rafmacalaba/data-use-mentions-extended huggingface dataset:
WPS3784 about 50 per cent higher on poor quality roads compared to good quality roads.<sup>6</sup> For many farmers roads are not accessible or totally inadequate and head carrying is the only available mode of transport. Given the uncertainty of the magnitude of such a reduction in transport costs on producer prices, we perform the simulations using two pass-through assumptions that is the extent to which the fall in transport costs is reflected in higher producer prices for farmers: a 100 percent pass-through and a 50 percent pass-through.

We follow Deaton (1989, 1997) whereby the short-run effects of price changes are directly proportional to income shares. In particular, holding all the other components of household income constant and assuming that coffee produced in Rwanda is entirely exported (there is almost no domestic consumption of coffee), the direct short-term welfare impact of a price shock on household _i_ is given by: Where _Y_ 0 is initial income of household _i_ , Δ _Y_ is the change in income following a change in the price of coffee _,_<sup>φ</sup> _icoffee_ is the share of coffee income (sales) in total household income as provided by the EICV survey and _P_ 0 _coffee_ the initial average producer price of coffee _._ Table 3 shows the different sources of income for Rwandan households, including income derived from coffee sales. At the national level, close to 57.6 percent of households’ income comes from labor market activities and almost 26 percent of income is derived from the sale of agricultural products. In rural areas, agricultural income accounts for close to 42 percent of total income, most of which is derived from the sale of food crops (33 percent) while a smaller portion comes from the sale of commercial crops,

> 6 A World Bank study found that lack of maintenance on the road between Gitarama and Kibuye raised vehicle operating costs from a 1989 level of $1.00 per kilometer to almost $3.40 per kilometer in 1996. The recent
We use annual, country-level data for the period 2000-08 which cover about 90 countries. The insurance variables were mostly taken from the insurance data provider AXCO, although asset data were also taken from national sources. The independent variables were obtained from a variety of sources including AXCO, the _International Financial Statistics_ (IMF), the _World Development Indicators_ (World Bank), and national sources. Table 2 shows the definitions, sources and descriptive statistics of the variables. Table 3 shows the correlations between the dependent and explanatory variables.

As shown in Table 2, there is substantial variation in the insurance variables. NONLIFE, the most ubiquitous form of insurance, ranges from virtually 0 percent (e.g. Angola, Bangladesh, Myanmar) to over 3.5 percent of GDP (e.g. USA, UK). LIFE, a form of insurance which emerges at more advanced stages of development, ranges from virtually 0 percent (e.g. Kazakhstan, Syria, Vietnam) to over 10 percent of GDP (e.g. South Africa, Portugal). ASSETS range from virtually 0 percent (e.g. Tanzania, Kenya, Saudi Arabia) to over 90 percent of GDP (e.g. Sweden, UK, France).

In line with the previous section, we group our explanatory variables in four categories. First, we consider _economic variables._ Our main income level variable is GDP per capita (GDPPC). Our measure of income inequality is the fraction of income held by the richest 20 percent of the population (INCOMETOP20) and we use annual inflation (INFLATION) as a proxy for inflationary expectations. As mentioned before, we add two additional economic variables to examine the non-life sector. We include the number of passenger vehicles per 1,000 inhabitants (CARS) because most countries require at least third party liability insurance (comprehensive car insurance is usually voluntary but also common in many countries). We also consider trade activity, the sum of import and export activities
leaders’ sample as our preferred prediction for the social costs of the program. In order to estimate WTA, we run a series of probit regressions to estimate equation 5 with different configurations of the matrix X j. Without any covariates, these estimations yield a γ that gives us an estimate of the median WTA across all of the communities. With additional conditioning, we can refine these estimates for subsamples of the population. For example, including state fixed effects as covariates gives separate median WTA estimates for each state. Further covariates permit the prediction of median WTA for communities with varying configurations of the covariates.

Table A9 contains the leader sample probit estimations used to predict willingness to accept. These estimates show specifications with no controls (column 1), with state fixed effects (columns 2-5), and with a full set of covariates intended to reflect the opportunity cost of participation, including forest type, ejidal wealth indices, primary economic activities, use of PSA land, and geographic characteristics. Beneficiaries seem more responsive to changes in the offer price than non-beneficiaries. We observe this also in a simple graph showing the proportion of yes responses against the difference between the offer and the actual payment (Figure A4), which illustrates a steeper drop for beneficiaries than non-beneficiaries when the net gains (the difference between the hypothetical and the asking price) increase. This helps justify our use of the coefficient estimates from the beneficiary sub-sample to predict non-beneficiary WTA in our simulations. The state fixed effects explain a significant amount of the variation in responses (compare columns (1) and (2) in Table A9). We use the estimated coefficients from this table (A9) to calculate γ and θ from equation 5 above, and then the median value of WTA as written in equation 6.

