rafmacalaba/data-use-mentions-extended huggingface dataset:
difficulties. It could lead to an over-parameterized specification, and hence to imprecise and unreliable estimates of the contribution of the individual infrastructure indicators. In our framework this is a concern not only for the usual reasons of multicollinearity – indeed, several of the infrastructure indicators we shall use are fairly highly correlated --<sup>13</sup> but also because, as described below, we shall use a nonlinear procedure to estimate the parameters of the production function. In these conditions, a parsimonious specification with relatively few regressors is much more likely to result in stable estimates robust to alternative choices of initial values.

For these reasons, we follow a different strategy. We use a principal component procedure to build a synthetic index summarizing different dimensions of infrastructure.<sup>14</sup> We focus on three key infrastructure sectors: telecommunications, power and road transport. This choice is consistent with previous literature on the output impact of infrastructure, which has typically focused on one of these individual sectors, most often telecommunications. The synthetic infrastructure index is the first principal component of three variables measuring the availability of infrastructure services in these three sectors. Specifically, the variables underlying the index are:

- (a) _Telecommunications_ : Number of main telephone lines, taken from the International Telecommunications Union‘s World Telecommunications Development Report CDROM. As a robustness check, we also experiment with an alternative measure , namely the total number of lines (main lines and mobile phones), from the same source.

- (b) _Electric Power_ : Power generation capacity (in Megawatts), collected from the United Nations‘ _Energy Statistics_ , the United Nations‘ _Statistical Yearbook_ , and the U.S. Energy Information Agency‘s _International Energy Annual_ .<sup>15</sup>

- (c) _Roads_ : Total length of the road network (in kilometers), obtained from the International Road Federation‘s World Road Statistics, and complemented with information from

> 13 For instance, in our panel data set
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sup>12</sup> This higher use for India specifically can be conveniently expressed as a ratio of 1.18 between the two shares. The average ratio across all 20 countries is 1.30, with 13 nations having a ratio greater than one. Finally, Column 8 of Table 1 lists the average hourly wage paid to Indian workers on outsourced contracts. The range across the top 20 countries is from $7 to $12, with an average of $10. As the average wage on oDesk for data entry 12 To put these figures in perspective, 0.9% of the U.S. population in the 2010 Census of Populations was born in India. These numbers are not exactly comparable, as our measure is based off of ethnicity, rather than country of birth, and includes South Asia more generally. Nonetheless, even after taking these features into account, the role of Indians on oDesk is perhaps twice as strong as the overall Indian population share. As a second comparison point, Kerr and Lincoln (2010) estimate the ethnic Indian share of U.S. inventors to be about 5% in 2005 using patent records from the United States Patent and Trademark Office. This second comparison point uses the same name matching approach as the current project. It thus suggests that Indians may use oDesk somewhat less as a share of total users compared to their general presence in high-tech sectors.

10
# Outcome Variable Collection Periods

||**Basline**|**Midline**|**Weekly**|**Endline**|
|---|---|---|---|---|
|**Psychological Well-being**|||||
|PHQ9|X|||X|
|Life Satisfaction Index|X|||X|
|Stress Index|X||X|X|
|Sociability (Total)|X||X|X|
|Sociability (Positive)|X||X|X|
|Self-Worth Index|X|||X|
|Locus of Control|X|||X|
|Allocation Decision Game||X||X|
|Stability Index||X||X|
|Physiological Well-being Index|X|||X|
|**Gender Dynamics**|||||
|Gender Perceptions - Work|X|||X|
|Gender Perceptions - Violence (IPV)|X|||X|
|**Financial Well-being**|||||
|Savings|X||X<sup>_∗_</sup>|X|
|Borrowing|X|||X|
|**Economic Decision Making**|||||
|Risk Preference||X||X|
|Time Preference||X||X|
|**Other Outcomes**|||||
|Cognitive Ability|X||X<sup>_∗_</sup>|X|
|Physical Health|X||X<sup>_∗_</sup>|X|

