rafmacalaba/data-use-mentions-extended huggingface dataset:
, payouts from a policy covering the first phase of the monsoon, if paid immediately, could be used by farmers to help fund crop replanting later in the monsoon season. In practice to date, payouts have not been made until after the end of the monsoon, in part because of delays in receiving certified rainfall data from government rainfall stations. Over time, ICICI Lombard has begun using automated rain gauges that allow them to measure rainfall immediately; this in principle should allow payouts to be made more quickly, and by increasing

> 22 The role of adverse selection and moral hazard in insurance markets has been the subject of a very large literature, see for example Pierre-André Chiappori and Bernard Salanié (2000), Amy Finkelstein and Kathleen McGarry (2004), Hanming Fang, Michael Keane and Dan Silverman (2008), John Cawley and Tomas Philipson (1996) and Michael Rothschild and Joseph Stiglitz (1976).

23
all bigger establishments with over 50 employees and then sampling at the lower end.<sup>18</sup> Although the UBI covers the entire country, due to confidentiality issues and limits on the capacity to share this data outside of the Uganda Bureau of Statistics (UBOS), our data have coverage only of Kampala and furthermore we are restricted by the abridged version of the data and statistics that have been provided to us. Our data cover the Greater Kampala metropolitan area for a sample of 2,342 firms, each with information on their GPS location, total number of employees, value added per worker, and industry classification. Value added is calculated as the net output of a sector after adding up all outputs and subtracting out all intermediate inputs but labor.

# **6.2 Matching the data to density measures**

We measure the effects of density on firm productivity by matching the firm coordinates and data from the UBI survey with our population, employment, and Landscan ambient population data, used elsewhere in this paper. As detailed in section 1, our data on population are from the 2002 census and are at the level of 174 parishes within the Greater Kampala administrative area. We assign that data to the 1km grid square level, through a weighted sum to the survey area numbers, so as to be consistent with the spatial resolution of Landscan. Our employment data are from the 2002 Census of Business Establishments [COBE] and provides the GPS location of supposedly the universe of firms and employment in Greater Kampala.<sup>19</sup> The data allow us to study density effects at two spatial scales. First is the coarser one with 1km grid squares upon which population and Landscan data are defined. For this each firm point in the UBI dataset is overlaid by the 1km grid square level data on population, employment, and Landscan. Each firm is then assigned own-square measure

> 18Business establishments were first stratified by industry sector and within each industry sector, business establishments were further
emigration rates of tertiary educated workers for 61 developing countries. These estimates are based on three main statistical sources (US Census data on the skill structure of immigration, OECD data on immigration per country of origin, Barro and Lee (2000) data describing the skill structure in sending countries). They had to rely on a set of assumptions. First, for non-US countries, they use OCDE migration statistics which report limited information on the origin of immigrants<sup>4</sup> . Second, they transpose the skill structure of US immigrants on the OECD total immigration stock. For example, migrants from South Africa to Great Britain are assumed to be distributed across educational categories in the same way as migrants from South Africa to the United States. This assumption is particularly tentative for countries which do not send many migrants to the United States. Adams (2003) used the same methodology to update the emigration rates of 24 labor-exporting countries in 2000. Beine et al (2003) used Carrington and Detragiache’s data to predict the growth impact of the brain drain. Yet, given the assumptions, the evidence concerning the consequences of skilled migration for developing countries remains not only limited but also largely inconclusive.

The purpose of this paper is to build an exhaustive international database on the brain drain describing the loss of skilled workers for all developing and developed countries. The majority of high-skilled workers go to industrialized countries. Building on Carrington and Detragiache (1998) and Adams (2003), we focus on the South-North and North-North brain drain. We are aware that a brain drain can be observed outside the OECD area (migration of skilled workers to the Gulf countries, South Africa, Malaysia, Hong-Kong, Singapore, Taiwan, etc) but, at this stage, we do not take these ‡ows into account<sup>5</sup> .

We collected data on the immigration structure by educational attainment and country of birth from all OECD receiving countries. Census and register data are (or will be
cyclical factors themselves.<sup>5</sup> Moreover, while single-country longitudinal studies may be useful for studying the impacts of some institutions that experience discrete changes (e.g., minimum wages), they are less suited for other institutions (e.g., unions) that evolve more gradually over time.

