rafmacalaba/data-use-mentions-extended huggingface dataset:
directly (and correctly) aimed at avoiding that demand falls too much and consumers’ hording may move demand for food up, future work could use general equilibrium models to quantify this channel. Finally, we abstracted in this paper from changes in import policy. Tariffs and other barriers to imports are high on food products and governments may lower them as food prices rise. While reduction in trade barriers is generally efficient, short-term policy changes such as tariff suspensions may contribute to price escalation by shifting out demand in the short term without bringing the longer-term benefits of more open food markets (Martin and Anderson, 2012).

# **References**

<mark>Aguiar, A., Chepeliev, M., Corong, E., McDougall, R., & van der Mensbrugghe, D. (2019). The GTAP Data Base: Version 10.</mark> _<mark>Journal of Global Economic Analysis, 4(1)</mark>_ <mark>, 1-27.</mark> Baldwin, R. (2020). “The supply side matters: Guns versus butter, COVID-style.” VoxEU. March 22, 2020.

Baldwin, R. and B. Weder di Mauro (eds) (2020a). Economics in the Time of COVID-19, a VoxEU.org eBook, CEPR Press.

Baldwin, R. and B. Weder di Mauro (eds) (2020b). Mitigating the COVID economic crisis: Act fast and do whatever it takes, a VoxEU.org eBook, CEPR Press.

Espitia, Alvaro, Nadia Rocha, Michele Ruta (2020a). “Trade and the COVID-19 crisis in developing <u>countries”. Washington, DC: World Bank; and VoxEU. April 9, 2020).</u> Espitia, Alvaro, Nadia Rocha, Michele Ruta (2020b). “Database on COVID-19 trade flows and policies”. Washington, DC: World Bank.

EUI-GTA-WB (2020). “Covid-19 Trade
|Biology|0.044<br>0.506|-0.218|
|Physics|0.005<br>0.340|-0.144|
|History|0.070<br>0.464|-0.202|
|Chemistry|0.030<br>0.504|-0.214|
|Geography|0.056<br>0.424|-0.182|
|Philosophy|0.064<br>0.407|-0.173|
||_Educational Attaintment_||
|Short-Cycle Incomplete|0.495||
|Short-Cycle Complete|0.342<br>0.012||
|Bachelor’s program Incomplete|0.101<br>0.502||
|Bachelor’s program Complete|0.062<br>0.485||
||_Formal Labor Market Outcomes_||
|Works in 2013|0.517<br>0.547|0.473|
|Avg. Monthly Wage (2013)|909,590.9<br>1,120,830.8|548,288.1|
|N|30,803<br>96,504|201,051|

Note: The sample corresponds to the universe of students who took the high school exit exam (Saber 11) in 2005. The information on educational attainment comes from SPADIES (System for Dropout Prevention of Higher Education), and labor market outcomes for higher education graduates are from OLE (Labor Market Observatory for Education). For high school graduates and those with incomplete higher education, we impute labor market participation and wages using household survey data from SEDLAC (Socioeconomic Database for Latin America and the Caribbean); see Appendix A.3 for details. Avg. Monthly wages are in Colombian pesos (COP).

24
WPS3784

# **Trade Costs, Export Development and Poverty in Rwanda**

Ndiame Diop<sup>a</sup> , Paul Brenton<sup>a</sup> and Yakup Asarkaya<sup>b*</sup>

## **Abstract**

For Rwanda, one of the poorest countries in the world, trade offers the most effective route for substantial poverty reduction. However, the poor in Rwanda, most of whom are subsistence farmers in rural areas, are currently disconnected from markets and commercial activities by extremely high transport costs and by severe constraints on their ability to shift out of subsistence farming. The constraints include lack of access to credit and lack of access to information on the skills and techniques required to produce commercial crops. The paper is based on information from the household survey and a recent diagnostic study of constraints to trade in Rwanda. It provides a number of indicative simulations that show the potential for substantial reductions in poverty from initiatives that reduce trade costs, enhance the quality of exportable goods and facilitate movement out of subsistence into commercial activities.

