rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 7. Incidence of main components of tax benefit system by deciles, share of final income, percent.**

<!-- Start of picture text --> 110<br>90<br>70<br>50<br>30<br>10<br>-10<br>-30<br>-50<br>poorest 2 3 4 5 6 7 8 9 richest total<br>SSC Direct Taxes Pensions Direct Transfers<br>Indirect Taxes In-kind Health In-kind Education Total<br>Note: deciles are defined based on market income.<br>Source: authors’ calculations using HBS-2017 data.<br><!-- End of picture text --> If we examine the incidence of main components of the tax benefit system separately for some representative household types, there are three categories of net payers, while other four benefit from the system (Table 10, Figure 8). The three categories of net payers are households with no children, and no pensioners, and families that consist of two working age adults and one or two children. They do benefit from the system through direct transfers, inkind health and education, but the amount of their contributions through direct and indirect taxes is higher than the benefits they receive. On net they contribute 4-12 percent of their final incomes. These types of families are among the least vulnerable.

The main beneficiaries are retirees-only households that get 69 percent of their final income as net benefits from the system, mainly through pensions (73 percent) and other direct transfers (8 percent). The households in this category are also the main beneficiaries of the health system in relative terms (9 percent of their final income is coming from this source). However, for obvious reasons, they do not benefit from the education system. Families without children, but with pensioners and working adults are also among net beneficiaries, receiving on average 33 percent of their final income mainly through pensions
# **1 Introduction**

What makes a survivor? For exporters, this question has become a focus of study since the seminal work of Besedes and Prusa (2006a) showed that the median duration of USbound export spells was, strikingly, only two years. Subsequent work has identified the importance of the sustainability margin of exporting in addition to the classical margins: the intensive (growth in volume) and the extensive (diversification of products and markets). Low-income countries in particular face very high exit rates from exporting, despite having entry rates similar to developed economies (Brenton, Saborowski and Uexkull 2009). It is for this reason that in developing countries a key element in achieving higher aggregate export growth is to reach higher survival rates for existing trade flows (Besedes and Prusa 2006a).

The growing literature which looks at the determinants of export survival and the duration of trade has followed several strands. Theoretical developments (using both representative and heterogeneous firms) such as the search-cost theory (Rauch and Watson 2003) or the literature on sunk costs have some explanatory power, and the importance of sunk costs in firms’ export decisions has been empirically verified (Roberts and Tybout 1997). With sunk costs one would expect long spells, but this tends to conflict with the low values found in the literature. It has therefore been argued that there is currently no given theoretical model that can fully explain the short trade durations that have been observed (Brenton, Saborowski and Uexkull 2009, Hess and Persson 2011b).

Empirical studies have focused on macro trade data at the country and industry level (usually from UN’s Comtrade database), the dominant focus being on specific determinants of export survival. Research in this vein has analyzed, for example: the impact on duration spells of differentiated versus homogeneous products (Besedes and Prusa 2006b); the effect of the GDP in origin or destination country (Hess and Persson 2011b); the importance of initial export values and fixed costs to export (Fugazza and Molina 2009
Even though the results in this paper cannot provide a conclusive answer to the question of what is leading price dispersion in multiple-destinations exporters, it opens the way for future research in this area. In fact, even though it is a standard practice to treat agricultural commodities as homogeneous goods, these commodities are perceived by consumers as differentiable in quality, according to production practices, seeds, geographical locations of production, sanitary and phyto-sanitary measures and food safety requirements. For this reason, differences in brand, quality and other aspects of heterogeneity make these products differentiated in international trade<sup>4</sup> . Allowing for the possibility that food products can be vertically differentiable amplifies the spectrum of existing opportunities for developing countries to exploit product differentiation and market niches in countries where consumers are willing to pay more for products that satisfy specific quality dimensions.

The remainder of the paper is organized as follows: Section 2 reviews the theoretical and empirical framework upon which this work is built. Section 3 reports how Tanzanian trade performed during the 2003-2009 period. Section 4 introduces the data used in this analysis and highlights some preliminary characteristics. Section 5 studies the variation in prices across firms and destinations to investigate whether the Tanzanian data shows evidence of price or quality competition. Section 6 concludes.

