rafmacalaba/datause-encoder-data huggingface dataset:
From Table 3 concerning the developing countries data, we see that, for any of the five variables used to measure regulatory performance, namely, mainline penetration ( _ml_ ), cellular subscription ( _cel_ ), mainline per employee ( _eff_ ), monthly subscription to fixed ( _p res_ ), and price of cellular ( _p cel_ ), there is at least one variable used to represent political accountability which significantly impacts it. Except when regulatory performance is measured by the monthly subscription to fixed, the sign of this impact is as can be expected, i.e., the higher the political accountability, the better the regulatory performance as reflected in higher output (increase in mainline penetration and cellular subscription), higher efficiency (increase in mainlines per employee), and lower prices (decrease in price of cellular). The apparently counterintuitive case where we find that higher political accountability (less risk of expropriation for operators and stronger checks and balances) leads to a higher monthly subscription to fixed service might in fact only reflect the extent of tariff rebalancing that typically takes place in developing countries during the early stages of the reforms. When we distinguish local accountability (regulatory governance) from global accountability, it is interesting to note that the latter is more often found to have a significant impact on regulatory performance. Nevertheless, in the cases when it is found to be significant, the effect of regulatory governance on regulatory performance has the expected sign, namely, a better regulatory governance leads to a higher output and a lower price.

The least we can say about the results obtained with the developed countries data set is that Table 4 which presents them does not convey the same messages. A general comment that should be made at the outset is that these results are poor compared with those obtained when the developing countries data set is used. Indeed, as can be seen from Table 4, some reasonable regressions could only be found when using either mainline penetration ( _ml_ ), cellular subscription ( _cel_ ), or monthly subscription to fixed ( _p res_ ) to measure regulatory performance. As to the impact of political accountability on regulatory performance, the only sensible results that could be recovered from the data on developed countries is a positive effect of regulatory governance ( _reg_ ) on cellular subscription ( _cel_ ) and a decrease in the monthly subscription to the fixed service ( _p res_ ) with a lowering of the currency risk 22
other hand, the impacts of climate change on yields of wheat and rice (C3 crops) varies more widely, with the possibility of large yield gains in Northern latitudes under global warming. In a recent panel study of the impacts of climate change in 2050 on Sub-Sahara Africa, Schlenker and Lobell (2010) find that maize is most severely affected and millet the least affected by climate change. Nearly all countries in the region experience yield losses in the expected scenario, with overall losses for cereals and oilseeds in the range of 10%; in the worst case outcome (5% probability), yield losses for most cereals and oilseeds in Southern Africa exceed 50%.

The foremost advantages of statistical approaches to analyzing climate impacts on agriculture (see also Table 1) are that they the data requirements are much lighter, they can be applied at a national or global spatial resolution, measures of goodness of fit can provide confidence that the model is faithfully capturing historical changes, and out-of-sample prediction can test the model’s validity. The greatest limitation of these studies focusing on yield changes is the absence of adaptation responses. One approach is to embed these yield “shocks” in an economic model which then allows for adaptation in the context of profit maximizing responses on the part of representative farmers (e.g., HBL 2009). However, it would be attractive to obtain estimates of adaptation alongside the climate impact analysis. This is leads us to the so-called Ricardian, or hedonic, analysis of climate change.

# _Hedonic approaches_

All the yield based studies of agricultural response to climate change assume that the crops continue to be grown in the same locations, thereby failing to account for potential adjustments in the mix of agricultural activities at any given location. The hedonic approach, popularly known as the “Ricardian” approach, and spearheaded by Mendelsohn, Nordhaus and Shaw 11
indicators used. Fourth, we discuss the challenges of causal identification and our proposed solutions. Fifth, we present and discuss our results, including microeconometric estimates of direct impacts and macrosimulation estimates of indirect impacts. We conclude by highlighting major policy implications and the next steps for further research.

# 2. INSTITUTIONAL CONTEXT AND REFORM DESIGN

16. The GOB has steadily improved the procurement environment since 2002, with support from two consecutive World Bank–funded projects (Public Procurement Reform Project I and II). In 2002, the government established the Central Procurement Technical Unit (CPTU), a national procurement policy unit. The government enacted a Public Procurement Law in 2006 and associated Procurement Rules in 2008, simplifying procurement procedures and making them more transparent.

17. As part of this broader procurement reform agenda, in 2009, the government commissioned the design of a custom-made e-procurement system, intended to improve access to public tenders and to speed up contract awards. Bangladesh’s e-procurement system is one of the world’s most advanced and comprehensive. It allows for a comprehensive, completely paperless management of the public procurement process starting from annual procurement plans to the contract award. It requires procuring entities to advertise tenders electronically, and firms to submit bids online. All administrative decisions about competing bids, from the opening of the bids to the awarding of contracts, are also recorded online. The system hence generates uniquely rich electronic records on the procurement process, which permits the tracing of decision-making processes in detail, such as the composition of bid-evaluation committees and the choices made by individual committee members.