### World Bank Policy Research Working Paper 3784, December 2005

_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._

> *<sup>a</sup> Trade Department, World Bank.<sup>b</sup> University of Virginia. We would like to thank Ataman Aksoy, John Baffes, Kene Ezemenari and Waly Wane
2006; von Lersner et al., 2008). Lastly, in terms of arguments grounded in political participation as the affirmation of in-group identity, it is not clear what identity deportees could be affirming unless they left their home country because of persecution on the basis of identity. Deportees are a relatively small segment of any country’s population, do not typically know each other, and often arrive in small groups. In place of these possible mechanisms discussed by other scholars, we posit one novel mechanism tying victimization to depressed politi– – cal participation fear and one novel mechanism tying extortion specifically to increased – political participation economic shock.

# **3.2 Victimization and Fear in the Context of Migration**

There is an extensive literature which shows that migrants tend to vote less than their native counterparts (e.g. Shaw et al., 2000; Cassel, 2002; Garcia, 2011; OECD, 2018). Internal migrants also participate less in formal and informal networks, and turnout declines in areas with larger proportions of migrants (Akarca and Tansel, 2015; Gay, 2012; Gaikwad and Nellis, 2020; Villamizar Chaparro, 2021). However, there is little literature on 1) the political participation among migrants who are deported to their country of origin or on 2) how variation in violence experienced while migrating affects political participation. We theorize that victimization such as extortion is correlated with increased fear among deportees. Furthermore, this fear is politically demobilizing.

What are the psychological impacts of migration journey violence? Experiencing abuse while migrating is predictive of depression and alcohol dependency (Altman et al., 2018). Among those individuals who experienced victimization during the migration process, such as being robbed or attacked, 21 percent are at risk for PTSD (Perreira and Ornelas, 2013). This is true not only in Central American migrants, but in migration populations around the world. For example, one meta-analysis of 113 articles confirms that exposure to violence during migration affects mental health; the
welfare dynamics analysis is highly relevant and helps shed further light on the processes driving these changes. To our knowledge, this is the first time such analysis is being implemented in the context of the Arab countries.

One major obstacle that hinders the analysis of welfare dynamics in the Arab countries is the ubiquitous absence of panel household survey data. Even when such panel surveys exist, they are often plagued by data quality issues such as attrition bias due to the fact that some households drop out of the sample in follow-up survey rounds. We overcome the lack of actual panel data in the Arab countries by constructing synthetic panels from repeated cross sectional survey rounds using the methods developed in Dang et al. (2014) and Dang and Lanjouw (2013). These synthetic panels allow us to examine the movements among different categories of objective welfare, including the low-income, middle-income, and top-income groups, and subjective welfare, including the groups of the unhappy, struggling, and satisfied, with a particular focus on the Arab Spring countries. Furthermore, since these synthetic panels are constructed from fresher rounds of the repeated cross sections, they are (much) less affected by the issues discussed above.<sup>3</sup> Our findings highlight the value added of undertaking welfare dynamics analyses with synthetic panels using both objective expenditure data and subjective well-being data from micro survey data.<sup>4</sup> The results suggest strong upward mobility for objective welfare in Tunisia and the Syrian Arab Republic, and downward mobility in the Republic of Yemen and the Arab Republic of Egypt. However, subjective well-being has deteriorated in most countries prior to and after the Arab Spring (2009-2012). The share of dissatisfied people increased while those of the happier

> 3 In addition, the definition of “panel data” may itself vary for different contexts. For example, a panel may be defined based on whether the household head remains the same or whether the residence, where the data are collected, remains the same. Our analysis
|72.8|27.2|
|Education|1.0|29.5|80.5|3.3|41.5|58.5|
|Miscellaneous|6.7|45.7|54.3|1.0||100.0|
|Non-food in kind||||0.2||100.0|
|Remittances||||7.6||100.0|
|Total|100.00|_46.5_|53.5|100.0|32.5|67.6|

a) The clothing category in the 1993 priority survey includes expenses for hairdressers and tailors, which were classified as non-tradable.