Interactions add to the challenge of identifying the effects of a specific law or policy. As Eichhorst, Feil, and Braun (2008) point out, it has become evident that two types of interactions are important to consider. One concerns interactions between different labor market institutions, with the impact of one being affected by another.<sup>6</sup> More generally, countries typically have “bundles” of complementary institutions (e.g., the lightly regulated Anglo-Saxon model, the Northern European flexicurity model, etc.) which make it difficult to isolate the effect of

> 4 In the first place, the institutions themselves are not exogenous variables but reflect the societies in which they exist.  This includes, for example, the country’s legal tradition (Botero _et al._ 2004); the strength of its family ties (Alesina _et al._ 2010); and civic attitudes (Algan and Cahuc 2006).

> 5 Model specifications can take into account this type of problem but require panel data series that are long enough to determine “normal” cyclical trends.

> 6 One example of this is the relationship between social insurance contributions and minimum wages.  How much the contributions affect employment will depend in part on who actually pays which in turn is affected by the minimum wage which limits the potential for shifting the costs to low-wage employees.
in the studies mentioned above, on the institutional environment as critical to explaining variation in FDI inflows. In contrast to Broadman et al. (2004), we look at cumulative FDI inflows per capita and use an aggregate EBRD-based indicator measuring the progress in building market-supporting institutions. We take FDI inflows per capita rather than in terms of GDP simply because the latter is positively correlated with higher quality of governance. As discussed earlier, the aggregate indicator is the average of EBRD scores limited to areas not directly associated with first generation reforms. The progress in these areas no longer strongly differentiates among transition economies, as was the case during the initial stages of transition, whereas institution and governance-related domains do. The index is averaged for the 1996-2002 period. The rationale behind averaging over the eight-year period is straightforward: a good climate has to exist for a sufficiently long time to affect investors’ decisions. As a second test, we also plot cumulative net FDI flows per capita and the aggregate indicator of quality of governance. As mentioned earlier, the quality of governance is defined here as an average value of the indicators of political stability, government effectiveness and regulatory quality. These three aspects of the business environment are critical to FDI inflows. The indicators pertain to 1996, 1998, 2000 and 2002 and range from –2.5 (lowest score) and +2.5 (highest score). For the same reason, we take the average over time (Table 5).

The variation in the progress in structural reforms and the quality of governance corresponds very closely to the variation in cumulative FDI net inflows per capita over 1990-2003. Countries whose institutions were below 40 percent of the level in industrialized market economies have failed to exceed US$ 500 dollar worth of cumulative net FDI inflows per capita (Figure 3). So have the countries with the negative values of the aggregate governance indicator (Figure 4).

> 5 There are two exceptions to this general rule: First, investments in natural resource sectors tend to flow
dollar coefficient is still lower than the pre-crisis level (though it has become larger in size and statistically significant in comparison to the mid-crisis period). Interestingly, the weight of the Japanese yen continues to be significant in Indonesia (0.31), Thailand (0.12), and the Philippines (0.10), even though the yen was never important in pre-crisis Indonesia or the Philippines.<sup>26</sup>

**_Rolling regressions._** Using 18-month period data for July 1997-December 1998 as a midcrisis sample may be problematic because exchange market developments were quite volatile, erratic, and dynamic during the crisis. Regional contagion, delayed currency attacks (Korea) and large exchange rate depreciations at times of political uncertainty (Indonesia) may have altered pattems of exchange rate movements over the course of events. Therefore, we next examine midcrisis exchange rate movements in more detail by dividing the sample into a series of 3-month periods."

Figure 3 reports the results of such rolling regressions for the period January 1990 - December 1999. The 10-year period is divided into a series of 3-month sub-samples, by rolling over the sample by one month each. More concretely, a total of 118 regressions are run, using the first sub-sample period as January-March 1990, the second as February-April 1990, and so onall up to the final sub-sample period of October-December 1999 (see the Appendix Table for a summary of the regression results).<sup>2 8</sup> Estimated regression coefficients of the US dollar, the Japanese yen, and the euro are plotted against time: a thick solid line for the dollar; a fine solid than a conscious policy shift to exchange rate stabilization, or the authorities' concem about too rapid an appreciation of the currency when growth momentum was about to pick up. Whatever the interpretation, it appears that the authorities continue to regard the US dollar as the most relevant reference currency for their exchange rate policies.

26 Again, the observed post-crisis importance of
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 19. Expansion of the Internet market in Senegal**

<!-- Start of picture text --> a. Internet service trends, 2000–08<br>0.9 20<br>0.8 18<br>16<br>0.7<br>14<br>0.6<br>12<br>0.5<br>10<br>0.4<br>8<br>0.3<br>6<br>0.2 4<br>0.1 2<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br><!-- End of picture text --> <!-- Start of picture text --> b. Internet service, ECOWAS, 2008<br><!-- End of picture text --> <!-- Start of picture text --> 14 350<br>12 300<br>10 250<br>8 200<br>6 150<br>4 100<br>2 50<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br>Sierra Leone Liberia Niger Guinea-Bissau Mali Burkina Faso Mauritania Gambia Senegal Benin Ghana Cote d'Ivoire Cape Verde Togo Nigeria<br><!-- End of picture text --> _Source:_ World Bank, including the Information and Communications for Development database. _Note:_ ECOWAS = Economic Community of West African States.

