rafmacalaba/data-use-mentions-extended huggingface dataset:
24 fragmentation and vote-selling? Or of problems of weak sanctioning power of voters because of state-led violence and coercion in elections?

**_Mass media:_** The potential role of media organizations, such as radio, seems obvious as purveyors of information to large numbers of citizens, especially the poor. Indeed, donors have been engaged in setting-up and supporting community radio in various parts of Africa to broadcast public-interest programming for democratic values, human rights, and the importance of health and education. Yet, there is little rigorous evidence of their impact; recent work using new and unique data from Benin yields surprising conclusions for the advocates of community radio. Keefer and Khemani (2011a, 2011b) find that greater access to community radio in Benin is _not_ associated with the ability of citizens to extract greater benefits from their government; instead, the impact of radio programming seems to be to persuade people to invest more of their own private resources into the education of their children and the health of their family. While this is a useful role for mass media to play in development, it is unrelated to the accountability challenge.

This contrasts with evidence from the post-Depression-era USA, when radio had just begun to spread, that greater access to radio enabled citizens to receive greater benefits from government welfare spending (Stromberg, 2004). It also contrasts with the experience from Uganda and elsewhere when media were used purposefully to disseminate information about leakage in public resources; in such cases they were instrumental in stimulating public opinion and action to reduce such leakage (Reinikka and Svensson, 2005, as discussed above). This suggests that investments in building capacity of media alone, and advocating their independence from the state, may not be sufficient to achieve greater accountability—investing further in well-designed messages, either those that can enable voters to coordinate on policy performance, or those that are endorsed by the state in an effort to improve efficiency in its public spending (as in the Brazil examples discussed above) is needed.

Although theory suggests an accountability
R., T. Lefevre and S. Sherstha (2000), Techno-Economic Databases for Environmental Policy Analysis in Asia: Requirements and Barriers, _Pollution Atmospherique_ , pp. 79-88.

- United Nations Environmental Program (UNEP) (1992) Atmospheric Brown Clouds. Regional Assessment Report with Focus on Asia, UNEP Nairobi.

- United Nations Framework Convention on Climate Change (UNFCCC) (2011). Report of the Conference of the Parties on its sixteenth session, held in Cancun from 29 November to 10 December 2010, Addendum Part Two: Action taken by the Conference of the Parties at its sixteenth session, UNFCCC, Bonn, Germany.

- United Nations Framework Convention on Climate Change (UNFCCC) (2013). Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23 November

24
0.170|_−_0.010|5003|
||(1.000)|(0.050)|(0.056)<sup>_∗∗∗_</sup>|(0.058)||
|||[0.188]|[0.014]<sup>_∗∗_</sup>|[0.909]||
|Satisfaction index|0.000|0.110|0.094|0.124|5002|
||(1.000)|(0.044)<sup>_∗∗_</sup>|(0.051)<sup>_∗_</sup>|(0.049)<sup>_∗∗_</sup>||
|||[0.060]<sup>_∗_</sup>|[0.134]|[0.072]<sup>_∗_</sup>||
|Health service delivery index|0.000|0.055|0.082|0.044|2845|
||(1.000)|(0.061)|(0.074)|(0.063)||
|||[0.277]|[0.330]|[0.473]||
|Clinic quality index|0.000|0.151|0.057|0.260|254|
||(1.000)|(0.157)|(0.183)|(0.184)||
|||[0.277]|[0.606]|[0.290]||
|CDPE index|0.000|0.161|0.153|0.168|501|
||(1.000)|(0
Sherwood, B., & Maidman, A. (2017). rqPen: Penalized quantile regression. R package version, 2.

- Soergel, Bjoern, Elmar Kriegler, Benjamin Leon Bodirsky, Nico Bauer, Marian Leimbach, and Alexander Popp. 2021. "Combining Ambitious Climate Policies with Efforts to Eradicate Poverty." _Nature Communications_ 12(1): 2342.

- Stifel, David, and Luc Christiaensen. 2007. "Tracking Poverty over Time in the Absence of Comparable Consumption Data." _The World Bank Economic Review_ 21(2): 317-341.

- Tarozzi, Alessandro. 2007. Calculating Comparable Statistics from Incomparable Surveys, With An Application to Poverty in India.” _Journal of Business and Economic Statistics_ 25(3), 314-336.