**Table 13** Evolution **of the Tradable and Non-tradable Components of the CPIa)**

||_Apr_|_il 1995_|_Janua_|_rv 1996_|
|---|---|---|---|---|
|_Components_|||||
|_of the CPI_|_Tradable_|_Non- tradable_|_Tradable_|_Non- tradable_|
|Food|146.3|150.3|155.5|133.9|
|Total|155.3|139.7|159.3|133.4|

a CPI index is the INS index for "ouvrier, employd qualifid on artisan traditionnel." Base period is October 1992 to November 1993. For the breakdown of posts into tradable and non-tradables, see text.

What are the implications for poverty? To the extent that there is relatively little differentiation in the rise of nontradable and tradable goods prices in the CPI consumption basket, differences in the share of importables in the consumption basket across socioeconomic groups or between the poor and nonpoor will have relatively little influence on poverty outcomes. Moreover, in 1993 there was relatively little difference between the poor and nonpoor population in the share of tradables in total household expenditure (table 14
potentially involve long recall periods, which could result in recall bias. This would affect accuracy of information on some job characteristics. Evidence suggests that we can draw fairly accurate information on labor market dynamics from retrospective data. More elaborate information about job characteristics such as fine distinctions between different employment statuses are less reliable (Assaad et al., 2018). In our analysis, we focus on transitions in and out of different jobs, so we have reasons to believe that the data is reliable for this purpose. Recall of job start and end dates might be trickier. This is evident in the important number of respondents who could not recall the exact month or year when they started or ended a job. Since information on starting and ending dates of a job are essential for constructing the synthetic panel, we excluded from our dataset 19 individuals who could not recall the year where they started or ended any of their jobs. It is, however, easier for respondents to recall the year they started or ended a job rather than the specific month. For example, one third of the individuals who reported the starting year of their first job had a missing starting month. To avoid losing those who did not recall the specific month, we assigned a random number between 1 to 12 when the month was missing. In doing so, we considered the following: a) Respondents who ended and started two different jobs in the same year and reported the month of ending the former job are assigned a starting month for the latter job that is equal to or greater than the month when they ended their former job.

b) Respondents who ended and started two different jobs in the same year and reported the month of starting the latter job are assigned an ending month for their former job that is equal to or less than the month where they started their latter job c) Since the survey ended before the end of 2020, we make sure that in 2020 the month assigned is less than or equal to the month where the
opportunity to track how attendance responds to a variety of conflict-related shocks.

However, in order to accurately identify the factors that most affect schooling during active conflict, it is necessary for mobile phone penetration to be high and for access to mobile phones to be relatively unaffected by important conflict-related shocks. Evidence from the last nationally-representative survey in 2014 suggests that access to mobile phones was high prior to the conflict, and evidence from the WFP mobile phone surveys suggests that access has remained high during the conflict. Additionally, there is no evidence that any of the shocks analyzed here affect either the number of active mobile phone numbers in a household or other household characteristics that are unlikely to rapidly change. Thus, although the survey misses households without access to mobile phones who potentially have worse access to education, the relatively high penetration of mobile phones allows an analysis that is representative of the majority of the population.3 Using the WFP surveys, we illustrate a number of patterns in access to schooling during the conflict in Yemen. First, we demonstrate that sudden changes in violence and fragility (e.g., conflict fatalities) need not lead to a change in access to schooling in all cases. In particular, we estimate the change in school attendance in response to southern secessionists capturing the capital of the internationally-recognized government (IRG), a partial ceasefire unofficially declared by the de facto authorities in the north (DFA) and Saudi Arabia, and in response to a sudden and violent campaign led by the DFA to capture a northern governorate from the IRG. Each event was unexpected, caused a substantial change in violence, and one of these events had a significant impact on peoples’ livelihoods. However, in all cases, there was little change in school attendance in affected regions. These findings are further corroborated by a lack of correlation between violence in the district of residence and school attendance for the entire sample of households.

