rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.1 Descriptive evidence for lease and sale transactions**

Descriptive information for the close to 900,000 transactions for which new leases were signed and prices reported after January 2021 are presented in table 2, overall (column 1) and for commercial and personal land in the entire sample (columns 2 and 3), for the roughly 60% of the sample where tenant data is available (columns 4 and 5), and for the 9% of the sample for which accessing registry data was feasible (column 6). Parcels for commercial land are slightly larger (2.3 ha) than those for personal land (1.6 ha). Mean and median annual lease rates are US$ 117.5 and 89.6 per ha, respectively. The average number of lease transactions per village over the entire period was close to 170 (192 and 69 for commercial and personal land, respectively), implying that new leases were recorded in about 10,889 villages in Ukraine Of nearly 340,000 leases conducted in 2021 only, about half were concluded before or after the moratorium on land sales was lifted for individuals. Although the fact that the date of land market opening was known well in advance makes increases of land rents in anticipation of land sales markets likely, implying our estimate is a lower bound, this allows us to use an indicator variable for lifting of the sales moratorium to obtain an estimate of the short-term effects of this policy change on lease prices. While the number of leases conducted in 2024 remains, with some 125,000, much smaller, a similar strategy can be applied to assess the impact of land sales market opening to legal entities on the lease price paid by such entities.

While more than 70% of leases are for terms of less than 14 years, which provides the most flexibility given the current legal environment of a minimum lease term of 7 years, about 17% and 11% are concluded for terms between 14 and 20 or
of the Congo has the highest annual number of lightning strikes globally. Importantly, as Figure 5 shows, lightning intensity varies greatly within African countries, implying that this instrument can generate meaningful variation in electricity access. Data on land elevation are provided in the geo-referenced component of the DHS datasets.

Columns 2 and 5 of Table 3 confirm the significant and negative relationships between the proximity to electrification grids on the one hand, and both the elevation of the land and lightning on the other. These negative correlations are found in both the LSMS and DHS samples. In addition, the high values of Cragg-Donald Wald F-statistics and the Windmeijer F-statistics show that our instruments are strong.

Given that proximity to the road networks and the electricity grids are two potentially endogenous variables, their interaction is likely to be endogenous as well. To instrument this interaction term, we use the interaction term between proximity to our hypothetical road networks (the instrumental variable used for proximity to the road networks) and terrain elevation (one of the instrumental variables used for proximity to the electricity grids). Column 3 of Table 3 shows a positive correlation between the instrument and the outcome in both the LSMS and DHS samples. Here the high values of the Wald and Windmeijer statistics also demonstrate the strength of this instruments.

Figure 5: Lightning flash intensity <!-- Start of picture text --> Lightning density per K^2<br>0.0 - 0.003<br>0.004 - 0.0006<br>0.007- 0.012<br>0.013 - 0.017<br>0.018 - 0.024<br>0.025 - 0.030<br>0.031 - 0.0610<br>0.0611 - 0.121<br>0.122 - 0.177<br>0.178 - 0.237<br>0.238 - 0.298<br>0.299 - 0.
## SPLIT DECISIONS

near the cutoff, suggesting a regression discontinuity design to evaluate the effects of migration on EPS job-applicants’ households. Migration by test-passers typically occurred within a few months of the KLT, and our household survey occurred in early 2010. Households are therefore surveyed about 3–5 years after potential migration began.

# 4.2. The regression discontinuity design

We estimate the effects of migration with the regression discontinuity design or RDD. Because RDD results can be sensitive to functional form assumptions, we use fully nonparametric sharp and fuzzy RDD (Hahn et al. 2001; Porter 2003; Nichols 2007; Imbens and Lemieux 2008). Because RDD results are also notoriously sensitive to bandwidth selection, we tie our hands with the asymptotically optimal bandwidth recently proposed by Imbens and Kalyanaraman (2012).<sup>14</sup> For each outcome _μ_ we first estimate the intent-to-treat (ITT) effect (where treatment _τ_ is migration) with sharp nonparametric RDD. This is the effect of barely passing the language test on “compliers” (Angrist et al. 1996)—those whose migration decisions were changed by the test result—whose families were willing to complete the survey, and who had scores near the passing threshold _s_ = 0:<sup>15</sup> We then estimate the treatment-on-treated (TOT) effect as fuzzy nonparametric RDD. This is the effect of migration by a household member: This again is the local average treatment effect on “compliers”, whose households com-

> 14We use the triangular kernel shown optimal by Fan and Gijbels (1996) and Cheng et al. (1997) for its boundary properties; Lee and Lemieux (2010) argue that the choice of kernel “typically has little impact in practice.”

