rafmacalaba/data-use-mentions-extended huggingface dataset:
hospital, pharmacy or traditional healer could be specifically selected for subsequent interview to validate their responses. Preferably these knewn attenders shoula be seeded among others<sup>to be</sup> interviewed.

It is not justified to extrapolate a morbidity rate found by<sup>using a</sup> recall period of less than 12 months into an annual rate,<sup>because of</sup> the seasonality and epidemicity of most conditions. Some countries have overcome this problem by extending dhe fieldwork of<sup>the health</sup> jurvey over a year (see section 5.8).

Morbidity results should be reported by symptoms or<sup>grouped into</sup> functional systems (eg. respiratory, digestive etc). It is probably futile to try to force them into medical diagnostic categories<sup>(eg.</sup> asthma, malaria etc) except for the limited number of conditions which are widely, and usually correctly, recognised by the survey population (Ross and Vaughan 1984). For these types of conditions it<sup>may be</sup> appropriate to have sets of questions in order to obtain<sup>cause</sup> specific morbidity.

In recent years, household surveys have increasingly been used for measuring the incidence or prevalence of specific diseases and the WHS could develop modules on cause-specific morbidity. Countries will have particular interests in certain diseases. In order to decide which topics to include in a national health survey it is often useful to use the kind of guide presented in Table 1. Questions on specific diseases should only be asked if interventions are to be planned around the results.

# Table I

How to select topics for inclusion in the World Health Survey (or, 'Setting Priorities in Public Health')

1. Prevalence or frequency

2. Severity 3. Ability to intervene (in terms of effectiveness, political and logistical feasibility) 4. Need for data to justify a programe 28
-0.022**|-0.007|-0.027***|
|Credit to government and state-owned||||-0.006|0.000|0.004|
|enterprises to GDP (%)|||||||
|Bank nonperforming loans to gross loans||||-0.012|0.015|-0.036*|
|(%)|||||||
|Bank credit to bank deposits (%)||||-0.003|-0.004|-0.000|
|Bank regulatory capital to risk-weighted<br>assets (%)||||0.009|0.016|-0.006|
|Lerner index||||-0.835*|-1.034**|-0.968*|
|Liquid assets to short-term liabilities (%)||||-0.001***|-0.002***|-0.002***|
|Observations|16,462|16,462|16,462|16,462|6,448|10,014|
|Countries|20|20|20|20|20|20|
|Pseudo R-squared|0.025|0.027|0.045|0.047|0.037|0.058|
|Selection equation|||Basel III Im|plementation|||
|Log of GDP (current US$)|1.083**|1.083**|1.083**|1.083**|1.166**|1.001**|
|<br>Foreign bank assets among total bank|-0.020|-0.020|-0.020|-0.020|-0.009|-0.
**Table 2: Poverty Transitions from Pre- to Post-fiscal Policy, Percent A: Men**

|||**Post-fiscal**|**APL**|**Post-fiscal**|**RPL**|
|---|---|---|---|---|---|
||**Poverty Status**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|
|Pre-fiscal|Poor|15.92|4.84|20.84|5.84|
||Nonpoor|1.14|78.1|1.37|71.95|
|**B: Women **||||||
|||**Post-fiscal**|**APL**|**Post-fiscal**|**RPL**|
||**Poverty Status**|**Poor**|**Non-poor**|**Poor**|**Non-poor**|
|Pre-fiscal|Poor|19.93|6.37|26.57|6.28|
||Nonpoor|1.56|72.14|1.86|65.29|

_Source_ : Authors’ calculations; ESS 2018/19 data. _Note_ : APL = absolute poverty line; RPL = relative poverty line.

As Figure 9 showed, the fiscal system shifts income distribution to the right. The mobility matrix in Table 3 also shows the share of individuals who transited from one income group to another as they moved from (1) market income to (2) disposable income to (3) final income.

