rafmacalaba/data-use-mentions-extended huggingface dataset:
the literature. At the grid-cell level, the marginal economic return of an additional drop of rainfall is highest in arid areas and slowly decreases as the weather becomes wetter. The first 500mL of precipitation each year increases GDP per capita growth by 1 percent; precipitation between 500mL and 1,000mL a year raises growth by about an extra 0.8 percent. Eventually, extra precipitation brings no additional GDP per capita growth (Fig Sup 1). The temperature curve in the baseline regression is similar to that found in a variety of studies and peaks at 20 degrees Celsius as observed in micro studies (BHM 2015).

Next, we explore the consequences of aggregating weather and economic outputs data at a coarser spatial resolution. In column 2, we show the consequences of aggregating data at the Administrative 1 level (one level below a country; i.e., a state in the United States). We determine population-weighted levels of total annual precipitation, average temperature and average GDP per capita. Results at the administrative 1 level (state) are similar in magnitude and significance to those at the cell level. They suggest that the first 500mL of precipitation brings an additional 0.8 percent of GDP per capita growth, and that passing from 500mL to 1,000mL increases against growth by 0.6 percent. The concave relationship between temperature and GDP per capita growth is also retained.

In column 3, we aggregate data at the country level, following the same weighting procedure. This time the results vanish for rainfall, consistent with findings from DJO, BHM and other studies. However, temperature remains significant and is consistent with previous findings. At the country level, the optimal level of temperature is also found to be 13.9�C, as in BHM.

Robustness checks are presented in the Supplementary Material. In Tables Sup 2, 3 and 4, we test different specification models at the cell, administrative 1 and country levels: first, we exclude the 10 major oil producing countries for which
open their own businesses, increasing the number of registered firms by 5%, while at the same time increasing wage employment by 2.2%. In the same vein, Branstetter et al. (2010), using Portuguese micro level data, investigated economic effects of a regulatory reform in Portugal. Their study found that the reform brought about an increase in the number of firms created as well as in the overall employment level. The authors note that the increase occurred predominantly among the firms that were at the highest risk of being deterred by heavy entry requirements. These were typically small firms, owned by entrepreneurs with relatively poor education level. And such firms commonly operated in agriculture, retail trade and construction sectors. The link between regulation and firm creation is further researched by Bripi (2016). Using data on time and cost to start a business and entry rates by industry in the period of 2005-2007, the author analyzes the impact of entry regulation on firm creation in Italy. The cross-sectional analysis of the data shows that the length and costliness of procedures contribute to lower entry in sectors with commonly high entry.

Economic theory suggests that employment as a reliable source of income can ultimately lift people out of poverty and reduce income inequality. Economies with poor quality business regulation have, on average, higher levels of income inequality. Furthermore, when business regulation is overly cumbersome, posing severe obstacles to companies’ daily operations, businesses become more prone to corruption and bribery. Paunov (2016) demonstrates that the

> 4 Bruhn 2013; Branstetter et al 2010; Klapper and Love 2010; Paunov 2016.

6
Recent literature has primarily focused on income taxes (personal and corporate -PIT and CIT, respectively) and value-added tax (VAT) rates due to their significant contribution to tax revenue in most countries (Vegh and Vuletin 2012; Vegh, et al 2017). These studies have made valuable contributions by constructing a comprehensive cross-country database that tracks the historical series of the top statutory tax rates for CIT, PIT, and VAT. Building upon these contributions and using this database, our analysis will concentrate on a sample of 33 countries, including 18 advanced economies and 15 developing economies, spanning a period of 40 years from 1980 to 2020. Additionally, we will extend the analysis to include other taxes, specifically focusing on the case of Argentina and updating the data whenever possible. However, it is crucial to interpret these results cautiously within the specific context of Argentina.

Argentina's tax structure deviates from international standards and theoretical recommendations, relying heavily on unconventional tax components that are more regressive and distortive. By 2021, Argentina's tax burden accounted for 29.1 percent of GDP ( **Figure 14** ), with nearly one-third (around 9.1 percent of GDP) coming from unconventional taxes, including financial transaction taxes, turnover taxes, taxes on foreign exchange purchases, and export duties.<sup>17</sup> In contrast to developing and advanced economies where VAT, CIT, and PIT make up a substantial portion of total tax revenue, in Argentina, they represent only 40 percent of the total tax revenue. Therefore, conducting standard tax rate cyclical tests on PIT, CIT, and VAT in Argentina may not provide as representative insights as in most other countries.

