rafmacalaba/data-use-mentions-extended huggingface dataset:
Panama—the information on both travel rates and expenditures by destination stems from a survey of 3,498 Panamanian households.

Foreign tourism has a much higher impact on the economy than domestic tourism. In absolute terms, foreign tourism expenditures are highest in Panama Province, but in relative terms they may contribute up to a fifth of the GDP in peripheral areas with low levels of provincial GDP, such as Bocas del Toro.

**Table 4. Estimated Foreign and Domestic Tourism Expenditures as a Share of Province GDP**<sup>a</sup>

||**Province**<br>**GDP**<br>**2005,**<br>**Million**<br>**US$**|**Percent of**<br>**Tourist**<br>**Nights**<br>**Spent by**<br>**Foreign**<br>**Tourists at**<br>**Each**<br>**Destination**|**Foreign**<br>**Tourist**<br>**Expenditures,**<br>**Million**<br>**US$/Year**<sup>**b**</sup>|**Domestic**<br>**Tourist**<br>**Expenditure**<br>**s, Million**<br>**US$/year**|**Foreign**<br>**Tourism**<br>**Expend-**<br>**itures, % of**<br>**GDP**<sup>**b**</sup>|**Domestic**<br>**Tourism**<br>**Expenditures
variability is captured by the country fixed effects. Therefore, the regression analysis here focuses on periods in which there are large, statistically significant improvements (two standard deviations) in any two‐year period.<sup>22</sup> _Policy uncertainty._ When firms are uncertain about future demand and future policies, their expected risk‐ adjusted returns may not exceed the costs of capital or the returns on liquid financial assets. This may make firms unwilling to commit to irreversible physical investment, a result found in a number of firm‐ level studies on advanced economies. In macroeconomic studies, the uncertainty generated by political risk has been shown to weigh on investment (Appendix 2).<sup>23</sup> The regression includes, as proxy for political stability, the International Country Risk Guide (ICRG) political stability rating. A higher index indicates greater political stability. The ICRG political risk index is a weighted average of ratings of government stability, socioeconomic conditions, investment profile, corruption, the role of military in politics, law and order, external and internal conflict, religious and ethnic tensions, democratic accountability, and bureaucratic quality.

_Data._ Data sources are drawn from Haver Analytics, World Bank’s World Development Indicators, Oxford Economics, as well as the International Monetary Fund. Investment growth denotes the annual growth rate of real gross fixed capital formation. In instances where data on gross fixed capital formation are not available, gross capital formation is used as a proxy.

_Methodology._ A fixed effects panel regression is used to estimate the correlates of investment growth in 73 EMDEs with populations above 3 million for the period 1998‐2015. The econometric framework is similar to that of Nabar and Joyce (2009). However, the focus in this paper is on investment growth, as a critical component of overall output growth (ultimately, the source of rising living standards), rather than changes in the investment‐to‐GDP ratio that would only capture changes in investment growth
Policy Research Working Paper 5467

# **Abstract**

This paper argues that legislative malapportionment, denoting a discrepancy between the share of legislative seats and the share of population held by electoral districts, serves as a tool for pre-democratic elites to preserve their political power and economic interests after a transition to democracy. The authors claim that legislative malapportionment enhances the predemocratic elite’s political influence by over-representing areas that are more likely to vote for parties aligned with the elite. This biased political representation survives in equilibrium as long as it helps democratic consolidation.

Using data from Latin America, the authors document empirically that malapportionment increases the probability of transitioning to a democracy. Moreover, the data show that over-represented electoral districts are more likely to vote for parties close to pre-democracy ruling groups. The analysis also finds that overrepresented areas have lower levels of political competition and receive more transfers per capita from the central government, both of which favor the persistence of power of pre-democracy elites.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the department to understand the role of history and institutions in the development process. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mbruhn@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and economic development: St Hill (1992). It argues that banking services play different roles in bringing about economic growth for economies at different stages of development, but it does not study transitions across payment systems. Beck et al. (2018) study the impact of mobile money adoption on entrepreneurship and economic development in a structural dynamic macro model calibrated for the case of M-Pesa in Kenya using firm-level data. Klapper and Singer (2014), relying on data from their Findex data set, advocate for using digital payments to enhance financial inclusion, particularly for women and rural dwellers in developing and emerging economies, thus implicitly arguing that digital payments can be adopted in developing economies as a means for overcoming social exclusion in the access to traditional payments services processed through formal bank accounts.