**Table** **<u>3: Mobility Matrices from Market to Disposable and Final Income, Percent</u>**

|**A: Men**|||
include the surrounding region (oblast) outside of the city or Tashkent (Tukmadiyeva, 2016). Although Tashkent is the focus of several propiska regulations, there are also significant concentrations of people living without registration in other urban areas outside of the capital, most notably in the city of Samarkand (Table 2). It is important to note that though these estimates are derived from official records, they are likely affected by non-response and

> 10 The propiska system traces its origin to the administration of serfdom in the Russian Empire, and thus substantially pre-dates the Soviet Union. 11 When the revitalized Soviet propiska system was introduced in 1932, the documents required to obtain permission to migrate were made available to citizens living in a city or workers’ settlement. Collective farmers, who represented most of the rural population were excluded (Bukley, 1995).

> 12 Details regarding rules and administration of the propiska system are summarized in appendix A.

> 13 Ukraine is the latest example of a country from the former Soviet Union eliminating the practice. These reforms were completed in 2019.

> 14 This designation echoed the Soviet system’s list of closed cities, which included Tashkent starting in 1956.

18
# Are Inflows of FDI Good for Russian Exporters?

Stavros Poupakis _University of Oxford_

**JEL** : F14, F23, O24.

**Keywords** : Exports, Quality, FDI, Russia, Unit values.

I am grateful to Beata S. Javorcik for her support and helpful guidance throughout this project. I am also thankful to Maurizio Bussolo, Arti Grover, and Denis Medvedev who helped at various stages of this project, to Guillermo Arenas for sharing the customs data, and to the World Bank for its financial support. The findings, interpretations, and conclusions expressed in this paper do not necessarily represent the opinions of the World Bank Group, its management or its Executive Directors. I would also like to thank Ana P. Fernandes, Alvaro Gonzalez, Christopher D. Miller and Farshad R. Ravasan for helpful comments on the draft. All errors are my own. Contact: Stavros Poupakis, Oxford Martin School, 34 Broad Street, Oxford, OX1 3BD, UK, stavros.poupakis@oxfordmartin.ox.ac.uk
# **I  Introduction**

Economic growth is universally recognized as a prerequisite for development. A strong momentum in investment reflecting rising productivity, healthy corporate profits, and robust exports has fueled a buoyant economic growth exceeding 7% a year in India for almost a decade. Economic growth has increased employment opportunities and allowed millions to emerge from poverty.<sup>3</sup> Economic expansion is also often accompanied by rising demands on the already scarce and often degraded natural resources (soils, fossil fuels, water and forests) and the increasing pollution footprint will negatively impact human health and growth prospects. In India’s case, a remarkable growth record has been clouded by a degrading environment and growing scarcity of natural resources. Mirroring the size and diversity of its economy, environmental risks are wide ranging and are driven by both poverty and prosperity. Environmental sustainability could become the next major economic challenge as India surges along its growth trajectory.

This study provides estimates of social and financial costs of environmental damage in India from three pollution damage categories: (i) urban air pollution, (ii) inadequate water supply, poor sanitation and hygiene, (iii) indoor air pollution; and three natural resource damage categories: (i) agricultural damage from soil salinity, water logging and soil erosion, (ii) rangeland degradation, and (iii) deforestation. The estimates are based on a combination of Indian data from secondary sources and on the transfer of unit costs of pollution from a range of national and international studies (a process known as benefit transfer). Data limitations have prevented estimation of degradation costs at the national level for coastal zones, municipal waste disposal and inadequate industrial and hospital waste management. Furthermore the estimates provided do not account for loss of non-use values (i.e. values people have for natural resources even when they do not use them).

## _Methodology for Valuation of Environmental Damage_

The quantification and monetary valuation of environmental damage involves many scientific disciplines including environmental, physical, biological
for primary education. Health employment figures are also from Nigeria Federal Expenditure Review of May 25, 1995 and only refer to the emplovees of the Federal GovemmenLi Data on military do not include personnel enlisted in paramilitary corps such as the Port Security Police (2,000) or the Security and Civil Defense Corps (unknown).

# Sao Tome & Principe

General Govemment employment estimate is from IMF Report 96170 of August 1996 and relates to 1993.

Senegal The unemployment rate is taken from Priority Survey (Direction de la Prevision et de la Statistique) for the year 1992. It does not include people who are currently underemployed as reported by Abdoulaye Seck (AF5SN) and is for.1992.

