rafmacalaba/data-use-mentions-extended huggingface dataset:
signi...cant, some of the coe¢cients on the control variables lose signi...cance. This suggests that the e¤ects of the control variables are not immediate but take some time to work themselves through, and that at the same time there is relatively limited autocorrelation in the control variables.

Column (4) reports a control experiment. We assigned the average date of reform to the three countries (Poland, Romania and the Czech Republic) that did not undertake a reform over the sample period and checked whether evidence of a credit acceleration around the average reform time emerges for non-reform countries. The result con...rms that the accelerations in ...nancial intermediation in the reform countries do not re‡ect a global trend in the region.

# 4.2 Cross-Sectional Approach

The analysis thus far has relied on identi...cation through di¤erences in the timing of reform, with the reform being treated as a zero-one event. As an alternative strategy we use two continuous measures of payment system development: (1) the number of credit transfers per inhabitant; (2) the number of all non-cash payments per inhabitant (including direct debits and cheques). The idea is that the number of credit transfers (and to some extent all non-cash payments) directly re‡ect the system’s use.<sup>16</sup> Yearly data are reported in the ECB Blue book for the period 1996-2001 for all countries in our sample except Bulgaria. Descriptive statistics reported in Table 1 show a huge variability in the use of non-cash payment instruments across countries which, in our sample, is unlikely to be attributable fully to variations in levels of economic development or culture. For instance, the number of credit transfers per inhabitant in 1996 is 30 in the Czech Republic and 4 in Hungary.

To capture the variation in the measures of payment systems development is mostly in the cross-section, we use an ordinary least squares estimator including year ...
value agricultural exports (e.g. horticulture products and meat) and labor-intensive manufacturing products such as clothing, textile, leather and leather products.

> 16 Gebreeyesus (2013) provides an extensive review of the Ethiopian reform process in the 1990s as well as industrial policy experiments in the 2000s.

> 17 Bigsten & Gebreeyesus (2007) find similar results using the same data source, while Tarfasa et. al. (2016), find a negative correlation between start-up size and growth of the MSEs.

> 18 These countries include: Kenya, Ghana, Zambia, Zimbabwe, Côte d’Ivoire, Ethiopia, Burundi, Tanzania, and Cameroon.

6
0.05|Assumed as a reasonable value. It is difficult to ascertain an<br>interest rate that would reflect the annual financial burden of<br>construction in an urban area that has developed over<br>centuries.|
|Depreciation rate of<br>capital|ρ|0.01|Assuming that structures last for 100 years on average, which<br>is consistent with Hallegatte (2009).|
|Unitary transport cost: fuel<br>and vehicle maintenance<br>(euros per kilometer)|t|0.416|Established from the French tax perception center URSSAF’s<br>reimbursement calculations for using private vehicles for work,<br>assuming an average annual mileage of 12,000 kilometers.|
|Fraction of land that can<br>be built on (net or roads<br>and public spaces)|s|0.62|Based on Corine Land Cover land use data.|
|Number of rich<br>households (millions)|N1|2.45|Assuming the population of the urban area of Paris is divided<br>into two equal-sized groups.|
|Number of poor<br>households (millions)|N0|2.45|Assuming the population of the urban area of Paris is divided<br>into two equal-sized groups.|
|Annual income of rich<br>households (euros)|Y1|80,244|Computed<br>based<br>on<br>the<br>FiLoSoFi<br>database<br>(INSEE‐DGFiP‐Cnaf‐Cnav‐CCMSA2015).|
|Annual income of poor<br>households (euros)|Y0|37,232|Computed<br>based<br>on<br>the<br>FiLoSoFi<br>database<br>(INSEE‐DGFiP‐Cnaf‐Cnav‐CCMSA2015).|

# 2.2 FLOODS AND FLOOD
<!-- Start of picture text --> Foreign Investment<br>5.0 1.8<br>4.5 FDI 1.6<br>4.0 1.4<br>3.5 Employment<br>1.2<br>3.0<br>Employment in Non-state-owned 1.0<br>2.5 Units<br>0.8<br>2.0<br>0.6<br>1.5<br>1.0 0.4<br>0.5 0.2<br>0.0 0.0<br>(Billion USD)<br>(Million Workers)<br>1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Shenzhen Statistics Bureau, various years.