- UN DESA. 2016. “Transforming Our World: The 2030 Agenda for Sustainable Development.”

- United Nations. 2018. “World Urbanization Prospects: The 2018 Revision.” Department of Economic and Social Affairs, Population Division. New York: United Nations.

- United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022: Methodology of the United Nations population estimates and projections. UN DESA/POP/2022/TR/NO. 4.

- Van Der Weide, Roy, Brian Blankespoor, Chris Elbers, and Peter Lanjouw. 2022. “How Accurate Is a Poverty Map Based on Remote Sensing Data? An Application to Malawi.” _Journal of Development Economics_ 171, 103352.

22
) argues that nutrient deficiencies in some blends is likely attributable to uneven distribution of granules in the bags due to improper blending (mixing). In such cases, the nutrient composition of the entire bag may be in compliance with the labeled nutrient content but non-uniform distribution within the bags means that samples taken from the bag are not in compliance. In addition, small farmers purchasing blends from open bags or in repackaged smaller bags are likely to receive products with nutrient contents that do not reflect the manufactured standard. Nutrient deficiencies in blends can also be attributable to manufacturing problems, poor control of blending procedures, and poor-quality blending equipment. Quality issues were much less prevalent in the compound fertilizers sampled in the IFDC studies.<sup>18</sup> Results from IFDC studies in Uganda and Kenya are consistent with the findings of quality issues arising in blends, likely due to problems in manufacturing (Sanabria et al. 2018a and 2018b).

> 17 Sanabria et al. (2018) write in their Uganda fertilizer assessment of the Bold et al. (2017) findings of these widespread and significant nitrogen shortages in urea: “the report does not identify or quantify the presence of materials that may be used to dilute nitrogen content in the urea samples. Dilution is the only possible way of reducing nitrogen content in urea. The nitrogen content in the samples used as evidence could be below 46% as a result of deficiencies in the use of the Kjeldahl method, especially when the method is applied manually and by personnel with limited experience analyzing fertilizers. A very common mistake is assuming that a lab with experience analyzing soils will perform well analyzing fertilizers.” In fact double testing of NPK samples by Asante et al. (2021) showed discordance between results of samples double tested in a Canadian lab and a Ghanaian lab. Both labs had used the Kjeldahl method. The Ghanaian lab initially identified significant nitrogen deficiencies but retested the samples after the Canadian lab found that the samples met nitrogen standards. Upon retesting, the Ghanaian
Peru, Sweden, and the United States reported charging premiums based on risk (Demirguc-Kunt and Huizinga, 1999). As of 2016, this number has increased to 55 countries. Figure 5 shows the percentage of countries that use risk-adjusted premiums by income group. Risk-adjusted deposit insurance is especially prevalent in high-income countries. In the 2016 BRSS survey, the percent of countries charging risk-adjusted premiums among the high-income countries was 78%. For the middle and low-income countries, the same percentage has been below 40%.

Although risk-adjusted deposit insurance provides a way to internalize banks’ risk taking, it is challenging to determine the premium that should be charged to the banks. Typically, banks’ examination ratings, capital adequacy, liquidity and asset quality are used to determine premiums. The FDIC, for example, assigns banks to several risk categories based on the banks’ capitalization level and supervisory rating, and then charges a different insurance premium for each risk category. Some of the measures used can be subjective and crude, and are usually better at explaining past and current risks rather than future risks. This naturally incentivizes banks to take on new risks once the premiums are determined as a way to game the system. It may also take time for the regulators to understand and price-in the costs of new and unfamiliar risks. The premiums are also based on banks’ stand-alone risk. The costs of insuring deposits and resolving failed banks, however, are much higher when multiple failures occur at the same time. Moreover, deposit insurance based on stand-alone bank risk can encourage banks to take excessive systemic risk. Pennacchi (2006), for instance, shows that banks have incentives to take on excessive correlated risk when insurance premiums are set to an “actuarially fair” value. Laeven (2008) shows that countries have typically underpriced deposit insurance because banks in many developing countries cannot afford to pay actuarially fair premiums. Nonetheless, empirical evidence suggests that risk-adjusted premiums work better than flat-rate premiums in reducing excessive risk taking by banks (Demirguc-Kunt and Detragiache 2002
Poiic RESEARCH WORKING PAPER 2031

# Summmary findings

NFo cotntry has achieved sustained economic development without substantially investing in human capital.. Previous studies have shown the handsome returns to various forms of basic education, research, training, learning-by-doing, and capacity-building. But education by itself does not guarantee successful development, as history has shown in the former Soviet bloc, Sri Lanka, the Philippines, and the Indian states of Kerala and West Bengal.