Central, non-central, education and employment data are provided by Abdoulaye Seck in the Senegal Resident Mission. Data on Education and Health are taken from a survey done by a local consulting firm in March 1995. Salaries for Health are entirely paid by the central administration. Contributions of local administrations are mostly for payment of salaries of temporaries (proctors, cleaning crews, etc.). This staff is not included in the total health employment. The Armed Forces, the largest component of military employment, is mostly made up of conscripts. The data on military employment do not include people enlisted in paramilitary corps such as the Gendarmerie (4,000).

GDP estimates are from World Tables 1995 and relate to 1993. Wages and salaries estimate is taken from Abdoulaye Seck (AF5SN) and is for 1994. Data on wages and salaries as multiple of per capita GDP was confirmed with Mr. Seck and is consistent with results of other francophone countries.
Figure A18: Decomposition of consumption difference between urban and rural areas <!-- Start of picture text --> (A) DOU (B) DB<br>150 150<br>100<br>100<br>50<br>50<br>0<br>0<br>Endowments Returns Endowments Returns<br>Contribution to the mean consumption difference (%) Contribution to the mean consumption difference (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distribution of percentage contribution of (1) endowments and (2) returns to the mean differences in the log per capita consumption expenditures between urban and rural areas in 18 countries based on the Oaxaca-Blinder decomposition. The share of interactions is not shown for the sake of presentation. WorldPop 250m is used for the DOU and DB methods.

39
# **Table 8. Bargaining Power**

|||**Midlin**<br>Control<br>Group|**e Survey**<br>Treatmen|t - Control||**Endlin**<br>Control<br>Group|**e Survey**<br>Treatmen|t - Control|
|---|---|---|---|---|---|---|---|---|
||Obs|Mean|Coeff.|_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Expenditures last 30 days (FDJ)**|||||||||
|Husband clothes and shoes|914|277|6|_0.931_|882|345|15|_0.881_|
|Beneficiary Woman clothes and shoes|914|468|162 **|_0.044_|882|499|111|_0.322_|
|Khat and Tobacco for male adults|844|5,250|-87|_0.888_|792|5,908|585|_0.600_|
|**Woman's participation in HH decisions**|||||||||
|Index 1: Woman participated in HH decisions|n.a.|n.a.|n.a.|n.a.|882|0.000|0.116 *|_0.059_|
|Index 2: Woman took decisions alone|n.a.|n.a.|n.a.|n.a.|882|0.000|0.078|_0.255_|

Notes _:_ Coefficients from an OLS regression of the left-hand side variable on a treatment dummy, controlling for strata dummies, surveytime dummies, and baseline traits (see Table 3 note).
(column 2 to 4 in Table 4). The latter two categories are also the most expensive ones in Brazil. Revolving credit card loans or overdrafts from the largest institutions carry between a 170 and 420 annual percent rate.<sup>14</sup> Treatment students are 5 percent less likely to have credit cards purchases than control students (a 1.75 percentage point decrease compared to the control group mean of 34.4 percent).

The results in the following section (4.b) show that treatment students are less likely to hold a formal job, i.e., a job with a written contract, which could potentially explain their lower use of credit. However, we do not believe this to be the case since the results in Table 4 are similar when we consider only the sample of students who have a formal job, according to RAIS, as shown in Table A2 in the Appendix. In column 8 of Table A2, we use RAIS data to examine whether the financial education program affected the wages of those who were formally employed. We do not find any statistically significant effect on wages.

In Table 5, we examine the effect of the financial education program on having credit contracts in default. Column 1 shows that treatment students are less likely to have credit contracts with repayment delays than control students. Here, delays are defined as being between one day and

> 12 Garber et al. (2019) provide stylized facts on the use of credit by individuals in Brazil.

> 13 In Brazil, new purchases only yield interest rates if they are not paid in full at the due date of the bill, even if there is an outstanding revolving balance resulting from preceding billing cycles. There are also purchases made in installments directly at shops, who receive their payment as the installments become due.

> 14 BCB, Relatório de taxa de juros, March 2019.

