rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7953

# **Abstract**

This paper studies the relationship between access to credit, demand shocks, and export market adjustments using firmlevel panel survey data for 24 economies in the Eastern Europe and Central Asian region. The study finds that domestic shocks to demand have a significant influence on the firm’s decision to participate in international markets (extensive margin) and on the firm’s share of foreign sales (intensive margin). Foreign shocks to demand only affect the firm’s share of foreign sales. Conversely, the role of financial constraints on either the extensive or the intensive margin is more nuanced. The results are robust to various specifications of financial constraints and different estimation methods.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at fdenicola@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
|167,357|0.20|0.00|0.40|0.00|1.00|
|Rural|167,357|0.60|1.00|0.49|0.00|1.00|
|**DB urban classifcation:**|||||||
|Core|167,357|0.35|0.00|0.48|0.00|1.00|
|Suburb|167,357|0.23|0.00|0.42|0.00|1.00|
|Town|167,357|0.19|0.00|0.39|0.00|1.00|
|Other rural|167,357|0.41|0.00|0.49|0.00|1.00|

Source: International Urban Poverty Database. Note: The statistics above are based on the household-level data pooled for 16 SSA countries. Real per capita consumption expenditures are deflated using a spatial deflator calculated using the WorldPop 250m dataset and the DOU method.

# **4 Empirical approach**

## **4.1 Updating urban classification in HBS**

In order to ensure consistent comparisons across countries, it is necessary to establish a standardized approach for defining urban areas, considering the wide variation in official national definitions. Building upon Combes et al. (2023), we employ both the DOU and DB approaches _._ While the DOU approach applies uniform population and population density thresholds to all countries, the DB approach provides more nuanced insights by identifying urban areas based on the relative density distribution within each country. Given the complementary nature of these 10
that extends to developing countries for the year 2009 alone.

2 Some earlier studies that analyze the effects of services reform on economy‐wide performance without using firm‐level data are Mattoo _et al_ . (2006) and Eschenbach and Hoekman (2006). The former relates services reform to economic growth effects while the latter concentrates on services reform, including liberalization, on economic performance in a sample of 20 transition economies using EBRD reform indexes.

> 3 Note, however, that Arnold _et al_ . (2011) also use FDI as one of the explanatory linkage variables using the share of the presence of foreign providers of services next to their EBRD measure of services liberalization.

3
between the parties and issues a recommendation after a public hearing. The parties can accept the recommendation or appeal to the Arbitration Commission of the PPP. Moreover, the Arbitration Commissions are no longer allowed to judge in equity but must issue a verdict based on the Law and the contract. The Experts Panel also reviews renegotiations that exceed 25% of the original value of the contract.

# _3.3.2 The effect of the new law_

The new law applied only to PPPs tendered after 2010.<sup>54</sup> Thus we compare PPPs tendered under the old law with those tendered under the new law. Of course, our comparison is only suggestive, because PPPs were not randomly assigned to the old and new laws.

Table 6 summarizes renegotiations under the new and the old law overall, distinguishing also between fixed and variable term PPPs. We have data from 98 PPPs, of which 57 were projects awarded before the 2010 Reform. Of all projects, 55 were fixed-term PPPs and 36 were variableterm concessions.<sup>55</sup> As column 1 shows, as of 2021, 55 of the 98 PPPs had been renegotiated 189 times. On average 20 percent of the total investment was a result of renegotiations. The average hides important differences, however.

> 53 The concessionaire does receive a contract management fee of 8% of the bid value for the additional works.

> 54 More precisely, the older PPPs could decide whether to abide by the reformed Law. None chose that option. 55 Recall that seven PPPs were tendered as fixed term, but then their term was made flexible post the 1999 Asian crisis. Thus, we excluded them from the comparison between fixed and variable term PPPs.

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Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey account for a reduction in wage inequality, as measured by the Gini index, of -0.30 in the period, while the whole reduction in the Gini was -8.95 points. Changes in the payment structure related to these racial and gender gaps account for -1.59 and regional and urban-rural gaps for -1.27, respectively. Taken together, the total effect of these three categories explain around 1/3 of the reduction in inequality in the period. However, these changes in payment structure were more important up to 2003. This may caution against expecting that such trends will continue to contribute to the drop in inequality in the coming years.

