENA regional level, and also in Jordan. MFI members of the regional microfinance network, Sanabel, signed a consumer protection and responsible finance document in 2008, committing themselves to standards of practice. Jordanian MFIs developed and signed a voluntary Code of Ethics covering disclosure and transparency, service standards, and ethical practices.

# _2.5 Financial infrastructure_

**Financial infrastructure underpins financial inclusion.** Moral hazard and adverse selection are reduced as collateral frameworks and credit information systems are improved, and loans can be made - at a lower cost and risk – to clients that previously would have been excluded. Without effective secured transactions laws and judicial (or non-judicial) processes, ideally with unified collateral registries to facilitate lenders‟ use of collateral as a guarantee, micro entrepreneurs may not be able to leverage current assets into access to finance. Low income individuals and microenterprises also need access to low cost and safe payment mechanisms, whether for salaries, benefits, remittances, or for business transactions.

**MENA has the lowest score on the Doing Business Legal Rights index of any region, however** .
studies the extent to which access to finance can explain the dispersion in marginal returns to capital across countries. The latter explores the role of distortions in the business environment in explaining the differential employment growth across firms of different sizes.

Our paper makes two contributions to the literature. First, it adds to the body of work replicating the theory of misallocation and the strategy to measure it from the data developed by Hsieh and Klenow (2009). We expand the literature in exploring a region of the world where the data requirements for the application of the methodology have left it relatively unexplored. We remove this limitation assembling comprehensive and comparable data on manufacturing form in four countries. A second contribution of our work stems from the illustration of the importance of adequate coverage of firms in the data, in terms of representativeness of the sectoral coverage of firms in the economy. We show that the misrepresentation of sectors in the ES leads to lower degrees of measured misallocation and subdued gains from reallocation than what you would otherwise estimate from the census data.

# **3 Background**

We motivate our study reviewing features of the structure of production and the magnitude of the development gaps that characterize the economies of Cˆote d’Ivoire, Ethiopia, Ghana, and Kenya. Then, in order to get a sense of the quality of the business climate in which firms operate in these economies, we provide a brief account of reforms and salient packages of government interventions that were implemented over the course of the years.

5
Table A8: Heterogeneous impacts of _Mais Educação_ by city size

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Impacts|on fifth-grade vari|ables||
|Dummy_Mais Educação_* year 2011|1.151|–3.476**|–2.259*|
||(1.321)|(1.498)|(1.361)|
|Dummy_Mais Educação_* year 2009|0.922|–4.074***|–2.689**|
||(1.112)|(1.462)|(1.348)|
|_Mais Educação *_log(city population)|5.197|4.678|7.956|
||(3.378)|(10.93)|(10.57)|
|_R_ <sup>2</sup>|0.667|0.786|0.789|
|Observations|2,988|2,988|2,988|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

<mark>Panel B: Impacts on ninth-grade variables</mark>

|Dummy_Mais Educação_* year 2011|–0.297|–3.852**|–0.766|
|---|---|---|---|
||(1.097)|(1.746)|(1.649)|
|Dummy_Mais Educação_* year 2009|–0.148|–5.373***|–2.572|
||(0.
informal sector activities play an important part in the lives of people in developing countries and Ghana is no exception.<sup>2</sup> Hart (1973) who coined the term "informal sector" ironically used a study of Accra and Nima in Ghana to show the importance of such activities for national income and employment estimates considerations. Both from a labor market perspective and also from a living standards and welfare perspective informal sector activity is important. Some estimates based on GLSS data indicate that on average in Ghana households earn more than three-fourths of their income from informal sector activities, i.e. activities which are not covered in our formal sector earnings discussion hitherto (GSS, 1996). It is stated that in areas such as rural Savannah almost 90 percent of household income comes from informal sector activity compared to 45 percent in Accra. Alternatively household activity generated consumption (subsistence consumption) accounts for around 27 percent of household consumption with 56 percent in rural Savannah and 2.6 percent in Accra, indicating the subsistence nature of informal sector activity for Ghanaian households. Estimates of informal sector incomes and output in Ghana suggest it to be a sizable percentage of current estimates of GDP. For instance non-farm enterprises are estimated to contribute at least 20 percent.

2 For some useful information on the characterization and definition of informal sector see Thomas (1992), Tumham, Salome and Shwarz (1989) and Suthuraman (1981) among others.

27
# _2b. Data for Regional Profiles and Correlation Analysis_

We draw heavily on the same data the World Bank uses in estimating global poverty both to enhance comparability of our analysis with the measures used by many international development agencies (e.g., United Nations, USAID, UK FCDO) and also to maximize country coverage and ensure that our primary findings are valid for the world. The main source of data is the Global Monitoring Database (GMD), an internal World Bank archive of harmonized micro-level income and consumption survey data from 154 countries. (For details, see World Bank, 2020a, Appendix 1A.) In addition, we supplement the GMD survey data with household survey data from the Luxembourg Income Study (LIS) for 8 countries.<sup>7</sup> These are the same data ingested into the Poverty and Inequality Platform (PIP), an interactive online computational tool for the Bank’s poverty and inequality estimates.

In total, our global analysis is based on nationally representative income and consumption survey data from 162 countries. These data are a subset of the 168 countries used for the March 2021 update to the World Bank’s global poverty estimates as described in Arayavechkit et al. (2021). These are the same data that are used to monitor SDG 1.1. We are unable to include six countries primarily because the data for these countries are reported in aggregate form (grouped data) and not available at the unit-record level.<sup>8</sup> The global poverty estimate we report in this paper, based on the per-capita allocation of household resources from the 162 countries in our analysis, is higher than the reported poverty estimate of 9.2 percent in 2017 based on the 168 countries (Arayavechkit et al., 2021). The primary reason the global estimate reported in this paper

> 7 These eight countries are Australia; Canada; Germany; Israel; Japan; the Republic of Korea; Taiwan, China; and the United States
Table 2: DID Results - static

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Ln(N frms)|Ln(N entries)|Ln(N exits)|Ln(N employess)|Ln(Wagebill)|Ln(Revenue)|
|_Full Sample_|||||||
|Ofshore _×Post_|-0.041|-0.669***|0.053|-0.044|0.041|-0.096|
||(0.03)|(0.10)|(0.11)|(0.06)|(0.08)|(0.11)|
|Observations|1,094|1,094|1,094|1,076|1,076|1,094|
|N sectors|61|61|61|60|60|61|
|R-squared|0.91|0.93|0.90|0.76|0.74|0.85|
|_Manufacturing Sample_|||||||
|Ofshore _×Post_|-0.112**|-0.808***|-0.214|0.044|0.196*|0.147|
||(0.03)|(0.11)|(0.13)|(0.06)|(0.08)|(0.08)|
|Observations|504|504|504|486|486|504|
|N sectors|28|28|28|27|27|28|
|R-squared|0.83|0.86|0.