The question is, when and how does education bring high payoffs?

Although theory has suggested a strong causal link betweern education and growth, the empirical evidence has not been unanimous and conclusive.

L6pez, Thomas, and Wang examine two explanatory factors. First, who gets educated matters a good deal, but the distribution of education is complex and not much has been written about it. They construct an asset allocation model that elucidates the importance of the distribution of education to economic development.

Second, how education affects growth is greatly affected by the economic policy environment. Policies determine what people can do with their education. Reform of trade, investment, and labor policies can increase the returns from education. Using panel data from 12 Asian and Latin American countries for 197094, they investigate the relationship between education, policy reform, and economic growtlh.

Their empirical results are promising.

First, the distribution of education matters. Unequal distribution of education tends to have a negative impact on per capita income in most countries. Moreover, controlling for human capital distribution and the use of appropriate functional form specifications consistent with the asset allocation model makes a difference for the effect of average schooling on per capita income. Controlling for education distribution leads to positive and significant effects of average schooling on per capita income, while failure to do so leads to insignificant, even negative effects, of average education.

Second, the policy environment matters a great deal. Our results indicate that economic policies that suppress market forces tend to dramatically reduce the impact of human capital on economic growth. Investment in human capital can have little
/sup> The second set of lines are thus about 20% higher than the first set of lines.

The nominal values of the poverty lines for different NSS rounds are evaluated using separate urban and rural price indices. Our price indices are mainly based on the all-India Consumer Price Index for Industrial Workers (CPIIW) as the deflator for the urban sector, and the all-India Consumer Price Index for Agricultural Laborers (CPIAL) as the deflator for the

> 27 In particular, one-year reference is used for clothing, bedding, footwear, education, medical (institutional) and durable goods. Mixed reference periods have been used for seven NSS rounds in our data, viz., rounds 55, 56, 57, 58, 59, 60 and 62.

> 28 The original Planning Commission lines correspond to rural and urban per capita monthly expenditures of Rs. 49 and Rs. 57 at 1973-74 prices. At 2005 purchasing power parity (PPP), these lines have a value of $1.03 per day in 2005. See Ravallion (2008) for further discussion, including comparisons with a higher international poverty line.

> 29 Neither set of lines is directly comparable to India’s current official “Tendulkar” poverty lines. At 2005 PPP, the Tendulkar lines have a value of $1.17 per day in 2005, incorporating a lower cost of living differential rural and urban areas than the original Planning Commission lines on which our poverty line series is based. The Tendulkar poverty lines are only available after 1993-94.

8
*|0.224|-2.380***|-2.685|0.614|0.027|-0.954|-2.486**|
||(0.552)|(0.236)|(0.540)|(1.745)|(0.401)|(0.820)|(0.887)|(0.980)|
|Number of observations|632|262|553|223|643|264|632|261|
|r2_w|0.373|0.491|||||||
|r2_b|0.031|0.012|||||||
|r2_o|0.009|0.033|||||||
|rho|0.751|0.873|||||||

Notes: This table reports panel regressions with individual firm fixed effects for the El Salvador COV-ES data. Columns 3 through 8 report marginal effects from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

49
economic theory tells us that it is optimal to subsidize an activity if it generates positive externalities -- i.e. the activity benefits agents other than those directly involved in the activity itself. The potential for positive externalities from FDI surely exists and available evidence exists that often this potential is realized. Incentives to attract FDI may be justified on the grounds of such externalities from inward FDI. However, such policies are not typically what proponents of industrial policy have in mind – indeed the thrust of such arguments is typically in favor of encouraging the development of _indigenous firms_ . It is worth keeping in mind that investment incentives and tax breaks to multinational investors work against their local competitors. Thus, if there exist local firms that could potentially compete with multinationals, the adverse effect of tax incentives to multinationals on such firms needs to be taken into account. The efficacy of investment incentives is also dubious – such policies can end up transferring rents to foreign investors without affecting their investment decisions.

