rafmacalaba/data-use-mentions-extended huggingface dataset:
taxes are sometimes ad valorem or sometimes the maximum of the ad valorem or specific tax rate. The results are reported in table 3.2.

**Input-output table.** The core input-output table for this section is the 1995 table produced by Goskomstat. (This was updated for the applications in Rutherford and Tarr (2008; 2010.)  The official table contained only 22 sectors, and importantly has little service sector disaggregation. Consequently, Russian input-output expert S. P. Baranov disaggregated this table into a 35 sector input output table. Baranov used unpublished data available to Goskomstat based on the surveys that were used to construct the 1995 table. The principal elements of this disaggregation were: a split of the oil and gas sector into oil, gas and oil processing; a split of the transport sector into railroad, maritime, air, pipeline, truck and other transportation services; the breakup of communication into post services and telecommunications; and disaggregation of the data in several business services sectors regarding market and non-market activities. The documentation by Baranov is available on the website www.worldbank.org/trade/russia-wto.

# **3.3. Results**

In our general WTO scenario, we assume that: (i) barriers against foreign direct investment are reduced as indicated in table 3.2; (ii) seven sectors subject to anti-dumping actions in export markets receive slightly improved market access. This is implemented as an exogenous increase in their export price as shown in table 3.2; and (iii) the tariff rates of all sectors are reduced by fifty percent.<sup>30</sup> We first discuss (and present in table 3.3) our estimates of the impact of Russian WTO accession on aggregate variables such as welfare and the real exchange rate, aggregate exports, the return to capital, skilled labor and unskilled labor, and the percentage change in tariff revenue. In order to obtain as assessment of the adjustment costs, we estimate the percentage of
We extend this approach in this paper to Tunisia and present much more detailed analysis for Egypt and Tunisia on whose status is changed by adopting a focus on household enterprises and who participates in them. Our specific research questions are (1) How do men’s and women’s employment rates change when adding enterprise-based detection questions to standard individual-level questions? (2) Was the additional market employment detected with projectbased approaches classified as subsistence work with individual measurement approaches? (3) For which women do we detect additional employment using project-based approaches?

# **2 Previous efforts to improve the measurement of female labor force participation in Egypt**

As an illustration of the challenges and innovations in measuring women’s economic participation, this section discusses efforts to improve measurement in Egypt. Efforts to improve the measurement of female labor force participation (FLFP) in Egypt date back to the early 1980s and efforts at the Central Agency of Public Mobilization and Statistics (CAPMAS) to improve the training of enumerators involved in the data collection for the Labor Force Survey (LFS). The enumerators’ manual used in 1983 clearly indicated that the definition of the labor force includes many unpaid activities done for the family, mentioning specifically family farming (Anker and Anker 1989). Other changes such as increasing the number of female interviewers and reducing the number of households per interviewer per day were also introduced in that year (Anker and Anker 1989). As shown in Figure 1, this effort paid off with a big increase in the measured FLFP rate from 7% in 1982 to 12% in 1983, with the rural rate in particular tripling from 4% to 12%.<sup>3</sup>

> 3 Given the way that CAPMAS presents the data in the LFS Bulletins from 1977 to 2007, we use a somewhat unconventional definition of the FLFP rate, which we are able to reproduce throughout the period. The numerator of the rate is the economically active female population aged 12 to 64 and the denominator is the
# **1 Introduction**

The number of preferential trade agreements (PTAs) has been increasing steadily since the 1980s, as has the number of signatories and coverage of non-trade policy areas in such agreements (Dür _et al._ , 2014; Lechner, 2018; Mattoo _et al._ , 2020). Non-trade provisions (NTPs) are a prominent feature of the more recent PTAs concluded by the EU and the US, as well as by many other OECD member countries. They pertain to such matters as environmental protection, labor standards and human rights and have become central to political support for PTA negotiations (Van den Putte, 2015; Lechner, 2016). The increasing prevalence of NTPs in PTAs in recent decades is accompanied by extensive heterogeneity in national non-trade outcome indicators. As discussed further in Section 2. The direction of change in some non-trade outcome indicators associated with NTPs has often been negative.