**Notes:** The “Baseline” survey was conducted with respondents before treatment assignment was revealed. The “Midline” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the
# A. Survey of Investors

One of the first survey studies was conducted by Barlow and Wender in 1955. They interviewed 247 US companies on their strategies to invest abroad. One of the questions asked was about the conditions that were required before companies proceed with foreign investment. Only 10 percent of the companies listed favorable foreign taxes as a condition for FDI, while another 11 percent mentioned "host government encouragement to companies". Together, these inducements were ranked fourth after currency convertibility, guarantee against expropriation, and host country political stability.

Those findings were confirmed by the survey of 205 companies conducted by Robinson in 1961. Perhaps the most important result of Robinson's survey was the considerable difference of opinion between the business community and the governments, with regards the major factors influencing decisions to invest. Tax concessions headed the list of government responses, while they were omitted from the list of private investor responses.

Next came the result of a field research conducted by Aharoni and published in 1966 on the way foreign investment decisions were made by U.S. manufacturing firms. The conclusions were that host government concessions did not bring about the decisions to invest. Income tax exemption was considered a very weak stimulant. Those investors, who did consider it, did it only marginally. In the word of one of the investors in the survey: _"Tax exemption is like a dessert; it is good to have, but it does not help very much if the meal is not there"._ It should be noted that in this case, as in the case of Robinson's interviews, host government officials interviewed in the field research believed income tax exemption to be a very powerful stimulus to FDI.

In a 1984 survey of 52 multinational companies, the Group of Thirty found that among 19 factors that were identified as influencing FDI flows, inducements offered by the host country rank seventh in importance for investment in developing _5_
.421|(***)|
|Net PPE / TA (Median)|0.220|0.215|0.201|0.203|0.207|0.215|( )|
|Cash / TA (Mean)|0.142|0.129|0.128|0.125|0.128|0.107|(**)|
|(Median)|0.060|0.056|0.053|0.049|0.057|0.039|(<sup>a</sup>)|

Source: CMA data (own calculations). TA stands for total assets. Tobin’s Q is defined as market value of equity + book value of liabilities over total assets. Real Sales Growth is sales growth adjusted for inflation (obtained from WDI). Interest coverage is defined as earnings before interest and taxes over interest payments. Gross PPE is property plan and equipment (i.e., fixed assets) at purchase cost, net PPE excludes accumulated depreciation. The t-test indicates significance for equality of the medians (or means) between years 1996 and 2000. ***,**,*, and<sup>a</sup> indicate significance at 1 percent,5 percent, 10 percent, and 15 percent, respectively.

17
then all investments since 1970, since 1980, and so forth.

Table 2, Panel A reports the performance of the complete portfolio. The bottom row of this panel reports the number of investments in each vintage year group. Relative to the S&P 500, the complete portfolio has achieved a PME = 1.15 since 1961. Over the long run, the portfolio has delivered 15 percent more than a counterfactual investment in the U.S. public equity market. Looking at indices available only in later decades, the PME, relative to the MSCI EM and World indices, is systematically higher, consistent with the superior average performance of the S&P 500 over the sample period. For instance, looking at all projects with vintage years after 1990, shortly after the initiation of the MSCI EM index, the IFC achieved a PME = 1.30 relative to MSCI EM, a PME = 1.23 relative to MSCI World, and a PME = 1.14 relative to the S&P 500.