# 5 WTA estimates

Figure 2 and Table A10 show the averages of the estimated median values of community willingness to accept, calculated using both parsimonious specifications (the first three columns of Table A9), and a specification using a large set
|0.05|0.20|0.17|
|Mean dep. var. control|2.31|0.00|0.42|0.00|
|SD dep. var. control|0.89|1.00|0.79|1.00|
|T1: _βE_+_βI_+_βG_ =0|0.69|0.69|0.00|0.00|
|T2: _βE_+_βG_ =0|0.34|0.50|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.59|0.76|0.00|0.00|

Note: this table reports results on the agricultural practices performed by the farmers in our study in the 2017 season. Using the full sample of 678 farmers, we run the following regression: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexiblei_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Final survey conducted in May 2017. The dependent variable in column 1 is a sum of individual dummies. Each dummy takes value of 1 if the farmer performed one of the so-called existing agricultural practices. In column 2, the dependent variable is the standardized index of the outcome in column 1, computed by standardizing each dummy individually, adding them all and standardizing the sum. We use the mean and standard deviation of the control group as reference for the standardized index. In columns 3 and 4, the dependent variables are analogous
importance of an (by economists) understudied link between labor and land markets, thereby improving our understanding of the structural transformation process in developing economies. Migration, by driving contractual change, can alter the efficiency of the agricultural sector, and have implications for equity and access. Second, we present three decades of national-level descriptive statistics on the nature of the relationship between tenants and landlords for India, one of the most populous country in the world which have a significant share of the population in agriculture. Third, we introduce a novel instrumental variable strategy for emigration: stripping the pull instrument from distance-related components strengthens exogeneity and can be applied in other contexts.

The rest of the paper is structured as follows. In section 2, we provide background on migration in India. Section 3 introduces the data. Section 4 presents descriptive statistics. Section 5 outlines a theoretical framework. Section 6 presents the empirical identification strategy. Section 7 presents the results. Section 8 concludes.

# **2 Setting: Migration in India**

India is often thought of as a country with low internal migration. Several studies point to India’s low internal labor mobility and localised labor markets, which are associated with regional wage gaps, efficiency losses and high local vulnerability (Jayachandran, 2006; Topalova, 2010; Chari et al., 2021). In 2001, merely 2.8% of India’s population consisted of individuals who had moved permanently to a different district in the last five years. In contrast, 9% of Brazil’s population had moved permanently to a different municipality in the last five years, and 10% of China’s population had moved to a different prefecture in the last five years (Kone et al., 2018). Research into the barriers to migration and labor mobility pointed at the role of social networks (Munshi and Rosenzweig, 2016), political and cultural inter-state differences (Kone et al., 2018) and restrictive labor laws (Adhvaryu, Chari and Sharma, 2013
scorecard treatment (“true” announcement), and half of the groups assigned to control (“false” announcement). This provides a “placebo” where the FOs are told that scorecards might happen but in fact do not. Comparing announcement to no announcement within the scorecard treatment provides a test of the extent to which awareness of the scorecards can itself boost efforts of the FO in training these specific farmers groups, while additionally comparing true and false announcement tells us about FOs’ ability to observe the scorecards. While we control for logbook treatment assignment dummies in our analysis of the announcement effect, we are not powered to study their interactions.

3.1 Data We use two sources of data: administrative data from our partner NGO, OAF, and survey data administered on a random subset of our study sample. First, we exploit OAF’s administrative client database for two years, 2013 and 2014. The client database includes the names of all farmers who register for OAF membership in the season, the name of the group to which the farmer belongs, the OAF Field Officer and Field Manager responsible for the group, the total amount of credit requested by the client, the quantity requested of specific agricultural inputs,<sup>19</sup> the number of repayments, and the proportion of the loan repaid. We use the 2013 client database as our sample frame. We track whether these farmers continue with the service by merging with the 2014 client database. If a client in 2013 does not register for the service in 2014, she is considered a dropout. If a farmer was not on the client list in 2013A, but registers in 2014, we consider her to be a new member. Some clients registered in 2013 (in and outside our study sample) switch farmer group in 2014; we do not consider these farmers to be either dropouts or new members. Hence, we report changes in membership net of these switches. For instance, if two clients were registered in group X
,833|3,833|3,833|3,835|
|Countries|88|88|88|88|
|Chi-Square|233.1|103.7|36.48|68.73|
|Wald Statistics|0|0|1.55e-09|0|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. “XR Practices”, “Current Account”, “Capital Account”, and “Export Proceeds” refer to the binary IMF indicator for restrictions in exchange rate practices, current account, capital account, and repatriation of export proceeds, respectively. For each category, “Open” and “Closed” refers to the binary IMF indicator suggesting “no restrictions” and “at least one restriction”, respectively. Sample period 1966-2011 as dictated by availability of data. Wald Statistics refers to the marginal statistical significance levels of Wald tests that the sensitivity of “Open” observations is equal to that of “Closed” ones, and distributed as χ<sup>2</sup> (1). These regressions follow the methodology in Lewis (1997). See footnote 17 for details.

