rafmacalaba/data-use-mentions-extended huggingface dataset:
stands out with relatively few public sector operations (only 7 out of 15 countries/territories with at least one lending operation).<sup>24</sup> This pattern is consistent with the notion that incentives to initiate public sector reforms are significant across LICs and MICs. As such, the decision by governments to _borrow_ for public sector reforms (rather than using potential grant-funded resources) could be seen to signal political commitment to such reforms – although this is not consistently the case, as will be discussed below.

Furthermore, a closer look at which countries borrow for public sector reforms at least in part contradicts a widespread perception, as reflected in Schacter (2001), that public sector reform efforts are pre-dominantly driven by donor pressures in countries dependent on external aid. In fact, only 25 percent of the countries with at least one World Bank public sector operation in the past decade had aid dependency ratios of more than 10 percent, and a number of MICs with little dependency on aid have sought multiple operations.<sup>25</sup> Thus, while there is a group of low income, aid dependent countries that seek World Bank support for public sector operations, the larger share of such operations has actually been focused on countries with modest to low levels of aid dependency. This suggests that other incentives to initiate public sector reforms among those noted in section two, such as globalization and regional integration and ̳contagion‘, play a role in a substantial number of countries.

> unit (OPCFM), Fee-Based Services (FBS) and IDF (Institutional Development Fund) grants were included, an even wider spread would result.

> 24 Several countries in the region have fee for service arrangements with the World Bank which are not counted here.

> 25 Based on the Bank‘s Development Data Platform (DDP) database results for  ̳Net Official Development Assistance (ODA) received in % of GNI‘ for the 87 countries with at least one public sector operation over the past decade.
# Faini (1994).

Regarding the elasticities that reflect regional market power on imports and exports, in our central elasticity case we chose the value of four, which was the value taken by de Melo and Tarr (1992) for the selected products for which market power was present in their model. These values were halved and doubled in the low and high elasticity scenarios.

# **Trade shares**

The share of Cameroon's total exports which go to CEMAC and its share of total imports from CEMAC are shown in table 2, by sector. As shown in figure 2, these values are rather important to the results. They were calculated from unpublished data provided by the Department of Statistics and National Accounts of the Cameroonian Ministry of Economy and Finance. The original data, which was slightly more disaggregated than the sectors in our model, provided the shares of each product category in total trade, intra-regional trade and trade with the rest of the world, along with the absolute totals for each. We aggregated these data to obtain the shares relevant to our model. In table 1, the aggregate shares of exports to and imports from the region were calculated as a weighted average of the shares in table 2.

# V. **Results**

In table _5_ we present the results of our policy simulations with our central elasticities. This involves selection of the policy instruments shown in columns 1-3, the specifics of which are explained in table 3. The results are dependent on the extent of market power by Cameroon in its regional markets. As discussed above, however, given the size of Cameroon in its regional markets, it is likely most appropriate to assume that Cameroon has market power on both its imports and

-19-
that embarked on a fast-growth transformation process like Russia, ensuring equitable growth would require special attention on the poorer population groups that may lag (further) behind.<sup>4</sup> Second, we examine Russia’s welfare mobility over different time windows of varying lengths. In particular, we study a 20-year time period, from 1994 to 2015, and we further divide this period into short-run and medium-run periods. The former include four periods 1994-98, 19982004, 2004-09, and 2009-15, while the latter include two periods 1994-2004 and 2004-15. The reason for this division is twofold: first, the major financial crisis in 1998 forms a natural dividing line for pre- and post-crisis periods, and second, we want to analyze time periods of roughly equal lengths for better comparability.<sup>5</sup> This detailed dissection of the time periods offers more granular analysis than previous studies, and can uncover new insights on the dynamics resulting from the country’s economic growth. To our knowledge, no other study on Russia has offered such a detailed temporal breakdown as we attempt here.

Furthermore, studies of mobility and inequality have primarily focused on analysis of shortterm mobility. An operational obstacle to the type of welfare dynamics analysis we provide here is the prerequisite of panel survey data, which track income or consumption of the same households (or individuals) over time. Fortunately, we can exploit multiple rounds of panel data from the Russia Longitudinal Monitoring Surveys that span over two decades from 1994 to 2015. Very few, if any, transitional countries can offer the type of long-running, nationally representative panel household survey data that Russia does.<sup>6</sup>

> 4 But we return to discuss top income earners in the robustness checks section.

> 5 A major technical issue with any (long-running) panel survey is attrition over time; analyzing panels of varying lengths can help provide better comparison and more robust results. We also experiment with other ways to divide the time periods
impose distinct legal conditions under which use of each can be permitted. While all three require evidence of injury to the domestic, import-competing industry, antidumping also requires evidence that this was caused by low-priced (‘dumped’) imports, countervailing duties require evidence that this was caused by foreign-government subsidized imports, and safeguards require evidence that injury was caused by an unexpected import surge.<sup>48</sup> Overall, according to metrics such as frequency of use and import coverage, the most empirically important of the policies is antidumping. Nevertheless, safeguards use has been important for certain countries and especially during certain periods, and there is also some evidence that countervailing duty use may be increasing for some countries over time.