As expected, returns are positive according to the IRR and TVPI. The long-run IRR is 13.4%, 13
br>difference)|No change in preference for girl<br>children; some positive effects<br>observed in terms of female<br>child survival and educational<br>attainment|
|Sekher<br>(2010)|Different CCT<br>Programs across<br>India|See Annexures section of<br>Sekher (2010)|In-depth interviews<br>with key stakeholders|Qualitative (desk<br>evaluation of<br>operational aspects of<br>CCT programs)|Short-term and long-term<br>operational problems associated<br>with CCT programs. Concerns<br>of public trust in these programs|
|Sekher and<br>Ram (2015)|Dhanlakshmi<br>Scheme in seven<br>Indian states<br>(Andhra Pradesh,<br>Bihar,<br>Chhattisgarh,<br>Jharkand, Orissa,<br>Punjab, and Uttar<br>Pradesh)|Balance child sex ratio,<br>improve investments in<br>girls, change families’<br>mindsets towards girls|Survey data from 4,000<br>beneficiary and non-<br>beneficiary households<br>in eight blocks across<br>five states (Punjab,<br>Bihar, Orissa, Andhra<br>Pradesh, and Jharkand).<br>In-depth interviews and<br>focus group discussions|<br>Mixed methods.<br>Quantitative element<br>was quasi-<br>experimental<br>(propensity score<br>matching)|Some immediate positive gains;<br>no change in preference for girl<br>children|
|Anukriti<br>(2017)|Devi Rupak,<br>Haryana, India|Reduce family size and<br>balance child sex ratio|Pooled birth-history<br>data from the NFHS<br>and the District Level<br>Household Survey of<
Atkin, D., B. Faber and M. Gonzalez-Navarro (2016). “Retail Globalization and Household Welfare: Evidence from Mexico,”forthcoming, Journal of Political Economy.

Atkinson, A. (1970). “On the Measurement of Inequality,” Journal of Economic Theory, vol. 2(3), pp. 244–263.

Autor, D., D. Dorn, and G. Hanson (2013). “The China Syndrome: Local Labor Market Effects of Import Competition in the United States,” American Economic Review, 103(6), pp. 2121–2168. Benabou, R. and J. Tirole (2006). “Belief in a Just World and Redistributive Politics,” Quarterly Journal of Economics, vol. 121(2), pp. 699–746.

Benjamin, D. and A. Deaton (1993). “Household Welfare and the Pricing of Cocoa and Coffee in Cˆote d’Ivoire: Lessons from the Living Standards Surveys,” The World Bank Economic Review vol. 7, pp. 293–318.

Besley, T. and T. Persson (2013). “Taxation and Development,” Chapter 2, pp. 55–110 in Handbook of Public Economics, vol. 5. Edited by Alan J. Auerbach, Raj Chetty, Martin Feldstein and Emmanuel Saez.

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Costinot, A.,
numerous papers have investigated the internationalization of R&D activities (Guellec and van Pottelsberghe de la Potterie 2001; Patel and Vega 1999; Picci 2010). This paper builds on and extends these two streams of literature by looking at the role of migrant inventors in fostering the internationalization of innovation activities.

Specifically, the paper looks at inventor diasporas and transnational inventive activities between developed and developing countries, which is gaining momentum from a development perspective (Clemens et al. 2014; Montobbio and Sterzi 2013). It also aims to see whether differences emerge across the type of technological internationalization—co-inventorship vs. R&D offshoring. Finally, the extent to which countries’ characteristics govern these potential relations is also investigated—that is, whether the least similar countries, for which informal barriers are more acute, have the greatest potential to benefit from diaspora networks.

The study extends the existing literature in a critical way. Contrary to most migration research, which retrieves migration data from decennial censuses, it exploits inventors’ information listed in patent applications. Inventors make up a specific class of workers at the upper end of the skills distribution.<sup>2</sup> Differently from former approaches to inventor migration that use ethnic name recognition algorithms to infer their migratory background (Agrawal et al. 2011; Breschi et al. 2014; Breschi et al 2015; Kerr 2008), direct nationality information of the inventors is used, allowing for the introduction of a richer range of origin and destination countries. Admittedly, the use of nationality data does not come without its limitations, the most important being

> 2. In this respect, the PatVal survey shows that 76.9% of European inventors underwent tertiary education (26% with PhD) (Giuri et al. 2007). Similarly, the RIETI-Georgia Tech inventor survey shows that 94% of US inventors have college degrees (46% with PhD), with 88% of the cases for Japanese inventors (13% with PhD) (Walsh and Nagaoka 2009
Tonga, Samoa or Fiji which could then be withdrawn as cash, again at very low transaction costs. To date this new method seems to have very low take-up.