What was behind the reduction in such gaps? The answer to this question is far from clear, and probably varies among these groups. With respect to regional gaps, the conclusions drawn by Dix-Carneiro and Kovak (2017) may provide some guidance. They found that regional wage premia decreased substantially for regions more affected by trade liberalization, and this effect instead of vanishing in the long-run actually exacerbates, with more affected regions observing relative decreases both in average wages and employment. Even though the authors do not explore this possibility, their finding can explain the convergence in regional gaps if the regions most affected by trade liberalization are precisely those that exhibited the largest premia. Ad ̃ao (2015), in the context of the commodity boom, argued that the regions more affected by the rise in commodity prices were precisely the ones with lower average wages. Therefore, regional gaps should decrease. Similarly, if the commodity boom benefited mostly rural areas, it should have also contributed to the reduction in the gap between urban and rural areas.<sup>16</sup> Freguglia and Menezes-Filho (2012) use data from RAIS to investigate the decrease in inter-regional wage differentials and show that nearly 2/3 of the differences in regional wage premia are explained by differences
than zero during the crisis years for countries in all income groups, except for _CIF_ in highincome countries. Table 5 also presents Wald statistics that test if the behavior of flows during the crisis years or in the immediate aftermath was significantly different from the one observed in the run-up. The Wald tests show that the decline in capital inflows by foreigners and capital outflows by domestic agents during the crisis years (in comparison to the average flow in the previous two years) is statistically significant for all income levels, including _CIF_ in high-income countries. Furthermore, the Wald tests show that gross capital flows remain at depressed levels, or decline even further, during the two-year period after the onset of the crisis.

Figure 4 shows that the median retrenchment in gross capital flows around crises is rather large. For instance, _CIF_ in high-income countries on average declines from inflows of 5.5 percent of trend GDP during the pre-crisis year to outflows of 4.3 percent during the first postcrisis year. In middle-income countries, these flows reverse from 0.4 to -2.5 percent of trend GDP over the same period. In low-income countries, _CIF_ declines from around 0.2 percent of trend GDP during the two years preceding the turmoil period to around -1.7 percent of trend GDP during the year following the onset of the crisis. Similar numbers are estimated for _COD_ .

The analysis so far has included the global financial crisis that hit countries in 2008. However, the empirical evidence in Milesi-Ferretti and Tille (2010) suggests that this latest crisis has been marked by a significant decline in capital flows around the world. A re-estimation of equation (4) around this episode, reported in the top panel of Table 6, reproduces their findings. The Wald tests suggest a significant retrenchment in capital flows during in 2008 and the following year in comparison to the pre-crisis period for all income groups.

To test if the 2008 global financial crisis is driving our results and as a robustness exercise, we re-estimate our event study
the effect on recruitment is unknown to us or if there is no effect on recruitment, we set the variable to 0.

Table 1 provides summary statistics on the final merged data set.<sup>7</sup> The first panel shows summary statistics for the three cooperatives together, the second panel shows statistics for Pichilingue and Sargento only, and the third panel shows statistics for Abreojos only.

While the logbook data is at the individual fishing transaction level, the data are aggregated in our tests of the model’s implications to the species-year-month-week level by averaging prices and totaling catch. This is because our own field visits suggested that cooperatives do not usually

> 6. Available at http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. 7. Observations where the market price is less than the cooperative price are dropped due to concerns about measurement of market prices. This affects only 762 transactions out of approximately 42,000 individual fishing transactions for which we have cooperative price data.

1
on the JHIC score, weighted by patient load, is 3.93 percentage points. This yields annual estimated gains in consumer surplus of USD 2.4 million in nominal exchange rates, compared to an operational cost of USD 242,000 for scaled-up program per year. This gain in consumer surplus is 10 times the cost of the program but it may still be underestimated, both because we have assumed it accrues for only one year and because we have excluded inpatients, who may value quality even more, from this computation.

# **IV.5 Additional Results**

Having demonstrated the impacts of the regulation on the JHIC score and the market structure, we now present three additional results before turning to potential mechanisms. Specifically, we assess

> 24Costs were higher in the pilot because of a single office in the county headquarters, which increased travel costs and the fact that inspectors were seconded from different government institutions and transferred from other regions, resulting in a salary supplement. In a scaled-up version, the number and location of inspectors can be flexibly determined to minimize costs.