**Figure 1.7    Productivity of Selected Sectors in Shenzhen (output per worker), 1993–2004**

<!-- Start of picture text --> 2.5<br>Farming, Forestry, Animal<br>Husbandry and Fishery<br>2<br>Transportation, postal, and<br>telecommunication services<br>1.5<br>1<br>0.5<br>0<br>1993-1998 1999-2001 2002-2004<br><!-- End of picture text --> _Source:_ Author’s calculations based on data from the Shenzhen Statistics Bureau, 1994, 1999, 2002, 2005, 2006, 2007.

**_Technology learning, innovation, upgrading, and strong links with the domestic economy._** One of the key strengths of the SEZs is that they have a high concentration of very skilled people, including many R&D personnel, especially in the HIDZs and
# **4. Conclusions**

Using the world-wide World Bank Enterprise Survey, we have examined how femaleheaded firms compare with male-headed firms in productivity level and growth, and how the cultural, institutional and business environments shape the female leadership effect. We find that conditional on a rich set of controls, female-headed firms robustly exhibit a lower productivity level by around 15 percentage points, and labor productivity growth by around 2 percentage points. The productivity level and growth disadvantages are significantly larger in manufacturing than in services—in fact, there is no productivity growth disadvantage at all in service sectors. In addition, the female leadership disadvantage is much more pronounced in SMEs, but non-existent in non-SMEs. We also find that female leadership disadvantage is larger where (i) personal networks are more important, as in the case of higher local usage of trade credit, higher local use of bank finance, a worse rule of law, and lower local internet penetration; and (ii) where competition is more severe, as in the case of higher local informal competition, and stronger national individualistic culture. Contrary to the literature, we do not find that female business leaders suffer more disadvantage where corruption is more severe. The findings provide confirmation of some of the lab and field experiment results on gender—such as women’s difficulty with or indifference to competition—and other evidence on personal network access, and they also show a lack of support for the notion that corruption hurts women more than men. The lack of female business leadership interaction with corruption could show the selection effects of female business leaders from women in general.

We also pay particular attention to African countries. In contrast to the world average, African countries overall have little female disadvantage on productivity performance. But in manufacturing sectors, the female disadvantage in productivity level and growth is more pronounced than the world average, while in service sectors there is _advantage_ in productivity growth. Female leadership disadvantage in productivity performance is more amplified where local access to bank finance is greater,
<!-- Start of picture text --> Quatar Malaysia<br>rs slope = 1.761 (0.049) —— re<br>Ke) 2 inte} 8 ° “ “a fs) ©<br>O78 2 © Sp ooo ° aa Org ° slope = 1.118 (0.107)<br>S|] gpoogien so BP oe ° x 2 2° &, °<br>] oo Tete a. guia 002 © o50 ° ° ogb.o 00, cB O ° —7— 735<br>A P. ae a0 BS © 00 2p ° N praia co Booed ab ee<br>fo} = ae ad : a ° °o eer S oo o. ° °<br>o ee > g oO eae ae ope<br>0 .01 .02 .03 .04 0 005 .01 015 .02 025<br>Share of migrants 2001 Share of migrants 2001<br>Saudi Arabia UAE<br>2 me s 0<br>° ° wa °<br>& 3% 2° a ° ae &<br>. ae ns pee cSt ob es ° A : 00 ® 0° ° Bo, ————<br>N | EEO 8° OEE Bite Doe 0 ramen<br>oO 2, ee oO pee ee oe ° o °<br>0 .02 .04 .06 .08 0 .005 .01 .015 02<br>Share of migrants 2001 Share of migrants 2001<br>© Share of migrants 2011 95% Cl —- Fitted values<br><!-- End of picture text -->
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" this analysis we have created new income, expenditure, and infrastructure variables that can be compared across countries. There are inevitably conceptual and measurement problems in the creation of such global variables. Our purpose here is to look for broad patterns of infrastructure use by households of different levels of economic well-being. We caution the reader not to make too mnuch of individual results.<sup>9</sup> Cross-country income and expenditure variables were created by converting local currency to 1998 US dollars, using first the official currency exchange rate in the survey year and then the IJ.S. consumer price index.<sup>10</sup> Our cross-country infrastructure variables classify infrastructure options in each sector as "advanced", "intermediate", or "basic" solutions (Table 2). Sections 3, 4, and 5 of this paper examine "advanced" solutions, which are typically provided by a utility (electricity, in-house water taps, sewer connections, and telephones). Section 6 looks at intermediate and basic solutions - more informal or private forms of infrastructure service (e.g. in the energy sector, kerosene would be an intermediate, and wood a basic energy source).