One reason that convenience may win out is that the amounts saved through better financial literacy may just be too trivial to warrant action. This is especially the case for the East Asians (who tend not to remit that much, and when they do, remit large amounts), and the Sri Lankans (who were using cheap methods to start with). But even for the Pacific Island sample, the savings from switching to one of these cheapest methods might amount to NZ$6-12 per $200 transaction, which may only merit the costs of learning a new method and trying it out if people remit very frequently. Given that the baseline survey revealed an average frequency of remitting for the Pacific Island sample of five times per year, the annual saving from switching to the cheapest methods is just NZ$30-60. The scope for changes in ultimate outcomes may be greater for financial literacy transactions which focus more on savings and budgeting behavior, or those which allow people at risk of obtaining expensive credit to avoid very expensive loans (e.g. Bertrand and Morse, 2011).

# **5. Conclusions**

Our results show that simple financial education training for migrants can change their knowledge about the costs of remitting and lead them to look around more at prices. There is also some suggestive evidence from the Pacific Island sample that coupling this with information on different sources of credit may help migrants to avoid some of the most expensive forms of credit. Nevertheless, we find no big changes in ultimate outcomes – migrants avoid switching to more expensive or less transparent remittance channels, but do not change the amount or frequency of remitting.

Thus despite simply informing remitters about remittance costs being a relatively cheap and uncontroversial intervention, it will not necessarily lower average costs from remitters switching to cheaper methods. Instead governments targeting reduced average money transfer costs may need
), we have not seen it included as a control in the structural adjustment literature.

This paper aims to illuminate long-standing debates about the impact of structural adjustment in SSA agriculture by addressing all of the above-mentioned issues. We adopt a novel quantitative, sectoral and long-term approach, in which we control for country-specific determinants and incorporate detailed information on the pace of reforms, the nature of post-reform market structure, pre-reform policies and weather conditions. Such detailed information is taken from two new datasets. First, we use the market structure indices compiled in Delpeuch and Leblois (2011) to inform the

> 4 Swinnen and Rozelle (2004) show how supply responses were intimately linked to the nature of agricultural policies under state control in different regions of Asia and Europe: where the state had tried to force industrialization in part by taxing agriculture, eliminating distortions resulted in output growth; where the state had tried to stimulate agricultural production by subsidizing inputs or output, market pricing resulted in output decline.

> 5 Differences in the legal and economic environment and enabling institutions have also been identified as a determinant of supply response (Jayne et al., 1997; Kherallah et al., 2002). However, this factor is more likely to explain broad differences in outcome between developing regions than within SSA, where the legal and economic environment and enabling institutions are relatively homogeneously low.

3
is important to note that countercyclical fiscal policy requires expenditures to move countercyclically to the business cycle, whereas revenues and fiscal balance should move procyclically to the business cycle; that is, revenues should _increase_ during economic booms (when GDP increases), dampening the cycle, and _decrease_ during recessions, softening the economic downturn (when GDP decreases). Therefore, we will interpret the derived correlation coefficients as being in line with the goal of countercyclical fiscal policy if they are negative for expenditures and positive for revenues and fiscal balance.

The sample covers 184 countries in the period 2000-2022, including 17 MENA countries: Algeria, Bahrain, the Arab Republic of Egypt, Iraq, the Islamic Republic of Iran, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, the United Arab Emirates, the West Bank and Gaza, and the Republic of Yemen (other countries in the MENA region are excluded for lack of consistent data).

