rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10789

# **Abstract**

Casual observation suggests that intra-national trade costs remain high in low- and middle-income countries. Precisely estimating them is crucial for guiding policies aimed at optimizing economic efficiency within a country’s borders. This paper estimates intra-national trade costs for six lowand middle-income countries in Africa and Eastern Europe: Kenya, Madagascar, Nigeria, Rwanda, Tanzania, and Georgia. The analysis exploits unit-level price data collected by countries’ national statistical offices for consumer price index calculation purposes. It applies the price differential methodology, which aims at estimating trade costs while accounting for the possibility of imperfect competition among intermediaries, controlling for spatial variation in markups. The findings show that the intra-national trade costs in the sample of countries are between 2.5 and 14 times larger than previous estimates for the United States using the same methodology.

This paper is a product of the Transport 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 bernardo. diazastarloa@economicas.uba.ar and npkhikidze@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
6 days over a two week period. Enumerators administered a short “intake” interview to all consenting patients seeking care for an acute illness. After the patient’s consultation with the provider was complete, the enumerator recorded details of all blood tests performed, medications prescribed, medications purchased, and fees paid. A randomly chosen subset of these patients were selected for a more detailed home-based follow up survey on the day after their clinic visit. As part of this visit, a trained enumerator performed an RDT on the patient to confirm his/her malaria status.<sup>11</sup> Table 1 uses the 628 patient observations in the Control group to give an overview of how acute illnesses are treated in clinics in Bamako. Panel A shows average indicators regarding malaria beliefs and treatment outcomes. Panel B reports treatments that often accompany malaria medications: antibiotics, which are often given for fevers but are not indicated for malaria, as well as injections/IV, which are used in severe malaria treatment. Panel B also shows how often patients were referred to a hospital, the recommended course of action for severe malaria. Finally, Panel C shows average costs in West African CFA (at the time of the experiment CFA 610 was approximately USD 1), split by the type of treatment received.

Looking at means in the whole sample of 628 (column 2), treatment for malaria is very common, with 31 percent of patients prescribed treatment for simple malaria and 30 percent for severe malaria. Twenty-one and 26 percent of patients purchase medication for simple and severe malaria, respectively (Panel A).<sup>12</sup> Malaria treatment is expensive: while the average patient not treated for malaria reported spending CFA 2,793 ($4.58 at the average exchange rate of CFA 610 per USD that prevailed during the study period), simple malaria patients paid CFA 4,475 ($7.34) and severe malaria 11RDTs detect antigens for malaria, which remain in the
the consumption linkage of urbanization appears to operate mainly through an income effect (which increases the quantities of the usual crops) rather than a substitution effect.

We use census data to construct the share of rural employment in nonagriculture/nonhousehold activities, which should capture the rural nonfarm employment channel.<sup>30</sup> Again, urban-rural remittance data are not available for the period of this analysis; therefore, we use the number of intradistrict rural-urban migrants as a proxy for the remittance channel. We also include the share of ruralurban migrants in the total rural population to control for the possible influence of rural poverty on migration.

Finally, we use the total cultivated area over the rural population as a direct proxy for the rural land/labor ratio channel. To the extent that urbanization increases the demand for agricultural goods, thus raising the return on land cultivation, this ratio can also capture part of the consumption linkages channel.

Column 1 in table 5 shows the coefficient of the urban population obtained by running specification (2) for the reduced number of observations for which all of the new variables are available. Adding the set of new variables brings the urban population coefficient to zero (column 2).<sup>31</sup> All of the new variables have the expected sign, although rural nonfarm employment has a nonlinear effect on rural poverty. For relatively low shares of rural nonfarm employment (i.e., below 32 percent of total rural employment), increases in this share are associated with increases in rural poverty, whereas the opposite is true when nonfarm/nonhousehold employment is greater than the 32 percent threshold. This finding suggests that rural nonfarm activities tend to be more profitable than agriculture only when nonfarm activities represent a substantial part of the rural economy. When these activities are 31
# 5. Conclusions

There are diametrically opposed views on the role of government-owned banks. Those who subscribe to the development view maintain that these institutions can play a useful role by alleviating credit constraints and lending to firms that generate positive externalities. Those who subscribe to the political view maintain that government-owned banks only exist to provide political rent. While there is ample evidence that politics does play a role in the activities of government-owned banks, government-owned banks could still play a useful role despite these political failures (Rodrik, 2012). Assessing this possible role is, however, complicated by the presence of reverse causality.