> 15For individual _i_ the outcome is _μi_ , treatment status is _τ i ∈{_ 0 _,_ 1 _}_ where treatment is migration, and _s ≡_ [raw score] _−_ 120. Then _μs↓
sensitive to Nigeria’s overall growth performance than the consumption of poorer Nigerians. Thus, it is important to separate out the periods before and after the 2016 recession when constructing the GICs and testing the robustness of the backcasts. Once they have been constructed, the GICs are then used to adjust the pass-through rate applied to each decile of the consumption distribution, holding the overall _average_ pass-through fixed at 1.<sup>26</sup> Relaxing the assumption of distribution-neutral pass-through alters the backcasted trend, but even under the assumption that more growth was passed through to poorer Nigerians during the decade to 2018/19, poverty reduction over that period remains in single digits. Figure 5 shows that using the 2010/11-2018/19 GIC – which was slightly pro-poor – in the backcasting exercise results in a larger reduction in poverty over the decade, such that the estimated poverty headcount rate is 47.9 percent in 2009 (1.6 percentage points higher than our main backcasted result). Conversely, using the 2015/16-2018/19 GIC results in a more

> 23 In 2018/19, _overall_ inequality in Nigeria was moderate: the Gini coefficient was 35.1. However, spatial inequality – especially along the north-south divide – was far more pronounced (Lain and Vishwanath, 2021; Blumenstock et al., 2021).

> 24 Data on spatial inequality of non-monetary indicators also suggest that inequality did not significantly drop in the decade prior to the COVID-19 crisis. According to the DHS, the north-south gap in terms of education and basic infrastructure did not close substantially over this period (Lain and Vishwanath, 2021).

> 25 Labor market indicators from the same period show similar patterns (see Jenq, Lain, and Vishwanath (2021)).

> 26 This means that the average growth in consumption is the same as in the main results in Figure 1.

18
represented as U.S. dollars per metric ton. All prices are converted into real terms by using the U.S. consumer price index obtained from the Bureau of Economic Analysis. World oil production, obtained from the International Energy Agency, is measured as millions of barrels. Figure 1 plots the growth of oil price and production over the period 1992-2022.

Country-specific real GDP series are obtained from Haver Analytics or International Financial Statistics (International Monetary Fund), and are measured in billions of U.S. dollars, at 2010 prices and exchange rates. Current account balances (as percentage of GDP) are calculated using net current account balances data (excluding exceptional financing) from the International Financial Statistics database in U.S. dollars and gross domestic product data in U.S. dollars (from Haver Analytics). Country-specific real effective exchange rates are obtained from the World Bank.

Since we would like to distinguish between the effects of energy price shocks on current account balances of commodity exporters and commodity importers within EMDEs, we identify countries as oil exporters or oil importers by using the World Bank criteria. Specifically, a country is classified as an oil exporter when, on average in 2017-19, exports of crude oil accounted for about 20 percent or more of total exports. Countries for which this threshold is met because of re-exports are excluded.<sup>8</sup> When data are not available, judgment is used. Other countries are considered as oil importers. Natural gas exporters are identified as those having natural gas exports account for at least 5 percent of their total exports based on data obtained from the United Nations Comtrade database; other countries are considered as natural gas importers. Similarly, coal exporters are identified as those having coal exports account for at least 5 percent of their total exports based on data obtained from the United Nations Comtrade database; other countries are considered as coal importers.

Current account surplus countries are identified as those having a positive median current account balance over the sample period;
**Figure 8: Distribution of quasi-fiscal deficits between consumer categories**

<!-- Start of picture text --> 0.1%<br>2.3%<br>6.4%<br>Residential<br>Commercial<br>41.8% Industrial<br>26.4%<br>Government<br>Export<br>Other<br>22.9%<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on utility data.