> 25See examples cited in Chege et al. (2022) such as De Allegri et al. (2019), Zeng et al. (2018), and Borghi et al. (2015).

> 26There are no systems for measuring nosocomial infections, vital statistics are incomplete and are not linked either to facilities or to geographical areas at a sufficiently granular level. Even if mortality data were available, the sample size requirements for sufficient power are exceedingly large. If we use a value of a statistical life of USD 50,000, which may be relevant for very poor populations, the intervention would have to save an additional 9 lives over 8 million outpatient visits for benefits to exceed costs (Li, 2020). To be well powered, this requires samples of more than 1 billion patients in each treatment arm, assuming mortality rates common to the literature (National Academies of Sciences, 2018; World Bank, 2020)
Venezuela”, Journal of Economic Inequality 1: 67-99.

Glewwe, Paul and Phong Nguyen (2002) “Economic Mobility in Vietnam in the 1990s”, World Bank Policy Research Working Paper No. 2838. Gottschalk, Peter (1997) “Inequality, Income Growth, and Mobility: The Basic Facts”, Journal of Economic Perspectives 11(2): 21-40.

Gottschalk, Peter and Enrico Spolaore (2002) “On the evaluation of economic mobility”, Review of Economic Studies 69: 191-208.

Instituto Nacional de Estadística, Geografía e Informática (INEGI) (1998) Documento Metodológico de la Encuesta Nacional de Empleo Urbano, INEGI: Aguascalientes, Mexico.

Islam, Nazrul (1995) “Growth Empirics: A Panel Data Approach”, Quarterly Journal of Economics 110(4): 1127-70.

Jarvis, Sarah and Stephen P. Jenkins (1998) “How much income mobility is there in Britain”, The Economic Journal, 108: 428-443.

Lokshin, Michael and Martin Ravallion (2004). “Household Income Dynamics in Two Transition Economies”, Studies in Nonlinear Dynamics and Econometrics 8(3), Article 4. http://www.bepress.com/snde/vol8/iss3/art4 Luttmer, Erzo F.P. (2002) “Measuring Economic Mobility and Inequality: Disentangling Real Events from Noisy Data”, Mimeo. Harris School of Public Policy Studies, University of Chicago.

McCulloch, Neil and Bob Baulch (2000) “Simulating the Impact of Policy Upon Chronic and Transitory Poverty in Rural Pakistan”, Journal of Development Studies 36(6): 100-130.

McKenzie, David J. (2001) “Estimation of AR(1) models with unequallyspaced pseudo-panels”, Econometrics Journal, 4: 89- 108 29
, if the attendant rise in inequality ends up stifling prospects for economic betterment at the bottom of the distribution, then this will also be associated with disenchantment with the overall process.

# **_4.2. Analysis of Perceptions of Mobility_**

Now, using data from the survey carried out in 2014, we examine mobility experienced by households based on their self-assessed position in the “economic ladder” relative to where they were five years ago. If the respondent placed the household at a higher rung relative to where they considered themselves to be five years ago, we interpret that rank improvement as upward mobility. Similarly, if the household is placed at a lower rung of the ladder than where it was five years ago, we regard that as downward mobility. The household is deemed to have experienced no mobility if the rank is reported unchanged. The survey also asked about expected position in the same ladder five years from the time of interview and we use responses to that question relative to the current position to define expected upward, downward or no mobility. The results are presented in Figure 9 and show that 62 percent of Nepalis have experienced upward mobility, 6 percent have experienced downward mobility and 32 percent remained unchanged. Considering expected mobility, an overwhelming 86 percent of Nepalis expected to do better in the future with only a small fraction, 1 percent, expecting to do worse.

**Figure 9: The Proportion of Nepalis Moving Up, Down or remaining the Same in the Economic Ladder**

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reported portfolio information for each fund in any given year. For example, our sample of mutual fund holdings for 2005 contains portfolio data for the Fidelity Worldwide Fund as of October 2005 and portfolio data for the Scudder Global Fund as of December 2005. In sum, we collect end-of-year detailed information on portfolio holdings between 1991

> 16 Funds that focus on both debt and equity are excluded from the analysis, even though they do invest a significant share of their portfolios in foreign stocks.

