rafmacalaba/data-use-mentions-extended huggingface dataset:
## Policy Research Working Paper 10350

# **Abstract**

This paper presents a meta-analysis of the infrastructure research done over more than three decades, using a database of over a thousand estimates from 221 papers reporting outcome elasticities. The analysis casts a wide net to include the transport, energy, and digital or information and communication technology (ICT) sectors, and the whole set of outcomes covered in the literature, including output, employment and wages, inequality and poverty, trade, education and health, population, and environmental aspects. The results allow for an update of the underlying parameters of interest, the “true” underlying infrastructure elasticities, accounting for publication bias, as well as for heterogeneity stemming from both study design and context, with a particular focus on developing countries.

This paper is a product of the Infrastructure Chief Economist Office. 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 author may be contacted at vfoster@worldbank.org, mvagliasindi@worldbank.org, ngorgulu@worldbank.org, stephane.straub@tse-fr.eu, and jain.dhruv@tse-fr.eu. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End
party affiliation and by membership in local Congress or Consultative Conference. These findings thus show that private firms have followed dual-track routes, one external and one internal, for gaining commercial advantages: political connection and human capital of owners. Whereas the external route relies more on the power of the government, most likely originating from preferential treatment, the internal route resorts to the strengthening of the firm's own competitiveness to win in the market system. The importance of both routes in determining profit rates of private firms highlights the complexity of the BE in China.

_Evolution of firm governance mechanisms_ **.** To measure Chinese firms’ reliance on formal legal institutions, we use two indicators on the efficiency of local courts in solving business disputes. The first is the proportion of local firms resorting to the legal system in the event of a business dispute; the second is the percentage of firms being satisfied with the legal system's resolution. Both measures are based on the private firm surveys. These measures are chosen because firms will resort to courts to resolve business problems or commercial disputes only when they have sufficient confidence in the quality of the court system (Long 2010; Li et al. 2008).

Chinese private firms have increasingly relied on formal mechanisms such as courts to resolve business disputes instead of resorting to private negotiations or suffering in silence, and they are more satisfied with court resolutions over time, implying more confidence in the legal system (see panels A and B in table 9). Strikingly, the share of firms using the court system to resolve disputes more than tripled, rising from 9 percent in 1995 to 32 percent in 2006. Equally surprisingly, the share of disputes resolved via the route of “appealing to the local government” rose from 2 percent to 21 percent, an increase by 10 times. Interestingly, the ratio of being satisfied with dispute resolutions by courts has increased from 54 percent in 1995 to 83 percent in 2006; and the corresponding satisfaction rate via appeals to local governments has
# **1 Introduction**

The accumulation of international reserves and public debt in emerging economies is puzzling because economies facing default risk pay high interest rates on their liabilities and receive low interest rates on their reserves.<sup>1</sup> Why, then, do economies paying significant sovereign spreads prefer to accumulate international reserves instead of paying back public debt? Moreover, what is the social welfare effect of reserve accumulation? Even though a large and growing literature addresses these questions, all previous studies have ignored the interaction between the central bank and the government by assuming a consolidated entity that simultaneously chooses reserves and debt.<sup>2</sup> In practice, the central bank often manages international reserves, the government issues public debt, and policymakers may have different incentives driving their choices. This is particularly possible if the central bank is independent from the government. Furthermore, Figure 1 illustrates a positive association between the accumulation of reserves and the widespread adoption of central bank independence (CBI) legislation in Latin America. To the best of my knowledge, this is the first study to explore the role of CBI in the accumulation of international reserves.