# 5.3 Quasi-fiscal deficits under benchmark performance

As explained in section 2, benchmark performance is defined as 100 percent bill collection rate, 10 percent T&D losses, and staffing at benchmark levels. A breakdown is provided in Table 8.

**Table 8: Quasi-fiscal deficits under benchmark performance**

|**Country**|**Absolute QFD under**<br>**benchmark performance**<br>**(current US$ million)**|**Relative QFD unde**<br>**(% current GDP)**|**r benchmark perfor**<br>**(% revenues)**|**mance**<br>**(% cash collected)**|
|---|---|---|---|---|
|Benin|-$27|-0.3|-13|-13|
|Botswana|$442|3.0|158|159|
|Burkina Faso|$60|0.5|23|23|
|Burundi|$14|0.4|72|97|
|Cameroon|-$8|0.0|-2|-2|
|Cape Verde|-$5|-0.3|-6|-7|
|Central African
of the individual countries is as follows: there are only 5 countries with less than 40 observations, there are 21 countries with 100 observations or more and the remaining countries have between 40 and 92 time-series observations.

The regressor variables intended to capture the cross sectional variation in the GDP, GDP Deflator and speed of adjustment coefficients were taken from the FinStats and Financial Sector Development Indicators of Al-Hussainy _et al._ (2010) and Beck _et al._ (2000). The economic development indicators are GDP per capita as a measure of economic development, the foreign trade to GDP ratio as a measure of an economy’s openness (both were obtained from the World Bank Central Database), the Kaufmann _et al._ (2010) overall public governance quality indicator, the degree of integration in prudential, business conduct and overall financial sector supervision of Melecky and Podpiera (2012), a central bank political and economic independence indicator, and an indicator for previous financial crisis experience (also from Melecky and Podpiera, 2012). A list of the final set of economic, financial and institutional development indicators that we used is provided in Table 3.

> 22See http://ec.europa.eu/economy ~~f~~ inance/euro/adoption/conversion/index ~~e~~ n.htm.

> 23Details regarding the computation of the filter are available from the Census Bureau’s website available at: http://www.census.gov/srd/www/x12a/.

17
- 31 -

|**Table 9. Summary statistics: Crop production data**|
|---|

|Variable|Abb.|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Crop production at each district (ton)|_q_||||||
|Cereal||1,292|1,125,720|1,487,816|<br>0.2|9,017,332|
|Potato||1,481|<br>189,000|236,465|<br>5.4|1,516,768|
|Oilseeds||1,133|<br>133,064|209,732|<br>0.2|1,741,450|
|Vegetables||1,550|184,637.5|413,477.0|<br>120.0|3,696,046|
|Transport time to the nearest city (hour)|_CITY2_|<br>5,456|<br>1.69|1.63|<br>0.05|<br>13.70|
|Transport time to the nearest border point (hour)|_BP2_|5,456|<br>13.25|7.69|<br>1.12|<br>48.55|
|Market Access Index based on transport time|_MAI2_|5,456|<br>0.06|0.06|<br>0.00|<br>0.55|

The dynamic panel regression is performed for each of the crops. First, the standard lagged value instruments are found to be largely valid, except for the model for cereal production ( **Table 10
**Inequality and Welfare Dynamics in the Russian Federation during 1994-2015**

Hai-Anh H. Dang, Michael M. Lokshin, Kseniya Abanokova, and Maurizio Bussolo<sup>*</sup>

# **JEL** : C15, D31, I31, O10, O57

Key words: welfare dynamics, poverty, inequality, pro-poor growth, panel data, household surveys, Russia

