rafmacalaba/datause-encoder-data huggingface dataset:
helps keep the administration of courts and the judicial system within the judiciary. Increasing the budget of the judiciary does not correlate with an increase in resolution rate. Perversely, in their data the larger the budget the slower the disposition rate for both civil and criminal cases. Voigt (2012) reaches similar findings as regards the number and size of courts: there is insufficient evidence that more resources relate to greater effectiveness.

Third, there is mixed evidence that increasing the number of judges or courts – an indirect measure of access - increases judicial effectiveness. Dimitrova-Grajzl et al (2012) find that in the case of Slovenian district courts, judicial staffing has an insignificant effect on court output. That is, when endogeneity is addressed, the number of cases resolved by Slovenian courts does not statistically significantly increase with the number of serving judges. Furthermore, the results indicate that the primary driving force of case resolution in Slovenian courts of first instance is the demand for their services: ceteris paribus, the number of cases resolved increases with caseload. This in turn implies that judge productivity increases with caseload.

Garoupa et al (2010) conclude that promoters of court specialization in Spain for the purposes of reducing case duration should be cautious in their optimism. By analyzing the length of cases in family courts compared to regular courts in specific family matters, the evidence shows that there was similar duration in specialized family courts. Gouveia et al (2017) find no evidence that larger courts handle more cases in Spain.

Court specialization is not associated with more effective judiciary either. Beenstock and Haitovsky (2004) show that the number of case dispositions in Israeli courts is independent of the number of serving judges, and that “productivity,” as measured by completed cases per judge, varies directly with the caseload per judge. These results suggest that the productivity of judges is endogenous; for the same caseload judges complete more cases under pressure, and complete less when new judges are appointed. Coviello et al (2019), using data from Italian courts, do however find evidence for specialization leading to faster disposition times. They estimate that when judges receive more cases of a certain type, they become faster, i.e., more likely to close cases of that type in any one of the corresponding hearings.

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empirically—the removal of licenses requirements, the removal of technical requirements, and the reduction of fees and charges for administrative procedures.

Overall, 70 (35) of 1,874 districts, 22 (18) of 196 provinces, and 3 (2) of 25 states in Peru eliminated at least one regulatory barrier in 2014 (2013). Among these, eliminating operating license requirements was the most common regulatory change (Table 2). The extent to which different municipalities eliminated regulatory barriers to competition in 2013 and 2014 is illustrated in the map of Peru in Figure 2. It shows substantial variety in the reform municipalities. Notably, the map reveals significant differences between both years with several municipalities eliminating regulatory barriers to market entry and competition only in one of the two years.

Several establishments benefitted from more than one eliminated regulatory barrier within a year because the responsible district and the higher tier province municipality both eliminated barriers that are inconsistent with national law. On average, establishments located in reform district or province municipalities benefitted from 4.5 eliminated barriers in 2013 and 5.4 in 2014. In 2013, illegal licenses requirements were the most common type— establishments in licensereform municipalities benefitted on average from 4.1 eliminated illegal license requirements; establishments in technical- and fee-reform municipalities benefitted on average from the elimination of 1.9 and 1.02 such barriers, respectively. In 2014, the removal of regulatory barriers was more evenly distributed among these types. Establishments in license-, technical- and feereform municipalities benefitted on average from the elimination of 2.5, 4.1, and 1.6 such barriers, respectively. The fact that many establishments benefitted from more than one eliminated barrier allows us to also estimate the impact of the intensity of municipal reforms.

# **4.3 Matched census-reform data set**

We match the data on regulatory changes across the 1,800 municipalities in Peru in 2013 and 2014 with establishment census panel data from 2008-15, using the 6-digit location codes providing the exact location of establishments at the district-municipality level. Since we observe subnational reforms at three different levels of aggregation (departments, province municipalities, and district municipalities), we then aggerate the number of reforms (by the different types of regulatory changes) for each establishment. Thus, establishments can be impacted by 0, 1 or multiple (up to 20) different reforms by different subnational government tiers in our matched data.

