rafmacalaba/data-use-mentions-extended huggingface dataset:
G20 members collectively had 3.2 percent of their import product lines subject to TTBs in 2011; for comparison, this is more than double their import coverage (1.5 percent) from a decade earlier, as reported by column (2).<sup>6</sup> Six of the eight individual G20 emerging economies had higher levels of import coverage in 2011 relative to 2001 – the two exceptions are Mexico and South Africa. In comparison, the cohort of six _high income_ economy G20 members in the sample combined to increase their coverage to only 1.9 percent of import product lines in 2011, up from 1.8 percent in 2001.

Columns (3) through (7) of Table 1 report information for each of the 24 policy-imposing countries’ use of temporary trade barrier policies in 2011 based on an alternatively constructed measure that _trade-weights_ policy use by bilateral, product-level (HS-06) import data. The four cohorts of policyimposing economies in Table 1 are each ranked by column (3). For example, among the cohort of emerging economy G20 members, this coverage ranged from a low of 0.3 percent of imports (Mexico) to a high of 6.3 percent of imports (India). Turkey, China, Argentina, Brazil and Indonesia also had a significant share of imports affected by TTBs in 2011. From the non-G20 cohort of emerging economies, Pakistan, Peru and Thailand each had more than 1 percent of imports subject to TTBs in 2011.

Column (4) provides information to disentangle the extent to which the country relies exclusively on its antidumping policy to implement TTBs. For example, while 6.3 percent of India’s imports were subject to some TTB policy in 2011, 5.8 percent of India’s imports were subject to antidumping alone. The differential of 0.5 is thus the percentage of India’s imports subject to some _other_ TTB policy. Antidumping is the most commonly used TTB policy for each economy, and India’
- 33 -

transportation--roads or railroads--as necessary first infrastructural investments (Rondinelli and Ruddle, 1978; Rondinelli, 1983). The consumption data from Africa and Asia reinforce this notion, highlighting the importance of road access to rural towns in stimulating agricultural consumption linkages. Beyond that, the spotty record of rural electrification programs alone in fostering industrialization (Fluitman, 1983; Okelo, 1973) suggests a need to review evidence on complementarities, sequencing and necessary additional conditions for generating productive impact frid infrastructural investments. Institutional infrastructure will also be essential in fostering the transition to a more specialized, productive rural economy. Efficient rural financial markets will be particularly important. It appears that improvements on this front will require integration of the existing informal credit markets with the formal banking system. Labor markets are also essential, and their increased efficiency will follow in the wake of improved communication and transport infrastructure.

3 Because much current writing emphasizes the need for investments in infrastructure, policy makers can all too easily overlook the collateral need for investments in people. Since services will be among the most rapidly growing rural nonfarm a4tivities, investment in human capital will likely be essential for realizing those potential gairs. Services depend more on skilled people than on equipment and infrastructure.

* Many programs of direct assistance to rural nonfarm enterprises, although by no means all, have been cost effective. 14 The record of technical assistance and projects providing modern workshop facilities have been most checkered; two-thirds of 17 recent project evaluations have found benefit/cost (B/C) ratios below one (Haggblade, 1982; Kilby, 1979). On the other hand, credit projects, especially those supplying working capital, have enjoyed greater success. A recent evaluation of seven small
Bird et al. (2006) measure tax effort as tax revenue as a share of gross domestic product (GDP). Data for both measures of the dependent variable came from the World Development Indicators (WDI). The model was estimated using cross-section data with mean values for the years 1990 to 1999 for 110 transitional and developing countries.<sup>4</sup> _Table A2_ in the Appendix presents a summary of the results. It can be seen that institutions and governance play a significant role in the determination of the level of tax effort in transition and developing countries using the Kaufmann et al. (2004) data set and the International Country Risk Guide database. The ICRG provides an alternative set of data to the Quality of Governance Index, with special emphasis on aspects affecting private foreign investment decisions. The data contain annual values for indicators of the quality of governance, constructed by Stephen Knack and the IRIS Center, University of Maryland, and provided by The PRS Group. The results indicate the institutional factors are always statistically significant at the 1% level with substantial quantitative effects. Using panel data Torgler et al. (2010) extend the previous studies covering a time period of 16 years using the ICRG risk guide data (1990-2005). They also implemented the Kaufmann et al. (2006) data set working with the years 1996, 1998, 2000, 2002, and 2004. Such an analysis goes beyond the previous studies that have explored this framework, mainly in a cross-sectional environment (e.g., Bird et al. 2006, 2008). Their results also indicate that governance and institutional conditions have a strong impact on tax performance.