**Figure 14. Tax burden (as percent of GDP)** 40

**Figure 15. Selected tax rates (1990-2022)**

<!-- Start of picture text --> 34.1 40<br>35<br>1.4<br>30 29
and organizational form.<sup>1</sup> However, once we drop the former Socialist countries, organizational form becomes the most important firm-level explanatory variable.

In comparing the different institutional theories, we exploit the World Business Environment Survey (WBES), a major cross-sectional survey conducted in developed and developing countries in 1999 and led by the World Bank. We use survey responses from 7760 firms in 80 countries to questions about property rights and firm characteristics. The survey contains data on small as well as large firms, and on private corporations and partnerships as well as publicly traded firms.<sup>2</sup> In order to compare the different theories and to examine the relative influence of firm effects versus country effects, we use variance decomposition analysis. This methodology is well established in the corporate strategy literature in the context of decomposing profitability into corporate and industry effects (Schmalensee, 1985; Rumelt, 1991; McGahan and Porter, 1997, 2002; Khanna and Rivkin, 2001).<sup>3</sup> The methodology allows us to focus directly on the general importance of these effects in explaining property rights without any assumptions on _causality_ or structural analysis.

Our analysis also uncovers a methodological issue that has not received adequate attention in the literature. The explanatory power of several institutional theories depends on the proxies used to represent these theories. We identify several potentially significant scaling issues that occur if empirical tests do not pay attention to non-linearities arising due to the way the proxies are scaled. These scaling issues have the potential to overturn conclusions drawn from tests.

This paper is closely related to the recent work of Stulz, Karolyi, and Doidge (2004) who investigate variation in the ratings of governance in large firms in a large sample of countries. They find that most of the variation in governance ratings across firms is explained by country characteristics rather than firm characteristics. They attribute this finding to the increased incentives of firms in better legal environments to adopt better governance structures.

> 1 Firms
# **Natural Resources and Total Factor Productivity Growth in Developing Countries: Testing A New Methodology**

Kirk Hamilton, Esther Naikal, and Glenn-Marie Lange _“Productivity isn’t everything, but in the long run it is almost everything. A country’s ability to improve its standard of living over time depends almost entirely on its ability to raise its output per worker.”_ _Paul Krugman_

## **1. Introduction**

As the quote above suggests, in a world of decreasing marginal returns to factors of production, the only way to ensure increasing growth and prosperity is to increase the efficiency of factor use. Rather than focusing on a sole production factor such as labor, economists beginning with Solow (1957) have developed methods of growth accounting which derive an indirect measure of the rate of change in the efficiency of use of all factors of production, termed ‘total factor productivity’ (TFP) growth or ‘multi-factor productivity’ (MFP) growth. With the exception of recent work at the OECD (discussed below), estimates of TFP growth at the macroeconomic level have excluded natural resources as a factor of production – a serious omission when considering developing countries. Natural resources make up on average 47% of total wealth in low-income countries, 36% in Sub-Saharan Africa, 27% in lower-middle-income and 17% in upper-middle-income countries. A particular concern is that agricultural land makes up 62% of natural wealth in low-income countries. The omission of natural resources was largely owing to a lack of comparable cross-country data on natural resource stocks and flows, measured according to System of National Accounts (SNA) standards.

The publication by the World Bank of _The Changing Wealth of Nations 2018_ (Lange, Wodon and Carey 2018) has filled this gap, with data on the value of natural resource stocks and flows across 141 countries and spanning the period from 1995 to 2014. The OECD pioneered methodology to include natural resources (minerals and energy) in TFP estimations for OECDmember countries and a few other
of spatial econometrics to a research question that has traditionally been investigated datasets. We instead use a framework that allows for more using cross-country panel accurate of the fact that both climatic shocks and urbanization are modeling potentially correlated over time and across space.

The structure of is in our a spatial dependence approach approximated by symmetric row-standardized weighting matrix _W_ . The spatial lag of each variable can then be derived by multiplying the matrix by a vector of observations. Our baseline approach relies on calculating Queen contiguity-based spatial weights. The Queen approach implies that spatial dependencies for each cell are calculated based on the eight neighboring cells that surround the destination cell. Given that each cell corresponds to roughly 55 _×_ 55 km, this approach captures spatial dependencies within square-shaped areas whose sides are 165 km in length each. Spatially lagged drought is then calculated by taking the average value of drought from across the neighboring cells. As part of our robustness checks, we vary the assumed structure of spatial dependence by introducing alternative weighting matrices.