<!-- Start of picture text --> 20 1<br>15 0.75<br>10 0.5<br>5 0.25<br>Reserves (Avg Latin America)<br>Independence (Avg Latin America)<br>0 0<br>1970197519801985199019952000200520102015<br>CBI Index<br>Reserves (% GDP)<br><!-- End of picture text --> Figure 1: International Reserves and Central Bank Independence _Notes:_ The figure presents the average levels of international reserves and central bank independence for 11 Latin American countries: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela. The left y axis presents the reserves-to-GDP ratio using data from the IMF and the right y axis reports
Near East (UNRWA), IDMC, JIPS and IOM. While there are significant numbers of people displaced by natural and man-made disasters, as well as development projects, the focus of this report is on conflict-induced displacement only. The circumscribed focus of this paper recognizes the unique character of displacement emerging in fragile, conflict-affected and violent settings, and the need to appropriately tailor development responses to these contexts and the underlying drivers of conflict.

# **Box 1: Terminology used in this Report**

**Refugees** : Individuals displaced outside their country of nationality or habitual residence as a consequence of generalized violence, conflict or a well-founded fear of persecution, and are therefore in need of protection.<sup>8</sup>

**Asylum-seekers** : Individuals who are seeking international protection under the 1951 Convention Relating to the Status of Refugees (1951 Convention) but whose claims for refugee status have not yet been determined.<sup>9</sup>

**IDPs** : Individuals who have been forced or obliged to leave their homes or places of habitual residence as a result of, or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, and who have not crossed an international border and therefore remain under the legal protection of their own government.<sup>10</sup>

aims at addressing gaps in disaggregated data (by location, sex, age and diversity) and promoting evidence-based responses to displacement in the context of the search for durable solutions. See: www.jips.org.

6 IAMC’s Information Management Working Group, chaired by OCHA, was established in 2006 to provide a forum for humanitarian organizations to strengthen information management in order to support improved decision making in emergency preparedness and response. See https://www.humanitarianresponse.info/en/topics/imwg.

7 UNSD, established in 1947, is the highest decision making body for international statistical activities, bringing together chief
0003)|0.0027***<br>(0.0003)|
|Observations|888,813|888,813|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.6351|0.7738|0.3912|0.6314|0.7655|0.3887|
|Kleibergen-Paap rk Wald F-stat||||111.795|111.795|111.795|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using the baseline estimation sample. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
average compared to the next best policy of using information on incidence from the year before. Zooming in on the part of the distribution up to $400,000, the amount spent on this type of program, the cell phone data targeted policy performs over 300% better on average (Panel b, Figure 6).

The second type of targeting focuses on travelers from particular origin areas. This is more dicult in terms of implementation because it requires the ability to identify travelers coming from specic areas, but has the potential of being more cost-eective since resources are focused on the highest risk travelers. I base the targeting strategies on the district the individuals are coming from and the month in which they are traveling. Therefore, if a district-month is chosen as receiving targeting, then every person traveling from that district to the pre-elimination area in that month would be treated with the policy. I assume again that 94% of targeted travelers are tested based on the experience in Richard Toll. Four strategies are again compared: (1) the implausible one of the government randomly choosing district-months (non-targeting); (2) the government choosing district-months randomly within the malarial season and then during the rest of the year; (3) the government using monthly malaria incidence in the prior year to order the district-months from those with the highest to the lowest incidence; and (4) a cost-benet value is calculated for each district-month based on the number of travelers and the variable cost and uses incidence from the previous year to calculate the total impact of the imported cases (the benet).

Panel a of Figure 7 shows the cost curves for these four strategies zoomed in on the relevant budget of $400,000. Note that the cost starts at $63,928, which is the total xed cost for all the facilities in the ve districts, since it is assumed that training and preparations are done across all health facility areas but only particular travelers are targeted across these areas. Using the
affords. Insurance is realized as a subsidy to patients when they seek care and the flip side of that subsidy is a payment to providers. But providers respond to the financial incentives created by how those payments are structured and therefore the value of the insurance is critically tied to the nature of these responses. The lack of non-price mechanisms combined with the difficulties of structuring price incentives for appropriate care imply that health insurance could prove to be a very expensive tool for improving financial protection that actually _lowers_ the quality of health care in LMICs. Ultimately, it is the prices in the crises that matter.