Looking at further factors and focusing more on tax administration using US and data from Turkey, Torgler, Demir, Macintyre and Schaffner (2008) report that positive attitudes towards the tax authority (e.g., how taxpayers rated tax administrations‘ job, their honesty and fairness, and their helping and information behavior) and tax system significantly
fact that the system applies a set of procedural rules common to all participant countries, hence eliminating the “home bias effect” introduced when working with patent data from one single office. Appendix S2 enumerates the advantages of using PCT data for economic analysis, discusses how countries make use of this system, and details the extent of overlap with other patent data sources, such as the USPTO and the EPO.

More importantly for the sake of the present analysis, PCT patent applications are the only ones recording the nationality of the inventors. The reason for that is as follows: because not all countries are PCT contracting states, only national or resident applicants of a PCT contracting state can file PCT applications. In order to verify that applicants meet at least one of the two eligibility criteria, the PCT application form asks for both nationality and residence. In parallel to this, US laws bind the applicant also to be the inventor; US laws also request the applicant to be an individual, not a firm. Thus, if a given PCT application includes the United States as a country in which the applicant has considered pursuing a patent—a so-called designated state in the application—all inventors are listed as “applicants/inventors,” and their residence and nationality information are, in principle, available.

All in all, between 1990 and 2010, the share of inventors’ records for which we can retrieve nationality and residence information is pretty high, around 80% of the cases.<sup>8</sup> Admittedly, this coverage is unevenly distributed over time—around 60–70% during the 1990s and 70–95% during the 2000s—as well as across countries—the United States (66%), Canada (81%), the Netherlands (74%), Germany (95%), the 8. The use of the word “record” here signifies the unique combination of “inventor name” and “application number.” 13
of changes in food prices at various market segments is rare in LMICs (Adewopo, et al., 2021; Zeug, et al., 2017; Galtier, et al., 2014).

For ex-ante and ex-post assessment of food security and market functioning, it is crucial to monitor food prices at sub-national levels, especially in fragile contexts where majority of households are living at the brink of poverty, and limited capacity or opportunity exists for local safety nets. In most LMICs, price monitoring and reporting systems for major staple commodities are rife with various intractable challenges, including planning time, costs, and logistical constraints to collect data at relevant granularity (i.e. market segments, commodity sub-types, spatial and temporal scales, etc.) to derive national and sub-national intelligence on market or price dynamics (FAO, 2017; Galtier, et al., 2014). Yet, governmental institutions, development agencies, and market stakeholders rely on such intelligence to make decisions about appropriate development and humanitarian interventions or investments. The underlying commodity price data are typically generated by designated national agencies or third-party institutions, through snapshot surveys of markets or implementation of arguably non-transparent proxy methods (Green, et al., 2013; Kalkuhl, et al., 2016). Proper food system planning and appropriate intervention require robust data and credible insights for time-relevant, targeted action, and reflexive impact assessment. At best, current official data represent a lagging rear-view of food security status and changes, and at worst, they misrepresent or under-represent the critical nuances of market signals in space and time, thereby eroding the confidence of decision-makers and data users.

Within the past decade, researchers have proposed and tested various innovative approaches to bridge the food price data gap in fragile contexts, producing mixed results (Kisan, 2015; Zeug, et al., 2017). The inherent applicability of these approaches can be adjudged relative to core elements of data innovation, including replicability, synchronicity, validity, and velocity over time and space. Data
-|---|---|
|_Nqt_+1 = 1|0.9375|0.0740|0.0105|0.0041|0.0022|
|_Nqt_+1 = 2|0.0473|0.8603|0.1477|0.0062|0.0043|
|_Nqt_+1 = 3|0.0152|0.0603|0.7996|0.0372|0.0043|
|_Nqt_+1 = 4|0.0000|0.0055|0.0380|0.8822|0.1041|
|_Nqt_+1 = 5|0.0000|0.0000|0.0042|0.0702|0.8850|

Note: Markov transition matrix of countries moving from the _i_ th capital control quintile at time _t_ to the _j_ th quintile at time _t_ + 1, estimated by maximum likelihood from annual data of 99 countries.

To address the potential endogeneity in capital controls, we estimate state-dependent impulse

> 15Data resource: the Direction of Trade Statistics (DOTS).