* Dang (hdang@worldbank.org; corresponding author) is economist with the Survey Unit, Development Data Group, World Bank; Lokshin (mlokshin@worldbank.org) is manager of the Survey Unit, Development Data Group, World Bank; Abanokova <u>(kabanokova@hse.ru) is junior research fellow with Higher School of Economics, National</u> Research University, Russia; Bussolo (mbussolo@worldbank.org) is lead economist with the ECA Chief Economist’s office, World Bank. We would like to thank Sam Freije-Rodriguez, Vladimir Gimpelson, Andras Horvai, Gabriela Inchauste, Stephen Jenkins, Anna Lukyanova, Moritz Meyer, Olga Shabalina, and participants at seminars at the Institute for Social Policy (HSE, Moscow), ECA Chief Economist’s Office, and Poverty Global Practice, World Bank for useful feedback on earlier versions. We would also like to thank the UK Department of International Development for additional funding assistance through its Strategic Research Program (SRP) and Knowledge for Change (KCP) programs. We also acknowledge support from the NRU-HSE Basic Research Program. This paper is a background paper commissioned for ECA’s flagship report on distributional issues.
Other assumptions of the standard competitive labor markets model could also be relaxed. In one of the few attempts to do this, Babiker and Eckaus (2007) explore the impact of constraints on real wage adjustment and on inter-sectoral flows of labor in different regions of the world and find that China and India could be the countries most adversely affected by the structural change induced by carbon pricing, largely because these are the regions that need most reallocation of labor as a result of environmental policy.<sup>3</sup> It would be very useful to consider the implications of various models of labor market functioning more specific to developing countries (e.g. as reviewed by Behrman, 1999) for the employment and wage impacts associated with a shift towards green policies.

As an example, consider the model of Satchi and Temple (2009). As they point out, “At present, we know relatively little about how changes in sectoral productivity translate into labor market outcomes.” Yet green policies appear likely initially to lead to lower productivity in the energy sector, with pervasive knock-on effects in the rest of the economy, unless at the same time other market failures, such as those involved in the provision of energy supply infrastructure, are corrected. Satchi and Temple set up a smallscale general equilibrium model, in the tradition of labor market search models, designed to characterize typical middle-income countries (their calibrations use Mexican data). There are three sectors: a formal urban manufacturing sector, an informal urban sector, and a rural sector. Underemployment – essentially, employment in the informal sector – arises because of job/worker matching frictions in the formal sector. The urban wage (in the formal sector) is endogenously determined, unlike in Harris and Todaro (1970). The rural sector is assumed to be perfectly competitive with constant returns to scale and full employment, thus ruling out the type of surplus labour considered in many low-income countries.

The authors consider the impact of productivity shocks in different parts of the
The paper will make use of data from the completed Mogadishu pilot survey and geo‐referenced maps and three example EAs to explore the following questions: (1) How might a given method be implemented in the field, given the information available and the security constraints? (2) What information is necessary to generate sampling weights, necessary for representative estimates, and how should those weights be calculated? (3) What are the implications in terms of precision and bias for each of the methods described above? (4) What are the implementation concerns for each method, including the options for verification and the impact of non‐household structures?

The next section briefly describes the literature as it relates to the questions above, followed by section 2 describing the data. Section 3 addresses research questions 1 and 2 by giving further detail on the methods considered. Section 4 presents simulation results covering questions 3 and 4. Section 5 concludes by offering some discussion of the overall performance and potential future applications.

# **1. Literature Review**

The most common method for collecting household data in Sub‐Saharan Africa is to use a stratified two‐ stage sample, with census enumeration areas selected proportional to size in the first stage and a set number of households selected with simple random sampling in the second stage (Grosh and Munoz, 1996). Since administrative records are often incomplete and most structures do not have postal addresses, as is the case in Mogadishu, a household listing operation is usually necessary prior to the second‐stage selection. However, due to the security concerns cited above, listing was not feasible.

A number of alternatives for second stage selection can be used when household lists are not available. A common alternative used in both Europe (see Bauer, 2014, for recent examples) and in the developing world is a random‐walk. The Afrobarometer survey, which has been conducted in multiple rounds in 35 African countries since 1999, and the Gallup World Poll, which conducted surveys in 29
7 We examine the links between individuals created by memberships in voluntary associations and social norns, which potentially excludes other dimensions of social capital. First, we do not examine the impact of any sense of affiliation with a nation or nation-state or any measure of distributional or ethnic conflict within the polity. Second, we do not examine as 'capital' the institutional "capacity' either of specific government or non-government organizations nor in the broad sense of society possessing a well known and legally sanctioned set of "rules of the game." Third, we do not analyze any 'cultural" values or attitudes, such as degrees of compassion, altruism, respect, tolerance<sup>4</sup> . Fourth we do not examine the issues explored recently by Knack and Keefer (1996) on the relationship between trust, norms of civic cooperation, associational activity and aggregate economic growth and investment rates.