The data on TTBs is sufficiently comprehensive - at least relative to the other border policies we examine in Section 3 - that we are also able to present measures that illustrate policy variation and thus which can address some of our chapter’s main questions.<sup>49</sup>

> 47More recent research has utilized these data for other purposes, including studies such as Broda, Lim ̃ao and Weinstein (2008) and Blanchard, Bown and Johnson (2016)), so as to validate their primary results (that focus on determinants of applied tariffs) with empirical analysis of other border barriers.

> 48For a comprehensive survey of economics research on antidumping, see Blonigen and Prusa (forthcoming); on countervailing duties and subsidies, see Lee (forthcoming); and on safeguards, see Beshkar and Bond (forthcoming).

> 49The empirical analysis in this section updates and extends much of the information initially presented in

28
(1999), ‘Household income and child schooling in Vietnam’, _The World Bank Economic Review_ **13** (2), 211–256.

- Behrman, J. R. and Rosenzweig, M. R. (2005), ‘Does increasing women’s schooling raise the schooling of the next generation? Reply’, _American Economic Review_ **95** , 1745–1751.

- Bhalotra, S. and Rawlings, S. B. (2011), ‘Intergenerational persistence in health in developing countries: The penalty of gender inequality?’, _Journal of Public Economics_ **95** (3), 286–299.

- Binder, M. and Woodruff, C. (2002), ‘Inequality and Intergenerational Mobility in Schooling: The Case of Mexico’, _Economic Development and Cultural Change_ **50** (2), 249–267.

- Bj ̈orklund, A., Lindahl, M. and Plug, E. (2006), ‘The origins of intergenerational associations: Lessons from Swedish adoption data’, _Quarterly Journal of Economics_ **121** , 999–1028.

- Bjorklund, A. and Salvanes, K. G. (2010), ‘Education and family background: Mechanisms and policies’.

- Black, S. E. and Devereux, P. J. (2011), ‘Recent developments in intergenerational mobility’, _Handbook of Labor Economics_ **4** , 1487–1541.

- Blanden, J., Gregg, P. and Machin, S. (2005), ‘Intergenerational Mobility in Europe and North America’, _London. London School of Economics. Centre for Economic Performance_ .

- Bourguignon, F., Ferreira, F. H. and Menendez, M. (2007), ‘Inequality of opportunity in Brazil’, _Review of income and Wealth_ **53** (4), 585–618.

- Bowles,
# **3.2 Data**

Our analysis considers all 25 administrative regions (oblasts) in Ukraine, including Kyiv city as a special region. For the purposes of this paper, the pre-conflict period (2008-2012) is defined as the 5-year period before the Maidan protests in 2013, and the post-conflict period is between 2014 and 2019 (the last year of our data). All regional statistics for the pre-conflict period, including population and average per capita income numbers, are based on the official information gathered by the State Statistics Services of Ukraine (UkrStat). Our data also includes gross bilateral migration flows between oblasts for the same period, which were reported in UkrStat’s demographic yearbooks. Information on these flows reflects the official registrations of residence recorded by the State Migration Service of Ukraine.

Descriptive statistics show that Donetsk and Luhansk regions were among the most populous oblasts in Ukraine before the onset of the conflict in 2014 (Figure 1b). With 4.5 million and 2.3 million residents, they were ranked 1st and 7th among all regions, respectively. They also had relatively high real gross regional product per capita (2nd and 9th, respectively). Compared to other regions, however, migration flows into Donbas were relatively small. Incoming migrants constituted only 0.26 percent of local population in Donetsk, and 0.28 percent in Luhansk, putting them among the least attractive regions (24th and 25th among all) for migrant arrivals (1c). Among the most important sources of migrants were neighboring regions. Donetsk, for example received 22.8 percent of all its inflows from Kharkiv, 20.7 percent from Luhansk, and 12.3 percent from Dnipropetrovsk.

Migration outflows from Donbas were also small. While the nation-wide interregional migration outflow rate stood at 0.56 percent of population annually, it was only 0.3 and 0.4 percent in Donetsk and Luhansk— the 2nd and the 7th lowest rates among all regions, respectively. Before the conflict, migrants
of at least ZMK 4,000 per urban parcel, in most instances much higher) upfront rather than in installments may be unaffordable for credit constrained households, limiting transition to the formal regime and reducing pubic revenue (as property tax can be levied only on titled properties). Converting customary land into state land to allow document issuance may be resisted by local traditional authorities and give rise to informal substitutes issued by chiefs that can weaken transparency.

