rafmacalaba/data-use-mentions-extended huggingface dataset:
> capitalization of emerging markets.<sup>12</sup> **13** Moreover, some of the outstanding equity in

> emerging markets-as well as in many developed countries-is not publicly traded because it belongs to the families or corporations that control the companies. In sum, even though we cannot provide precise estimates, one can argue that international mutual funds hold a large and significant proportion of the publicly available equity.

Table 2 shows that the presence of mutual funds has grown substantially during the 1990s. Though net equity flows have declined from their **1993** peak-about **27** billion dollars to Latin America and 21 billion dollars to Asia-the relative importance of mutual funds has grown substantially. For example, dedicated emerging market equity funds held $21 billion in Latin-American stocks at the end of **_1995._ By** December **1997,** their holdings had increased to $40 billion. While mutual funds' growth in Asia has been less pronounced, the presence of mutual funds is still important in many countries. Overall, dedicated emerging market mutual funds held **$77** billion in Asia at the onset of the crisis (December **1996).** While the absolute amount of mutual fund investment in transition economies is not comparable to that in Asia and Latin America, funds' growth in these transition economies has been remarkable. In fact, in terms of market capitalization, mutual

> 12 The data come from Emerging Market Funds Research, which collects aggregate data of emerging market mutual funds to track the net cash flows of nearly 1,400 international emerging market equity funds, with an average position of about $120 billion in **1996.** The data set covers both **U.S.** registered and offshore funds as well as funds registered in Luxembourg, United Kingdom, Ireland, Cayman Islands, Canada, and Switzerland. It includes both open and closed-end funds. Our data start with the Mexican
sup> The procedure for constructing the variables is detailed in Fan, Lang and Stulz (1998). It entails three steps. First, we create four matrices of inter-industry relatedness coefficients. This involves computing the coefficients between each pair of over 400 industries defined in the input-output tables. We follow an approach similar to Lemelin (1982) in measuring inter-industry relatedness. The building block of this approach is the _Use Table of the 1992 Benchmark U.S. Input-Output Accounts._<sup>_1 0_</sup> The _Use Table_ reports

> 9 We are unable to obtain input-outpu **t** ables for individual Asian countries.

> '° The Accounts reports commodity flows between pairs of over 400 non-governnent and non-household industries. See Lawson (1997) for details.

9
Finally, in recent years, crowd-sourced platforms such as Numbeo,<sup>9</sup> which rely on user inputs on property prices in various locations around the world, have added their own house price index along with publicly available per square foot price ranges for properties within the city center and outside the city center. These platforms add more distributional aspects to the average house prices and point predictions published by other indices, but suffer from the reliability of the self-reported data.

Despite the apparent issues in comparability across countries, the listed sources are the most comprehensive databases on property prices currently available for a larger set of countries. Therefore, many papers draw on these indices to conduct country or region-specific analyses on property price developments over time (e.g., Girouard et al. 2006; Igan & Loungani 2012; Yoshino & Helble 2016). One of the earliest systematic presentations of house prices is a historical time series data set of nominal residential property prices in 13 advanced economies by Borio et al. (1994). Some studies that provide comparative assessments combine the data sources outlined above or enhance them with some primary data collection on some additional countries that are not yet covered by the indices (e.g., Deghi et al. 2020).

# 2.2 Determinants of House Prices

The volume of research on the housing market, particularly estimating its impact on real economic activity, has experienced a steep influx after the global financial crisis in 2008–2009. Most studies in this realm investigate the various channels through which housing and house prices affect macroeconomic and financial outcomes, particularly as housing bubbles are associated with significant output losses (e.g., Catte et al. 2004; IMF 2008; Jordà et al. 2015). Single-country studies on house prices and house price developments mainly focus on developed economies, particularly OECD countries, EU countries, and the United States or Canada (e.g., Alter & Mahoney 2021; Davis & Heathcote 2005; Knoll et al
The present paper analyzes the relationship between the number of days of paid maternity leave and the proportion of workers in a typical registered private firm that are females. As indicated above, we also explore the idea of lower demand by employers for female workers when the cost of maternity leave falls on the employers as opposed to the government (discussed in detail below). The analysis is based on firm-level survey data for a large cross-section of mostly developing countries. Thus, we capture the full impact (short-run plus long-run) of maternity leave. In this sense, our approach is like that of Fipps et al. (2001), who argue that “ever having” rather than “currently having” a child is generally the more appropriate measure for studying the family gap.