We use monthly household consumption aggregates as income proxies in this analysis because the consumption data are considered more accurate and reliable than the self-reported income data. For the purposes of this analysis, the poorest households are those with the lowest per capita income proxy."' The pooled sample of households from all countries is similarly divided into twenty quantiles of 5 percent each. We divide households in the urban and rural areas of each country into "income" deciles by per capita consumption. We present the results by decile or quantiles of 5 than one survey year was available for a particular country, we used the most recent survey year.

9 This note of caution is particularly important for country-specific results. Some but not all LSMS
**Figure 11: Cross-country bivariate correlations with GDP and GDP-per-capita: Geographical scale of population dispersion and dispersionadjusted sparsity**

<!-- Start of picture text --> Panel A: GDP and dispersion Panel B: GDP and sparsity<br>100000 100000<br>10000 10000<br>1000 1000<br>100 100<br>10 10<br>1 1<br>0.1 0.1<br>0.01 0.01<br>0.01 0.1 1 10 0.0001 0.001 0.01 0.1 1 10 100<br>Panel C: GDP-per-capita and dispersion Panel D: GDP-per-capita and sparsity<br>100 100<br>10 10<br>1 1<br>0.1 0.1<br>0.01 0.1 1 10 0.0001 0.001 0.01 0.1 1 10 100<br>PICs Carribean SIDs Other countries<br>Geographical scale of population dispersion ('000 km; log scale) Dispersion-adjusted population sparsity (square km per person; log scale)<br>Source: National census statistics sourced from PopGIS via SPC for Pacific Island countries and World Bank subnational database otherwise; World Bank Development<br>Indicators database; Authors’ analysis.<br>GDP (US$b; log scale)<br>GDP per capita (US$'000; log scale)<br><!-- End of picture text --> 25
on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production.

As we reported in Balestreri, Rutherford and Tarr (2009), the MFN tariff rates, multiplied times the trade flows, exceed the collected tariff rates. That is, using MFN tariff rates for all trade, the weighted average tariff rate exceeds the collected tariff rate of 8.4 percent for the economy as a whole. Thus, they exaggerate the protection received by Kenyan industry and agriculture. This is due to tariff preferences to regional partners and due to other preference items or tariff exemptions. We assume that zero tariffs apply on all imports from the East African Customs Union and from COMESA.<sup>24</sup> Thus, we apply the MFN tariff rates only on the trade flows from outside of these African regions (EU and Rest of World in our model) and take a weighted average tariff rate of the MFN rates on the non-East African regions. The resulting weighted average tariff rate on non-East African imports still exceeds 8.4 percent. We then equiproportionally reduced all the MFN tariffs in our model so that the estimated collected tariffs on imports from the EU and Rest of World divided by the total value of import is 8.4 percent.

# **Share of Market Captured by Multinational Service Providers**

It was necessary to calculate the market share of multinational firms in the services sectors by region of the model. Take the banking sector as an example. We need to know the share of the market captured by Kenyan, EU, African and Rest of the World firms, where the countries in the regions are defined in table 1. This entailed acquiring a list of all banks operating in Kenya along with their market share, and, when the bank is owned by multiple parties, allocating the ownership across the regions of our model. The database Bankscope was sufficient for this task in most cases, but websites of the banks had to
rice cultivation, and associated groundwater extraction was drawing down the water table.