13
# **1. Introduction**

One of the mandates of the Millennium Development Goals is a sharp reduction in infant mortality across the developing world, yet uncertainty remains over how best to achieve this goal. Long-term reductions in mortality are generally believed to be driven mainly by improvements in medical technology (Preston 1980; Acemoglu and Johnson 2006; Cutler et al. 2006). The extent to which changes in income per se affect health status, including the mortality and morbidity of young children, remains controversial. A good deal of this controversy arises from the fact that there are likely to be feedbacks from health status to income—for example, both Gallup and Sachs (2001) and the World Health Organization (2001) argue that improvements in health status would increase rates of economic growth. On the other hand, Pritchett and Summers (1996), who instrument economic growth with investment levels, terms of trade shocks, the black market exchange rate premium, and price level distortions, argue that there is a strong causal relation running from income to health status. However, their identification and conclusions have been challenged by, among others, Jamison et al. (2004) and Deaton (2006), who argue that the co-variation between income and health is likely to be driven primarily by omitted variables such as country-specific differences in institutional quality, female education, and the capacity to absorb medical technology,.

In this paper, we extend the discussion of the interrelationship between health and income with an investigation of GDP and infant mortality for a large set of developing countries between 1975 and 2004. We focus on infant mortality not only because of its pervasive presence in much of the developing world, but also since it is less likely than adult mortality to be affected by reverse causality from health to income. We address the natural covariation of trending series such as GDP and infant mortality in a variety of ways to limit the potential for spurious 2
et al. (2015) show that the quantity of water used is smaller compared to a conventional one. Based on the data for the 1995-2000 period on 81 countries, Lio and Liu (2006) find a significant positive relationship between the adoption of information and communication technology (ICT) and agricultural productivity. They also find that returns from ICT in agricultural production of the richer countries are about two times higher than those of the poorer countries.

# **5. Other Factors Driving Voluntary Investment on Green/Clean Technologies**

Unlike the general perception that the private sector does not have the interest to invest in new green/clean technologies because of their cost disadvantage as compared to conventional or non-green technologies, some empirical literature shows otherwise. Fama and French (2007) suggest that returns or payoffs are not the only criteria investors use to make decisions on green/clean investments. They argue that investors can buy assets as consumption goods rather than strictly based on returns or payoffs. Many information technology companies (e.g., Google, Facebook, and Twitter) think, during the initial public offerings, that the market for clean technologies will expand in the future even if they are expensive and not profitable for now. Ng and Zheng (2018) provide empirical evidence by comparing the investors’ demand between 99 green energy companies and 93 matching samples of non-green energy Fortune 500 firms. The results from their Capital Asset Pricing Model show that green energy portfolios perform comparably or better than a matching non-green energy portfolio. Contrary to the traditional perception that full filling the social responsibility is costly for firms, they find meeting the environmental objectives not affecting firms’ market performance.

Using sample data of firms from France, Germany, Italy, the Netherlands, and the UK during the 1985-2011 period, Colombelli et al. (2020) show a positive impact of innovation on the market value of firms. They also find that firms operating in sectors with a high propensity for green technologies yield a significant positive effect along with the stock of green technologies. Based on the financial performance data of 420 energy firms in OECD countries over four years (2013-2016), Hassan (2019) shows that green investment promoted by renewable energy policies in OECD countries improves firms’ financial performances.

37
employed in the second SISBEN, using the following specification: where _π_ is the coefficient of interest, which, if positive, implies that the probability of being informal increased among the eligible people relative to the non-eligible in different sub-samples close to the threshold. _S_ is the control for the year in which the first SISBEN was conducted, and _γi_ is the individual fixed effect.

The corresponding regression results are reported in Table 4. Using the ID matching, the table shows that informality among the eligible population increased by 4.0 to 5.5 percentage points (panel A). The results are similar but slightly lower in magnitude (2.6 to 4.0 percentage points) when using the Expanded match, as reported in panel B. The corresponding Figure 2 shows, on the left graph, the informality rates relative to the eligibility threshold as determined in the first SISBEN using data from the first (solid line) and second (dash line) SISBEN. The graphs on the right are derived from data in the left graphs. These graphs indicate the difference in informality rates for the post minus the pre period relative to the eligibility threshold. These graphs show that the decline in informality rates is lower for people to the left of the threshold (those eligible for the SR), consistent with the direction and magnitude of the results estimated when using the variation in implementation dates across municipalities.