# **2. Literature Review**

Before starting the empirical analysis, it is useful to classify the main models of trade and heterogeneous firms and review the predictions that are most relevant for this paper. This section will focus on the predicted relationship between firms’ fob export prices and: revenue, quantity, GDP, GDP per capita and distance. This brief review draws on Baldwin and Harrigan (2011) and Manova and Zhang (2012). The classification of the reviewed models is summarized in Table 1. Even though it is based on Baldwin and Harrigan (2011), unlike their analysis the fob export price in Table 1 is intended as the unit price of a product at the _firm level_ , not the unit price of a particular
Fig. 5 Sectors’ contribution to annual value added growth in 2014-16 and 2017-19 (in %) Fig. 6 Firms’ annual employment growth (in %) <!-- Start of picture text --> 6 6<br>5<br>4<br>4<br>3<br>2<br>1 2<br>0<br>-1<br>0<br>-2<br>-2<br>EGY JOR LBN MAR PSE TUN<br>-4<br>Other sectors<br>Manufacturing and services - ES comparable<br>Source: the authors’ calculations based on UN National Source: the authors’ calculations based on EBRD-EIB-WB<br>Accounts Main Aggregated Database (2015 USD). Enterprise Survey.<br>2014-16 2017-19 2014-16 2017-19 2014-16 2017-19 2014-16 2017-19 2014-16 2017-19 2014-16 2017-19<br><!-- End of picture text --> Many productive firms might not be able to reap the scale and efficiency benefits from trade and innovation activity because of the weak business environment and state dominance in the economy. According to the latest WEF Global Competitiveness index, MENA countries perform worse than peer economies in terms of macroeconomic environment, labor market efficiency, technological competitiveness and business dynamics (Figure 7). Under this challenging business environment, the traditional channels of development and performance might be blocked or narrowed considerably.

Fig. 7 Global competitiveness Index <!-- Start of picture text --> Upper Middle Income (average) MENA (selected 6 economies)<br>Institutions<br>120<br>Innovation Infrastructure<br>80<br>Business Macroeconomic<br>dynamics environment<br>40<br>Market size 0 Health<br>Technological<br>Skills<br>competitiveness<br>Financial market Commodity<br>development market efficiency<br>Labour market<br>efficiency<br><!-- End of picture
Policy Research Working Paper 9152

# **Abstract**

Accurate estimates of men’s and women’s employment are at the heart of understanding sources of productivity and economic growth and designing well-targeted, gender-sensitive labor policies. How respondent selection in household and labor force surveys affects these estimates is a key question, for which experimental evidence outside of high-income settings is limited. Leveraging two concurrent, national surveys in Malawi that differed in their approach to respondent selection, the analysis shows that compared to the best practice of privately interviewing adults about their employment outcomes, the common “business-as-usual” approach that permits the use of proxy respondents and non-private/group interviews leads to significant underreporting of employment across a range of wage- and self-employment activities, with stronger effects for women and for a longer (12-month) recall period. Under the business-asusual approach, the main factors linked to under-reporting include household wealth, proxy reporting, and potential difficulties associated with interpreting/answering on questions regarding household non-farm enterprises.

This paper is a product of the Development Data Group, Development Economics. 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 tkilic@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._
). Statistical analysis of data from household surveys is normally used to refine the set of indicators included in the index and to estimate their respective weights. Information collected from each program applicant is then used to construct a household‐specific ‘PMT score’, which is a proxy for the family’s relative welfare level in the population. This score is used to determine eligibility for safety net programs and potentially also the level of benefits. A household is eligible for the program if its PMT score falls below a predetermined cutoff score.