In this paper, we use a unique dataset that matches firm-level information with bank-level information covering 36 emerging and developing economies over 2012-20 to assess if government-owned banks achieve their mandate by targeting credit constrained firms or firms that are more likely to generate positive externalities. We find mixed evidence that government-owned banks play a useful role. On the one hand, we find that government-owned banks target credit constrained firms, but only when they operate in countries with good institutions. We also find that state-owned banks are especially useful for credit constrained firms during recessions, when credit is likely to be particularly scarce. On the other hand, we find no evidence that government-owned banks are more likely to lend to firms that innovate or that engage in green activities.

These results suggest that government-owned banks are effective in terms of the quantitative allocation of credit because they do lend to firms that tend to lack access. However, government-owned banks are less effective in the qualitative allocation of credit as they do not seem to target firms that generate positive externalities. We also find that there is substantial regional heterogeneity and that state-owned banks are less likely to play a useful role in the Middle East and North Africa. In future work, it would be interesting to further explore the role of bank heterogeneity both within and across countries.

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bottom 40%, computed from the difference in (per capita) consumption of the bottom 40% of two different distributions at two points of time and _not_ of the same group of households over time. In contrast, consumption growth of the “non‐anonymous” bottom 40% of the distribution would measure the difference in consumption of the same group of households (those in the bottom 40% of the initial year) over time. The implications of anonymity are important to recognize for the correct interpretation of Figure 3.

How much difference does “anonymity” make in measuring shared prosperity in India? Although NSS does not allow for a tracking of expenditures by households over time, the India Human Development Surveys (IHDS) for 2004‐05 and 2011‐12, which include a panel component, offer the opportunity for a test. Between 2005 and 2012, annual per capita consumption growth among households who were in the bottom 40% of the consumption distribution in 2005 (9 percent) was much higher than that of the total population (4.7 percent); whereas consumption growth of the anonymous bottom 40% (4.4 percent) was on par with that of the overall population.<sup>6</sup> In other words, the least well‐off in 2005

> 5 It is possible, for example, for the consumption Gini to remain constant if inequality among top three quintiles falls, even as the share of the bottom 40% in total consumption declines.

> 6 See Balcazar et al (2016) for more details on the incidence of consumption growth across the distribution, using IHDS data for 2005 and 2012.

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are given by <sup></sup> = 0114 and <sup></sup> = 031. The unemployment rates for female and male unskilled workers, <sup></sup> and <sup></sup> , both in proportion of the relevant active population, are set at 0029 and 0041, whereas the corresponding rates for female and male skilled workers, <sup></sup> and <sup></sup> , are set equal to 0249 and 0146. By implication, <sup></sup> = <sup></sup> = <sup></sup> + <sup></sup> = 0637 whereas <sup></sup> = <sup></sup> = <sup></sup> + <sup></sup> = 0544. Similar results hold for <sup></sup> = 0363 and <sup></sup> = 0456.<sup>21</sup> All these values match the actual data on the distribution of employment and unemployment available for 2016, published by Morocco’s Haut-Commissariat au Plan (HCP). Based also on HCP data, the share parameters <sup></sup> , <sup></sup> , and <sup></sup> are set at 0414, 0631, and 0184.

To estimate the subjective employment probability of labor type 
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<!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> Subsidies**<br><!-- End of picture text --> <!-- Start of picture text --> Consumable income<br><!-- End of picture text --> <!-- Start of picture text --> • Copayments and user fees for<br>education and health services ****<br><!-- End of picture text --> <!-- Start of picture text -->
-<br><!-- End of picture text -->