# **3.2 Measuring Urban Extent**

To measure the extent of land within each cell in a we use built-up grid given year, remotely sensed data from the World Settlement Footprint (WSF) (Marconcini _et al._ , 2020). Employing multitemporal optical satellite imagery, the WSF provides a global 30m resolution of human settlements. Using this novel resource, we first calculate the total number of built-up pixels that a given grid cell contained in 1985 and we use this variable as a baseline control of urban land present in each grid cell. As our dependent variable, we calculate the increment, i.e. the total number of new built-up pixels that were built in a given cell in a given year. Figure 1 below illustrates this methodology by displaying the trajectory of urban growth in Uganda’s capital city, Kampala. The figure displays five-year increments in built-up pixels, showing how Kampala’s urban extent gradually expanded within the specific grid cell where it is located.<sup>6
# **2 Context**

I conduct the study with low-income female entrepreneurs in Jordan. In this section, I provide information on the study context as relevant to the experimental design. To do so, I outline an overview on women’s economic participation and digital access in Jordan, and I discuss how online spaces are used by male and female business owners.

## **2.1 Country Setting**

**Women’s mobility and work:** At 15%, Jordan has the world’s third lowest female labor force participation rates (figure A.1). In contrast, men’s labor force participation exceeds 60%, resulting in a striking gender gap in labor supply despite comparable education levels across men and women (figure A.2). With relatively high literacy rates, the underutilization of women’s human capital entails substantial economic losses for them and their households (Ait Ali Slimane, Lundvall, Mohindra, Al Abbadi, Kurshitashvili, and Hisou 2020) and suggests the existence of untapped potential for growth among them.

Existing efforts to explain women’s exclusion from the labor market have highlighted conservative social norms and the lack of flexible work opportunities as main contributors (Kaasolu, O’Brien, Hausmann, and Santos 2019). In a World Bank survey in urban areas of Jordan, 96% of Jordanians agreed that it is generally OK for women to work. However, this rate drops to 54% if women would have to leave their children with relatives, to 38% if women would have to work in mixed workplaces, and further to 26% if women would need return home from work after 5pm (Bank 2018). These rates are consistent with norms around women’s work, as expressed in national surveys in Jordan. Over 80% of Jordanians believe that jobs should be reserved to men if they are scarce, and over 84% report that the children of a working mother suffer (World Value Survey, 2018)
br>variables|9.36|0.0000|226.8|0.0000|

Notes: This table reports the results of a regression of two variables reflecting the school expansion. Column 1 reports a linear probability regression in which the outcome is a binary indicator equal to one if a new school was introduced to a municipality in the period 1998–2009, and 0 otherwise. Column 2 reports an ordinal logit regression in which the outcome reflects the timing of a municipality’s first school new school (number of years after 1997). Only municipalities that received a new school during the period1998–2009 are including in the second regression. Both regressions include state dummy variables. Population size is based on the average total population between 1997 and 2009. Robust standard errors are reported in parenthesis. ***, **, and * denote statistical significance at 1%, 5% and 10%.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

32
remarkable and generally acknowledged among them, as identified by Tybout (1992):

- a) Market size (e.g. population and GDP). Although some economies are quite large, most are not. Hence, with the exception of countries such as Poland or Romania, the size of the domestic market for manufactured products is relatively limited. This means that the strategy of companies must be oriented towards the external sector and thus, must necessarily bear in mind the macroeconomic aspects that are translated through channels such as direct foreign investment, exchange rates and immigration.

- b) Access to manufactured inputs (e.g. merchandise imports and exports). The set of choices of domestically produced intermediate inputs and capital equipment is also often limited.

- c) Human capital (e.g. school enrollment, primary). Low rates of secondary education (less than 90%) and an insufficiency of technicians and scientists in countries like Hungary of Lithuania also affect the mix of goods manufactured and the factor proportions used to produce them.

> 5 A study on the experience of the start of the internal European market in 1993 (that had as one of its main objectives the increase in company efficiency) shows that during the 1987-1996 period, there has not been an increase in efficiency in the productive sectors of the Union. This paper argues that a possible cause of this stagnation is the possibility that the efficiency has a cyclic character and, therefore, the recession suffered in the beginning of the 90‘s could explain this situation (Esteban, Gallizo and Hernández, 2002).

> 6 In this research paper, the data of Malta and Cyprus have not been taken into consideration since they are considered as Mediterranean countries. On the contrary, data from Moldova (not in the EU) with the sole objective of homogenizing the sample of the block of countries considered as Eastern European.

4
these considerations, we employ the sum of the rent from energy depletion (crude oil, natural gas, hard coal, and lignite) and rent from oil and minerals (bauxite, copper, gold, iron, lead, nickel, phosphate, silver, tin, zinc). Each rent is computed as production volume multiplied by the difference between international market price and average unit production cost.<sup>18</sup>

# **4.2. Empirical Strategy**

While Knack and Keefer (2007) use an averaged cross-section to investigate the impact of institutional quality on public investment, we employ the system GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998).