We contribute to the existing literature in several ways. First, estimates of the long-run effects of maternity leave are largely restricted to a handful of rich countries. Whether the results apply to other countries is not discussed. We fill this gap in the literature by focusing on a large cross-section of countries. Second, there is substantial variation across countries in who bears the cost of maternity leave. Demand for female workers may contract when such costs fall on the employers as opposed to the government. However, there is no formal evidence on the issue. We attempt to fill this gap in the literature by analyzing how the maternity leave and female workers relationship differs depending on who pays for maternity leave (employer, government, or both). Our results confirm that the maternity leave and female workers relationship is much larger (more positive) when maternity leave is funded by the government vs. the employer. Third, the use of individual-level data that is pervasive in the existing literature suffers from selection bias. Women who are eligible for maternity leave and those who decide to have children are different in several characteristics from the rest. Failure to control for these selection effects can significantly bias the estimates of the impact of maternity leave (see for example, Hashimoto
significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA.

> 2 UNHCR defines a protracted refugee situation as “one in which 25,000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country” (2012: 23).

> 3 The figures are based on refugees (including those in refugee‐like situation). Persons in refugee‐like situations include “groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained” (UNHCR 2014: 39). The number of refugees and people in refugee‐like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19% in the world and 8% for SSA).

12
with the rest of the Caribbean. Like the other four countries in this study, Suriname is a member of the Caribbean Community (CARICOM).

In recent years, Haiti has been rocked by severe economic and political crises. Accordingly, with a 2022 GDP per capita of US$1,748 (in current US$ according to the World Bank’s World Development Indicators), Haiti is by far the poorest of all five countries in the sample. The second and third poorest countries are Suriname with a GDP per capita of US$5,859 and Belize with a GDP per capita of US$6,984. The two richest countries are Dominica with a GDP per capita of US$8,351 and Saint Lucia with a GDP per capita of US$13,031. Indeed, in our survey, households in Haiti are markedly more likely to indicate that they find it challenging to meet their basic needs (see Table 1) than households in the other four countries. Here, ability to meet basic needs is defined based on two questions asked to households: (i) “In your opinion, how much does a home like yours need to cover your basic needs, per month?” and (ii) “Does your household currently have [amount mentioned in response to question (i)] to cover the cost of basic necessities?” In Haiti, only 14 percent of households responded positively to this last question. The share of households indicating that they can meet their basic needs ranges from 42 to 44 percent in the other four countries.

On non-monetary development outcomes Haiti also compares relatively poorly. It ranked 158 out of 193 countries on the UNDPs Human Development Index in 2022, a score classified as “medium” (but close to low) human development by the UNDP. The ranking of Suriname (124) is also considered “medium”. The scores of Belize (118), Saint Lucia (108), and Dominica (97) are considered “
windows – i.e., one window timed around the _imposition_ of the original import restriction and one timed around its _removal_ .

Our results provide evidence of a number of previously undocumented features of the export response to TTB removals. On average and relative to other exporters, China’s exports resume quickly and aggressively after a foreign trading partner removes an antidumping import restriction. However, for all other exporters, the evidence is not nearly as positive. Other emerging economy exporters have a particularly difficult time regaining lost export sales and restoring export market share after a trading partner removes an import restriction, especially in the case of exports sent to _other_ emerging economies.

Section 5 concludes with a discussion of a number of questions these results raise for future research as well as policy implications.

# **2 Temporary Trade Barriers Imposed by Emerging Economies**

An increasing number of emerging economies have imposed antidumping, countervailing duties and safeguards – collectively referred to here as temporary trade barriers (TTBs) – over an increasing share of their imports. Table 1 summarizes newly constructed indicators on TTB use through 2011 for fifteen emerging economies broken into two cohorts – members and non-members of the G20.<sup>4</sup> The approach follows the methodology proposed in Bown (2011), which reported data on policy use through 2009 for a more limited set of countries.<sup>5</sup> As a point of comparison, the lower third of the table reports policy use by nine high-income economies also included in the _Temporary Trade Barriers Database_ .

Consider first column (1) of Table 1 which reports the share of import product lines, at the 6- digit Harmonized System (HS-06) level, over which the policy-imposing economy had at least one import-restricting TTB in effect in 2011. To clarify, this measure captures the “stock” of products covered by the accumulation of TTBs over time and that were in effect in 2011. The emerging economy