To clarify key concepts, it is worth recalling the basic definition of a payments system. The Federal Reserve Bank of New York defines a payment system as “the mechanism--the rules, institutions, people, markets, and agreements--that make the exchange of payments possible.” One of the earlier attempts to analyze the digitization of payment systems can be found in Bossone (2001). The author points at the emerging issues of electronic payment systems offered by private actors outside the banking community and national financial regulators. The Bank of England (2014) offers a typology of digital payment systems. _Wrappers_ create a digital interface for traditional payment systems to improve their accessibility; _mobile money_ is a new type of payment system that does not require having a bank account; _credits and local currencies_ are new or alternative currencies, generally making use of existing payment systems; while _digital currencies_ incorporate both a new payment system and a new currency such as cryptocurrencies. Another approach is suggested by Natarajan (2019) who proposes four stages of innovation in payments. The first, second and fourth stages proposed by this author are like the types described by the Bank of England (2014). The third, however, differs by introducing the notion
– have had to deal with a heavy load. In Kenya, home to over 20 million children aged 14 and younger, the virus outbreak and the nine-month-long period of school closures have led to disruptions in education and child development that have potentially longlasting implications. When schools shut their gates in March 2020, few children could resume their education – even if imperfectly – with remote learning, likely increasing pre-existing inequalities, as children from poor and less educated households fell further behind.<sup>47</sup> In particular, girls living in poor households, who were less likely to adopt remote learning when needed and to go back to school after the restrictions were lifted, seem to be particularly at risk.

These early signs of increasing inequality are, to some extent, also reflected in the latest data from the Kenya Continuous Household Survey (KCHS). While school attendance rates remained largely unchanged between 2019 and 2020 for children who live in households with college-educated heads, they fell by almost two percentage points for those living with families whose head is less educated. Over the same period, a similar contrast is observed in terms of place of residence: rates significantly dropped in rural areas, but not in urban centers. Moreover, although we observe a significant reduction

> 46 Table A8 in the appendix reports results of probit regressions of changes in behavior on the first differences of in-person, remote and alternative education (in addition to the same control variables), indicating that these findings are robust to nonlinear specifications. We opt for first differences instead of fixed-effects models to avoid the problem of incidental parameters (Neyman and Scott 1948).

> 47 As mentioned previously, recent evidence indicates that, even if children do have access to virtual and hybrid education, these alternatives are still less effective than face-to-face learning (Halloran et al. 2021). The heterogeneity in terms of quality may be even larger for remote and alternative education, and this is something that we cannot account for in our analysis.

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# Notes

As noted by Leibbrandt et al (1996: 4), Rk is a form of rank correlation coefficient, because it measures the extent to which the relationship between Yk and the rank distribution of total income coincides with the relationship between Yk and its own rank distribution.

**2** This analysis ignores feedback effects, that is, the effects that a change in any source income share might have on distribution within any source income. Of course, such an assumption might be quite unrealistic for large changes in any source income share.

**3** This household survey was conducted by the International Food Policy Institute, working in collaboration with the Egyptian Ministry of Agriculture and the Egyptian Ministry of Trade and Supply.

**_4_** For more details on this 1997 Egypt Integrated Household Survey, see Datt, Joliffe and Sharma (1998).

5According to the data, 90 percent of rural households in the survey own their own house.

**6** Only the simple correlation between transfer income and total income (0.204) is lower than that for livestock income. The simple correlations for the other source incomes are: nonfarm income, 0.362; and rental income, 0.442.

7 It should be noted that the definition of nonfarm income used here is narrower than those used in other studies. For example, Reardon (1997) includes migration (remittances) income in nonfarm income while Chinn (1979) includes rental income.

8For other estimates of the share of nonfarn income in total rural income, see Reardon (1997) on Africa, and Hazell and Haggblade (1993) on Latin America.

91t should be noted, however, that these Ginis of per capita household income for Bolivia and Brazil are based on the distribution of overall (that is, urban and rural) incomes, while the income Ginis used in this study are based on rural household income. In theory, one would expect that the distribution of rural household income to be
among the 2,213 observations appearing in one or more tests reported below is 33%. (Appendix Table A1 provides summary statistics for all variables in the analysis.) The PDMS data constitute an unbalanced three-dimensional panel: surveys were conducted in 2006, 2008 and 2011, and designed to obtain information on aid flows for the previous calendar year (2005, 2007 and 2010 respectively) for each donor-recipient pair. Each successive survey round improved the quality and comprehensiveness of data. Guidance on definitions was strengthened and comparability of data reported by donors and by recipients was enhanced in later rounds (OECD, 2011; OECD 2008). The number of recipient countries choosing to participate in the survey increased from 34 in 2006, to 54 in 2008, and 78 in 2011. These 78 recipients account for about three-fourths of total aid worldwide (OECD, 2011).