One final insight from the literature on linkage effects of multinational firms is worth mentioning. As Rodriguez-Clare (1996a) notes, we should “...expect multinationals to generate more linkages when they come from regions that are farther away.” The implication for industrial policy with respect to multinationals is clear – if a country does want to attract FDI, it ought to focus on firms that come from far off regions and are interested in serving the local market.

In a recent paper, Barrios et. al. (2004) construct a model wherein the competition effect generated by a multinational is eventually dominated by the positive externalities it generates. Using plant level panel data from the manufacturing sector in Ireland (a country whose economic development has been influenced greatly by multinational firms) they show that such a model indeed describes the Irish experience with FDI. Alfaro and Rodriguez-Clare (2003) use plant level data from several Latin American countries and they make the important point that many empirical studies testing existing theoretical models often use inappropriate measures to evaluate the linkage
# **3.5. Descriptive Statistics**

Table 1 presents the descriptive statistics for the key covariates, and the full results are found in Online Appendix 2. Note that the monetary consumption variables are expressed in nominal terms. Thus, the increases across time can be attributed primarily to inflation, which is taken into account by our multivariate analyses through the inclusion of time fixed effects. The shock measures are based on geospatial data, as explained above. The flood shock affected 29 percent of the sample in 2015. In 2016, 40 percent of the sample was affected by the drought. Nine percent of the sample was affected by sequential shocks, i.e. both of drought in 2016 and flooding in 2015.

The drought and flood shocks led to a substantial drop in maize yields, which could jeopardize consumption outcomes particularly for poor and vulnerable households. The value-based consumption figures are in nominal terms, potentially masking the true impact of the shocks on consumption since inflation was high over this time period as well.<sup>18</sup> Households did experience reductions on their average caloric intake, and experienced lower food consumption scores over time as the shocks unfold. School participation rates were in fact higher in the flood year, and unchanged from 2013 in the drought year.

The humanitarian response to the 2015 floods and particularly to the drought in 2016 was of unprecedented scale with almost 40 percent of the population expected to receive either in-kind food or cash transfers or some combination of the two, distributed in-kind or in cash. The vast majority of such transfers were made by the World Food Programme (Babu et al., 2018). Aid distribution eventually covered all 24 affected districts out of a total of 28 districts in Malawi (Babu et al., 2018). As shown in Table 1, aid distribution rates were much higher in 2015 and 2016 versus 2013, with a TA-level average of more than one distribution per household. The summary statistics in 2016 provide a rough proxy for aid
large; young versus old; male versus female-owned; located in Maputo versus in other places; exporters versus nonexporters; foreign vs non-foreigner), we estimate the following model for formal firms with at least five employees:

> 7 In the Enterprise Survey questionnaire, capital stock is only available for manufacturing firms and not for services firms. As an alternative, some regressions use last year investment as a proxy for capital when estimating the results for all sectors.

14
- Hallegatte, S., A. Vogt-Schilb, M. Bangalore, J. Rozenberg. (2017). Unbreakable: Building the Resilience of the Poor in the Face of Natural Disasters. Climate Change and Development; Washington, DC: World Bank

- Hallegatte, S., J. Rentschler, J. Rozenberg. (2019). Lifelines: The Resilient Infrastructure Opportunity. Washington, DC: World Bank.

- Hirabayashi, Y., R. Mahendran, S. Koirala, L. Konoshima, D. Yamazaki, S. Watanabe, H. Kim & S. Kanae (2013) Global flood risk under climate change. Nature Climate Change 3, 816–821 (2013).

- Husby, T., H. L. de Groot, M. W. Hofkes, and T. Filatova. 2015. “Flood Protection and Endogenous Sorting of Households: The Role of Credit Constraints.” Mitigation and Adaption Strategies for Global Change. doi:10.1007/s11027-015-9667-7

- Jongman, B., P. Ward, J. Aerts. (2012) Global exposure to river and coastal flooding: Long term trends and changes, Global Environmental Change, Vol. 22(4), p. 823-835

- Kulp, S.A., B.H. Strauss (2019) New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications Vol. 10, 4844

- Lall, S. V., and U. Deichmann. 2012. “Density and Disasters: Economics of Urban Hazard Risk.” World Bank Research Observer 27: 74–105.