18. The initial e-procurement roll-out between FY2011-12 and FY2015-1610 focused primarily on the four largest government agencies with the largest number and value of contracts. The aforementioned three—BWDB, LGED and RHD—and the Bangladesh Rural Electrification Board (BREB). BWDB, LGED and RHD share a similar organizational structure, with procuring entities at multiple hierarchical levels. Figure 1 illustrates this structure for the example of LGED. Each of the three agencies has 64 districtlevel procuring entities, one for each district. These district PEs are the focus of this study, because they are responsible for the largest share of tenders, both in number and value. They also shape comparable clusters, with similar tasks and levels of authority. BREB was excluded from the study, as it only has a few central PEs.

> 10 Please note that our dataset used for the analysis also includes 2 additional years after the initial roll-out (FY16-17 & FY 17-18) in order to observe PEs once they fully adopted e-procurement.

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# **1. Introduction**

Intimate partner violence (IPV) is pervasive, with about one in three women in richer as well as poorer countries reporting abuse over their lifetime (Devries et. al. 2013). It is increasingly being recognized as an important part of the development agenda (WHO 2014, Michau et al. 2015, UN Women 2015a). While there were no targets for violence against women in the Millennium Development Goals, the new Sustainable Development Goals explicitly call for the “elimination of all forms of violence against women and girls” (UN Women, 2015a). IPV tends to be higher in low income countries, being 65.64% in Central Sub-Saharan Africa and 41.73% in South Asia, compared to 27.85% and 21.32% in Central Europe and North America, respectively (Devries et. al. 2013). It not only harms the physical and mental health of women, it also harms children (Ellsberg et al. 2008, Aizer, 2011, Rawlings and Siddique, 2019). In the last decade, many countries have implemented laws criminalizing IPV, but the extent to which such laws are upheld varies hugely (OECD Development Centre, 2014).

This paper considers how IPV varies with macroeconomic conditions, in particular, male and female unemployment rates, using comparable data for 31 developing countries. It considers male and female unemployment shocks, each conditional upon the other. Since these are correlated with one another but may have different impacts on violence, omitting either one will tend to bias the coefficient on the other and this study shows that this is the case.<sup>2</sup> Of particular interest is how violence against individual women is associated with improved aggregate employment prospects for women. This is important because many interventions in developing countries, for example, microcredit schemes and skills training programmes, are designed to increase women’s employment. The direction

> 2 Independent variation in male relative to female unemployment rates may arise for instance because recessions have larger impacts on sectors of the economy that are more intensive in male-labour (Anderberg et al. 2016; Eaton et al. 2016) or on account of added worker effects that lead to more women seeking work during recessions (Bhalotra and Umana-Aponte 2015 and references therein).

2
percent to the average airline ticket price.<sup>30</sup> Based on the studies summarized in Table 2, it is assumed that the price elasticity of demand (i.e., the percent change in travel demand in response to a one percent increase in the ticket price) is -1.0. In this case, a general 2 **–** 4 percent increase in ticket prices might therefore be expected to reduce overall travel demand **–** for affected airlines by around 2 4 percent.

**Table 2. Estimates of the Price Responsiveness of International Air Travel by Trip Type**

|Type of trip|Medianprice-elasticityestimate<sup>a</sup><br>Range|
|---|---|
|Long-haul international business|-0.27<br>-0.48 to -0.20|
|Long-haul international lesiure|-1.00<br>-1.70 to -0.56|
|Long-haul domestic business|-1.15<br>-1.43 to -0.84|
|Long-haul domestic leisure|-1.10<br>-1.23 to -0.79|
|Short-haul business|-0.70<br>-0.78 to -0.60|
|Short-haul leisure|-1.52<br>-1.74 to -1.29|

Source: Gillen et al. (2002). Note:<sup>a</sup> Price elasticity refers to the percent change in travel demand in response to a 1 percent increase in ticket price.