# How Are Health Insurance Schemes Structured?

We start by mapping the overall coverage of health insurance across LMICs using data from the Demographic and Health Surveys, which are nationally representative health-related surveys that target women aged 15-49 and their children under the age of five in LMICs, with a traditional focus on maternal and child health. Questions on health insurance coverage were sporadically included in the late 1990s for some countries like Bolivia, Jordan, Peru, and <mark>Türkiye,</mark> and a standard question (v481) was introduced in 2003. As of April 2022, of 426 surveys ever fielded, 100 surveys in 62 countries have health insurance data available. We compiled all the health insurance questions to create a single database of just under 5 million observations, representative of countries with a total population of about 3.5 billion people; for additional details, see Appendix A available with this paper at the JEP website. When combined with OECD health insurance data (Scheil-Adlung 2014), our DHS-based data allow us to present a unified picture of health insurance coverage and its correlates across multiple countries, adding to previous studies that have focused on single or small groups of countries (for example, Amu et al. 2022; Barasa et al. 2021; Wang, Temsah, and Mallick 2014; National Population Commission NPC/Nigeria and ICF International 2014).

For LMICs where data was available from either the Demographic and
The medium-term economic returns to increased MA are very difficult to estimate, especially on such a large scale. The only large-scale analysis I am aware of is Donaldson & Hornbeck (2016). The authors regress changes on the log of land values across 2,327 US counties between 1870 and 1890 on changes in the log of MA, and find an elasticity of 0.5, which is quite robust to different choices of _θ_ . This suggests that in the medium run MA translates into economic activity at a ratio of 2:1. However, present-day Africa may be very different from the historical US.

I thus also run some regression similar to Donaldson & Hornbeck (2016), examining the effect of log(MA) on GDP per capita (Kummu et al., 2018) and the International Wealth Index (Lee & Braithwaite, 2022) using the dataset of 1379 nodes/cities. I control for country fixed effects and also for the log of port outflows in 2020Q1, adding one so that the log is zero in non-port cities. Since MA is endogenous to GDP, I apply the network finding algorithm from Section 4.2 (with EU parameters but not checking against OSM) to generate a hypothetical network with 2869 edges, shown in Appendix Figure A14, and use the MA measure based on this network as an instrument for MA. Table 7 reports the results for MA denominated in both road distance and travel time.

Table 7: Returns to Market Access in Africa ( _N_ = 1379 Nodes)

|Dependent Variable:|log(GDP|/Capita)|I|WI|log(|IWI)|
|---|---|---|---|---|---|---|
|Estimator:|OLS|IV|OLS|IV|OLS|IV|
|_Variables_|||||||
|log(MA): Distance|0.3221<sup>_∗∗
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sector model in which technological progress in the production of durable good is faster than in the rest of the economy. Whelan (2003) develops the two-sector approach to establish the longrun properties of the real NIPA aggregate data.

Our finding highlights the importance of examining growth in a detailed sectoral perspective. Most of the existing analyses focusing on the structural changes are based on twosector (agriculture/non-agriculture) or a three-sector (agriculture/manufacturing/services) model with a few exceptions. For example, Ungor (2017) analyzes a nine-sector model for Korea, Taiwan, China, and Latin American countries and finds that productivity growth in manufacturing and wholesale is important in increasing aggregate productivity growth. Although our study empirically examines industries at an even more detailed level than that of Ungor (2017), it may not be practical to develop and analyze a growth model distinguishing very narrow industries. However, it is worthwhile to think about how to model the effect of reallocation (i.e., sectoral shift) in an existing two-sector model, because our empirical evidence suggests that industrial shift (e.g., from textiles to electronics) may provide a key to explain sustainable growth of income and productivity in Korea.

First, we have to think about how to deal with returns to investment when there is ongoing structural change. In particular, sectoral shift suggests that new investment is more likely to concentrate on the new, growing industries. A steep decrease in marginal returns to investment in a traditional one sector model may not be suitable to explain the returns to investment when there are multiple sectors and firms at different stages of the life cycle. Moreover, the marginal returns to investment may behave differently due to differential rates of R&D activity and other innovations.