We present basic descriptive statistics on dry season production of pulses and boro rice in Barisal in Appendix Table A5; our household survey data used to construct this table is described in Section 3.3. Consistent with the context above, the production of pulses, relative to boro rice, is associated with much less intensive irrigation but are also lower profits for farmers; these lower profits may be in part a consequence

> 4These villages are mapped in Appendix Figure A1.

> 5Most irrigation in Barisal originates from pump-operated shallow tube wells (67% in our study sample, compared to 5% using surface water).

8
# **References**

- **Abel, Guy J and Nikola Sander** , “Quantifying global international migration flows,” _Science_ , 2014, _343_ (6178), 1520–1522.

- **Adhikari, Samik and Ugo Gentilini** , “Should I stay or should I go: Do cash transfers affect migration?,” _World Bank Policy Research Working Paper_ , 2018, (no. 8525).

- **Beegle, Kathleen, Joachim De Weerdt, and Stefan Dercon** , “Migration and economic mobility in Tanzania: Evidence from a tracking survey,” _Review of Economics and Statistics_ , 2011, _93_ (3), 1010–1033.

- **Blattman, Christopher, Eric P Green, Julian Jamison, M Christian Lehmann, and Jeannie Annan** , “The returns to microenterprise support among the ultrapoor: A field experiment in postwar Uganda,” _American Economic Journal: Applied Economics_ , 2016, _8_ (2), 35–64.

- **, Nathan Fiala, and Sebastian Martinez** , “Generating skilled self-employment in developing countries: Experimental evidence from Uganda,” _The Quarterly Journal of Economics_ , 2014, _129_ (2), 697–752.

- **, , and** , “The long term impacts of grants on poverty: 9-year evidence from Uganda’s Youth Opportunities Program,” Technical Report, National Bureau of Economic Research 2019.

- **Brown, Ryan** , “EU tests idea in Gambia: Would more opportunity at home mean fewer migrants?,” Christian Science Monitor 2018. `https: //www.csmonitor.com/World/Africa/2018/1126/EU-tests-idea-in-GambiaWould-more-opportunity-at-home-mean-fewer-migrants` , [Online; accessed 20-August-2019].

- **Brudevold-Newman, Andrew, Maddalena Honorati, Pamela Jakiela, and Owen Ozier** , “A firm of one’s own,” _World Bank Policy Research Working Paper_ , 2017, (no. 7977).

- *
21 mechanisms and uniform wage setting. Labor is also frequently highly immobile across countries, and it is very difficult or expensive for employers to be perfectly informed about the acquired skills of potential employees.

At the same time, there is empirical evidence indicating that rates of return to education are not able to reflect cross-country quality differences in education. For example, data on rates of return collected by Psacharopoulos (1994) show that rates of return to education tend to decline with higher per capita income. For example, the returns to any level of education are highest in Africa and lowest in the industrial countries. As such, if rates of return did accurately reflect quality differences in education, then the data would imply that the quality of education is generally higher in the developing countries as compared to the developed countries, which is very unlikely<sup>34</sup> . Consequently, qualitative differences in education are probably not well captured by the available data on country-specific rates of return to education.

# **4.2. Measuring Innovation and Technology Adaptation**

## **USPTO Patent Counts**

One of the most basic measures of the level of domestic innovative activity taking place within an economy that bears commercial value is the annual number of U.S. patents filed by residents of the country. According to the U.S. Patent and Trademark Office (USPTO) website, a U.S. patent for an invention is the grant of a property right to the inventor(s), issued by the USPTO. The right conferred by the patent grant is, in the language of the statute and of the grant itself, "the right to exclude others from making, using, offering for sale, or selling" the invention in the United States or "importing" the invention into the United States. Note that while there are several patenting offices in the world, we have chosen to only use U.S. patent data. This is because different patenting agencies have
66 Table D.5: Resources, Industrialization & Employment, Cross-Section, I2D2 Database