And second, we demonstrate that other conditions associated with conflict aside from the location of violence can have a
have only limited information about their customers). However, the provider’s view of its customers’ and non-customers’ access to financial services may be seriously deficient. An interesting comparison between provider surveys and user surveys is obtained by the Genesis/Finmark study of Swaziland mentioned earlier. In a first, provider survey, estimated access percentages for transactions accounts, savings accounts, credit and insurance were obtained by surveying experts and providers. However, as became clear in the subsequent design and implementation of a 600–household survey on usage of financial services, neither the range of providers nor the close substitute services had been adequately covered in the provider survey. Access as measured by household respondents was over twice that measured in the provider survey.<sup>30</sup> The main reason was that the providers and experts surveyed at the provider stage were not sensitized to the importance of nontraditional forms of the services being examined. The banks gave data on full-service transactions accounts, but did not mention ATM-enabled savings accounts; the importance of store credit, of savings at informal savings clubs ( _stokvels_ ) and of insurance with burial societies were all neglected at that stage. Of course these aspects could have been surveyed if they had been explicitly considered, but the process of considering matters from the household’s side, as in designing and piloting a household survey, is far more likely to elicit a fuller understanding of the substitute products and services for certain traditional formal financial sector products.

# **5. Expert surveys.**

Experts can be surveyed in order to obtain information about conditions that are general across the economy as whole or some region of it. Highly relevant examples would be the surveys of Transparency International and the World Bank’s _Doing Business_ team. The respondents to the community-level questionnaires in the LSMS and similar household conditions surveys are also expert respondents in this sense.

The sort of information that can be sought in this way includes such matters as how much it costs and how many days it takes
- **Borrowed Money from Informal Source** (Non_Bank_Borrow): Money borrowed from sources other than banks. Original variable names in dataset: q805 and q810_ Dummy variable equals 1 if any individual in the household who has borrowed money borrowed the money from a source other than a bank (relative, friend, private money lender, NGO, other), equals 0 if money was borrowed from banks.

- **Trust in Banks** (Trust_Bank): Level of trust in Banks and the financial system. Original variable name in dataset: q303j Dummy variable equals 1 if there is some or complete trust in the banking system, equals 0 if there is some or complete distrust or neither trust or distrust.

The World Bank Development Indicators (WDI, 2014)

- “NY.GDP.PCAP.PP.KD” - GDP per capita, PPP (constant 2011 international $)

- “NY.GDP.PCAP.PP.KD.ZG” - GDP per capita, PPP annual growth (%)

- “NY.GDS.TOTL.ZS” - Gross domestic savings (% of GDP)

- “SI.POV.25DAY” - Poverty headcount ratio at $2.5 a day (PPP) (% of population)

- “SI.POV.2DAY” - Poverty headcount ratio at $2 a day (PPP) (% of population)

- “SI.POV.4DAY” - Poverty headcount ratio at $4 a day (PPP) (% of population)

- “SI.POV.5DAY” - Poverty headcount ratio at $5 a day (PPP) (% of population)

- “SI.POV.DDAY” - Poverty headcount ratio at $1.25 a day (PPP) (% of population)

- “SI.POV.NAHC” - Poverty headcount ratio at national poverty line (% of population)

25
is not significant for importers that impose domestic sourcing.

The results by industry summarized in Table A-6 in the Appendix are less conclusive than the results by sector, because of the incompleteness of the data at this level of disaggregation.<sup>8</sup> They nonetheless confirm that the border effect is greater for importers with preferential treatment provisions in government procurement, and more precisely estimated for manufacturing industries.

> 8 The PPML estimates converged for 152 industries out of the 177 in the ITPD-E database.

18
the evidence is mostly indirect, given that it is not based on actual indicators of the scale and nature of linkages between MNC affiliates and local suppliers. Furthermore, information on firm-level characteristics is also often limited in these data sets, restricting the analysis of the impact of characteristics of MNC affiliates and local suppliers on productivity spillovers.

As indicated by findings from some recent studies that benefit from using rich data sets, one way forward is to conduct larger-scale, purpose-built surveys to obtain information on the extent and nature of inter-firm linkages, as well as information on performance indicators of local suppliers. The World Bank Enterprise Survey (WBES) could play a central role in this endeavor, given its extensive coverage of a large variety of developing and emerging economies. The growing evidence identifying backward linkages as the main channel transmitting productivity from MNC investments suggests that
35 Report to USAID, R-643-AID, Santa Monica, California, Rand Corporation, June, 1971.