10
– revenue (Currie, Lin and Meng, 2014). This structure where medical staff are salaried but still have an incentive to generate revenue to sustain the – clinic exists in many other settings across the developing world, including private, mission, and NGO-run medical facilities.

CSComs are one of the most important sources of care in Mali: according to the 2012-2013 Demographic and Health Survey, 47 percent of mothers in Bamako who sought care for a child under five with fever or cough took the child to a CSCom. The quality of care across CSComs varies and is typically poorest in rural areas. Our study was conducted in the capital city of Bamako and a nearby suburb, where clinics offer a higher standard of care and are usually headed by a trained medical doctor (as compared to a national average of roughly one third (PMI, 2016)). All clinics in our sample have at least one salaried physician on staff, and in over 90 percent of clinics that have a pharmacy, there is a physician present when the pharmacy is open (see Appendix Table B1).

One of the most commonly-treated illnesses in CSComs is malaria. Despite recent progress, malaria is the second largest cause of mortality in Mali, accounting for roughly 14 percent of all deaths and 19 percent of deaths among children under five (IHME, 2018). Although the parasite is endemic in all parts of the country except the sparsely populated northern desert, rates of transmission are substantially lower in urban areas. For example, in 2015 the estimated prevalence of the parasite in children under five in Bamako was six percent, as compared to 36 percent nationwide (PNLP et al., 2016).

Malaria infections are classified as either “simple/uncomplicated” or “severe”. Simple malaria is not life threatening if treated promptly, and is characterized by non-specific, flu-like symptoms including fever, chills, and headache. If left untreated, simple malaria can progress to severe malaria. In this stage of the disease, patients often suffer
65 years). To define our sample, we used two criteria: (i) we excluded age groups which may have been exposed to historical malaria that afflicted Sri Lanka before 1950; (ii) we focused on the rural sample. Note that the rural sample does not include any household that has moved to urban areas with all the members, but the split households where some members stay behind in the village are part of the sample. The number of adult females who were born after 1950 and are currently residing in rural areas is 10,850. The sample for the wage regressions are, however, smaller. Among females in our main sample (10,850), 42 percent are employed. About a third of those employed are self-employed. We have thus complete information on wages and other relevant variables for 2,918 females who were born after 1950 and live in rural areas. The dependent variable in the wage regression is deflated using the region specific consumer price index.

A key piece of information for our analysis is the amount of land under LDO restrictions in a DSD. We draw this information from the Agricultural Census of 1998. We estimated percentage of agricultural land under LDO leases (including permits and grants). The DSD identifiers in the HIES (2002) and Agricultural Census allow us to merge individual level data from HIES 2002 with data on percentage of land under LDO leases from Agricultural census. The geographic information including travel time from surveyed DSDs to major urban centers with population of 100 thousand or more are drawn from the Geographical Information System (GIS) database. The travel time is estimated using the existing road network and allowing different travel speed on different types of roads.

A critical variable for our instrumental variables analysis is the historical district level malaria prevalence rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman
the current season shock (in this last case only for those children born during the rainy season).

# Figure 8

# **Height-for-Age**

We also use the pooled child-level DHS sample to examine the effect of weather shocks on height-for-age; an indicator of long-term health status. In contrast to weight-for-height, there are two differences in the relationship between rainfall shocks and height-for-age. First, there is no point to correlating contemporaneous rainfall shocks with current height for age, as child height-for age is only 19
.03|328|0.243|0.136|-0.107<sup>**</sup>|-0.078|-0.093<sup>*</sup>|
|||||(0.047)|(0.052)|(0.049)|
|±0.04|395|0.234|0.177|-0.057<br>(0.044)|-0.058<br>(0.047)|-0.075<br>(0.045)|
|_Controls_|||||||
|Socio-ec.<br>background||||No|Yes|Yes|
|Educational<br>background||||No|No|Yes|

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’s gender, the student’s age, and a dummy indicating whether the student’s family owns a real estate property. Controls denoted by “educational background” include the student’s grade point average at the highest three completed education levels, dummies indicating the type of school attended (public or private), the term of the program in which the student is enrolled, and dummies indicating the study area. DS stands for discontinuity sample and equals the cut-off points for the Economic Need Factor (ENF) where the loan <u>amount increases by additional 10% of tuition.</u> 25
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