Given the prominence of non-trade issues in recent debates on globalization in general and trade agreements in particular, whether the inclusion of NTPs in PTAs supports the realisation of underlying non-trade policy objectives, i.e. they actually work to promote climate and socioeconomic goals, is a fundamental policy issue. This is the primary – research question analyzed in this paper does inclusion of NTPs in PTAs improve associated non-trade outcomes? We address this question by exploiting new data on trade agreement coverage (Mattoo _et al._ , 2020) and the synthetic difference-in-difference (SDID) estimator recently proposed by Arkhangelsky _et al._ (2021) to address endogeneity concerns. We extend the literature by allowing for the potential effectiveness of NTPs to depend on the nature of the commitments made. Specifically, we distinguish between NTPs depending on whether they take the form of binding, legally enforceable commitments or non-binding (non-enforceable) soft law provisions that only provide a framework for policy dialogue on the matters covered.<sup>1</sup> In both cases, NTPs provide a potential focal point for technical and financial assistance to support cooperation
study their export participation and entry decisions.<sup>10</sup> Finally, we use two alternative measures to proxy for the regulation intensity at the product level. First, we use the WTO Specific Trade Concerns (STC) Database, which provides product-level (HS4) information on trade concerns raised in the WTO’s SPS and TBT committees. The main advantage of this database is that it captures the “revealed” stringency of standards, as perceived as important trade barriers by exporters, rather than counting the number of standards that may or may not be trade impeding (Fontagné et al., 2015; Fontagné and Orefice, 2018). We classify products to be regulation intensive if at least a concern was raised to one of the committees. There are 312 specific concerns raised on 124 products in the SPS committee and 317 specific concerns raised on 848 products in the TBT committee, together covering a broad range of 863 products across different industries (see Appendix Table A.4 for the distribution of STC-products across industries). Second, we use an alternative measure of regulation intensity based on prior notice requirements by the United States’ Food and Drug Administration (FDA). This measure covers a total of 272 HS4-products, mainly in food, drugs, and medical devices, which require prior notice to the FDA.<sup>11</sup> To construct the data set needed for the first part of our analysis (firm-level), we expand the initial exporters database to make it squared so that every firm-destinationyear combination is an observation. For the second part of our analysis using the product-level data, we first aggregate firms’ exports data (available at HS6-level) to the HS4-level, which is the level available in the WTO STC data set. Then, we expand the data set so that each firm-product that exported at least once is combined with every destination and year. There are 12,918 firms exporting 1,197 HS4-products to 115 destinations, and there are 102,295 firm-product
4.5 million per year on average (table 20). Many African countries have done significantly better in this area (figure 22). Notable is the absence of private investments in the power and transport sectors. Countries such as the Democratic Republic of Congo, Liberia, Nigeria,

**Figure 21. ODA commitments by sector**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>ICT Transport WSS Power<br>US$ million per year<br><!-- End of picture text --> _Source:_ AICD Fiscal Matrix 2011. _Note:_ ODA = official development assistance; ICT = information and communications technology; WSS = water supply and sanitation.

Uganda, Kenya, and Senegal have captured between 1.8 and 2.5 percent of GDP. But in 2008 and 2009 the ICT sector attracted more than $80 million in foreign direct investment (FDI), most of it from mobile operators (World Bank 2009d).

42
for both cross-sectional and panel data, respectively. Both tables 5 and 6 show that the log of GDP per capita is positively correlated with the SCI score, statistically significant at the 1 percent level.

