rafmacalaba/datause-encoder-data huggingface dataset:
# I. FSAL **Characteristics**

# (A). Types of Reform

Reform identification in this paper is based on a recent OED methodology to identify the presence or absence of policies in Bank-assisted financial sector reform programs across countries.<sup>5</sup> OED classified financial sector policies in sixteen broad categories. The categories, which OED grouped according to their major objectives, are given below:

- (a) _Removal of Distortions:_ Interest rate liberalization Indirect monetary instruments Dismantle directed credit Capital account liberalization

## (b) _Increased Competition_

Bank pnivatization Competition laws Differential taxation/regulation of banks Foreign ownership laws _(c) Improved Financial System Infrastructure_ Central Bank law Prudential regulations, banking law Non-bank regulations Money market<sup>6</sup> Rights and obligations of financial agents

## _(d) Strengthening of Individual Financial Institutions_

Bank supervision Bank re-structuring-re-capitalization Bank institutional reforms See _Financial Sector Reform: A Review of World Bank Assistance,_ OED, preliminary draft, February, 1997. I am deeply indebted to OED and, in particular, Nicolas Mathieu, for providing the data that summarizes the individual interventions. Without it, this project would not have been possible.

6 Although the OED draft does not explicitly state that there is no overlap between the "money market" category and "indirect monetary instruments," it appears that the categories are mutually exclusive.

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been questioned by analysts who have dug into the reports (Eadson, 2008). Given that many of the actions generating greenhouse gas emissions are taken at multiple scales, activities could be organized at multiple scales, ranging from households, farms, and cities at a local scale to regions within a state, states, regional units that cross state boundaries and the globe (Kates and Wilbanks, 2003).

# **What Efforts to Reduce Greenhouse Gas Emissions Are Being Taken**

# **at Less Than a Global Scale?**

It is not possible to do a full inventory in this paper of all of the projects going on across the world at multiple scales. What I can do is focus on some of the projects that have been organized at a local level, at the level of state governments in the United States, and at a regional level in Europe, and discuss some of the efforts to reduce emissions substantially.

# **_Local-Level Projects and Alliances to Reduce Local-Level Externalities_**

One of the most successful efforts made by many local governments across the United States has been to reduce the level of fine-particulate air pollution (which in some cases has reduced greenhouse gas emissions as well). Pope et al. (2009) have just completed a major study of the impact on life expectancy of particulate matter in the air sampled over the period from 1979 to 2000 for 51 metropolitan areas (including more than 200 counties).<sup>1</sup> Metropolitan areas across the nation have reduced air pollution levels by one-third. Pope et al. also found that increased life expectancy during this period was associated with reductions in fine-particulate air pollution

> 1. This study also illustrates the importance of effective monitoring efforts. The U.S. Environmental Protection Agency (EPA) maintained the Inhalable Particle Monitoring Network from 1979 to 1983 for 61 metropolitan areas. The monitoring effort was not continued until the passing of the National Ambient Air Quality Standards for Particle Pollution in 1997. EPA then developed the Aerometric Information Retrieval System, which has continued the monitoring process. The maintenance of reliable, comparative information over time is a very important step in coping with large-scale externalities, both to assess who is complying with policies and to compare the effectiveness of diverse strategies in different units.

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facilities’ traditional input based budgets were increased by the average amount of P4P payments to treatment facilities on a quarterly basis during the entire 23-month treatment window. As a result, treatment and comparison facilities had the same levels of financial resources on average throughout the study. Therefore, the differences in outcomes between the two groups at followup must be attributed to the difference in incentive structures and not to a difference in available financial resources.

# **c. Estimation**

Given the reassignment of districts between the treatment and comparison groups before the start of the study, and the limited number of districts that could be assigned to the treatment and comparison groups, we view our study as quasi-experimental. While the sample is balanced at baseline on outcomes and characteristics, it is possible that the reassignment of districts was correlated with something unobservable to us and related to health outcomes. However, redistricting took place within the context of a decentralization agenda that was led by the Ministry of Local Government, and we find no evidence that it was driven by or related to health outcomes (MINALOC 2004).<sup>8</sup> Therefore, we think it is likely that any relevant unobservable factors were likely to be invariant over the time period of the intervention.

Therefore, we will use the difference-in-differences methods that control for unobserved time invariant characteristics. This method compares the change in outcomes in the treatment group to the change in outcomes in the comparison group. By comparing changes, we control for observed and unobserved time-invariant characteristics as well as for time-varying factors that are common to the treatment and comparison groups. We show below that the final assignment to the treatment and comparison groups is orthogonal to pre-intervention observable variables, leading us to assert that there should be no correlation between this assignment and nonobservables that drive program effects.