# **4. Empirical results**

**Over the past two decades, fiscal policy tended to be more countercyclical in richer economies.** Figure 1 shows the correlation between the cyclicality of several fiscal policy variables and countries’ income levels. Most low- and middle-income countries feature procyclical or cyclically neutral expenditures, whereas most high-income countries feature countercyclical expenditures. At the same time, high-income countries also saw their revenues and fiscal balances move more strongly in line with the business cycle, corresponding to the desired outcome under countercyclical fiscal policy. This indicates that richer countries were generally more successful in achieving countercyclical fiscal policy than countries with

assistance benefits, and employer social benefits in cash and in kind (World Bank 2023). For the Arab Republic of Egypt, we amend this variable with data from World Bank internal sources as follows (for the years 2003-2023): 4.9%, 5.1%, 5.5%, 11.2%, 7.8%, 10.3%, 12.2%,
.01|0.02|0.01|
|total|1.00|1.00|1.00|1.00|

_source: own calculations, using GTAP8 data._ While Italy is similar to other high income economies, in terms of service intensity, it is an outlier in terms of service sector productivity. We make this claim on the basis of estimates of absolute and relative productivity levels, using data from the EU-KLEMS database and following the methodology developed by Inklaar,Timmer and van Ark (2008).<sup>31</sup> On this basis, US multi-factor productivity (MFP) in market services (i.e. non-public) in 2007 was 145% of 1995 levels. At the same time, in 2007 Italy’s productivity was 84.8 percent of the US level for 1995. Italy, along with a number of other Eastern and Southern European states (and Japan) lag relatively far behind Northern Europe and North America in terms of service sector productivity.

As a follow-up to our discussed in Section 3 regarding emerging evidence that the cross-country pattern of productivity in services is linked to regulatory regimes, in Table 5 we have organized the data from Figure 1, related to productivity and regulation indexes, transformed for regression analysis. As such, all 31This involves first constructing detailed output (value-added) and input (labour and capital) price indexes, and then making productivity comparisons on the basis of Tornqvist indexes. The resulting data provide cross-country benchmarking of absolute productivity for selected years, implicitly linked by relative productivity growth rates for the intermediate years. See Francois, Manchin, and Norberg (2012) for further discussion. The data used as the basis for our calculations are the EU KLEMS database (November 2009 release) and the GGDC Productivity Level database (Inklaar et al. 2008).

29
households prefer to construct expensive toilet types, typically sceptic tanks and toilets with a nearby bathroom (Coffey et al., 2014, Coffey and Spears, 2017). Coffey et al. find that the typical household in their survey of 5 Northern Indian states would like to invest in a toilet costing Rs 21,000 on average, which is substantially higher than the maximum subsidy amount of Rs 12, 000. Figure 6 shows a similar pattern in our data. This figure plots toilets costs and subsidy amounts taken over time, as reported by households survey respondents. Since 2010, though the median reported subsidy amount has been fluctuating around Rs. 12,000, the median reported toilet cost has been steady around Rs. 26,000.

If micro-credit was used by subsidy eligible households to cover the funding gap between the subsidy amount and the actual toilet construction costs, then we would expect impacts on sanitation loan uptake to be higher in areas where toilet costs are generally higher. On the other hand, for similar reasons as for delays in sanitation subsidy disbursement that we described in the previous section, we would expect to see a reduction in the loan to new toilet conversion rate if the toilet turns out to be more expensive than anticipated when taking the sanitation loan, as higher toilet costs make sanitation investments less attractive relative to other investments.

To look at this, we make use of some unique data we collected from one mason in each village on the minimum and maximum cost of constructing the same twin pit and septic tank toilet. We take the average of the minimum and maximum costs reported by masons to calculate an average cost over the different toilet types. We construct an indicator for ‘high GP toilet cost’ if the mason reported an average toilet cost in his/her community that was higher than or equal to the median toilet cost across villages, and ‘low GP toilet cost’ otherwise.

Table D in the Appendix compares these two different village types in terms of a set of observable 23