<!-- Start of picture text --> +<br><!-- End of picture text --> <!-- Start of picture text --> In-kind transfers<br>• Monetized value of education<br>and health services<br><!-- End of picture text --> <!-- Start of picture text --> Adjusted income<br><!-- End of picture text --> _Source:_ Adapted from Lustig 2018. * No information on household consumption was available for 2017. Thus, none of the indirect taxes such as the Value Added Tax are incorporated in this exercise. ** In Uruguay, subsidies per se are nonexistent. However, some products have fixed prices (such as a specific kind of milk, fuels, and others), which could have welfare effects. Since there is no information on purchases for 2017, these are also not considered. *** These are benefits of the social security system that require some conditions to be eligible: unemployment insurance, maternity and family benefits, disability and sickness allowances. ****
markets is associated with entry barriers and, thus, lower entry rates of new firms. Using 1995, 2004, and  2008 data from the Chinese Industrial Census, Brandt, Kambourov, and Storesletten (2020) indicate that a key factor underlying the dispersion and dynamics of aggregate total factor productivity and wages across Chinese prefectures were entry barriers, which in turn were linked to significant state presence in economic activities. The reduction in SOE employment in prefectures between 1995 and 2004 was systematically linked to the reduction in entry barriers, and prefectures where SOE employment fell over the period saw faster growth in wages and labor productivity, capital per worker, and aggregate total factor productivity. Further, Iootty, Pop and Pena (2020) show that certain firm characteristics in Romania, notably state ownership, matter in explaining differences in markup performance as a proxy for competition (a higher markup  indicative of lower competitive pressure). The authors find that both majority and minority owned SOEs tend to demonstrate the highest markup premiums when compared with domestic privately owned companies, especially in the manufacturing sector. The average difference in markup is higher for companies with minority state ownership (28.9 percent) than for fully state-owned companies (20 percent). However, in manufacturing, markups of fully state-owned firms (100% state shareholding) are the highest on average, at 52.7 percent,  compared to domestic privately owned firms. Iootty and Dauda (2017) also found that Chinese manufacturing SOEs had higher markups than non-SOEs compared to their POEs peers, even after controlling for firm location and whether they were subsidized.

# 3. Data Sources

The analysis relies on data from two main sources and a taxonomy of sectors. The data sources are (1) Romania MoF firm-level data for the period 2011-20, and (2) the World Bank Businesses of the State (BOS) database for Romania, and (3) the taxonomy of sectors developed by Dall'Olio et al. (2022b).

## 3.1 Romanian Firm-Level Data

*
the exception of Vanuatu, which publishes a purposeful disaggregation of the census results by island on PopGIS, the data cannot be manipulated to present results at the island-level.

## Geospatial data

The geospatial data we used were from several different sources. The PopGIS platform provides ‘shape files’ (the geo-coded outlines for specific regions) for each country, corresponding to the geographical disaggregation of the data. The locations of cities and towns of interest were pinpointed using OpenStreetMap and Google Maps; some coordinates were automatically generated using the name of the town, others were manually identified. Distances were calculated using QGIS software.

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estimation. First three columns use only Pichilingue observations, and next three columns use only Abreojos observations. Columns 1, 2, 4, and 5 include fixed effects at the species-month-fishing team level, while columns 3 and 6 include fixed effects at the species-week-fishing team level. “Num. groups” indicates the number of unique combinations at each level used in estimation. Standard levels clustered at the level indicated in the “Clustering” row.

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with USSD, electronic forms, and IVR (MobileDataGathering 2011). For reasons explained below, SSEPS also employed a voice-operator approach.

> 2 The face-to-face, device assisted approach has also been used with devices other than mobile phones, such as tablet and notebook computers and is more broadly classified as Computer-Assisted Personal Interviewing (CAPI). See IRIS Center (2011) for a detailed review of CAPI software options and Caeyers et al. (2012) for an analysis of the performance of CAPI vs. traditional Pen-and-Paper Interviewing (PAPI).

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the subsidy amount prior to the completion of their toilet. This share is meant to be small though and only to be provided when the household can show that significant works have already been completed (e.g. by having dug a pit).

> 7Outside of the study area, the MFI first started offering sanitation loans to their clients in 2009. The MFI has (among other motives) a social mission, committing itself to strive to improve clients’ health. To that end, it has introduced various health related programs, including emergency loans to support clients in meeting unexpected health expenditures among other unanticipated needs. Through customer surveys, which the MFI conducts to understand their clients’ needs, they realized that there was strong demand for credit to construct toilets - 90% of clients expressed an interest in taking sanitation loans -, which most clients lacked. Understanding that such a loan product does not generate income directly and immediately, the MFI decided to meet their customers’ demand and offer the product, but at a lower interest rate than income generating

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will be critical to protect public investment budgets and try to increase resources going to the sector in the case of oil importers. Doing so will be a smart choice for governments looking to create jobs and growth. The fiscal challenge will be the toughest for the poorest countries of the region since they are the least likely to be able to attract private financing for infrastructures required to meet population’s needs.