> 4 The only G20 countries not included in the policy-imposing economy
56

# Table III.6

Source of Contraceptives, by Vomenrs Education, for Selected Countriesa

|||Source of Co|ntraceptives||
|---|---|---|---|---|
|Country<br>Education|Government<br>(Percent)|Commercialb<br>(Percent)|<br>NGO<br>(Percent)|Otherc<br> <br>(Percent)|
|Colombia (1986)|||||
|None|64.6|15.4|16.9|3.1|
|< Primary|44.5|34.5|19.9|1.1|
|Primary Comp.<br>|36.6<br>|34.4|27.2|1.8|
|Secondary +|19.2|57.6|22.0|1.2|
|Egypt (1984)|||||
|None<br>|32.2|65.5|1.2|1.0|
|< Primary<br>|32.6|64.7|1.5|1.3|
|Primary Comp.<br>|24.7<br>|72.2|1.3|1.9|
|Secondary +|15.7|82.2|1.4|0.7|
|Jamaica (1984)|||||
|<Primary<br>|90.0|10.0|--|--|
|Primary<br>Comp.|59.3|27.3|__|9<sup>.</sup>4<sup>d</sup>|
|Secondary|50.0|.50.0|__|__|
|College|53.9|46.
comprehensive review for modeling money demand in Egypt. These studies are reviewed in detail and summarized in Annex VIII.

Scobie (1983) is one of the early empirical studies on estimating money demand in Egypt. It was conducted to examine the monetary impact of subsidy expenditures on domestic inflation, the balance of payment, and the exchange rate. These variables were determined simultaneously within a framework of the monetary approach of the balance of payment. The demand for real money balances was estimated within a system of four structural equations, including domestic inflation, the black market exchange rate, and the balance of payments equation by adopting a two stage least squares estimation using annual data for the period 1947–1981. The study concluded that the elasticity of real money demand was estimated to be 0.38 in the short run and 1.42 in the long run. With respect to inflation, it was found to be -0.86 in the short run and -2.71 in the long run.

El-Erian (1987) adopted Ordinary Least Squares (OLS) framework to identify the absolute and relative determinants of currency substitution that took place during the period 1980–1986. He used data related to individuals’ expectations regarding foreign balances. A reduced form of the money demand model was estimated using quarterly data of relative holdings of foreign currency deposits and specifications of the associated impact on the portfolio balance decisions of asset holders. Empirical results indicated that agents tend to hold foreign balances for two main reasons: (1) higher financial yields due to expectations of exchange rate induced capital gains; and (2) greater political stability and fewer institutional changes, especially banking regulations. Results also proved that the impact of enhancing these causal factors to influence agents to limit currency substitution will require a time lag to alter the actual holdings of foreign currency balances.

Alami (1999) tested for a long-run equilibrium relationship (co-integration) between the ratio of foreign currency denominated deposits to total liquidity, the expected rate of change of the exchange rate,
Borraz, F. and M. Olarreaga. 2011. “Services ownership and access to Safe Water: Water Nationalization in Uruguay.” In _Services, Trade and Development,_ eds. M. Mashayekhi, M. Olarreaga, and G. Porto, 265-282. United Nations Conference on Trade and Development.

Cameron, A., J.B. Gelbach, and D.L. Miller. 2008. “Bootstrap-Base Improvements for Inference with Clustered Errors.” _The Review of Economics and Statistics_ 90 (3): 414-427.

Chong, A., and F. Lopez-de-Silanes. 2005. “Privatization in Latin America: Myths and Realities.” Stanford University Press.

Driscoll, J. and A. Kraay. 1998. “Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data.” _Review of Economics and Statistics_ 80 (4): 549-560.

Galdo, G. and B. Bertha. 2005. “Evaluating the Impact on Child Mortality of a Water Supply and Sewerage Expansion in Quito: Is Water Enough?” OVE Working Papers 0105, Inter-American Development Bank, Office of Evaluation and Oversight (OVE), Washington DC.

Galiani, S., P. Gertler, and E. Schargrodsky. 2005. “Water for Life: The Impact of the Privatization of Water Services on Child Mortality.” _Journal of Political Economy_ 113 (1): 83-120.

Galiani, S., P. Gertler, E. Schargrodsky, and F. Sturzenegger. 2005. “The Benefits and Costs of Privatization in Argentina: A Microeconomic Analysis”, in _Privatization in Latin America: Myths and Realities_ , eds. A. Chong and F. Lopez-de-Silanes, 67-116. Stanford University Press.

22
a novel dataset assembled by the International Finance Corporation (IFC) that captures stock market data from over 150 countries for the period 2015-2020. We employ a crosscountry fixed effects panel to uncover relationships between stock market growth and issuer composition. Our results show that stock market capitalization, often the most popular headline indicator for stock market development, does not necessarily reflect broader firm participation. Instead, a growing stock market tends to be related to an increase in the number and size of seasoned equity offerings, indicating growth at the intensive margin. Greater trading activity, however, is associated with entry of new issuers in the stock market.