25. Schultz, T. Paul, "Interrelationships Between Mortality and Fertility', in Ronald G. Ridker (ed.), Population and Develogment -- The Search for Selective Interventions, The Johns Hopkins University Press, 1976, pp 239-289.

26. Schultz, T. Paul, "Fertility and Child Mortality over the Life Cycle Aggregate and Individual Evidence", American Economic Review, Vol. 68, No. 2, May 1978, pp 208-215.

27. Scrimshaw, Susan C. M., "Infant Mortality and Behavior in the Regulativn of Family Size", PoRulation and Development Review, (1979) 28. Simmons, George B., "The Policy Implications of the Relationship between Fertility and Socio-Economic Status", Population Planning Working Paper No. 24, University of Michigan, June 1977.

29. Simon, Julian L., The Effects of Income on Fertility, Carolina Population Center University of North Carolina at Chapel Hill, 1974.

30. Taucher, Erica, "Effects of Declining Fertility on Infant Mortality Levels: A Study Based on Data from Five Latin American Countries", Latin American Demographic Centre (CELADE), Report No. 82-2-222, Report to the Ford and Rockefeller Foundations, 1982.

31. Warwick, Donald P., "The Indonesian Family Planning Program: Government Influence and Client Choice", Pogulation and Development Review, Vol. 12, No. 3 (September 1986).

32. Wheeler, David, "Human Resource Development and Economic Growth in Developing Countries: A Simultaneous Model", Staff Working Paper No. 407. World Bank, 1980.

33. Winegarden. C. R., "Socioeconomic Equity and Fertility in Developing Countries", Economist (Leiden), vol 128, pp.530-557, 1980.

34. Zachariah, K. C., and Sulekha Patel, "Determinants of Fertility Decline in India -- An
**Table 1. Nutritional status of children in Kagera region, Tanzania. 1991-1994**

||Percentage<br>|of children whose z-score is below –1 SD<br> <br>|
|---|---|---|
||Height for age|Weight for age<br>Weight for height|
|female|0.68|0.62<br>0.26|
|Male|0.72|0.65<br>0.29|
|Total|0.70|0.63<br>0.28|
||Percentage|of children whose z-score is below –2 SD|
|female|0.35|0.25<br>0.05|
|Male|0.43|0.30<br>0.060|
|Total|0.39|0.28<br>0.06|

**Note** : For Kagera region the DHS data in 1996 report 42% of kids under five stunting, 36% under-weight and 11% wasting. (The percentage reflect those children whose z-score are below –2) Source: Kagera Health and Development Survey (KHDS).

**Table 2. Nutritional Status in Tanzania for Children Under-Five**

||Percentage of children whose Height z-score is belo|w –2 SD|
|---|---|---|
||Height for age<br>Weight for age|Weight for height|
|1991-92|0.43<br>0.29|0.06|
|1996|0.44<br>0.31|0.07|
|1999|0.43<br>0.29|0.06|

**Source** : REPOA (2004), calculated using DHS 1991/92, DHS 1996 and TRCHS 1999.

19
ratio of final use (residential, public, and half of transportation) and intermediate use (industry, and half of transportation) of the TFC is 0.94, average value over 2000-2022. Finally, about 87% of the Turkish TPES is imported, coming from GLORIA database. In the model, this corresponds to the share of imported carbon in total carbon input.

- Wage share of output in core goods and energy sectors are around 39% and 0.08%, calculated from GLORIA database.

- Renewable to fossil energy employment ratio is 0.15, calculated from Global Trade Analysis Project (GTAP)-Power and Gender Disaggregated Labor Database (GDLD) of the World Bank.

> 27To obtain the renewable-fossil energy ratio, total final consumption (TFC) would be the appropriate measure. Yet, it is unclear to what extent electricity is generated by renewables and fossil energy. Hence, we use the ratio of total primary energy supply (TPES) as a proxy.

65