21
Finally, we combine the direct and indirect channels to assess the total (net) contribution of GVC participation to wage inequality within countries (Figure 5c).<sup>12</sup> We find that GVC participation is linked to higher wage inequality in the top high-income countries, such as the US and Ireland, which in most cases is driven by the indirect contribution of GVCs through its links with worker RTI. At the same time, GVC participation is associated with reduced wage inequality in most low- and middle-income countries (in particular Kenya and Ghana, but also Mexico), as well as the bottom high-income countries (Central Eastern and Southern Europe, in particular, Czechia and Poland) where the direct reduction in wage inequality is stronger than the indirect contribution.

The findings can be interpreted as follows. Our results suggest that in countries that mostly receive offshored jobs, GVC participation reduces wage inequality, despite widening the gap in the RTI of work between offshorable and non-offshorable occupations. However, in rich countries that mostly offshore jobs, GVC participation widens wage inequality as it benefits mainly workers in non-offshorable occupations in services.<sup>13</sup>

# 5. Conclusions and policy implications

In this paper, we investigated the relationship between GVC participation and the RTI of workers and its contribution to within-country wage inequality. We used a unique dataset combining worker-level, countryspecific RTI measures based on a pooled sample of survey data for 47 (38, resp.) countries at all development levels, applying the methodology of Lewandowski et al. (2022), with measures of backward and forward GVC participation at the country-sector level based on the method of Borin and Mancini (2019, 2015). We find that the relationship between GVC participation and the RTI of workers is complex and depends on the nature of GVCs, occupations and sectors. This study also finds that GVC participation contributes to wage inequality within countries directly and indirectly through its relationship with workers’ RTI.

First, we found that the relationship between
# **1. Introduction**

Damijan et al. (2003) examine different channels of global technology transfer to transition countries. These authors study the impact of direct technology transfer through foreign direct investment (FDI), intra-industry knowledge spillovers from FDI, firms‘ own research and development (R&D) accumulation and R&D spillovers through trade for total factor productivity (TFP) growth of local firms. Using firm-level data for eight transition countries for the period 1994–1998, this research found that technology is being primarily transferred to local firms through direct foreign linkages.

In the specific context of Latin American countries, Ramirez (2010) estimates whether FDI flows and other relevant variables have had a positive and significant effect on private investment spending over the 1980–2002 period. On the other hand, Schiff and Wang (2010) examine the impact on TFP in Latin America and the Caribbean (LAC) and in other developing countries (DEV) of trade-related technology diffusion from the North, education and governance. The main findings are: i) education and governance have a much larger direct effect on TFP in LAC than in DEV, while the opposite holds for the North‘s R&D; and ii) education and governance have an additional impact on TFP in R&D-intensive industries through their interaction with trade-related technology diffusion from the North in LAC but not in DEV.

According to Suyanto et al. (2009), the mixed evidence of productivity spillovers leads to the celebrated argument that firm-specific characteristics or absorptive capacity may influence the ability of domestic firms in gaining productivity spillovers from FDI and trade. The most commonly used measure of absorptive capacity in the literature about this topic is the extent of R&D expenditure (Findlay, 1978; Glass and Saggi, 1998; Wang and Blomstrom, 1992).

Kathuria (2000) shows – in the context of the Indian manufacturing sector - that local firms that invest in learning or R&D activities receive high productivity spillovers, whereas the non-R
[i<sup>7</sup> lcy RESEARCH WORKING PAPER 1729

# Summary findings

In discussions about regional trade arrangements (RTAs), one concern has been whether the discriminatory trade barriers applied in RTAs encourage high-cost imports from member countries at the expense of lower-cost goods from nonmembers. But evaluations of the impact of RTAs have been hampered by a lack of appropriate empirical procedures for assessing their influence on the level and direction of trade.