Questions on likelihood of land loss suggest high levels of perceived tenure insecurity: 43% of respondents perceive some risk of losing their land in the next 5 years.<sup>18</sup> Tenure insecurity is higher in rural than in urban areas (45% vs. 37%) and varies across regions with levels highest in the East where with 71%, insecurity is pervasive, followed by 43% in Lusaka and the North, and 21% and 11% in the West and South, respectively. Even if a more conservative measure of insecurity (“perceived land loss very likely”) is used, 26% of land owners are tenure insecure, a figure that is very high in global comparison. In Malawi, much lower levels of tenure insecurity had far-reaching effects on productivity and gender equality (Deininger _et al._ 2019).

> 16 This would include determining the range of people to which land can be transferred, the type of approvals needed to bequeath within the family and the risk of these not being granted, and women’s ability to make decisions on transferring land. Once such a regulatory assessment is completed, a suitable communications strategy could then be defined.

> 17 As per Kaunda (2017), Zambia’s integrated land information system (ZILMIS) includes some 600,000 documents of which at most 200,000250,000 are estimated to be complete titles. An average of 9.8% of respondents claiming to have title translates, at an average household size of 5.3 and with a national population
microfinance institution) to collect deposits and process payments, including payments on smallscale loans (Lyman et al., 2006; Siedek, 2008; Flaming et al., 2011). Results from recent field experiments in Africa that are beginning to shed light on the types of financial services that could benefit underserved market segments and overcome impediments to their uptake are also summarized. Finally, the role that technological innovation could play in bridging some of the geographic and informational divides that currently characterize the African financial landscape is explored.

# **3.1 Branching and Agents**

While the number of bank branches per capita remains low in sub-Saharan Africa compared to non-African developing countries (Figure 4), Kenya provides an example of an African country that underwent a significant bank branch expansion in recent years. According to the World Bank’s Global Financial Development Database, the total number of bank branches in Kenya increased from 2.7 per 100,000 adults in 2006 to 5.6 in 2015, while the number of ATMs increased from 2.9 per 100,000 adults to 9.7 over the same period. Those figures had declined slightly, perhaps due to the COVID-19 pandemic, but still stood at 4.7 branches and 7.3 ATMs per 100,000 Kenyan adults in 2020.

While many Kenyan banks, including some state- and foreign-owned ones, expanded their branch footprints from 2000 to 2015, the expansion strategy of one private domestic bank in particular, Equity Bank, stood out for its potential impact on outreach to under-served market segments. This institution has played a key role in fostering financial inclusion in Kenya and in neighboring countries in East Africa while remaining profitable in the process. In the period 200624
Duraiswamy and Duraiswamy (1995) used the DHTP data to estimate earning functions for public and private sector workers, and decompose earnings differentials into two parts – one reflecting differences in productive characteristics or “endowment” and the other reflecting differences in pay structures, or premiums, attached to particular characteristics common to both sectors (Blinder and Oxaca 1970). The authors found that wages are higher in the private sector and that about 87% of the sectoral wage difference could be attributed to the higher premium paid by the private sector, while the rest could be attributed to the superior human capital of private-sector workers. They showed that the endowments and the returns on these endowments are higher in the private sector, except for female and scheduled caste (SC) and scheduled tribe (ST) workers.

Madheswaran (1998) also applied Blinder-Oxaca (1970) decomposition to the DHTP and showed that the overall unadjusted wage differential (average public wage divided by average private wage) was 0.85, and that superior human capital endowments of workers in the private sector account for 29% of this difference.<sup>3</sup> For females, however, especially those from SC and ST, public wages are higher than private wages. Similar to Duraiswamy and Duraiswamy (1995), Madheswaran found that returns to productive characteristics were higher in the private sector, except for female and SC/ST workers.

Using DHTP data, Lakshmanasamy and Ramasamy (1999) model workers’ choice of sector of employment with the expected sectoral wage differential as one of their explanatory variables. Madheswaran and Shroff (2000) used a sub-sample of science graduates, around 40% of the DHTP sample, to examine public-private wage differentials among men and women. These authors also found that, on average, wages in the private sector are higher than the private sector wages.

# **4. Data, Definitions and Stylized facts**

Data for this study come from Round 50 and Round 55 of the all-India household survey collected routinely by the
# **5.2 The “Product” Spreadsheets – CVD-CTY-Products**

Each user country also has a spreadsheet presenting information on the Harmonized System (HS) product codes listed in the countervailing duty investigation petition. The CVD-CTY-Products spreadsheet has the same 3 initial column headings (Columns A through C) for each of the using countries, listed in table 2.2.1 .