Coverage of aid within these recipients is comprehensive over donors. Most of the “emerging” bilateral donors and a few of the smaller multilateral donors and vertical funds are dropped from the analysis, due to missing data on other donor variables from the DAC database or public opinion surveys. Dropped donors collectively account for a total of only 1.4% of aid reported in the survey.<sup>4</sup> The 34 donors remaining include the 22 traditional DAC donors, Korea (a new DAC member in 2011), Turkey, 7 multilateral organizations, and 3 vertical funds.<sup>5</sup> The panel is unbalanced not only with respect to time, but also with respect to donorrecipient pairs: not all donors provide aid to all recipients in each survey year. These features of the data create potential selection problems, considered in more detail below.

> 4 Their share of all aid worldwide is undoubtedly greater, although much of what is popularly termed “aid” from China and some other new donors would not meet the DAC‟s definition of official development assistance (ODA). 5 Bilateral donors are listed in Appendix
<!-- Start of picture text --> 5%<br>0%<br>-5%<br>-10%<br>-15%<br>-20%<br>0 0.2 0.4 0.6 0.8 1 1.2<br>Latitude<br>Impact on GDP of +3°C<br><!-- End of picture text -->

**Figure 9** . Percentage variation of GDP against latitude

# **9. Conclusion**

In this paper, a new set of climate change damage functions has been presented, improving earlier estimates in several ways. First, functions and parameters are provided with a large regional disaggregation (140 countries) and in a format which, by referring to the latest GTAP social accounting matrix, makes them easily employable in many general equilibrium models. Information from new, recently available studies, mostly from the non economic literature, has been processed in such a way that parameter values for economic variables, like labor productivity, can be estimated. Because of the wealth of primary data utilized in this exercise, it has also been possible to detect non-linearities in many impacts of climate change.

Although our estimates are mostly intended for use in multi-sectoral macroeconomic models, we undertook a simple aggregation procedure to verify the order of magnitude of the various impacts, as well as their distribution. Our findings confirm that the negative effects of climate change will be mainly borne by developing countries, located in tropical regions.

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(2005): “Trade, Wages, and the Political Economy of Trade Protection: Evidence from the Colombian Trade Reforms,” _Journal of International Economics_ , 66(1), 75–105. 8 (2007): “Distributional Effects of Globalization in Developing Countries,” _Journal of Economic Liter-_

- _ature_ , 45(1), 39–82. 8

- Guimaraes, P., O. Figueiredo, and D. Woodward (2003): “A Tractable Approach to the Firm Location Decision Problem,” _Review of Economics and Statistics_ , 85(1), 201–204. 4.1

- Harrison, A., J. McLaren, and M. S. McMillan (2011): “Recent Perspectives on Trade and Inequality,” _Annual Review of Economics_ , 3(1), 261–289. 8

- Helpman, E., O. Itskhoki, M.-A. Muendler, and S. J. Redding (2017): “Trade and Inequality: From Theory to Estimation,” _Review of Economic Studies_ , 84, 1–36. 6

- Hummels, D., R. Jørgensen, and J. M. . C. Xiang (2014): “The Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm Data,” _American Economic Review_ , 104(6), 1597–1629. 8

- International Monetary Fund, World Bank, and World Trade Organization (2017): “Making Trade an Engine of Growth for All: The Case for Trade and for Policies to Facilitate Adjustment,” Washington DC. 3

- Kovak, B. (2013): “Regional Effects of Trade Reform: What is the Correct Measure of Liberalization?,” _American Economic Review_ , 103(5), 1960–1976. 1, 2.2

- McFadden, D. (1973): “Conditional logit analysis of qualitative choice behavior,” in _Frontiers in Econometrics_ , ed. by P. Zarembka, pp.
# **2.2 Data Collection**

The sample frame for the Chaldal survey was all small and medium sized suppliers (“vendors”) who had supplied goods through Chaldal in the last 3 months. The survey was implemented by computer-assisted telephone interviews (CATI), fielded in June 2021 by the Chaldal call center. The vendor list provided by Chaldal included 346 firms. However, some firms could not be reached or had closed, and 35 refused to participate. The final sample consists of 127 firms with a completed interview. Some 42 percent of interviews were conducted with the owner of the firm, 34 percent with a manager, and 51 percent with someone in the sales department.

Data on Daraz vendors was collected through an online survey, sent to vendors who had filled at least 50 orders on Daraz during the last 6 months by email. The target sample included 14,660 firms in Bangladesh, Pakistan, Nepal, Sri Lanka, of which only owners were interviewed. Data collection took place in July 2021. A total of 1,842 firms responded to at least some questions, with 1,549 continuing to the end of the questionnaire. Interviews were conducted only with firm owner/directors.

The full questionnaires used to collect data from firms supplying Daraz and Chaldal are available in Appendix A4. Given the different structures of these two e-commerce platforms, the two questionnaire are similar but not identical.