Although actual household income is in theory a more accurate measure of welfare (and indeed self‐ reported income is currently used as a main eligibility criterion for the Samurdhi program), it can be difficult to accurately measure or estimate reliably in countries like Sri Lanka, where a large proportion of households are self‐employed or informally employed.<sup>5</sup> The value of informal transfers like gifts and remittances is also difficult to measure. If income self‐declarations are difficult to verify, households have a temptation to understate income in order to qualify for benefits. In cases like Sri Lanka where accurate data on household consumption is also burdensome to collect and difficult to verify, self‐reported consumption faces similar drawbacks as a targeting approach. The PMT offers a way to estimate a household’s welfare level without utilizing self‐declared income or consumption.

> 5In the first quarter of 2016, one=third of all workers were own account workers (Department of Census and Statistics, 2016).

3
A strand of the literature studies spillovers from protests. Several papers have documented that protests in Tunisia and Egypt have inspired protesters elsewhere (Bamert et al, 2015; Hale, 2013; Lynch, 2013; Saideman, 2012). Similar spillovers were observed from other revolutionary periods, such as the Eastern European democratization in the 1990s and the revolutionary wave of 1848 in Europe (Hale, 2013; Weyland, 2010, 2012).

The remainder of the paper is organized as follows. Section II presents the data and the empirical strategy. Section III shows the results. Section IV presents robustness checks. Section V concludes.

# **II. Data and Empirical Strategy**

Data on protests are from the Armed Conflict Location & Event Data (ACLED) Project. Protests are defined in the data set as _“non-violent demonstrations, involving typically unorganized action by members of society.”_ This category covers peaceful protests, protests with interventions (e.g. arrests), and protests in which excessive force was used against protesters (e.g. causing injuries and deaths). Note that this definition does not include violent demonstrations and mob violence. The data coverage starts in 1997 but is severely imbalanced, rendering difficult the use of earlier years to conduct the spatial analysis. We aggregate the number of protests at the monthly (and quarterly) level.

Figure 1 shows the evolution of the number of protests at a monthly frequency as recorded by ACLED—in absolute number (Panel A) and in per million capita (Panel B). Note that starting in 2010 and subsequent years more countries were added to the data set—see Panel A of Appendix Table A1 for the list of countries included in the data set. That explains the big jump in the series in the year 2010. We hence restrict our sample to start in January 2010 when using the ACLED data set.<sup>2</sup>

**Figure 1: Evolution of the Total Number of Protests**

*
accuracy of the estimate is significantly reduced. A monthly beta estimate, for example, requires at least five years' data (60 data points) to be used while a daily beta can be calculated for a single year (around 250 data points). The latter estimate is the more meaningful if a company is subject to changes in its operating environment or regulatory regime, as explained in Appendix A1.4.

Examples shown in Appendix A3 show the extent of the interval problem. Beta estimates for British Gas fall as the interval length is increased while those of the two US water companies rise with the interval length, as expected from the fact that the former is large and frequently traded while the latter are infrequently traded. US gas companies show a mixture of trends, reflecting the variety of sizes of the companies in this group. Beta estimates for the UK RECs rise with the interval length, a slightly surprising result which implies that these companies are relatively infrequently traded compared with the index as a whole. It should be noted, however, that the confidence intervals for the longerinterval estimates are very wide, as shown in Appendix A4. Taking the case of five-year asset beta estimates for BT, daily data produce a 95% confidence interval of 0.81 to 0.92, weekly data give a range of 0.79 to 1.02, while the monthly estimate lies between 0.61 and 1.03.

The response taken here to these problems has been to use daily beta estimates in order to obtain a narrow confidence interval for betas calculated over fairly short periods and to deal with the interval problem in the following ways. As explained in Section 6. 1, a largesecurity index results in a smaller interval-effect bias than an all-share index. Industry averages have been calculated; where there is a range of company sizes, this procedure should overcome the bias involved in using any single estimate. This is indicated by the case of US gas companies given in Appendix A3: the average daily, weekly
the Black Box”, Working Paper (2010).

- Deichmann, Uwe, Somik Lall, Stephen Redding, and Anthony Venables, “Industrial Location in Developing Countries”, World Bank Research Observer 23:2 (2008), 219-246.

- Delgado, Mercedes, Michael Porter, and Scott Stern, “Clusters, Convergence, and Economic Performance”, Working Paper (2010).