These findings also hold for a subsample of only low- and middle-income countries. For this group, the paper additionally finds that higher stock turnover is related to lower market concentration, though the magnitude is marginal. This result is of particular relevance given that less developed markets are more likely to be dominated by a few firms. Such markets are also more likely to have a high concentration of listed firms in a handful of industries. Additionally, we examine the sectoral diversity of equity issues but do not find any evidence that this improves as stock markets increase in size and activity. It is important to note that our results do not capture causal 3
In the first step, we select a set of comparable non-monetary indicators that are available in both the NLSS and GHS (Table _2_ ). The advantage of using the NLSS and GHS is that the questionnaires on household characteristics, assets ownership, consumption frequency and demographics is the same and asked using the same recall periods (see Table _4_ ). Moreover, we can ensure comparability between the different indicators using data for from the 2018/19 NLSS and 2018/19 GHS. While the two surveys differ in their data collection schedule – with the 2018/19 GHS being collected in two visits in July-September 2018 and January-February 2019 and the 2018/19 NLSS being collected over 12 months – they provide information for part of the same year making it possible to check if the assumptions behind the survey-to-survey imputations are plausible. Column 3 in Table _2_ shows the difference in means for each variable used in the survey-to-survey exercise and available in the 2018/19 NLSS and 2018/19 GHS. Zone population adjustments are applied to the 2018/19 GHS data to ensure that the zone-level population estimates match those from the 2018/19 NLSS. The indicators are overall highly comparable, with only a few variables – such as household head’s employment variables and consumption frequency of food-items – showing larger differences possibly due to seasonal variation. In Section 6, we test the robustness of the results to different specifications of the consumption model that do not include this set of variables. Variables to be included in the consumption model are selected using stepwise selection with an optimal p-value of 0.01.

In the second step, we develop a consumption model using the selected variables and consumption data from 2018/19 NLSS. Variables used in the consumption model include regional dummies, demographics of the household (dependency ratio), household head characteristics (gender, employment category), living conditions (main source of cooking fuel, toilet availability), consumption frequency dummies (food and non-food items), asset ownership (air conditioning, washing machine,
# **1. Introduction**

Economic development requires a government that can successfully implement policies, protect property rights, and deliver public goods and services. A necessary prerequisite is the ability to raise and spend fiscal resources effectively. But many governments, particularly in low- and middle-income countries, lack these capacities (Besley and Persson, 2010). High tax compliance costs due to cumbersome regulations and harassment by tax officials deter investment, encourage tax evasion, and undermine economic growth (Hindriks et al., 1999; Djankov et al., 2006; Coolidge, 2012; Alm et al. 2015; Jerbashian and Kochanova, 2016a). The public procurement of goods and services is often rife with collusive practices and corruption, resulting in the misallocation or waste of resources and poor quality infrastructure (Auriol, 2016; Center for Global Development, 2014).

This paper investigates whether the use of information and communications technology (ICT) by government, or “e-government,” can strengthen governments’ capacity to raise and spend resources effectively. Most countries have invested heavily in ICT over the past two decades to improve revenue mobilization, budget preparation, and budget execution, and to deliver a variety of services to citizens.<sup>1</sup> However, very little is known about the impact of these investments on government efficiency. We aim to fill this gap by empirically examining the effects of two aspects of e-government: the electronic filing and payment of taxes (e-filing) on tax compliance costs and corruption; and electronic procurement (eprocurement) on the competitiveness of public procurement and corruption. We employ novel crosscountry data on e-government implementation, as well as country- and firm-level data on the business environment.

E-filing should, in theory, reduce tax compliance costs. Taxpayers have more flexibility about when to file taxes, are less prone to errors while filing standardized forms, and, ideally, do not need to spend time with tax officials in face-to-face interactions, which also reduces opportunities for corruption. Similarly,
within occupations, focusing on Malaysia where the primary source of data was collected. Second, we estimate the extent to which digital and nondigital skills are used in each country, given the occupational structure of the individual labor markets.

In addition to providing the answers to these questions, the data and methodology add value in two ways. First, this is the first paper to use data from a Southeast Asian country to construct occupational skills profiles that include cognitive, socioemotional, and digital skills. Most studies use the occupational skills profiles reported in the U.S. Occupational Information Network (O*NET).<sup>2</sup> However, O*NET tends to misrepresent the occupational skills in low- and middle-income countries due to differences in job tasks, skills, and production technologies across countries (Caunedo, Keller, and Shin 2021; Lewandowski et al. 2021; Lo Bello, Sanchez-Puerta, and Winkler 2019). The Malaysia

> 1 Deming and Kahn (2018) also find that people management (linked to supervision, leadership, non-project management, mentoring, staff), which can be classified as a socioemotional skill, has a much lower correlation with their measures of basic and advanced digital skills: 0.24 and -0.06, respectively (p. 351).

> 2 See for example Acemoglu and Autor (2011) for the U.S.; Goos, Manning, and Salomons (2009) for Western Europe; and Apella and Zunino (2018) for Latin American and Eastern and Central Europe countries.

3