- Lall, S. V., M. K. A. Lundberg, and Z. Shalizi. 2008. “Implications of Alternate Policies on Welfare of Slum Dwellers: Evidence from Pune, India.” Journal of Urban Economics 63: 56–73.

- Muis S., M. Verlaan, H. Winsemius, J. Aerts, P. Ward (
CIS (Figure 4.4). Fighting corruption and its sources is thus critical for encouraging job creation.

The tax burden has been shown to hamper employment creation mainly in the EU transition economies, where there are in place efficient tax-collecting systems and the informal sector is relatively small. Both the tax burden (figure 4.5) and tax regulation are regularly perceived by firm managers in ECA countries as one of the main obstacles for business (ICA Survey, World Bank). Hence, a predictable and transparent tax code as well as reasonable tax burden on new firms could help greatly new firm formation and survival.

Finally, firm expansion and job creation are supported by an efficient legal system. Such a system is only being developed in ECA. The legal structure and security of property rights in ECA lag behind those in the US or EU15. Expectedly, the legal system is most developed in EU transition economies, less so in other European transition economies, and least developed in CIS and Turkey (Figure 4.6). Strengthening the legal system: ensuring greater security of property rights, improving contract enforcement and improving the efficiency of dispute resolution will advance the investment climate and thus be conducive to employment generation.

22
Figure A3 Comparison of empirical data and simulated data (a) Log employment density (b) Log capital density <!-- Start of picture text --> 0 2 4 6 8 5 10 15 20<br>log employment log capital<br>Model Data Model Data<br>(c) Prots density (d) Log employment | K<br>0 200 400 600 5 10 15 20<br>profits K percentiles<br>Model Data Model Data<br>(e) Prots | K<br>8 10 12 14 16 18<br>K percentiles<br>Model Data<br>.3 .2<br>.15<br>.2<br>.1<br>density density<br>.1<br>.05<br>0 0<br>.008 8<br>.006 6<br>density .004<br>4<br>log employment<br>.002<br>2<br>0<br>400<br>300<br>profits 200<br>100<br>0<br><!-- End of picture text -->

Notes: comparison of rm-level outcomes from rm data (EIA, Annual Industrial Survey) and the SMM simulated data. Panels (a) to (c) present the kernel density estimates of log employment, log capital and prots. Panels (d) and (e) present the mean of log employment and prots conditional on percentiles of the log capital distribution.

50
17 difference regression of Col. (1) where we control for initial conditions suggests that urbanization correlates with both. The Beta coefficient is twice as large for FMXGDP than for AGXGDP, likely due to agricultural exports requiring rural labor. In the panel specifications (Cols. (2)-(4)), only FMXGDP survives. Likewise, lags of AGXGDP have positive but insignificant coefficients (Web Appx. Table D.3). Indeed, not all agricultural exports may be associated with urbanization. Some crops require large amounts of labor and cities may develop too slowly for the correlations to appear with panel regressions. Web Appx. Table D.2 then shows stronger correlations for MFG than for SERV, and positive correlations for SERV, and FIRE or non-FIRE services.

The results shown so far are aligned with Propositions 1-4. We further test these propositions in the next section, and in particular the fact that the mechanism of the urbanization process might also influence the employment composition of urban areas.

# **4. Resources, (De-)Industrialization, and Urban Employment**

**Model.** We rely on IPUMS census microdata for 62 sample countries accounting for almost two-thirds of the world’s total urban population, which is high considering that we ignore most developed economies. In total, we obtain 184 census samples during the period 1990-2015. For our cross-sectional country ( _c_ )-level analyses, we then select for each country the closest year to the year 2000 and estimate the following model:

EMP _c_ 00 = _α_ + _β_ MFGSERV _c_ 00+ _γ_ NRXGDP _c_ 60 _−_ 00+ _δ_ DEINDU _c_ 80 _−_ 00+ _ε_ URB _c_ 00+ _Xcκ_ + _μc_ (10) In this equation, EMP is a sector’s labor share in the urban areas of country _c_ c. 2000. Since most censuses are centered around the year 2000, we use information from 2000 for MFGSERV in country _c_ , the mean of NRXGDP in country _c_ in the