# **V Conclusion**

In this paper, we explore whether an expansion of non-contributory health insurance for the poor discouraged workers from entering the formal sector. Although this study was conducted using data from Colombia, it explores the broader question of how linking social benefits to 19
|Other|-0.133|(0.174)|
|---|---|---|
|Main source of lighting|||
|(baseline=Electricity/Gas)<br>|||
|Kerosene<br>|-0.0824|(0.118)<br>|
|Candles/Batteries|0.0745|(0.173)|
|Other|-0.386**|(0.164)|
|Main heat source for cooking|||
|(baseline=Firewood)|||
|Charcoal|1.286**|(0.592)|
|Kerosene/Oil|0.12|(0.142)|
|Electricity/Gas|-0.111|(0.397)|
|Other|0.392*|(0.232)|
|Type of garbage disposal|||
|(baseline=Bins)|||
|Compound|0.12|(0.175)|
|Empty Lots/streets/rivers|0.0721|(0.165)|
|**Assets**|||
|TV|-0.129|(0.122)|
|Fridge|0.0795|(0.140)|
|Stove|-0.135|(0.119)|
|Bicycle|0.143|(0.104)|
|Car|0.0267|(0.162)|
|Iron|-0.0684|(0.116)|
|Constant|1.436***|(0.522)|
|Observations|4,545||
|Robust standard errors <br>*** p_<_0.01, ** p_<_0.|in parentheses<br>05, * p_<_0.1||

Source: Authors’ estimations based on GHS Data 34
group inequality at market income but falls behind the Pensioners group at final income (Figure 14, Figure 15). Not all the groups show a reduction in inequality; the two fiscal groups with children in school show an increase in their between-group inequality, even though they continue to have a negative contribution to between-group inequality (their average income is below the total average).

<!-- Start of picture text --> Figure 11. Inequality decomposition by fiscal categories Figure 12. Inequality decomposition by care categories<br>0.30 0.30<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Market Disposable Consumable Final Market Disposable Consumable Final<br>Income Income Income Income Income Income Income Income<br>Within Between Total (Theil) Within Between Total<br>Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations.<br><!-- End of picture text --> For care categories, the within component of the Theil starts at 0.20 at market income and ends at 0.14 in final income, while the between component fall is much smaller, from 0.03 to 0.02. Accordingly, the contribution of the between-group component to total inequality in the care categories falls only by 1 percentage point, from 14 to 13 percent. There is also one group that dominates in terms of its contribution to within-group inequality: Married Couple with Dependents, which represents about half of within-group inequality. This group also shows a large fall (from 0.10 to 0.07) in its within-group inequality when moving form market to final income (Figure 15). Across care groups, the No Dependents group has the highest contribution to between-group inequality for all income concepts, and it is the only group for which the between-group inequality falls through the fiscal system (Figure 16). The Married Couple with Dependents has a negative contribution to between-group inequality, even though it increases at final income.
# Table A.3: Summary Statistics