22
household members that have returned from abroad in the past 5 years and when they returned, but with no additional information. In contrast with Bangladesh and Nepal, no information on current migrants is collected. As the number of return migrants from overseas captured by each wave of the Pakistan LFS can be small relative to the working age population, we pool together the 2014/2015 and 2017/2018 waves of the survey to increase the number of return migrants in the Pakistan sample. In all three countries, an individual is categorized as a return migrant if he has returned from overseas in the past 5 years, which is the recall period used in all three surveys. Return migrants in the sample are thus relatively recent returnees, which must be kept in mind in the interpretation of the results. For all three countries, since over 95% of temporary migrants (and thus return migrants) are males, we restrict our analysis to males of working age (between age 20 and 59).

# **3.2 Descriptive Statistics**

Descriptive statistics of the sample of return migrants compared to nonmigrants are reported in Table 1 for individuals aged 20 to 59, as well as the p-value for the corresponding t-tests between two samples.<sup>8</sup> As shown in Table 1, return migrants to South Asia are by a very large majority males, reflecting the very low share of female migrants (Ahmed and Bossavie 2021).<sup>9</sup> This gender pattern is driven by very low levels of domestic female labor force participation (Farole et al. 2017; Amir et al. 2018; Bulmer, Shrestha, Marshalia, et al. 2020), combined with difficult work conditions and risk of abuses at destination, as well as regulations in sending countries which in some cases explicitly restrict female migration to avoid abuses (Ahmed and Bossavie 2021). Given the very low incidence of migration among female workers, we restrict the remainder of our analysis of return migrants’
Figure 6: Distribution of Mean Seasonal Temperature, by Remote Sensing Source and Agro-ecological Zone <!-- Start of picture text --> Tropic-warm/semi-arid (n = 9,095) Tropic-cool/semi-arid (n = 2,840)<br>.25 .3<br>.2<br>.2<br>.15<br>.1<br>.1<br>.05<br>0 0<br>20 25 30 35 15 20 25 30<br>Tropic-warm/sub-humid (n = 9,009) Tropic-cool/sub-humid (n = 5,886)<br>.5<br>.2<br>.4<br>.15<br>.3<br>.1<br>.2<br>.1 .05<br>0 0<br>15 20 25 30 10 15 20 25 30<br>Tropic-warm/humid (n = 3,280) Tropic-cool/humid (n = 2,960)<br>.6 .4<br>.3<br>.4<br>.2<br>.2<br>.1<br>0 0<br>15 20 25 30 16 18 20 22 24 26<br>Mean Seasonal Temperature (C) Mean Seasonal Temperature (C)<br>MERRA-2 ERA5 CPC<br>Density<br>Density<br>67<br>Density<br><!-- End of picture text --> _Note_ : The figure presents temperature distributions pooled across all countries and years, disaggregated by agro-ecological zones.
increasing over time as more sophisticated survey instruments may require hiring a better educated labor force.

This study assumes a constant annual increase of 2.5 percent in costs of survey implementation and technical assistance. It is worth noting that even if the annual growth rate of costs is constant, total survey costs may differ depending on the timing of survey implementation. For example, the total cost of implementing five surveys within a country is much smaller if all surveys are conducted in the first five years of the 15-year period between 2016 and 2030 than in the last five years. Even if the five surveys were to be conducted every three years, costs would vary depending on when the first survey starts.

In our analysis, we assume that all the 78 countries carry out surveys every three years, with the implementation of the first surveys uniformly distributed between 2016 and 2018. Under this assumption, 28 household surveys will be implemented every year. This assumption appears to be strong; however, the global World Development Indicators database of poverty estimates shows that the total number of household surveys conducted each year remained more or less the same in the last 10 years. Under these assumptions, the total survey cost over the 15-year period is estimated to be 20 percent higher than the total cost if there is no increase in costs of the survey implementation and technical assistance.

To appreciate the impact of the assumption of 2.5 percent annual growth in costs, we further estimate the increases in the total survey costs by assuming different growth rates such as 2 and 3 percent. The analysis suggests that a 1 percentage point increase in the growth rate increases the total cost estimate by approximately USD 70 million, which accounts for about 7.5 percent of the total costs if there is no growth in the survey cost.