The proxy variable used for data transparency is the World Bank’s Statistical Capacity Indicator (SCI). The overall SCI indicator is based on a diagnostic framework to assess the capacity of national statistical systems over time. This indicator is ideal as it covers both the quantity (frequency) and to some extent the quality aspects of data, building on studies that have mostly focused on the former (Williams, 2009; Hollyer et al, 2011). The framework has three dimensions: (i) Source data, (ii) Methodology, and (iii) Periodicity and timeliness of socioeconomic indicators. Each dimension is evaluated on criteria based on metadata information from the World Bank, IMF, UN, UNESCO, and WHO. The overall SCI score is the average of the three sub-indicators calculated for each dimension.<sup>5</sup> The source data dimension reflects whether a country conducts data collection activity in line with internationally recommended periodicity, and whether data from administrative systems are available and reliable for statistical estimation purposes. This dimension covers the micro-data aspect of data transparency that is essential given microdata are foundational for a country’s data system. Specifically, the criteria used are the periodicity of population and agricultural censuses, the periodicity of poverty and health-related surveys, and completeness of vital registration system coverage. A country can achieve a perfect score if it has conducted 1 or more population census in the last 10 years, 1 or more agricultural census in the last 10 years, 3 or more health surveys in the last 10 years, 3 or more health surveys in the last 10 years, and has a complete vital registration system.

The statistical methodology dimension measures a country’s ability to adhere to internationally recommended standards and methods. This aspect is captured by assessing guidelines and procedures used

> 5 The composite
# Import Uncertainty and Export Dynamics<sup>_∗_</sup>

Mariana Vijil<sup>_†_</sup> Laurent Wagner<sup>_‡_</sup> Martha Tesfaye Woldemichael<sup>_§_</sup>

## **JEL Classification** : F14, L25, O1.

**Keywords** : export dynamics, import uncertainty, supply chain unreliability, Customs, trade costs, firms.

> _∗_ We are grateful to Ana Margarida Fernandes and Hibret Belete Maemir for providing access to the Exporter Dynamics Database. We thank Jean-Fran ̧cois Arvis, Cyril Chalendard, Jaime de Melo, Enrique Fanta, Ana Margarida Fernandes, Pierre Mandon, Zafer Mustafaoglu, Martha Denisse Pierola, Patrick Plane, Mary-Fran ̧coise Renard, Shawn Tan as well as participants of the AFR External Sector Network Seminar at the IMF for their useful comments and discussions. All errors remain our own. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the Ferdi, IMF or World Bank.

> _†_ World Bank. Email: `mvijil@worldbank.org`

> _‡_ FERDI. Email: `laurent.wagner@ferdi.fr`

> _§_ IMF and CERDI, Universit ́e Clermont Auvergne. Email: `mwoldemichael2@imf.org`
Policy Research Working Paper 8924

# **Abstract**

Adequate water, sanitation and hygiene (WASH) in health care facilities plays a critical role in ensuring improved health care utilization and reducing disease burden due to reinfection. WASH in health facilities is now gaining momentum with the new SDG targets that governments have vowed to meet. This goal calls for a baseline examination of existing WASH conditions in health facilities. Using data collected through a census of all community health clinics in Bangladesh, this paper presents an analysis of the state of WASH in Bangladesh’s rural, public health facilities highlighting that the lack of functionality of WASH facilities is a widespread problem across the country. The paper also identifies priority areas for action when considering the prevalence of poverty and chronic undernutrition at the upazilla level.

This paper is a product of the Water 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 gjoseph@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
# **Farther on down the road: Transport costs, trade and urban growth in Sub-Saharan Africa**