- Duranton, Gilles, and Diego Puga, “Micro-Foundations of Urban Agglomeration Economies”, in Vernon Henderson and Jacques François Thisse (eds.) Handbook of Regional and Urban Economics, Volume 4 (Amsterdam: North-Holland, 2004), 2063–2117.

- Fernandes, Ana, and Ariel Pakes, “Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data”, Working Paper (2010).

- Fernandes, Ana, and Gunjan Sharma, “Together We Stand? Agglomeration in Indian Manufacturing”, Working Paper (2011).

- Foster, Lucia, John Haltiwanger, and C.J. Krizan, “Aggregate Productivity Growth: Lessons from Microeconomic Evidence”, in Dean, Edward, Michael Harper, and Charles Hulten (eds.) New Developments in Productivity Analysis (Chicago, IL: University of Chicago Press, 2001), 303372.

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# **8. References**

- American Automobile Manufacturers Association. 1996. _World Motor Vehicle Data._ Washington, D.C.

- Bates, J.J., H.F. Gunn, and M. Roberts. 1978. "A Model of Household Car Ownership." _Traffic Engineering and Control_ 19 (11/12): 486-491, 562-566.

- Beesley, Michael E., and John F. Kain. 1964. "Urban Form, Car Ownership and Public Policy: An Appraisal of Traffic in Towns." _Urban Studies 1_ (2).

- Bennathan, Esra, Julia Fraser, and Louis Thompson. 1992. "What Determines Demand for Freight Transport?" Policy Research Working Paper 998. World Bank, Washington, D.C.

- Button, Kenneth, A.D. Pearnan, and A.S. Fowkes. 1982. _Car Ownership Modeling and Forecasting._ London: Gower Publishing Company Ltd..

- Button, Kenneth, Ndoh Ngoe, and John Hine. 1993. "Modeling Vehicle Ownership and Use in Low-Income Countries." _Journal of Transport Economic and Policy_ 27 (January): 51-67.

- Canning, David. 1998. "A Database of World Infrastructure Stocks 1950-1995." Policy Research Working Paper 1929. World Bank, Washington, D.C.

- Chin, Anthony, and Peter Smith. 1997. "Automobile Ownership and Government Policy: the Economics of Singapore's Vehicle Quota Scheme." _Transportation Research A_ 31 (2): 129-140.

- Deaton, Angus. 1987. _The Demandfor Personal Travel in Developing Countries._ Washington, D.C.: World Bank.

- _The Economist._ 1996. "Taming the Beast-A Survey on Living with the Car." 22 June 1996, 1-18.

- Glaister, Stephen. 1981. _Fundamentals of Transport Economics. New_ York: St. Martin's Press.

- Hau, Timothy, D. 1992. "Congestion Charging Mechanisms for Roads
# **1. Introduction**

The basis for poverty and inequality analysis is household welfare, which is commonly measured by household consumption (Deaton and Zaidi 2002; Ravallion 2008).<sup>2</sup> A key component of welfare measurement is price adjustment. To compare the welfare level of households over time and across regions, household consumption aggregates need to be adjusted by accounting for inflation (that is, temporal price adjustment) and cost-of-living differences across regions (that is, spatial price adjustment).<sup>3</sup> Inappropriate adjustments of such price difference cause a bias in poverty estimation. Spatial price adjustment becomes particularly important when policy makers need to know not only who is poor but also where poverty is concentrated.<sup>4</sup> Despite the importance of spatial price adjustment on welfare measurement for poverty and inequality analysis, its underpinning theory is not necessarily clear to guide the practice. Many low- and middle-income countries apply different methods, without a rigorous methodological foundation, often facing the limited availability of price data suitable for spatial price adjustment as the biggest challenge.<sup>5</sup> The recently collected survey by the World Bank illustrates how the current practice of spatial price adjustment varies among Sub-Saharan African countries (Figure 1).<sup>6</sup> In its internal process of developing a regional poverty database, the World Bank collected from 47 Sub-Saharan African countries information about the data sources and methods used for price adjustment. In terms of data sources (Panel A), about 40 percent of African countries rely on survey unit values, which is a proxy measure of prices calculated by dividing households’ expenditures by the purchased quantity for each good based on household budget or welfare surveys. While unit values might be useful in cases where price data are lacking, the problems related to unit values have been reported in several studies (Gibson and Kim 2019; McKelvey 2011). Another 26 percent of the countries use market
# **Table S1.9: Robustness: including control for survey length as proxy for fatigue**