All of the individual outcomes relate to pregnancies and many women do not give birth in both waves of the survey. Hence, we treat the 2006 and 2008 household surveys, described below, as repeated cross-sections and estimate the following regression specification of the difference-in-difference model for individual outcomes:

> 8 According to MINALOC 2004, the objective of the decentralization was to enhance institutional development and capacity building for responsive local governance., to develop an efficient transparent and accountable fiscal and financial management system at local government and grassroots level.

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16 The other way to make this calculation is to assume a wage increment to education (taking the micro evidence discussed below at face value) and calculate the fraction of the labor force in each educational attainment category to derive the educational capital share. Table 3 shows the results of two calculations. The top half shows the fraction of the labor force in various educational attaintrnent categories in various regions. If one assumes a wage premia for each of these categories then one can calculate the share of the wage bill due to educational attainment.

<!-- Start of picture text --> K<br>S (W, - wo) ai<br>Educational capital share = "°<br>wL<br><!-- End of picture text --> where i represents each educational class and _a,_ are the shares of the labor force in each educational attainment category. Row A of table 3 shows the share of the wage bill that is educational capital in various regions if it is assumed that the wage increment to a year of schooling is 10 percent at all levels of education<sup>28</sup> . Under that assumption the educational share of the wage bill varies across regions from 26.3 percent (in SSA) to 62.1 percent in OECD and is 36.4 percent for the developing countries as an aggregate. Row B shows the share of the wage bill due to educational attainment if it is assumed that primary has a higher impact than secondary and secondary than tertiary. Under this assumption, the share is 49 percent for all developing countries and varies from 38 percent (in SSA) to 73 percent (in the OECD).

**28** In the Psacharopoulos (1993) report the developing country average was 11.7 percent.
locative role of the financial sector (Fisman and Love 2007). We compute these different financial needs for narrowly defined industrial sectors and match them with the disaggregated product-level data on international trade. Given that the explored transmission channels might operate differently in the short term and long term, we examine export survival at time horizons ranging from one to ten years. We also allow for the possibility that the size and the activity of domestic stock markets play different roles in shaping export survival at the product level.

Our three main empirical findings are as follows. First, well-developed stock markets rather than strong banks are key in promoting export survival of products from industries with a high share of intangible assets. This result supports the notion that stock markets have an advantage over banks when it comes to promoting innovative sectors of the economy that face increasing returns to scale and a pronounced technological change (see, e.g., Allen 1993; Aghion et al. 2004; Brown, Martinsson, and Petersen 2013, 2017; Hsu, Tian, and Xu 2014). Second, _active_ stock markets play the most important role for the export survival of products with high liquidity needs, possibly reflecting the role active stock markets have in monitoring the efficiency of working capital management (see, e.g., Gill and Biger 2013; Ben-Nasr 2016; Zeidan and Shapir 2017). By contrast, _large_ stock markets are often dominated by banks in promoting these products, especially when it comes to long-term export survival. Third, trade credit operates as a substitute for external finance from banks but not from stock markets. In addition, this substitution works only for _long-term_ export survival, suggesting that the channel can successfully operate only alongside well-established export links where exporters and their foreign customers already enjoy the mutual trust necessary for an extensive use of trade credit.

Overall, our results suggest that banks, large stock markets, and active stock markets all play different roles in shaping product-level export survival, depending on the examined transmission channel and the time horizon under consideration. This relates our work to three distinct strands of the existing literature.

First, the paper is related to the existing literature examining the distinct roles of banks and stock markets. This line of research has investigated the importance of banks 2
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typically captured by large firms and thus tend to substitute private investment. Furthermore they provide an unfair advantage to established firms as opposed to emerging firms and therefore are detrimental towards new entry or increase in entrepreneurs over time.

This study empirically addresses the central hypothesis of whether a reallocation of government spending from private subsidies to social and public goods under credit market imperfections encourages entrepreneurial activity. Our analysis exploits the GEM database, which covers 50 developed and developing economies between 2001 and 2009 and which includes all startups, regardless of their legal status. The Global Enterprise Monitor survey (GEM) covers at least 2,000 individuals annually in each country. The individual level data (approximately 650,000 usable observations) are generated through surveys which enabled the production of stratified samples, drawn from the data which correspond to the whole working age population in each participating country. Our empirical approach is to exploit individual level variation in entrepreneurship activity and cross-country variation in government spending. To achieve this we combine cross-country microeconomic individual level data with country-specific government spending data from the IMF’s Government Financial Statistics database (GFS). Most of the literature has focused on total entrepreneurial activity which includes self-employment. We follow Estrin and Mickiewicz (2011) and focus on entrepreneurs that aspire to create 10 or more jobs, which is important for economic growth. We include several individual level controls from the GEM data set and macroeconomic controls from the World Bank Development Indicators (WDI). Concerns about simultaneity bias are ameliorated in our use of aggregate explanatory variables because the individual decision of a potential entrepreneur should not affect country-level institutions or economic development. However, endogeneity may arise because the mean country-level individual entry outcome may affect some of the country-level variables, so we lag all our macroeconomics and institutional variables by one year.

