rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

The purpose of this paper is to survey prevailing electricity price levels and tariff structures as a reference for practitioners to benchmark tariff design across countries. The paper also provides simple indicators of the performance of tariff designs against standard design criteria such as cost recovery and affordability and investigates the extent to which tariff structures apply price differentiation across customer groups and consumption categories. The paper also considers to what extent prevailing electricity tariff designs are compatible with emerging technological trends in the sector, such as distributed generation, demand-side participation, and electrification of transport.

The paper adds to a sporadic existing literature surveying electricity tariff designs across countries and evaluating their performance against standard criteria such as cost-recovery and affordability. These range from regional studies involving Sub-Saharan African countries (Briceño‐Garmendia and Shkaratan, 2011; The African Development Bank Group, 2019) to global surveys spanning both developed and developing countries (World Bank, 1990; Foster and Yepes, 2006). Existing studies find systematic patterns in electricity tariff design across countries. Ore et al., 2017 show that countries in Central America commonly differentiate volumetric tariffs across customer groups. Komives et al., 2005 show that rising block tariffs are widely used for residential customers often as a form of social tariff, but that they are not very effective at reaching poor households since electricity consumption is not a very strong proxy for household income.

A recurrent finding in the literature is the difficulty that low-income countries face in achieving cost recovery; even for operating costs (Huenteler et al., 2020). For instance, Briceño‐Garmendia and Shkaratan, 2011 find that while about 80 percent of African power utilities achieved operating cost recovery, only 30 percent achieved full capital cost recovery. Later work by Trimble et al., 2016 finds that even the share of African power utilities reaching operating cost recovery had fallen to about 50 percent, while barely 5 percent attained full capital cost recovery. Furthermore, there are evident tensions between the achievement of cost-recovery and affordability in the
market opportunities. To the extent that these uses reduce the fixed costs of finding markets and buyers, Internet access might therefore increase exports.

# **3 Data and Variable Descriptions**

I created a comprehensive dataset from the World Bank’s Enterprise Survey data that includes all available information for manufacturing _firms_ for all available countries and years. This results in a database of over 46,000 firms from surveys that took place between 2002 and 2008.<sup>5</sup> There are data from firms that belong to ten manufacturing sectors, in more than 100 developing countries, and five different regions of the world. Table 1 presents a breakdown of the data.

The World Bank’s Enterprise Survey is intended to capture business perceptions of the biggest obstacles to enterprise growth, the relative importance of various constraints to increasing employment and productivity, and the effects of a country’s business environment on its international competitiveness. This comprehensive survey collects data from key manufacturing and service sectors in every region of the world. It uses standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across the world’s economies. Enterprise Surveys use either simple random sampling or random stratified sampling. The first rounds of surveys used a simple random sampling methodology whereas surveys done on or after 2006 use a stratified sampling methodology. Surveys that use the stratified sampling methodology use three levels of stratification: industry, establishment size, and region. Under stratified random sampling unweighted estimates are biased

> 5 There are over 46,000 observations from surveys in 106 different developing countries. However, there is many missing data. Missing data vary depending on which questions the firms were able and willing to answer. The sample of firms in the regressions that follow varies depending on the data that is available. The more variables that are used, the smaller the sample becomes.

8
maximization objective and the strategy of choosing locations based on local comparative advantages.

In sum, the literature finds that China’s ODI tends to be attracted to larger markets and rich resource areas just like other countries’ ODI. What differentiates Chinese investors is that they are more willing to invest in risky environments. At the firm level, evidence shows that more productive firms and less financially constrained firms are more likely to invest abroad. Investing abroad also makes investing firms more productive and grow faster. While studies on the determinants of China’s ODI are extensive, relatively little work has been done to evaluate the impacts of Chinese ODI.

# 2.4 Reports on BRI

Existing economic analysis of the BRI is very limited. Most of the available discussions are descriptive reports. For example, Johnson’s (2016) CSIS report provides a general assessment of the BRI and discusses the benefits of BRI to China. It also documents the responses from participating and non-participating countries. The Economist (2016) published a report on BRI countries using data from InfraPPP and Competitiveness Group/Long-term Asset Infrastructure to provide a general outlook of the ongoing infrastructure projects in each BRI country. American Enterprise Institute’s Scissors et al (2017) also provides a descriptive summary of China’s outward investment since the introduction of the BRI and argues that China’s increasing construction projects in BRI could become costly for recipient countries to maintain. Similar concerns have been expressed by Dollar (2017), who observes that most of China’s ODI still flows to rich developed countries. He also points out that Chinese investors’ willingness to take risk will benefit BRI countries. A similar point is made in another report by the Economist (2017) which characterized BRI countries as high opportunities but also high risk countries. UNDP China et al (2017) suggests that the initiative can help BRI countries achieve industrial diversification and economic growth and discussed the conditions for achieving the Sustainable Development Goals.