Prices for telephony remain modest, particularly via the Internet; end-user prices are
# **7.2 Access to markets and connectivity**

Since the homelands were spread across the country, the effects of their progressive integration in the South African economy might be expected to depend on their proximity to markets and connectivity (Harris, 1954; Krugman, 1991; Hanson, 1996; Fujita et al., 2001; Redding and Sturn, 2008; Donaldson, 2015). Failing to account for these mechanisms might bias our estimates, notably if initial employment in mining were correlated with proximity to markets and roads of the corresponding communities. Even though we initially accounted for the distance of the community to the border of the homeland, this measure does not necessarily capture the effect of the integration of former homeland communities in the South African economy.

To capture a community’s access to markets, we calculate its distance to the ten largest cities in terms of population in South Africa.<sup>27</sup> The linear distances are weighted by the city’s population. Figure 9 describes this measure for the sample of communities near the former homeland borders. Communities located inside homeland areas appear to have worse access to major cities, though there is substantial variation inside and out.

In a similar way, we account for the connectivity of each community, defined as proximity to main road. Formally, this measure is defined as the linear distance in kilometers of a community to the nearest main road (in 2001 as defined by Statistics South Africa). Figure 10 describes this measure and shows a similar pattern to that found with distance to major cities. However, the difference between communities just-inside and outside the homelands is substantially larger in this case.

Table 7 presents regression results that account for the introduction of these variables. In column 1 we include the measure of distance to major cities and the interaction with the homeland indicator. Both covariates are statistically significant. The closer a community is to a major city, the stronger is its growth in income. This effect is even more pronounced
in the coresidency rates across Bangladesh and India data, and the bias estimates reflect the differences in the severity of selection. Since we estimated the biases in IGRC and IGC for a number of different samples, one might wonder how the magnitude of the bias relate to coresidency rate across different samples. Figure 4 shows the relation between the coresidency rate and the estimated bias for both IGRC and IGC estimates. There is a clear negative relation between coresidency rate and the magnitude of bias in the case of IGRC, implying that comparing IGRC estimates from different data sets may not be appropriate. In contrast, there is no discernible relation between the bias in IGC estimates and the coresidency rate. An OLS regression of the bias in IGRC estimates on coresidency rates yield a coefficient of _−_ 0 _._ 22 which is significant at the 1 percent level ( _t_ statistic equals _−_ 2 _._ 50). The coefficient on a regression of the bias in IGC estimates on the coresidency rates is, in contrast, numerically very small (0.008) and statistically insignificant ( _t_ = 0 _._ 30 and P-value= 0.77). We provide an explanation of the low sensitivity of IGC estimates to coresidency rates below (please see section 6.2).

# **6. Toward an Understanding of the Results: Why Is the Bias in IGC Estimates So Low?**

The evidence presented above is strikingly consistent and clear: when a researcher works with a data set from a survey that uses coresidency for defining the household, the IGRC estimates are likely to be seriously biased downward; but the estimates of IGC in coresident samples are, in general, much closer to the estimates from the full sample.

It is important to appreciate that the coresident sample bias common in the household surveys in developing countries is best modeled as a truncation, not censoring. The most common problem in the context of household surveys in developing countries is that there is no information (on both dependent
of inter-district mobility, with the exception of low-educated individuals.

Mueller et al. (2020) employ the LSMS-ISA data from four countries, namely Ethiopia, Malawi, Tanzania and Uganda. They combine these household panel data with climatic data from NASA’s MERRA to investigate temporary migration responses to weather anomalies in the East African context. Using a linear probability model, they find that climate impacts tend to _decrease_ outmigration and, perhaps surprisingly, are most pronounced in urban areas.

Becerra-Valbuena and Millock (2021) combine LSMS-ISA Malawi surveys with satellite weather data covering the timespan 2000–2016 to estimate the probability of migration for reasons related to work and marriage separately for men and women. They find that overall droughts inhibit marriage-related

> 13 We here refer only to studies exclusively focusing on the causal links between climate change and migration, and exclude works devoted to other research questions that incidentally find climatic impacts on migration decisions.