||DP|FDC|NRM|UPC|
|---|---|---|---|---|
|Enumerator age|-0.529***|-0.383**|-0.118***|-0.497***|
||(0.145)|(0.154)|(0.040)|(0.113)|
|Enumerator gender (male)|-0.020|-0.004|0.002|-0.032**|
||(0.020)|(0.019)|(0.005)|(0.015)|
|Enumerator education|0.110|0.137|-0.075**|0.065|
||(0.106)|(0.119)|(0.036)|(0.108)|
|Enumerator from urban locality|0.449**|0.571***|0.008|0.432***|
||(0.177)|(0.196)|(0.045)|(0.144)|
|Survey length|0.002|0.001|-0.001|0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Observations|6,038|6,126|6,341|6,111|
|R-squared|0.491|0.461|0.241|0.485|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note
only the Islamic Republic of Iran, red and processed meat intake was associated with increased risk of obesity (OR 1.37) and higher mean waist circumference and BMI (Rouhani et al. 2014). High adherence to an unhealthy ‘western’ dietary pattern was a strong risk factor for overweight/obesity in one meta-analysis of 18 studies in HICs and LMICs (OR 1.65; 95% CI 1.45, 1.87) (Mu et al. 2017) but not in a meta-analysis of 8 studies from China (OR 1.34, 95% CI 0.98, 1.84) (Jiang et al. 2022).

## **Health outcomes – diabetes and hypertension**

The SRM for adults included 24 meta-analyses with T2DM and 11 with hypertension as the outcome. In the only study that summarized the evidence for dietary diversity, there was no evidence of an association with cardiometabolic risk factors, including T2DM and hypertension (Qorbani et al. 2022). High adherence to healthy dietary patterns is protective for both T2DM and hypertension, with relative risk estimates in the range of 0.79-0.86 for T2DM (Alhazmi et al. 2014; Esposito et al. 2014; McEvoy et al. 2014; Morze et al. 2020; Maghsoudi, Ghiasvand, and Salehi-Abargouei 2016). Adherence to a vegetarian diet also was associated with lower blood glucose levels (Dinu et al. 2017) and risk of T2DM (Lee and Park 2017). The odds of hypertension were lower (OR 0.87; 95% CI 0.78, 0.98) among adults with higher adherence to a Mediterranean diet(Cowell et al. 2021). Total dairy consumption was also associated with reduced risk of T2DM and hypertension, although there were very small numbers of LMIC studies included in these estimates (Chen et al. 2022; Feng et al. 2022; Mishali et al. 2019; Khoramdad et al. 2017; D. Gao et al
HEIs grew by 40% relative to 2000, yet the number of municipalities with other HEIs grew by only 10%. These differential trends define the relative availability of higher education programs within a specific community. Our analysis seeks to uncover the effect of opening SC HEIs.

We focus on higher education supply as of 2004, one year before students in our sample graduated from high school. Of the 300 HEIs operating at the time, only one-third were SC HEIs. SC programs in Colombia are also provided by SENA ( _Servicio Nacional de Aprendizaje_ ). This public institution has been providing workforce training for many decades; it only started offering SC programs in 2003 and had a negligible SC market share by 2004. It is not an HEI and operates outside the purview of the Ministry of Education. As a result, we do not include it in our analysis.

Of the 918 municipalities in our sample, 14% had an SC HEI within a 10 km radius (Table 3, municipality-level panel). This figure was 28% for other HEIs. Moreover, during our period of analysis, the vast majority of SC HEIs were located in cities that already

> 6Own calculations using data from the National System of Information on Higher Education (SNIES, in Spanish).

6