# Adam Storeygard<sup>_∗_</sup>

# This draft: April 2012

**Keywords:** Urbanization, Transport costs, Infrastructure, Roads, Sub-Saharan Africa

**JEL classification:** F15, O18, R11, R12, R4

## **Sector Board:** Urban

> _∗_ Tufts University. Email: adam.storeygard@tufts.edu. I am grateful to David Weil, Nate BaumSnow, and especially Vernon Henderson for their advice and support. I thank Katherine Casey, Uwe Deichmann, Leo Feler, Andrew Foster, Delia Furtado, Jake Goldston, Walker Hanlon, Erik Hurst, Blaise Melly, Sriniketh Nagavarapu, Mark Roberts, Nathan Schiff, Michael Suher, Ishani Tewari, Matt Turner, Nicholas Wilson, Junfu Zhang, and seminar participants at Brown, CIESIN, Columbia, Georgia State, Maryland, MIT, Notre Dame, NYU, Syracuse, Toronto, Tufts, UC Irvine, Wharton, EconCon, the Midwest International Economic Development Conference, and the annual meetings of the Center for the Study of African Economies (CSAE) and the North American Regional Science Council (NARSC), for helpful comments and suggestions, Uwe Deichmann and Siobhan Murray for access to and guidance on the roads data and NARSC and the World Bank’s Research Support Budget and Knowledge for Change Program for funding. All errors are my own.

1
for some destinations (such as high-income countries) than others. Additionally, higher education may also reduce policy barriers to many destinations due to the higher demand in destination countries.

In summary, this paper shows that, unlike the intentions of ‘root cause’ policies, emigration towards highincome destinations generally increases with development. The response is much smaller for low-income origin countries during early phases of development relative to the response from middle-income origin countries. However, development is strongly associated with shifts in the composition of migration. First, migrants sort into high-income and more distant destinations, which arguably offer higher returns, than low-income neighboring countries. Second, emigration to high-income destinations becomes more intensive in formal education. These patterns reflect the multiple transformations that accompany development, including rising incomes, rising global integration, demographic shifts, and rising human capital stocks. This paper presents these stylized patterns of migration in the context of the transformations that happen as low- and middleincome countries develop.

# 2. Data and empirical strategy

The data sources and empirical strategy for this paper draw heavily from Clemens (2020). The discussion that follows briefly describes the data sets used and the empirical strategy.

## 2.1 Data

**Migration** : The analysis uses the most comprehensive data set on bilateral migration stocks for each year, in ten-year intervals, spanning from 1960 to 2020. This data set allows the migration pattern of countries to be traced over time—a significant improvement in methodology compared to earlier studies. Migration stocks for years 1960, 1970, 1980, 1990, and 2000 are taken from Özden et al. (2011) and those for 2010 and 2020 are taken from UN DESA (2020). These data sets have migrant stock information, collated from censuses and surveys at destination and origin countries, for 226 countries and territories across the world and provide estimates for the 50,850 migration corridors.

A migrant, in these data sets, is typically a person who is born in a country that is different from
WPS3397

# **_A Unified Framework for Pro-Poor Growth Analysis_**

# B.  Essama-Nssah<sup>•</sup>

Poverty Reduction Group The World Bank Washington, D.C.

# Abstract

Starting with a general impact indicator as an evaluation criterion, this paper offers an integrative framework for a unified discussion of various concepts and measures of propoor growth emerging from the current literature. It shows that whether economic growth is considered pro-poor depends fundamentally on the choice of evaluative weights. In addition, the framework leads to a new indicator of the rate of pro-poor growth that can be interpreted as the equally distributed equivalent growth rate. This is a distribution-adjusted rate of growth that depends on the chosen level of inequality aversion. Illustrations based on data for Indonesia in the 1990s show a strong link between growth and poverty reduction in that country. A decomposition of the observed poverty outcomes reveals the extent to which changes in inequality have blunted the poverty impacts of both growth and contraction. Finally, the results also demonstrate that absolute and relative indicators of pro-poor growth can lead to conflicting conclusions from the same set of facts.