26
development (in the fourth quartile of the IMF financial development index). The estimates show a stronger and significant association between financing from banks and NBFI and the extent of innovation in countries with a high level of financial development (Table 7, OLS, column High FD). Interestingly, the coefficients on financing by other is positive and significant in the sample with low financial development countries for both the innovation score and adoption score (Table 7, OLS, column Low FD), perhaps suggesting that low financial development entails a lower access to external sources of formal financing for innovative firms.

**Table 7: Financial development, firm innovation and the sources of funding of fixed assets**

||**OL**|**S**|**Lo**|**git**|
|---|---|---|---|---|
|**Variables**|**Low FD**|**High FD**|**Low FD**|**High FD**|
|**% Fixed assets funded by banks**|0.0493|0.0810**|0.0526|0.0841*|
||(0.0323)|(0.0397)|(0.0365)|(0.0447)|
|**% Fixed assets funded by NBFI**|0.1785|0.2642***|0.1814|0.4113*|
||(0.1735)|(0.0715)|(0.1826)|(0.2316)|
|**% Fixed assets funded by other**|0.0703**|0.0182|0.0714*|0.0179|
||(0.0354)|(0.0512)|(0.0375)|(0.0504
percentage juvenile to the regulator in the electronic logbook data, but not the length distribution. I obtained a supplementary electronic logbook dataset for a group of vessels that report length distribution data to their owners. These vessels represent 56% of landings and I received their data from Sociedad Nacional de Pesquer ́ıa (SNP), a consortium of fishing companies, in January 2020 (Englander, 2022).

To calculate juvenile catch for each set, I first use the length distribution values from sets in the SNP electronic logbook data to impute length distributions for non-SNP sets, based on

> 43My results are robust to measuring juvenile catch in terms of tons of juveniles caught (Figure A6).

33
level, CONEVAL jointly with the World Bank, produced a panel of municipal poverty maps for 1990, 2000, 2005, and 2010.<sup>13</sup> More recently, the World Bank has produced a comparable municipality-level poverty map for 2014 combining the inter-census survey of 2015 with the 2014 household survey. This long-term panel of poverty maps allows an analysis of local trends and drivers of income, poverty, and inequality in Mexico.

Figure 1. Per capita household income growth across municipalities, percentiles, 2000–14 <!-- Start of picture text -->
1.30<br>1.25<br>1.20<br>1.15<br>1.10<br>1.05<br>1.00<br>0.95<br>0.90<br>2000 2005 2010 2014<br>Percentile 10 Percentile 50 Percentile 90<br>household income index<br>municipal mean per capita real<br><!-- End of picture text -->

_Source:_ Calculations using the poverty maps of 2000, 2005, 2010, and 2014. _Note:_ Unweighted average. The index shows the changes in a given percentile of the distribution of the municipal mean household income per capita. Municipalities at the n percentile in 2000 and 2014 do not necessarily correspond.

Figure 2 **.** The share of municipalities that reduced inequality, by income group, 2000–14 <!-- Start of picture text --> 2000-2005 2005-2010 2010-2014<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>municipalities
Chad, and Niger- have a per capita GDP of US $ 500, which is 21.5 percent of the per capita GDP in PNG (US $ 2,333).

The patterns are robust to concerns about the accuracy of reported GDP figures. For example, some have suggested that there is too much volatility in reported figures, even in years where there have been few observable changes (e.g., Howes et al. 2022). However, Figure 9a illustrates that the figures are qualitatively identical when using an additional source for per capita GDP aside from those reported in the World Development Indicators;<sup>29</sup> and Figure 9b illustrates that the figures are identical when using the five year average of GDPs in the underlying regression and the projection to reduce potential noise.

As described above, the inability of economic activity in the resource sector to spill over and benefit the majority of the population through public transfers, direct employment, or spillovers of increased economic activity in other sectors is possible in resourcedependent economies (e.g., IMF 2012). However, we illustrate that higher-than-expected deprivations in PNG are even extreme relative to other resource-dependent economies.

Figure 10 reports how much larger actual deprivations are than the level of deprivations projected by economic activity alone. The figure reports this difference separately for PNG and the average for all resource-dependent countries that are low- or middleincome, as defined by the IMF (e.g., IMF 2012).<sup>30</sup><sup>_,_31</sup> For the MPM itself and for each deprivation aside from the educational attainment of adults, the difference was larger in PNG than the average for all other resource-intensive low- and middle-income countries. The point estimate for the other resource-dependent economies was below the lowest projection in PNG for all indicators; and for four of the seven total indicators (6 deprivations and the MPM), the lowest projection in PNG is still higher than the highest