|Dep.Var. =<br>**Empl. Sh. (c. 2005):**|(1)<br>MFG|(2)<br>FIRE|(3)<br>MFGFIRE|(4)<br>NTRI|(5)<br>NTRI2|(6)<br>WAGE|(7)<br>SELF|(8)<br>UNPAID|
|---|---|---|---|---|---|---|---|---|
|**Panel A:**<br>Vars. in 2000|||||||||
|MFGSERV2000|0.12***|-0.04|0.08*|0.09|0.14|0.44**|-0.48***|-0.15|
||[0.035]|[0.039]|[0.043]|[0.127]|[0.144]|[0.180]|[0.136]|[0.118]|
|NRXGDP1960_−_2000|-0.06|0.03|-0.03|0.18|0.03|0.41|-0.30|-0.15|
||[0.056]|[0.045]|[0.076]|[0.127]|[0.209]|[0.294]|[0.221]|[0.121]|
|DEINDU1980_−_2000|-0.26|0.14|-0.12|0.12|1.42**|-0
complaint generation based on individual utility maximization. Data sources and estimating equations are treated in Section 4; econometric results are reported in Section 5, along with simulations which explore the implications. The final section provides a summary and conclusions.

# **2. Environmental Complaints in China**

China's citizens are far from passive about the environmental performance of neighboring factories. During 1991-93 the environmental authorities received over 130,000 complaints per year, mostly related to air, water and noise pollution (Table 1). Plaintiffs visited provincial and local regulators over 79,000 times per year and sent more than 53,000 letters. Air pollution received the most attention, with over 20,000 letters and 15,000 visits per year. Noise pollution accounted for over 27,000 complaints, while water pollution dropped from 35,000 to around 23,000.6 Table 2 shows that the propensity to complain varies widely across China's provinces. In 1993, there were around 30 complaints per 100,000 individuals in Shanghai and Tianjin, but less than 5 per 100,000 in Gansu, Xinjiang and Inner Mongolia. A provincial map of the propensity to complain (Figure 1) shows that its geographic distribution is far from random. The incidence of complaints is generally highest in the urban/industrial centers of east China; lower in the middle provinces; and lowest in China's least-developed regions -- the western hinterlands. Across provinces,

**6** We have no explanation for the fall in water pollution complaints, which seems to have affected personal visits but not letters. This could represent a lagged reaction to a large increase in water pollution charges which began in 1990. However, it seems very doubtful that the impact could have been this large. For an extensive discussion of Chinese water pollution charges and their impact, see Wang and Wheeler (1996).

4
We find no evidence of an “age gap” in perceptions of climate change. Older respondents seem to be as (or even more) concerned about climate change as younger respondents. However, we do find that older individuals are more likely to object to tax increases to finance climate adaptation and mitigation policies, which is likely to reflect the general decline in willingness to pay taxes with age rather than a specific lack of willingness to finance climate change policies.<sup>3</sup> Past exposure to natural disasters, a person’s physical and psychological health, the degree of risk aversion, sources of information, trust in institutions, and whether a respondent was planning to move away also affect perceptions and willingness to pay for climate change, but none of these confounding factors reverse the climate change − age gradient estimated by the most parsimonious models. The positive age gradient is also robust to a range of specifications, definitions of the dependent variable, and corrections of potential omitted variable biases.

This paper contributes to the literature on the determinants of individual perceptions and engagements in climate change actions in several ways. First, it develops a theoretical model that shows that the relationship between age and concerns about climate change and the willingness to pay for climate change actions is ambiguous and depends on various factors. Second, the paper distinguishes between climate-related beliefs and actions toward climate change mitigation, an important distinction, as one can believe that climate change is real and manmade but still not be willing to bear an additional tax burden to mitigate its impacts. Third, the LiTS data allow us to account for the wide range of determinants of climate beliefs that earlier studies have identified as varying across different age groups, including ideology, attitudes toward risk and fairness, trust in government, primary sources of information, and health conditions which can exacerbate climate morbidity.

The paper is organized as follows. Section 2 reviews relevant literature on generational differences in beliefs about climate change. Section 3 discusses the study’s theoretical framework and empirical