Our methodology can be considered superior as it exploits the longitudinal dimension and, as specified by Roodman (2006), jointly addresses several potential econometric problems since it is specifically designed for situations with (1) few time periods and many individuals; (2) a linear functional relationship; (3) a single left hand side variable that is dynamic, depending on its own past realizations; (4) independent variables that are not strictly exogenous (possibly correlated with past and current realizations of the error); (5) unobserved heterogeneity at country level; (6) and heteroskedasticity and autocorrelation within individuals, but not across them.

Given our dataset spanning 144 countries over 25 years, a typical persistence in the investment variable, some of the explanatory variables (income and price in particular) that may be endogenous with public investment, a likely presence of specific country fixed effects, and idiosyncratic errors that are heteroskedastic and correlated within but not across individuals, the system GMM is instrumental in addressing all these issues.

To test hypothesis (i) we adopt the same specification as in Keefer and Knack (2007):

> 18 Oil exports have been used as a robustness check.

11
*US$ per hour:**<br>**Non-qualified workers**|
|---|---|---|
|**GCC oil exporters**|4.5|3.0|
|**Oil importing countries**|1.5|1.0|
|**Developing oilexporters**|3.0|2.0|

Source: Authors’ estimates <mark>Second, we adopted more conservative multipliers than the estimated multiplier of 4 proposed by Schwartz et al. (2009).</mark><sup>10</sup> <mark>In this adaptation we use the multipliers for 2007/08 in the Egyptian IO table. The type II multiplier (the ratio of all jobs created to the number of direct jobs) varies between 1.09 and 1.82. Those multipliers suggest that the transport and communication sectors, for instance, have far greater potential to create induced and indirect job effects than the roads and bridges construction sector. This indicates that when investment decisions are made with the objective of creating jobs, consideration should be given to both direct and indirect employment effects as well as the type of skills required to implement projects.</mark> Third, this adaptation also estimates and accounts for the share of each input factor for infrastructure projects relevant for MENA countries and based on standard technologies for each subsector that were not considered in the LAC estimates.<sup>11</sup> The shares, presented in Appendix Table 1, indicate that labor accounts for a much larger share of costs in water and sanitation than in roads, ports and electricity generation and access. The sanitation sector is most promising as

> 7 Schwartz et al. (2009) assume hourly wages gross of benefits to be respectively US$6 and US$3 for qualified and non-qualified workers in LAC.

> 8 The data in Gardiner and Theobald (2007) for Latin America are consistent
evidence that index insurance has the possibility of improving welfare for rural households by providing resources to invest in the subsequent planting season, which in turn results in more land sowed. The payments may also prevent households from resorting to costly coping mechanisms, such as reducing consumption, as evidenced by the result for household expenditures. Lastly, there appears to be some interaction between formal insurance payments and remittances that reduces the burden of private transfers for relief assumed by migrants. Additionally, we conclude that future evaluations of CADENA or similar programs would benefit from more detailed agricultural production data and administrative records.

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primary measure of success.

The data that we use comes from micro-level household surveys collected by the Development Economics Group (DEC) of the World Bank, the International Income Distribution Database (I2D2). This data base consists of already existing data sets that have been collected and standardized. Most original country datasets are labor force surveys, budget surveys or living standards measurement surveys, and all are nationally representative. The data are an updated version of the dataset described in Montenegro and Hirn (2009).<sup>2</sup> These data include four sets of consistently defined and coded variables: (i) demographic variables, (ii) education variables, (iii) labor force variables, and (iv) household per capita consumption.

Not all variables are available in all countries and years. In our analysis, we only use surveys where we can identify whether the worker is an own account worker, owner or wage and salary employee. Most countries datasets are available for multiple years from the period 1984 to 2010. We only use the most recently available survey in each country in this analysis. We

> 2 The datasets for India and Sri Lanka in the I2D2 did not allow us to separate own account workers from employers.  We therefore used labor force survey data from India and Sri Lanka to supplement the I2D2 data.

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<!-- Start of picture text --> Fig. 1a: India's urban shares, 1989-2005<br>0.70<br>0.60 Output<br>0.50<br>Employment<br>0.40<br>0.30<br>Plants<br>0.20<br>0.10<br>Notes: Figure plots urban shares of plants, employment<br>and output for each year using survey data of plants from<br>organized and unorganized sectors.<br>0.00<br>1989 1994 2000 2005<br>Urban shares<br><!-- End of picture text -->