# 3. Data and Stylized Facts

In this section,
39.7<br>34.0<br>5.4<br>3.8<br>1.6<br>Total LE-SE HE-SE Emplo Total LE-SE HE-SE Emplo<br>yer yer<br>40<br>80<br>30 60<br>20 40<br>10 20<br>0 0<br>60<br>10<br>8 40<br>6<br>4 20<br>2<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average including Mozambique, Zambia and Zimbabwe except for mobile money which is not available for Zimbabwe.

All three informal firm types identify similar motivation for not being registered with authorities, though the top rationale differs by firm type. Employers mostly point to the lack of information about how to register as an impediment, while also citing low benefits from 58 For HE-SE we do not find a statistically significant association. This is due to the education regressor, which is likely positive correlated with having a bank account. When the education dummies are dropped, we find a positive relationship between having a bank account and HE-SE.

32
the most attractive emerging economies for FDI.<sup>17</sup> Thus, Jordan experienced significant increases in FDI which have the potential to generate spillovers. Indeed, the World Bank (2012) attributes Jordanian growth over the period in no small part to these FDI inflows (with half of total investment coming from foreigners). Second, growth and FDI in Jordan were negatively impacted by the global financial crisis in 2009-2010. The external shock led to sudden stop in foreign investments which generated a quasi natural experiment allowing to test for the impact of MNE exits and thus the nature of FDI spillovers. After 2009, FDI into Jordan declined substantially and many foreign subsidiaries exited in 2009 and 2010 as MNEs adjusted their portfolios to reduce exposure to high-risk investments in developing countries. From Table 2, which presents the summary statistics, we can infer that the number of foreign-owned establishments, implying at least 10% foreign ownership, fell from 338 to 142.<sup>18</sup> As can be seen from Table 3 this observation is confirmed when looking at the share if foreign ownership considering all companies (including not foreign owned) and disaggregating the sample to the industry level. Interestingly, for foreign owned firms the share of equity hold by foreigners was quite high with roughly 70% in both years.

Nevertheless, even during the crisis, some MNEs continued to enter and expand the size of their Jordanian investment.<sup>19</sup> Thus, the Jordanian data has both increases and decreases in FDI, with this churning necessary for us to compare persistent and transitory spillovers. Third, Jordan is a setting in which local firms may be poised to take advantage of the spillovers. Overall, Jordan was a low middle income developing country that grew strongly over the last decade based on market-oriented reforms and a favorable external environment becoming a upper middle income developing country as of 2010. This suggests that its firms have the absorptive capacity to take advantage of spillovers while remaining sufficiently far from the technological
gay, bisexual, transgender, and intersex (LGBTI) individuals in India shows that the cost of such discrimination in employment and health sectors in the country could be as high as US$30.8 billion (Badgett 2014).

Discrimination against ethnic, religious, and sexual minorities is widely documented by international institutions and human rights groups. The most obvious manifestations of such divisions are the ongoing civil conflicts and the surge of the extremist group Daesh in the Middle East, which followed the Arab Spring and are, arguably, the consequence of deep-rooted hostility between Shia and Sunni Muslims **.** The immediate and long-term economic consequences of those wars in the region are incalculable. Similar struggles have affected Afghanistan, the Central African Republic, Pakistan, South Sudan, and Sudan for many years (Lattimer and Verbakel 2015). But even in non-war-torn countries, minorities often are the people most discriminated against. Following the economic downturn of 2008, racial discrimination in the workplace increased, posing, according to recent International Labour Office studies, tough questions about policies that have existed in the past decade (ILO 2011a). In the report titled _State of the World’s Minorities and Indigenous Peoples 2015_ , Minority Rights Group International reports that in Southeast Asia in 2014, minorities were evicted to make way for infrastructure and high-income housing developments in urban centers in Cambodia and Thailand. Similarly, in Myanmar, the Rohingya urban minority populations in Sittwe were at times openly discriminated against and violently targeted (Grant 2015, 148).

Discrimination often seems to go hand-in-hand with poverty and marginalization. In Vietnam, World Bank data show minorities face more barriers to obtaining access to education, credit, land, and the marketplace than do the Kinh majorAnti-Discrimination Law and Shared Prosperity 7
in labor income or remittances as strictly “causing” the observed poverty reduction. Nevertheless, this accounting exercise is useful in identifying empirical regularities and focusing attention on factors that are quantitatively more important in describing distributional changes. The rest of the section attempts to put into perspective magnitudes of changes in the three identified main drivers of poverty reduction: remittances, labor income, and demographic factors.