Yeats employs a new index for analyzing the static trade effects of an RTA. He examines changes in the regional "orientation" of exports and shows how this information can be employed in connection with the "revealed" comparative advantage (RCA) index to identify apparent "inefficiencies" in trade patterns. He applies the approach to statistics on Mercosur countries' exports to determine if recent trade is evolving along lines current compatible with these countries' current comparative advantage.

He does riot comment on the many other possible effects of RTAs, such as benefits from political cooperation, enhancing the credibility of reform strategies, or dynamic gains from trade. Nor does he focus directly on changes in trade with nonmembers, changes that accelerated rapidly because of the 1988-91 liberalization of trade in Mercosur countries. Thus the paper does not address the net welfare effects of trade creation and diversion relative to the 1988 trade policies of member countries.

The results show the most dynamic (fast-growing) products in Mercosur's intra-trade generally are capitalintensive goods in which members have not displayed a strong export performance in outside markets. Neither the RCA indices nor statistics about factor proportions indicate that Mercosur has a comparative advantage in those products. The evidence suggests that Mercosur's own trade barriers are responsible for these trade changes. Most-favored-nation tariffs on the fast-growing products are above the average for all imports and provide Mercosur members with significant preferences. These findings constitute evidence of the potential adverse effects of regional trade arrangements on members and on third countries, as judged by the variance in their trade patterns from what current comparative advantage would predict.
Figure 4 and Figure 5). Using the LiTS, the share of informal workers decreased from 16.1 percent in 2006 to 11.7 percent in 2010, then rose to 15.1 percent in 2016. Using the ESS, that same share started out at 12.8 percent in 2006, stood at 11.2 percent in 2010, but then recorded a large increase to 19.2 percent in 2016. In fact, the increase estimated from the ESS between 2010 and 2016 is the largest among all surveys.

To summarize, estimates of long-term trends suggest that informal employment in Russia has continued to grow in recent years. A comparison of trends from different surveys reveals that regardless of the data source used, informal employment appears to have increased in the first half of the 2000s, then briefly leveled-off or even fell, but then continued to increase. The deceleration in the relative size of informal employment in second half of the 2000s coincides with the global financial crisis.

> 11 This 6.2 percentage point increase in the rate of informal employment between 2000 and 2016 is due to an increase of 3.6 percentage points in salaried informals (who grew from 3.1 to 6.7 percent of employment) and a 2.6 percentage points increase of self-employed informals (who grew from 7.6 to 10.2 percent of employment).

11/67 Informal Employment and Worker’s Wellbeing in Russia
station. This served to build a complete list of routes for the city to determine the full extent of the transit network. The routes were then travelled in both directions to map them. If more stations were discovered through this process, then they were also included in the mapping process using the procedure described above.

Figure 1 illustrates the public transport network for each city. In Amman, the public transport consists of buses, coasters and services (owner-operated shared taxis running on fixed routes). In Beirut, the network consists of buses and microbuses (service and taxis are not included as these operate almost door to door).<sup>13</sup> And in Cairo, it consists of buses, microbuses, and metro.<sup>14</sup> These data not only allow to see the routes of the transit network, but also includes information about frequencies and timetables that will be used in the construction of various indicators (see section 3.1).

Built environment audits were conducted at selected locations to objectively assess the environment in which people are taking public transport. These were performed for 50 public transport locations in each of the three cities using the methodology developed by Safetipin to assess site safety (Safetipin, 2021). The audit locations were stratified based on geography, stop type, public transport modes available and time of day (peak, off-peak and nighttime).

To assess the built environment of a location, the location is rated along seven objective parameters to assess the perception of safety at this public transit stop. The seven parameters are summarized in Table 1, and the locations of the audits are illustrated in Figure 2.

> 13 Details relating to services and taxis can be found in the forthcoming Lebanon Public Transport Diagnostic. 14 The full list of transport modes covered in Egypt include 10 different options.

7
<!-- Start of picture text --> Figure 1.9 Costa Rica: Degree of Liberalization and Per Capita GDP<br>4,500 90%<br>85%<br>4,000<br>80%<br>3,500 75%<br>70%<br>3,000<br>65%<br>2,500 60%<br>2,000 55%<br>50%<br>1,500<br>45%<br>1,000 40%<br>1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>Per Capita GDP Degree of Lib. (X+M/GDP)<br>Export Promotion FTZ Law Reform<br>Import Liberalization<br>US Dollars Capital Account Liberalization Percentage<br>Intel’s Arrival<br><!-- End of picture text --> Source: Prepared by the authors based on figures from Banco Central de Costa Rica.

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