13
|19.7|-8.9|
|Intermediate inputs|15.2|11.0|8.7|-5.7|
|Capital goods|12.4|8.1|6.2|-5.5|
|Source: Team calculations<br>1997 are obtained from Ed|using TRAINS<br>wards (2005)|data at HS|6-digit level;|SACU tariffs from|

Notes: a. Change is based on the 2001-2007 period. *1995 tariff used in Madagascar for 1997 period *2002 tariff used for 2001 period for Mauritius, Zambia and Angola *2006 tariff used for 2007 for Angola and Malawi. 2008 tariff used for Zambia The end-use classification is based on the BEC classification obtained UN Statistics. Passenger vehicles are excluded as they are both a capital and consumption good. The percentage change in the tariff inclusive border price is calculated as ( _t_ 1  _t_ 0)/(1 + _t_ 0), where _t_ 1 and _t_ 0 refer to tariff rates in the final and initial periods, respectively. These rates do not include ad valorem equivalents and are therefore not directly comparable to those obtained from the WTO World Tariff Profiles 2009 and used in the prior table.

In addition to regional or unilateral measures to reduce tariffs, many SADC countries are members of the WTO and benefit from preferential access to developed markets through the Generalized System of Preferences, Everything But Arms and the Africa Growth and Opportunity Act (AGOA). For example, preferential access for apparel is important for Lesotho, Botswana, Namibia, Madagascar and Malawi, who have all seen a rise in exports as a result of AGOA (de Melo and Portugal-Perez, 2009).

In sum, the SADC FTA has largely eliminated within SADC tariffs, but many countries remain members of multiple overlapping and sometimes inconsistent agreements on the continent. MFN tariffs fell from the mid-1990s, although the degree of liberalization varies across members and effective protection remains high on consumer goods
# _Appendix Table A.7. Summary Statistics_

|||Total|||Unskilled|||Skilled||
|---|---|---|---|---|---|---|---|---|---|
||||Change|||Change|||Change|
||2002|2004|<br>2002-2004|2002|2004|<br>2002-2004|2002|2004|<br>2002-2004|
|**Personal Characteristics**||||||||||
|Observations|1746|1746||1065|1065||656|656||
|Female (%)|.36<br>|.36<br>||.31<br>|.31<br>||.46<br>|.46<br>||
||(.48)|(.48)||(.46)|(.46)||(.50)|(.50)||
|Minority (%)|.073|.073||.066|.066||.087|.087||
||(.26)|(.26)||(.25)|(.25)||(.28)|(.28)||
|Education (years)|9.25|9.38||7.21|7.30||12.41|12.61||
||(4.23)|(4.26)||(3.55)|(3.59)||(3.08)|(3.03)||
|Potential Experience|18.63|20.45||18.01|19.86||19.58|
27 by equation 6. The estimated coefficients on the industry dummy variables are interpreted as “inter-industry wage differentials” following Krueger and Summers (1987).

In the long run, the price decrease would affect the “general” component of wages. In our apparel industry application, as long as industry _j_ is female-intensive (we assumed that T&G is female-intensive), any decrease in the price of industry _j_ (T&G in our case) will affect<sup></sup> 1<sup>, which</sup> represents the gender wage gap. Of course, what time frame constitutes the “long run” is not clear at the start. Estimates of the relevant time frame for “long run” effects are rare. Robertson (2004) suggests that the Stolper-Samuelson effects begin to emerge in three to five years.

# **4.3. Main Results**

Table 5 presents the results of wage regressions for Cambodia and Sri Lanka in the short run and table 6 presents the results of wage regressions for both countries in the long run. The regression analysis was carried out using the methodology described in section 2. For our analysis, we used eight rounds of the Cambodian Socio-Economic Surveys that covered the 1996–2011 period, and the 1992–2002, 2008, 2011, and 2012 Sri Lankan Labor Force Surveys.<sup>21</sup> In both countries, working in apparel pays a premium compared to the economy average.<sup>22</sup> Column 1 of table 5 shows that the MFA-era wage premium was 39.2 percent in Cambodia and column 3 of table 5 shows that this premium was 6.7 percent in Sri Lanka. This result contrasts with table 4, which might be interpreted as demonstrating that the apparel sector is a low-wage “sweatshop” sector. In comparison to international wages, wages are indeed low in the apparel sector. But table 5 shows that relative to alternatives for similar individuals (
weil as the uses of services affecting health (Schultz 1985).