**Table 5.2.1 : Variables in the CVD-CTY-Products Spreadsheets**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**D. **|**CASE_ID**|Case identifier used to link observations across different elements of the database<br>[generated by us]|
|**E. **|**HS_CODE**|Harmonized System product code for the product under investigation|
|**F. **|**HS_DIGITS**|Number of digits of the HS product code reported in HS_CODE|

# **5.3 Canada (CAN)**

Canada’s countervailing duty cases are handled by the same authorities that investigate dumping. The Anti-Dumping and Countervailing Directorate of the Canada Border Services Agency (CBSA, formerly the Canada Customs and Revenue Agency, CCRA) examines subsidy issues, while injury determinations are made by the Canadian International Trade Tribunal (CITT). Before late 2000, the CITT initiated an investigation only in two cases: after a preliminary determination of subsidizing had been made by the CCRA or if an interested party referred the issue of injury to the tribunal. The data sources used for these figures are identical to those used for the Canadian dumping data described in section 3.

The Canadian CVD workbook contains the standard data items described above as well as a few additional data items described below.

41
**Table 6. Income & Transfers**

||**Midl**|**ine Survey**<br>||**End**|**line Survey**<br>|<br>|
|---|---|---|---|---|---|---|
||Control<br>Group<br>Mean|Treatment -<br>Coeff.|Control<br>_p-value_|Control<br>Group<br>Mean|Treatmen<br>Coeff.|t - Control<br>_p-value_|
|**Labor & Non-labor income in last 7 days**|||||||
|Total income (in FDJ)|8,312|3,216 ***|_0.000_|9,110|-96|_0.843_|
|Amount (in FDJ) of beneficiary woman's labor income|1,441|3,010 ***|_0.000_|1,112|-237|_0.210_|
|Amount (in FDJ) of other HH members' labor income|6,394|357|_0.349_|7,772|44|_0.920_|
|Amount (in FDJ) of husband's labor income|7,855|-1,211|_0.150_|10,271|270|_0.673_|
|HH had non-labor income|0.109|-0.047 ***|_0.001_|0.043|-0.005|_0.616_|
|Amount (in FDJ) of non-labor income|748|-111|_0.563_|287|88.57|_0.409_|
|**Intra-HH transfers in last 7 days**(as declared by woman)|||||||
|Beneficiary woman
Policy Research Working Paper 9066

# **Abstract**

Rising obesity rates are one of the most challenging public health issues in many emerging economies. The extent to which the nutritional composition of food consumed away from home is behind this rise, and the links with socioeconomic status, is not yet well understood. This paper explores this question by combining a representative restaurant survey that includes detailed information on the nutritional composition of the most widely consumed meals in Metropolitan Lima and a representative household survey with anthropometric measures of adult women. The findings indicate that the nutritional quality in restaurants located in the food environment of the households is significantly associated with higher rates of obesity and overweight. Up to 15 percent of the socioeconomic gradient in obesity is attributable to restaurant food quality, with sodium being the main driver. This highlights the importance of considering the food environment to inform public health policies, particularly for the poor.

This paper is a product of the Poverty and Equity Global Practice. 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 gfarfan@worldbank.org.

_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 for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 1:** Timeline of Activities

|**Season/Date**|**Activity**|
|---|---|
|_Pre-planting 2015_||
|January 2015<br>February 2015|Farmer Registration<br>Soil sampling<br>Baseline survey (farmer characteristics<br>and 2014 practices)|
|_Planting 2015_||
|March 2015<br>April-July 2015|Delivery of soil analysis<br>Orders of fertilizers<br>Intervention|
|August 2015|Follow-up survey (2015 practices)|
|_Harvest 2015_||
|October-December 2015<br>February 2016|Yield estimation<br>2015 Self-reported yields survey|
|_Commercialization 2015_||
|June 2016|2015 Commercialization survey<br>(prices, sales and costs)|
|_Planting 2017_||
|May 2017|Follow-up survey (2017 practices)|

31
mortality rates are not included in the mid-term evaluation report. Because they calculate these rates for five-year periods preceding the data of the survey, it is not yet possible to calculate these rates for a pure period of MVP interventions. The DHS data, however, can tell us the child mortality trends in the MVP countries. The most recent DHS surveys show large declines over 2003-2008/09: from 114.6 to 73.6 per 1000 in Kenya, 111.2 to 80.0 per 1000 in Ghana, and 200.7 to 156.8 per 1000 in Nigeria.<sup>13</sup> Any future rigorous impact evaluation of the MV intervention will need to take account of these large changes occurring around the intervention sites.

> 12 Kibaara et al. (2008, Table 8) shows a yield of 0.506 tons/acre in Western Lowlands in 2007 (that is, 1.25 tons/hectare). In 2004 it was 0.231 tons/acre.

> 13 The child mortality figures are for the five-year period preceding each survey.

15