## **_Migration and remittances_**

Nepal has experienced a drastic increase in remittances received from abroad since the late 1990s (World Bank 2006; World Bank 2013). While work-related emigration is not a new phenomenon in Nepal, what characterizes Nepal’s emigration pattern in recent years is the massive outflow of labor force to destinations other than India, which had been by far the most popular destination for Nepali migrant workers since the beginning of the 19<sup>th</sup> century (Seddon 2005). Based on data from 14
## 4.4 Democracy and Development

Finally, to rule out the possibility that the results are driven by subgroups of countries with extreme characteristics, we restrict the sample in two ways. First, we exclude highly autocratic countries, de...ned as those whose Polity IV score in 1990 takes the extreme value –10 ("strongly autocratic" in the language of the score producers). Second, we exclude highly developed countries, de...ned as those that are classi...ed by UNDP in 1990 as having “very high human development”based on the country’s Human Development Index (HDI). The two subgroups of countries are listed in Table 14. Note that the most developed countries are also the most democratic.

Tables 12 and 13 present the results with the restricted samples. The tables report the two early D-GMM baseline speci...cations for each of the three measures of ...scal performance. Overall, the prior patterns are preserved, strengthening the support for hypotheses (H1)(H3). The government balance regression in Table 12 column (2) shows stronger MTEF e¤ects both in magnitude and in statistical signi...cance. This result seems consistent with the notion that MTEFs as commitment devices are particularly well suited for democratic settings which Mueller and Stratmann (2003) argue are more prone to ...scal indiscipline.<sup>34</sup> The results in Table 13 excluding the most developed countries are comparable to the baseline results in Tables 6, 8, and 10, both in magnitude and statistical signi...cance, providing evidence that the MTEFs’ positive e¤ects on ...scal performance are not a phenomenon speci...c only to the most developed countries.<sup>35</sup>

# 5 Conclusion

In the last two decades more than 120 countries have moved toward a multiyear budget process. Although there has been much debate in the literature
Finally, the appendix section presents in detail the case of Argentina and provides a literature review.

# **II. Data and** Methodology

# II.a Data Description

Our sample contains data on firms from seven emerging economies: Argentina, Brazil, Mexico, Indonesia, Malaysia, South Korea, and Thailand. The countries in the sample are of particular interest, since they have undergone periods of financial repression, followed **by** financial liberalization and crises. Data on firms' balance sheets come from _two_ sources, the corporate finance database of the International Finance Corporation **(IFC)** and WorldScope. IFC has complee data for the 1980s; WorldScope has a large data set for mid and late 1990s. The data set contains a total of **1,973** firms. After removing outliers and firms that are in the sample for less than three years, we are left with around **800** firms., To compare the pre-liberalization period (mainly the 1980s) with the postliberalization period (mainly the 1990s), we combine data from both sources. Our sample comprises annual balance sheet data of publicly traded firms, from **1980** to **_1999.s_** Previous work on corporate finance, notably Demirgiig-Kunt and Maksimovic **(1995** and **1998)** and Aivazian, Booth, Demirgi-Kunt, and Maksimovic **(1999),** use similar data but only for the 1980s. We also add the case of Argentina, which was not studied before.

The data set contains detailed information on the capital structure of firms, but it does not include sources and uses-of-funds statements. We exclude from the sample liabilities.

**_5_**
# **1 Introduction**

Market concentration has garnered considerable attention in both media and academic discourse since the onset of the global financial crisis. Although the focus of the debate has centered on the United States, where fewer, larger, more productive firms account for increasing shares of economic activity over time (Philippon & Gutierrez 2018, OECD 2018, Grullon et al. 2019, Autor et al. 2020, De Loecker et al. 2020, Bessen 2022), there is evidence that concentration has been rising also in other advanced economies (OECD 2018, Affeldt et al. 2021) and, to a smaller extent, in selected emerging economies (Akcigit et al. 2021). Theory and a modest body of evidence suggest that market concentration tends to reduce capital investment (Guti ́errez & Philippon 2017); however, expectations and trends are unclear when it comes to foreign direct investment (FDI) - an activity typically undertaken by the few leading firms with a capacity to expand their operations across borders. Whether FDI is indeed becoming more concentrated across multinational enterprises (MNEs), in which countries and under what conditions, remains an open question, and a gap in our understanding of foreign investment dynamics.