No single approach to the provision of information or to analysis will be able to resolve the difficulty of research in this area. A great deal can be learnt from sophisticated analyses of retrospective survey data using statistical controls for confounding factors. However, such investigations have inherent limitations for the definite attribution of causality. Moreover it is impractical to collect information on all subjects of interest using such surveys. An important consideration for the WHS is that surveys should form at most only one aspect of a more extensive strategy aimed at understanding mechanisms and processes affecting health. Moreover it should be noted that the methodologically-sophisticated analyses needed to investigate determinants of health are likely to be beyond the capability of many developing country institutions.

# 3.MEASURING THE IMPACT OF INVESTMENTS IN HEALTH

# 3.1 Economic evaluation

The second objective of the proposed WHS is "to develop appropriate methodology for measuring the impact on health status of investments in health as well as other sectors". Economic evaluation techniques of cost-benefit analysis (CBA) and cost-effectiveness analysis (CEA) are methods for guiding monetary investment in any sector, and so are particularly appropriate to the stated WHS objective. In this section the problems of these techniques are examined at some length in order to identify data which could be collected and used to strengthen their methodology.

It is argued that the problems of such techniques undermine the usefulness of collecting data across countries for the purpose of evaluating the impact of investment on health. Although this is the commonly accepted objective of economic evaluation, even countryspecific studies of this nature should be treated with caution. On the 14
on the cross-sectional distribution of consumption (Chaudhuri, 2003)), neither of which are completely satisfactory. We show that measures of vulnerability that properly take covariate shocks into account can result in a very different understanding of welfare differences across groups than poverty measures at one point in time. This highlights the importance of considering such measures of vulnerability when designing poverty-targeted programs in many African countries where poverty is infrequently measured and covariate shocks are important.

Economists have recently incorporated rainfall data as an exogenous source of shocks in various empirical models to estimate ex-post impacts of weather (Dell et al., 2014). The impact of weather shocks on consumption outcomes for households has been analyzed in this way for a small sample of villages in rural Ethiopia by amongst others Dercon and Krishnan (2000); Dercon (2004); Porter (2012), and similarly food price shocks in urban areas have been analyzed using a household panel by Alem and S ̈oderbom (2012). In this paper we quantify the welfare impact of drought and price inflation at a national level in Ethiopia. We use community (woreda) level information on rainfall-induced crop-losses as the objective measure of rainfall shocks and control for the probability of drought in a given community using the historic distribution of drought. Conditional on the probability of drought, the timing of the shock is considered to be exogenous.

> 1The dataset we use has full coverage of all but the pastoral areas of Afar and Somaliland.

2
assumptions involved in adding vulnerability to the concepts behind poverty mapping. Section 4 provides estimates of poverty and vulnerability using data on Senegal, the distribution of communes by poverty- and risk-induced vulnerability rates, and several robustness checks. Section 5 provides background on the RNU, the recent expansion of the registry, and the operational implications of using vulnerability as part of the eligibility quota system. The last section concludes.

# 2. Expanding poverty mapping to measure vulnerability

## Targeting in social programs

The use of targeting in antipoverty programs in the context of limited resources has been widely studied, and there is a general consensus on the positive relationship between effective targeting and a program’s impact (Alatas et al. 2012; Brown, Ravallion, and van de Walle 2016; Coady, Grosh, and Hoddinott 2004).

3
Firm-Level Survey**

In order to support our preliminary findings, we conducted a directed survey and generated primary data from informed parties. These included firms involved in the value chain; government officials from the Ministry of Commerce and Industry, Customs, Directorate of Textiles; researchers; and academia. Information gathered from each source was cross-checked with other sources to ensure verification and validity.

Primary information was generated mainly from T&C firms of both countries. A total of 40 firms (20 in each country) were interviewed in 2016 to generate both quantitative and qualitative information. The firms are spread across India (Coimbatore, Bhilwara and Ludhiana) and Bangladesh (Dhaka, Narayanganj and Gazipur districts). Two sets of comprehensive structured questionnaires were used to collate information. The questions were related to firm size, items traded, quality of infrastructure and institutional services, impact of tariffs and taxes, NTBs, time taken in trade by stages, the role of trade policy and South Asia Free Trade Area (SAFTA), RoO, constraints related to value chains, and others. In addition, interviews were conducted with a 21