26
) is the end-of-period capital stock in region r_ _RORFLEX is the flexibility of expected net rate of return on capital stock in region r with respect to investment_ RORFLEX (r) is defined such that if a region’s capital stock increases by 1%, then its expected net rate of return on capital will decline by RORFLEX%. Hence a small value implies that the rate of return on capital is relatively insensitive to net investments. Higher RORFLEX thus imply that the expected rate of return on capital is low; in a certain sense this may reflect a weak investment climate.

# **Data**

The model used for this study draws data from the GTAP 6 database. Although this version of the GTAP database allows for 87 regions and 57 commodities, its coverage of MENA is rather limited. The database has separate data for Morocco and Tunisia, while the rest of MENA is aggregated into “the rest of North Africa” (RONAF), and “the rest of the Middle East” (ROMIDE). This study uses a 13 region by 16 commodity aggregation, which captures all the MENA sub-regions, key trading partners and key commodities. Details of the aggregation are provided in appendix table A1.

9
. China “reached over 40 billion yuan ($6.5 billion) in 2012, surpassing the United States for the second year to become the largest musical instrument market in the world.”<sup>14</sup> Statistics released by the China Musical Instrument Association “showed that, in 2012, the total world output of pianos was 500,000, with China accounting for 80 percent. Of the two million Western orchestral instruments in the world, 60 percent of them were made in China, and the country produced 80 percent of the 1.3 million violins made worldwide in 2012.”<sup>15</sup> On just piano exports, consider Box 1.

It must be noted nonetheless that just as the Human Development Index (and other indices) may correlate with GDP in some areas, the Cultural Trade Index, and the Cultural Exchange Index may also do the same. For example, China’s economic rise in recent years may explain why China has become the largest instrumental music market in the world. Yet that might not be

|**Box 1| Top P**<br>**Country**|**iano Exporters**<br>Amount in<br>US Dollars|**in 2014 by Cou**<br>Percentage of<br>total Piano<br>Exports|**ntry**<br>Rank|
|---|---|---|---|
|China|$150,625,000|19.9percent|1|
|United States|$134,090,000|17.7percent|2|
|Germany|$60,176,000|7.9percent|3|
|France|$35,777,000|4.7percent|4|
|Canada|$30,416,000|4percent|5|
|Japan|$27,668,000|3.
3 Next, many countries have experienced “premature” _deindustrialization_ (Rodrik, 2016), due to the removal of import substitution industrialization (ISI) policies adopted by nations to the 1980s or increased trade due to trade liberalization many up competition or rising productivity related to advances in automation in some nations. We discuss why “premature” _deindustrialization_ may not lead to de-urbanization. However, since the country experiences deindustrialization, its cities do so as well.

Using GJV16’s sample of 116 developing countries (as of 1960) and long-difference and panel regressions for the period 1960-2020, we first show that: (i) higher urban shares are found in countries with higher GDP shares of manufacturing & services, a proxy for industrialization broadly defined (including tradable services); (ii) countries exporting natural resources, whether fuel & mining products _or_ agricultural products, are also more urbanized; and (iii) urban shares are unchanged in deindustrializing countries.

Second, we take advantage of newly available data, including IPUMS census microdata for about 60 countries over time and I2D2 household and labor force survey data for about 90 countries over time, to examine the correlations between the sectoral structure of urban areas and industrialization, resource exports, and deindustrialization.<sup>3</sup> We study sectors not covered in GJV16 and informality, use panel regressions, and identify which parts of the city size distribution are affected by the structural change mechanisms mentioned above. Cities in industrialized countries have more in tradables and more while cities in resource- employment wage employment, rich and deindustrializing countries have higher shares of non-tradables and selfemployment. Differences between industrialized, resource-rich, and deindustrializing countries are stable across city sizes. Thus, the origin of the urbanization process also impacts the largest cities, hence countries’ “engines of growth” (World Bank, 1999, 2009).

Third, focusing on the mechanisms, we take advantage of novel data on urban construction across countries to shed light on the “quality” of spending in cities. For
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