# **<mark>4. Current infrastructure employment</mark>**

A comparison of employment shares by sector and country to international benchmarks provides a first-round assessment of the extent to which infrastructure activities could play a role in speeding up job creation in MENA. The shares are computed using ILO data that disaggregate employment into industrial sectors at the 1-digit ISIC level allowing an identification of electricity and water sector jobs as well as jobs in transport and communications. Data on construction is included as an important element of infrastructure investment. The construction category encompasses housing and building construction. These two activities are likely to be the main drivers of job creation, but other infrastructure investments are still likely to account for a significant share of employment creation.

Table 6 shows that MENA’s infrastructure sectors, including construction and infrastructure services, employ close to one-fifth of the regional workforce, or 18.2 million people. About 11 million workers are employed in construction while the remaining 7.5 million provide infrastructure services. Within infrastructure services, the transport and communication sectors are the biggest employers, representing jointly about 7% of total employment, while workers employed in energy and water represent approximately 1%. These aggregate numbers 8
Several reasons for these differences have been suggested. First, households’ and firms’ expectations are subject to “sticky information” and are updated more slowly than those of professional forecasts (Carroll 2003). Second, household surveys give the same weight to “informed” and “uninformed” consumers. Because uninformed consumers likely give excess weight to goods that are purchased frequently (for example, food) or have highly visible price changes (for example, gasoline), their assessment of inflation expectations can be biased upward when the prices of these products increase (Coibion and Gorodnichenko 2015; Coibion, Gorodnichenko, and Kamdar 2018a; Sousa and Yetman 2016). Yet, surveys of households and firms also have important advantages relative to surveys of professional forecasters—for instance, they can be designed to include a large number of respondents and have the flexibility to canvass different types of economic agents. For surveys of professional forecasters, bias may arise from respondents’ reluctance to reveal their expectations about inflation because they consider the information private (Cunningham, Desroches, and Santor 2010).

# 2.2. Market-based measures

The most commonly used market-based measure of inflation expectations is the breakeven inflation rate—that is, the difference between yields on comparable nominal and inflation-indexed bonds. In general, however, this difference consists of four components: expected inflation, an inflation risk premium, a liquidity premium, and other factors (Hördahl 2009; Christiansen, Dion, and Reid 2004). Hence, extracting expected inflation requires the use of strong assumptions, and any estimate of expected inflation is necessarily imprecise (Galati, Heemeijer, and Moessner 2011).

Another common market-based measure is the inflation swap rate based on derivative instruments, which again includes not only inflation expectations, but also the inflation risk premium and liquidity premium. A key advantage of the swap rate is that, unlike for break-even inflation rates, liquidity has a limited impact on its movements (Grothe and Meyler 2018). Both types of market-based measures have the advantage of being
In order to measure entrepreneurship and make the data comparable across countries, we developed a methodology that can be consistently applied across heterogeneous legal regimes and economic systems. Previous efforts have been made in this regard, but the great majority focused solely on the developed world, and did not take into account differences in legal systems, sectors, and economic structures (see United Nations, 2005).

The concept of entrepreneurship lacks a common language. Joseph Schumpeter defined entrepreneurship as “the assumption of risk and responsibility in designing and implementing a business strategy or starting a business” (Schumpeter, 1911). J.W. Gough stated that entrepreneurship “refers to a person who undertakes and operates a new enterprise or venture, and assumes some accountability for the inherent risks” (Gough, 1969). The entrepreneurial process centres on the discovery, creation, and profitable exploitation of markets for goods and services. Therefore, for the purposes of the analysis in this study, entrepreneurship is defined as: _The activities of an individual or a group aimed at initiating economic enterprise in the formal sector under a legal form of business._ Notably, this definition excludes informal sector initiatives. The exclusion is based on the difficulties of quantifying the number of firms in the informal sector, rather than on its relevance for developing economies (Nielson and Plovsing, 1997). The only way to measure the informal sector is through economic censuses, which due to their high costs are infrequently collected.

Following a definition of entrepreneurship, we create a standard unit of measurement. Generally, entrepreneurial activities are carried out in the form of “businesses.” However, due to the lack of a universally agreed upon definition of what constitutes a business, agencies have formulated either an economic, statistical or legal definition. For instance, the U.S. bases its 4