36. **Emissions pricing would induce other mitigation options beyond this reduction in demand** . These include more efficient operations (e.g., optimizing flight paths and reducing airport congestion, reducing average time spent idling on runways or circulating airports through advanced communication, navigation, and air traffic management) and improved efficiency of new planes (e.g., improving aerodynamics to reduce drag, more efficient engines, incorporation of lighter materials into the airframe). It is difficult to estimate the future development and deployment costs of many of these strategies, and hence the extent to which they would be incentivized by higher fuel prices over the longer run.<sup>31</sup> For cars and light trucks (at least in the United States), it seems reasonable to assume that a 1 percent increase in fuel prices will ultimately increase fuel economy by something in the order of about 0.2 percent (e.g., Small and Van Dender, 2006). For airlines the responsiveness might be lower, given already strong incentives to economize on fuel (which is expensive to carry) and ongoing efforts by ICAO to promote better fuel economy. Simply by way of illustration, suppose the fuel economy response is 0.1 percent (per 1 percent increase in the fuel price). Combining this with the assumed reduction in travel demand implies an overall fuel (and emissions) reduction in response to an 8 percent increase in fuel price of 3–5 percent. Given

> 30 Assuming that fuel costs comprise between 25 and 35 percent of total costs of airline (see GAO 2009), and assuming that the carbon charge is fully passed on to customers, an 8 percent increase in fuel costs leads to 2 **–** 4 percent increase in ticket price.

> 31 See, for example, Morris et al. (2009).

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(1982) Clandestine Employment: A Problem of Our Times. _The Underground Economy in the United States and Abroad._ Eds. V. Tanzi, D.C. Heath and Company. Lexington, Massachusetts.

De Soto, H.

(1989) _The Other Path._ Harper & Row, New York.

Frey, B., and H. Weck-Hanneman (1984) The Hidden Economy as 'Unobserved Variable'. _European Economic_ _Review,_ 26: _33-53._ Gupta, M.

(1993) Rural-Urban Migration, Informal Sector, and Development Policies: A Theoretical Analysis. _The Journal of Development Economics,_ June.

Gupta, P., and S. Gupta

> (1984) Black Economy: A Review of Methodologies. _The Unsanctioned Economy._

Huq, M., and M. Sultan (1991) 'Informality' in Development: The Poor as Entrepreneurs in Bangladesh.

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extraction may lead to institutional failures. Finally, Manzano and Rigobon(2001) argue that imperfect international capital markets allow countries experiencing commodity price booms to over borrow, eventually requiring policies that restrict growth when credit dries up during the inevitable downturns.

There is as yet limited consensus on the appropriate empirical proxy for measuring resource abundance. Learner (1984) argues that standard Heckscher-Ohlin trade theory dictates that the appropriate measure is net exports of resources per worker. Though this measure has been the basis for extensive research on the determinants of trade patterns (e.g., Trefler 1995, Antweiler and Trefler 2002, Estevadeordal and Taylor 2002)3 to date there has been essentially no empirical work testing its impact on growth.<sup>4</sup> A look at the unconditional correlation in figure la suggests that the most resource abundant country is Norway, followed by New Zealand, Trinidad and Tobago, Canada, Finland and Australia. Though these countries are mostly well-off, there is overall no obvious relationship between the Leamer measure and growth.

In fact, the only formal empirical tests for the resource curse are found in the work of Sachs and Warner (1995a, 1997a, 1997b, 1999, 2001a,b) who employ natural resource exports as a share of GDP as their proxy. Using cross sectional data employed previously by Barro (1991); Mankiew, Romer and Weil (1992); and DeLong and Summers (1991) across the period 1970-1990, they persistently find a negative correlation with growth, much to the alarm of many resource abundant developing countries.<sup>5</sup> Figure lb suggests that with this proxy the most natural-resource "abundant"

> 3Assuming identical preferences, a country will show positive net exports of resource intensive goods if if its share of productivity-adjusted world endowments exceeds its share of world consumption. Usually, the net exports are then measured with respect to the quantity of other factors of production, such as the labor force.

**4** It is worth mentioning that the cited references show that the HO model of factor endowments performs relatively well for natural resources net exports, but it performs less well for manufactures. The current debate in the trade literature revolves around the question of how the HO model might be amended (by considering, for example, technological differences across countries, or economies of scale) to help predict better the observed patterns of net exports across countries. But there is not debate about the use of net exports as a proxy for revealed comparative advantage in this literature.

**5** The other papers by Sachs and Warner (1995b, 1997b, 1999, 2001a, 2001b) contain the basic results of 1997a, at times using a slightly longer time span (1965-1990 instead of 1970-1989), and often including

3
given country. Among other things, the reports generally include a table that reports on the country’s financial structure broken down into institutional categories such as banks or pension funds. The aggregation level of institutional categories varies across reports. There is no standardized categorization of institutions; while one report may have “banks” as one institutional category, another report may have “private banks” and “state-owned banks” as institutional categories instead, which combined would be equivalent to the category “banks” in the former report. The table typically provides the following information for each institutional category: number of institutions, assets in (mostly) local currency units, assets as a percentage of total financial sector assets and assets as percentage of GDP. Note that not all reports report data in all four categories and while reports generally include a couple of years of historic data they may record data in one category for one year but not the next and often data just for one or two years are reported.<sup>5</sup> Using this financial structure information, we build a database from all financial structure information reported in table form in FSAP reports from the beginning of the program until mid 2009.