Our study confirms findings in the literature that government size, measured via total spending, has a negative effect on entrepreneurial activity. However, in contrast to the literature that has cited social 4
# **From Fiscal Cyclicality to Fiscal Stress: The Role of Asymmetric Public Consumption Rigidities in Emerging Markets**

**Daniel Riera-Crichton, Pilar Ruiz Orrico, Guillermo Vuletin**<sup>**1**</sup>

Keywords: Business Cycle; Cyclicality; Downward Rigidity; Public Consumption JEL: E320; E620; H500

> 1 Riera-Crichton: Bates College; email: drieracr@bates.edu Ruiz Orrico: World Bank; email: <u>pruizorrico@worldbank.org</u> Vuletin: World Bank; email: gvuletin@worldbank.org
used in regional studies in LAC (such as Cord et al. 2017; Ferrerira et al. 2012; Gasparini, Cicowiez, and Escudero 2013; and Vakis et al. 2016). This allow us to estimate income mobility for all LAC countries for which data are available using the exact same approach as the one proposed in this paper.

Results indicate that the methodology performs well in all the settings. In general, mobility estimates fall within the 95 percent confidence intervals of the true panel mobility. More importantly, the paper finds that a simple specification for the underlying income models that includes few time-invariant regressors is sufficient to obtain accurate estimates of mobility. Finally, the validation confirms that the method performs well at estimating household-level income growth (and not just join probabilities) between two rounds of surveys.

The next section summarizes the technique. Section 3 discusses the data and the empirical approach. Section 4 introduces the main validation results. Section 5 presents mobility results for 17 Latin American countries. Finally, Section 6 concludes.

# **2. Methodology**<sup>**3**</sup>

Let yit be round _t_ household log per capita income (where _t_ =1, 2) of household _i_ and _z_ the poverty line. The goal is to estimate the change in incomes between both periods (∆yi = yi2 −yi1) as well as all possible poverty dynamics: the proportion of poor households who escaped poverty (Pr(yi1 < z and yi2 > z)), who remained poor (Pr(yi1 < z and yi2 < z)), who became poor (Pr(yi1 > z and yi2 < z)), and who remained non-poor (Pr(yi1 > z and yi2 > z)).

The linear projection of income on a vector _xit_ of time-invariant characteristics (e.g., education of the household head under the assumption that the identity of the household head remains constant between rounds of data) of household _i_ in round _t_ is determined by where  _it_ is an error term. We can introduce superscripts to denote household observations surveyed in a given moment in time. Then, we can predict first round “unobserved” incomes of households surveyed in the second round (ŷi12 ) by multiplying their time-invariant characteristics 2 (xi1 ) and the first-round Ordinary Least Squares (OLS) estimates of parameters β̂1. We can obtain

> 3 This section largely relies on Dang and Lanjouw (2013), Dang et al. (2014), Cruces et al. (2015), and Vakis et al. (2016).

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BEU Green Deal CBAM EU Green Deal + CBAM
**Figure 6: Domestic retail prices and production of sugar**

_Source: Data is from KNBS, author calculations. Note: Domestic production of sugar has been smoothed using a four-month moving average as before._

**Subsequent to the sugar crisis international prices continued to fall, but after an initial period of decline, Kenyan prices rose slightly again most likely due to further local production shocks.** Looking back at Figure 2, Kenyan prices did not drop off as steeply as international prices after the crisis, and indeed even began to rise slightly shortly after the crisis.  While we do not have a sufficiently long period of import or cane production data to examine this, we can see from Figure 6 above that domestic production took another historically large dip from mid-2012, suggesting that further local production shocks were likely the cause for sugar prices not continuing their post-crisis decline.

**A combination of local production shocks and barriers to trade provide a plausible explanation for the sugar price surge of 2011.** In general we could summarize the drivers of sugar prices in Kenya with the following points:

- Kenya is a high-cost producer of sugar, and does not produce enough sugar to meet domestic demand (there is a structural deficit), even at current (and historical) high prices.  Problems between the cane sector and the sugar manufacturing sector, extensive government involvement and uncertainty about the future may play a role in maintaining this situation<sup>45</sup> .

- Kenya has formidable trade barriers in place including exceptionally high tariffs (100 percent), COMESA import quotas and NTB’s including a licensing regime that allows discretionary blocking of any sugar imports.  These trade barriers prevent any real price competition from imports and restrict the process through which imports can ease tight supply in the context of a local production shock.  This makes Kenya susceptible to price crises.

> 45 It is difficult to guess how exactly government has influenced prices.  ODI (2010) reports that “government still intervenes in price setting intermittently (e.g. to hold prices down when there are sugar shortages due to a poor harvest during which the price would otherwise rise)”, but given government role in production (broadly accepted to be expensive relative to the region) it is hard to guess what prices would be under a competitive market structure.

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