Keywords: Pro-Poor Growth, Evaluation, Indonesia, Inequality, Poverty Decomposition. JEL Classification: C15, I32, O53 World Bank Policy Research Working Paper 3397, September 2004 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ • The author is grateful to Erwin H.R. Tiongson and Jean-Yves Duclos for their insightful comments, and to
|Poverty gap|**4.6***|**4.4***|5.3|**4.2**|**4.3***|**4.2**|**4.4***|**4.5***|**4.9***|4.6|
|---|---|---|---|---|---|---|---|---|---|---|
||(0.4)|(0.4)|(0.5)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.4)|(0.3)|
|USAID Poverty gap|**26.3***|**25.3***|**27.5**|**24.9***|**25.1***|**25.2***|**25.4***|**25.5***|**26.9***|25.9|
||(1.1)|(1.1)|(1.3)|(1.1)|(1.1)|(1.2)|(1.1)|(1.1)|(1.1)|(1.1)|
|N|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|
**Figure 10: Sector employment shares by agglomeration size distribution**

<!-- Start of picture text --> Secondary sector Secondary sector<br>1970 Tertiary sector 2000 Tertiary sector<br>Core area's secondary industry share Core area's secondary industry share<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Smallest Largest Total Smallest Largest Total<br>City size category City size category<br>Percent Percent<br><!-- End of picture text --> Source: Brazilian Population Census of 1970 and 2000 Overall, the share of tertiary industry has increased rapidly from 54.8 percent in 1970 to 69.4 percent in 2000. The city size decomposition of the tertiary industry shows concentration of service sector employment in bigger cities. As we move up the urban hierarchy, the service industry share in 2000 increases from 62.8 percent among the smallest agglomerations to 71.2 percent among the largest. The manufacturing industry share in non-agricultural employment is highest in the medium size cities (30.2 percent in 2000). It suggests that manufacturing decentralization was relatively more intense from large to medium size cities.

Industrial decomposition within agglomerations shows a pattern of manufacturing suburbanization from the core to the suburb areas. The manufacturing industry share in the core areas, relative to the total city manufacturing industry employment, decreased from 64.2 percent in 1970 to 47.0 percent in 2000 (Table 9). The relative manufacturing employment share of the core areas decreases as a city becomes bigger. Manufacturing suburbanization is more distinct in bigger agglomerations and this process is also observed in the tertiary sector. The suburbanization of service industry shows a similar pattern as those of manufacturing. The service industry has experienced an overall increase in suburbanization over the period
PQ(C) is price of commodity c QINT(C, A) is the quantity of intermediate commodity, c, purchased by activity, a The price of the aggregate intermediate input now includes the rebate per unit of aggregate intermediate input: _PINTA_ ( _A_ ) = ∑ _PQ_ ( _C_ ) ⋅ _ica_ ( _C_ , _A_ ) − _REBATE_ ( _A_ /) _QINTA_ ( _A_ ) _c_ where: PINTA(A) is the price of the intermediate aggregate input PQ(C) is the price of the composite commodity ica(C,A) is the intermediate input c per unit of aggregate intermediate QINTA(A) is the quantity of aggregate intermediate input 8 We use the effective tax rates based on tax collection data. The rates differ by sector and differ from the reported statutory rate of 14% due to sector aggregation, the treatment of the informal sector and/or measurement error.

7
|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|Constant|0.109***|0.110***|0.109***|0.109***|0.109***|0.103***|
||(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|
|No. of obs|36,724|36,724|36,724|36,724|36,724|36,724|
|R-squared|0.288|0.288|0.288|0.288|0.288|0.288|

_Note_ **_:_** Results are from 2019-2022 panel regressions where the unit of observation is the village council and the dependent variable is the NDVI for fields planted with winter wheat in each of the relevant years. Crimea and areas that have been occupied since 2014 are excluded. GDD is cumulative growing degree days above 0°C between October 1and May 31 approximated hourly by sinusoidal distribution of minimum and maximum temperature (d’Agostino and Schlenker, 2016); and fall, winter and spring months are Oct-Nov, Dec-Feb, and Mar-May, respectively. ‘Any’ (Col. 5) is the maximum over the 8 two-week periods considered whereas ‘conflict intensity’ (col 6) accounts for the temporal dimension by taking a value of 1 for any of the 8 two-week periods when any conflict activity was observed. See appendix table 1 for full specification including weather variables. Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

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