For some countries, more than one FSAP report is available. Unfortunately, the reporting structure is almost never the same as in the previous report(s) for the same country and crosschecks of the data revealed that the reported information is not even necessarily consistent across reports for the same country. We therefore assume that the most recent report contains the most accurate information and only keep observations from the most recent report available. Our final database consists of an unbalanced panel for 89 countries over the years 1995-2008. We convert any variables in local currency units into 2000 constant US dollars using exchange rates from the IMF’s International Financial Statistics.

While we have data available for a broader array of institutions, we focus on three types. First, low-end financial institutions which include credit unions, building societies, community banks, cooperatives, microfinance institutions, cash lenders, mutual banks, postal banks, rural banks, savings and loans institutions, and thrift banks. This category is supposed to capture nonbank institutions that serve the low-end of the market, including small and medium-sized enterprises. Second, specialized non-bank financial institutions which comprise – among others – finance companies, factoring companies, banks specialized in housing, merchant banks, and

> 5 See Appendix Table 1 below for data availability across countries and categories.

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rollout of the coffee mills across locations. We address this concern in two ways. First, we control for trends in observable determinants of coffee mills rollout by including baseline coffee intensity interacted with linear time trends. This is expected to purge our estimates of the direct effects of coffee cultivation on our outcome. In addition, since we focus on students born during the period of the massive expansion of coffee mills, we control for possible confounders such as access to transport infrastructure, with the inclusion of road density interacted with time trends (Hoynes and Schanzenbach, 2009; Hoynes et al., 2016).

Second, we present event-study results to show that absent the coffee mills, the performance of students in treated and control locations would evolve along a similar trend by estimating the following specification: However, the recent wave of literature on difference-in-difference with staggered treatment, as in the case of our study, suggests that using the conventional two-way fixed effects (TWFE) estimators for models with staggered treatment may lead to biased estimates due to the presence of negative weighting and heterogeneity in the treatment effects (De Chaisemartin and D’Haultfoeuille, 2018, 2020a,b; Goodman-Bacon, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2024). To address these challenges and ensure the robustness of our estimates, we use the "imputation" estimator by Borusyak et al. (2024) to estimate our event study. This estimator ensures consistency in the presence of the TWFE and also deals with issues of negative weighting (Acemoglu et al., 2022; De Chaisemartin and D’Haultfoeuille, 2020b).

# **5 Results**

We begin by presenting descriptive evidence on the trends in test scores between students in treated and control locations born before and after the opening of the (closest) coffee mill. This allows us to check the trends in test scores between treated and control locations before and after the opening of coffee mills, thus providing a validity check to our identifying assumptions. As shown in Figure 5, we observe a similar evolution in test scores between control and treated locations before the arrival of coffee mills. However, the test scores of students born in treated locations after the opening of coffee mills begin to outpace their counterparts in control locations, suggesting a potentially positive impact of coffee mills on students’ test scores. Results from the event study analysis presented in the subsequent sections provide additional validation.

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lower bribes for both privatized and state-owned enterprises. This interpretation is consistent with results from previous studies (see, e.g., Ades and Di Tella, 2000) and the results in this paper that suggest that openness to imports, which might also increase competition, is correlated with lower levels of corruption.

# **IV. CONCLUSION**

Rather than discussing the political, macroeconomic and cultural factors that affect the overall level of corruption (see, e.g., Ades and Di Tella, 1999; Treisman, 2000; Lederman _et al.,_ 2001), this paper primarily focuses on how characteristics of firms paying and receiving bribes affect the equilibrium level of bribes in the utility sector. Our conceptual framework suggests that characteristics of bribe takers (i.e., the rents available for extraction in the utility sector, the extent of competition in the sector and the penalty functions faced by utility employees) and bribe payers (i.e., the firm's willingness to pay bribes, the leverage that bribe takers have over the bribe payers, and the length of the payers relationship with the takers) should both be important. Further, the multiple-equlibria nature of corruption means that bribe payments in the utility sector should be higher in countries where the overall level of corruption is higher.

The empirical evidence from a survey of around 2000 enterprises in 21 countries in Eastern Europe and Central Asia is remarkably consistent with the conceptual framework. We find strong evidence that bribes paid to utilities are lower in countries with greater capacity and competition in the utility sector and where the utility has been privatized. On the side of bribe payers, enterprises that are more profitable, enterprises that have greater overdue payments to utilities and _de novo_ private firms appear to pay higher bribes. Macroeconomic and political factors that contribute to higher corruption at the national level also appear to increase bribes in the utility sector.

The results from this study suggest that countries can reduce corruption in the utility sector through utility privatization and increased competition in the utility sector. In addition to reducing corruption by easing capacity constraints, privatization might improve internal incentives to reduce corruption while competition might reduces the utilities' ability to demand fractionalization).

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