This paper aims to bridge this gap by bringing new evidence to the question. Using granular data on FDI projects across the globe spanning two decades, we show that the share of the largest enterprises in total cross-border investment has steadily increased in the aftermath of the global financial crisis. This pattern is robust across various measures of concentration, modes of investment, source countries and targeted industries, regardless of the overall trajectory of aggregate FDI flows, which have grown in several regions during the same period. The surge in foreign investment concentration has been particularly pronounced in developing economies, reaching new highs in the last two years, with destination economies in Asia leading the way. This trend seems to be driven more by a declining number of investing firms in developing countries rather than rising project capital expenditures. The opposite
## **Figure 1: Overview of IPC distributions for 15 all countries**

The individual graphs show the entire distribution of IPC1 (green), IPC2, (yellow) and IPC3+ (red) for all 15 countries based on FEWS NET data. This distribution is based on the population-weighted IPC levels in subnational administrative regions. Prior to 2016, the reports were reported four times a year, afterwards three times a year (the vertical lines demarcate the reporting dates). The vertical axis runs between 0% and 100%.

<!-- Start of picture text --> Afghanistan Chad Ethiopia<br>Guatemala Haiti Kenya<br>Malawi Mali Mozambique<br>Niger Nigeria Somalia<br>South Sudan Uganda Zimbabwe<br>IPC1 IPC2 IPC3+<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text -->

# **2 Data**

In our study we rely on the Integrated Phase Classification Acute Food Insecurity (IPC) framework for the definition of famine risk and food insecurity (see Table 1). We obtain the data from FEWS NET as shown in Figure 1. These data are reported on the first-level administrative country subdivisions (districts).

One of our main innovations is to model food insecurity distributions for a given country, rather than a single index metric.1 An example for this would be the established IPC framework, which classifies districts in a country based on the IPC stage the worst 20th percentile of the population finds itself in. For illustration, consider the two populations in Figure 2. The left population is distributed as 60%, 10%, 10%, 10% and 10% in increasing severity phases from IPC1 to IPC5. The overall IPC phase for the entire region would, applying the standard classification threshold of 20%, be IPC4, as the
. BOOST dataset is compiled based on the raw data extracted from the national Financial Management Information System (FMIS). A typical BOOST consolidates the raw data into one single, coherent database that can be analyzed along various budget dimensions – administrative levels, functions, costing items, activities, programs, budget entities, as well as sources of financing of government expenditures.

About 18% of general government spending in 2017 or MDL 9.681 billion went to the provision of education in the country, while public spending on health care absorbed 13% or MDL 7.268 billion of total government spending in the same period (see Table 5). The lion’s share of education expenditures is channeled through the budgets of local governments, which collectively account for more than 3⁄4 of total spending, according to the 2017 sector execution data, with the remainder being channeled through the State budget. On the contrary, health care expenditures are more centralized with 86% of health care spending being directed through the National Health Insurance Company (NHIC). Meanwhile, the State budget accounts for about 12% of total spending on health, with the remaining of 2% going to local governments.

**Table 5. General Government Expenditure by Function and Level of Government, 2017.**

|**General Government Expenditure**|**SSFs**|**State**|**Local**|**Total**||
|---|---|---|---|---|---|
|National government functions||4,464,949|1,241,620|5,706,569|10%|
|Defense||557,406|10,854|568,260|1%|
|Public order, safety and judicial authority||4,021,082|20,387|4,041,469|7%|
|Economic activity|
could be transformed even more if the family members did not have to remain behind, and millions of dollars of remittances were instead converted into internal transfers within intact families.

# **3 But migration (and the prospect of migration) can change the family**

The model above follows much of the literature in taking the family unit as fixed when making migration decisions. But there are a variety of ways that migration can change how families form and dissolve, and how we conceptualize the family.

## **3.1 Migration and the prospect of migration can affect whether a marriage occurs in the home location**

Individuals who are thinking about migrating may factor this into their decisions about whether to form romantic attachments to people in the home location. For individuals who are in relationships, when migration opportunities increase, this can cause a reevaluation of the relationship. Carter and Wozniak (2023) conduct an event-study analysis to examine the impact of exogenously determined long-distance relocations of U.S. Army soldiers on marriage rates, finding that an additional move over a five-year period increases the likelihood of marriage for them by 14 percent, with marriage rates increasing sharply around the time of the move. They suggest that this comes in part from migration lowering the cost of evaluating a potential marriage partner. Their story is that both involve making long-term decisions about what the future will look like, and migrating causes individuals to spend time thinking about the future.

## **3.2 People can migrate specifically to get married**

Migration for the specific purpose of getting married can occur both with internal and international migration. Rosenzweig and Stark (1989) show how the marriage of daughters in India to kinship-related households in other villages can be seen as part of implicit inter-household risk-sharing agreement. They document that in the 1981 Indian Population Census, 80 percent of individuals residing in a place other than their place of birth were 12