# **IV. Total survey implementation and technical assistance costs**

Building on our discussion in the previous section, a baseline total survey implementation cost estimate is calculated assuming that 1) each country implements a multi-topic household survey
, it is only possible to differentiate between these sources in four of the 15 countries (Panama, Ecuador, Nicaragua, and Nepal). In these four countries, electrical generators for household use are rare. In Nicaragua and Nepal, less than I percent of households with electricity reported obtaining the electricity from a generator. In Panama and Nicaragua, 3.5 percent and 1.5 percent of households with electricity rely on generators. In Panama, poor households are somewhat more likely than rich households to rely on generators for electricity: nearly 12 percent of those in the poorest quintile who have electricity versus 2.3 percent of the richest quintile use generators. In Nicaragua, very few of the richest or poorest obtain electricity from generators; the Nicaraguan generator users are disproportionately concentrated in the middle-income quintiles.

> _13_ Information on telephone use is only available in 10 ofthe 15 countries. Sewer information is available in 12 countries, and electricity data in 14. These coverage figures reflect the percent of sample householdsfor _whom data on the service is available_ who have the service in their homes.

14 Most of the richest households come from South Africa, Panama, Russia, Ecuador, and Jamaica - the wealthiest countries in the sample.

6
%|44%|
|Mexico|18%|16%|58%|56%|9%|9%|15%|15%|
|Nicaragua|23%|21%|16%|15%|32%|32%|28%|28%|
|Paraguay|54%|53%|25%|25%|18%|17%|4%|4%|
|Peru|5%|5%|41%|40%|23%|23%|31%|31%|
|Panama|21%|19%|18%|17%|18%|17%|44%|42%|
|Uruguay|97%|97%|3%|3%|0%|0%|0%|0%|
|Venezuela|37%|35%|7%|6%|28%|28%|28%|28%|
|_LCR Totals_|_30%_|_29%_|_29%_|_29%_|_25%_|_25%_|_16%_|_16%_|

Sources: 1) Population density is from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total area as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” includes GAEZ classifications of ‘marginal’, ‘very marginal’, and ‘not suitable’.
more effective to give them access to a marketplace for business services where they can hire skilled specialists.

We test this idea through a randomized experiment in Nigeria, carried out as part of a government program intended to help small firms grow. We randomly assigned a sample of 753 firms (each with 2-15 workers) into a control group and four treatment groups. The first treatment group used the common approach of _training_ the entrepreneur, providing a mix of 25 hours online and 12 days in-class business training. The second treatment group used another approach that has been gaining popularity in which the government pays for business consultants to provide personalized _consulting_ to the entrepreneurs, with firms receiving 88 hours of support. The third and fourth treatments supported the entrepreneur to use an online marketplace for business services, with a declining subsidy over nine months. The _insourcing_ treatment group was linked to Human Resource (HR) service providers, who helped the entrepreneur recruit an accounting or marketing specialist to join her firm as a full-time employee (insourced worker). The _outsourcing_ treatment group was linked to Accounting and Marketing service providers, who could be contracted by the entrepreneur to carry out relevant business functions via their external specialists (outsourced professional). The training, insourcing and outsourcing treatments were designed to be of equivalent cost to the government, while the consulting treatment cost twice as much.

We carried out two rounds of follow-up surveys, at one and two years after the start of these interventions. This includes detailed measurement of 41 different business practices, representing both traditional activities (in finance/accounting, marketing/sales, operations/HR), and also more novel activities (in digital marketing). The training treatment does not have a significant impact on business practices, nor on firm growth. In contrast, the other three treatments result in statistically significant improvements on a range of business practices (including a physically verified subset) and these effects persist well over a year after the interventions ended. Most firms given the insourcing or outsourcing treatments chose to hire
WPS3808

# Network Effects of the Productivity of Infrastructure in Developing Countries*

## Christophe Hurlin **

### **Abstract**

Using panel data models we examine the threshold effects of the productivity of infrastructure investment in developing countries. We consider various specifications of an augmented production function that allow for endogenous thresholds. More precisely, these specifications are tested in a panel threshold regression. Our main robust result is the presence of strong threshold effects in the relationship between output and private and public inputs. Whatever the transition mechanism used, the testing procedures lead to strong rejection of the linearity of this relationship. In particular, the productivity of infrastructure investment generally exhibits some network effects. When the available stock of infrastructure is very low, investment in this sector has the same productivity as non-infrastructure investment. On the contrary, when a minimum network is available, the marginal productivity of infrastructure investment is generally largely greater than the productivity of other investments. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.

Keywords : Infrastructure, Threshold Panel Regression Models. J.E.L Classi.cation : C82, E22, E62.

## World Bank Policy Research Working Paper 3808, January 2006

_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._

> *I would like to thank Santiago Herrera for his support and his comments on a previous version of this work. I also thank Mohamed Belkir