# **2.3 Descriptive Statistics: Chaldal Survey**

Firms in the Chaldal sample are mostly established manufacturing or trading firms, and they range widely in size (table 1). The average firm has 749 employees, although this number is driven by a few very large firms, with 14 surveyed firms having a workforce of more than 1,000. In the sample, 36.6 percent of the firms have 1-19 employees (including the owner), 27.7 percent have 20-99 employees, and 35.7 percent have 100 or more employees (figure 1).
||Data<br>|TABL<br>Sources Used for Pro<br>|E 1:<br>ductivity Decomp<br>|osition<br>|||
|---|---|---|---|---|---|---|
|Country|Name of Survey/Census|Source|Period|Sectors|Panel|Threshold|
|Côte d’Ivoire|Registrar of Companies for the<br>Modern Enterprise Sectors|Business<br>Registrar|2003-2014|All<br>sectors|yes|None|
|Ethiopia|Survey of Large & Medium Scale<br>Enterprises|Manufacturing<br>Census|1996-2009<br>2012-2014|Manufacturing<br>only|yes|Employment ≥ 10|
||Survey of Large & Medium Scale<br>Enterprises|Manufacturing<br>Census|2010-2016|Manufacturing<br>only|no|Employment ≥ 10|
|Tanzania|Annual Survey of Industrial<br>Production (ASIP)|Manufacturing<br>Census|2008-2012|Industrial sectors<br>only|yes|Employment ≥ 10|
||Census of Industrial Production<br>(CIP)|Industrial<br>Census|2013|All<br>sectors|no|None|
|Bangladesh|Census of Manufacturing Industries<br>(CMI)|Manufacturing<br>Census|1995, 1997,<br>1999, 2001|Manufacturing<br>only|yes|Employment ≥ 10|
||Census of Manufacturing Industries<br>(CMI)|Manufacturing<br>Census|2005, 2012|Manufacturing<br>only|no|Employment ≥ 10|

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i) include the corresponding _Postt_ and _Entrejt_ dummies, while those in panel (ii) include the _Postt_ and _Entre ijt_ dummies; 6) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System).

11 This is indeed a general pattern that we observe throughout the analysis conducted in this paper. For simplification purposes, in the rest of the paper we mostly report the equivalent to column (iii) –our preferred specification —but more detailed tables are available upon request. We also conduct additional checks in the robustness and placebo sections.

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6

## **Table 2**

|**M**|**acroecono**|**mic**<br>**Ind**|**icators**|||||
|---|---|---|---|---|---|---|---|
||1990|1991|1992|1993|1994|1995|1996<br>(est.)|
|Real GDP growth rate|-3.896|<br>-8.4%|-9.7%|-14.2%|-23.5*|-11.8%|-7.9%|
|GNP per capita (US$)(Atlas)|..|2636|2753|2439|1913|1650|1514|
|Aseof GDP||||||||
|Current account deficit|||||-6.1|-4.2|-2.2|
|Fiscal Deficit||-14|-29|-12|-8.2|-4.7|-2.8|
|External Debt|..|..|..|..|23.8|23.0|21.4|
|Real exchange rate index<br>(1/1993=100)|..|..|125|108|54|47|39|
|Nominal exchange rate (1000 LC/$)|0.0006|0.0022|0.0336|1.288|49.86|147.0|200.0|
|Inflation<br>rate (year average)|..|..|1210|4735|891|376|75|

Data Source: World Bank Second, while total **employment** only
where _i_ index municipalities and _t_ index years. Capturing the distribution of income, _dit_ is (median or mean) per capita income and _git_ measures the inequality of its distribution. I care about enrollment in and quality of public pre-primary education, described by _pit_ and _qit_ , respectively. I also care about how much revenue policymakers spend on public education, _eit_ vs. on goods only obtainable from the public sector (like roads and parks), _fit_ . I consider both total per capita spending and the fraction of revenue spent on each category.

National inequality in Brazil has changed little in the last 50 years.<sup>29</sup> To avoid overstating the significance of subtle variation in inequality, and given a lack of inequality data for intercensal years, I assume that within-municipality income inequality is roughly constant over the sample period. Thus, it only enters the model in interaction.

The predictions that more unequal and higher-income municipalities are less likely to use revenue to expand public pre-primary enrollment, and more likely to increase its quality, implies: The predictions that more unequal and higher-income municipalities spend relatively less revenue on education, and more on goods without private substitutes (infrastructure), implies:

# **5.1**

There are two identification problems likely to affect the analysis of pre-primary education policy. The first is the potential for omitted variable bias. The second is the potential for observed per capita revenue to be endogenous due to reverse causality.

Observed per capita revenue is the result of factors affecting both the supply of funds and the population’s demand for funds. I want to rely solely on variation in revenue that comes from the supply side. Factors which affect demand for revenue may have a direct effect on pre-

> 29In 1960, the first year the Brazilian Census collected household income data, the national Gini coefficient was 0.59. It was also 0.59 as of the 2000 Census (Skidmore 2004).

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