# I. Data on Social Capital?

We cannot examine the effects of what we _mean_ by social capital, only what we _measure._ The sections below describe the survey and the procedure we used to construct our measure of social capital. The Social Capital and Poverty Survey (SCPS) was carried out in rural Tanzania in April and May of 1995 as part of a larger participatory poverty assessment exercise (Narayan, 1997). While the households were chosen randomly within clusters, the 3 Easterly and Levine (1996) have shown that a measure of "ethnic heterogeneity" is empirically associated with the adoption of bad economic policies, which they attribute to the importance of distributional conflict among groups.

' Explaining economic performance by such "cultural" characteristics such as the "Protestant work ethnic" or "Confucianism" has a history which is long and checkered, as it easily veers to self-congratulation or condescension. Recent entries include Harrison (1992) on culture and Fukuyama's examination of trust (1995).
Myanmar to grow in terms of economic and social development.

**Across the world, women face numerous challenges to participate equally in the economy as leaders, entrepreneurs, employees, consumers, and community members** . Laws and regulations continue to prevent women from entering the workforce or starting a business, resulting in lasting effects on women’s economic inclusion, labor force participation, and entrepreneurial aspirations. Moreover, barriers such as the lack of familyfriendly workplace policies, care and household responsibilities, lack of training or mentoring programs, access to finance, and a lack of safe transportation, create a situation that prevents women from achieving their full potential. This, in turn, prevents the whole of society from reaching its full potential. According to a World Bank Group study,<sup>2</sup> countries are collectively losing USD160 trillion in wealth because of differences in lifetime earnings between women and men and the situation for women in Myanmar, as this study confirms, is no different to the experience of women globally.

**Currently, the benefits from growth are not being shared equally in Myanmar.** The World Bank Group’s World Development Indicators (2019) shows that the labor force participation rate among those aged 15 and over in Myanmar is 77 percent for men but only 48 percent for women. The Myanmar Annual Labor Force Survey (2017)

> 1 Private sector matters for job creation, IFC, 2012.

> 2 Unrealized Potential: The High Cost of Gender Inequality in Earnings, World Bank, 2018.

3
Some studies consider the factors affecting fixed-internet access and usage and other aspects of the digital divide. For instance, income, installation fees, and age are shown to be significant factors predicting fixedinternet usage (Birba and Diagne 2012; Cerno and Amaral 2006; Katz and Rice 2003). Other socioeconomic variables such as education, gender, locality and household size are also found to be drivers for internet adoption and usage in general. For instance, Goldfarb and Prince (2008) find that low income, less-educated people spend more time online. Similarly, Penard et al. (2012) and Penard et al (2015) find that education and computer literacy increase internet usage. Gilwald et al. (2018) provide evidence on lower internet adoption rate among women and those who live in rural areas. Other infrastructure factors such as increasing the distribution of electricity and improving competition in digital infrastructure are found to help promote the adoption of internet services (Armey and Hosman 2016; Rodriguez-Castelan et al. 2019).

However, with regards to mobile internet, existing studies have a few limitations. First, previous research has mainly focused on understanding the determinants of mobile phone ownership in developing countries. Education, employment status, and type of electricity are found to be important factors (Aker and Mbiti 2010; Björkegren 2019; Forenbacher et al. 2019; van Biljon and Kotzé 2007). Second, studies on mobile internet adoption tend to focus on the supply side, emphasizing technological aspects such as the efficient compression of images or data delivery (Kim and Kim 2002). Funk (2005) presents how certain technological products such as the push mail service and micropayment systems promote mobile internet adoption. Third, only specific country or regional data are used in the few studies analyzing the demandside factors affecting mobile internet adoption. For instance, Srinuan, Srinuan, and Bohlin (2012) use data from Thailand to find that price, the availability of fixed telephony, and individuals’ age and location are strong determinants of mobile internet adoption.