|||||||Percen|tile||
|---|---|---|---|---|---|---|---|---|
|||Mean|SD|5th|25th|50th|75th|90th|
||Total Annual Sales|8,760|27,089|0|13|1,663|5,679|16,523|
||Input VAT|1,301|4,049|0|0|135|672|2,572|
||Output VAT|1,911|5,786|0|0|264|1,072|3,785|
||Net VAT Liability|546|2,305|-36|0|54|326|1,181|
|All Firms|Net VAT Liability _>_0|0.63|0.48|0.00|0.01|0.39|0.55|0.61|
||Has POS|0.17|0.37|0.00|0.00|0.07|0.13|0.16|
||Number of Card Transactions|1.93|5.21|0.00|0.06|0.33|1.39|4.33|
||Volume of Card Transactions|2,587|5,791|9|124|603|2,287|8,085|
||Share of Electronic Sales|0.22|0.25|0.00|0.03|0.11|0.32|0.59|
||Total Annual Sales|8,324|13,695|16|1,438|3,564|8,790|20,609|
||Input VAT|2,075|4,934|0|160|492|1,412|5,008|
||Output VAT|2,464|5,761|0|206|599|1,681|5,904|
||Net VAT Liability|346|1,512|-58|12|83|283|923|
|Retail Firms|Net VAT Liability _>_0|0.81|0.39|0.03|0.58|0.72|0.78|0.80|
||Has POS|0.52|0.50|0.04|0.33|0.41|0.47|0.50|
||Number of Card Transactions|3.06|7.23|0.01|0.12|0.48|1.85|7.83|
||Volume of Card Transactions|3,499|7,848|11|160|688|2,499|9,169|
||Share of Electronic Sales|0.22|0.25|0.00|0.03|0.12|0.33|0.60|
||Total Annual Sales|18,276|42,005|0|626|3,686|13,401|45,521|
||Input VAT|2,788|6,007|0|16|427|2,161|7,888|
||Output VAT|3,703|8,331|0|20|611|2,752|10,092|
||Net VAT Liability|792|2,384|-94|0|87|518|2,014|
|Wholesale Firms|Net VAT Liability _>_0|0.67|0.47|0.00|0.22|0.50|0.61|0.65|
||Has POS|0.16|0.37|0.00|0.05|0.13|0.16|0.17|
||Number of Card Transactions|0.94|1.39|0.00|0.04|0.24|1.13|4.21|
||Volume of Card Transactions|1,903|2,622|11|120|641|2,420|8,085|
||Share of Electronic Sales|0.22|0.25|0.00|0.02|0.11|0.33|0.60|

Notes: This table reports summary statistics of relevant variables for all firms, retail firms and wholesale firms in 2013. The number and volume of card transaction and the share of electronic sales are limited to firms with a POS. All monetary values and the number of card transactions are winsorized at the 99th percentile and displayed in thousands of Uruguayan pesos (1 USD= 43 UYU in July 2021). The percentiles columns for the binary outcomes (Net VAT Liability _>_ 0 and Has POS) show the mean outcome across the distribution of firms based on sales size. This Table is discussed in Section 3.4.

34
who had previously worked within these despotic administrations and they carried forward the capriciousness and abusiveness. In counterpoint, however, Hull (2012b) points out that the “haunting” of the postcolonial present by the colonial past might at times be overstated. Certainly, at the Capital Development Agency in Islamabad, Pakistan where he conducted field research, the continuities of colonial administration were overshadowed by the many innovations made during the postcolonial period, necessitated by new contradictions and demands specific to the time.

A central object of critique in development studies since the 1990s has been Structural Adjustment programs, which have been accused of undermining state capacity. Ayee (2001), for example, shows that a Civil Service Performance Improvement Program in Ghana from 1995 aimed to improve efficiency and service delivery through restructuring, but manifest primarily in salary restructuring and retrenchments. More recent work in anthropology has sought to complicate this “easy” narrative of neoliberal process. A special issue of _The Cambridge Journal of Anthropology_ by Bear and Mathur (2014), for example, sets out to “redefine the public good,” seeking to harness the power of that concept supposedly at the heart of the civil service for a just end rather than rule out state bureaucracies as merely tools for the powerful. They contend that a set of concepts, such as “transparency” and “human rights” have ensnared ordinary people around the world in bureaucratic practices and have rendered invisible the bureaucracies’ ethical positions. Their overarching conclusion is that the neoliberal reforms which accompany those concepts do not simply make bureaucracies more or less effective, but rather “generat[e] new kinds of intimate relations with the state” (Bear and Mathur 2014: 22). For example, Qureshi (2014) found that the marketization of an HIV/AIDS health program in Pakistan led the Ministry of Health officials into uneasy arrangements with outside NGOs, turning their attention to learning how to manage contracts with these NGOs rather than educate their own employees to carry out the needed tasks.

18
perspective of both the demand and supply sides. Furthermore, the overlap in the country coverage also enables us to assess whether there is consistency in the responses provided separately by firms and banks.

The FRS (Inmark Group) surveys cover around 900 SMEs per country per year, except in Puerto Rico, where approximately 600 firms were interviewed. In each country, the surveys cover the main cities, provinces, or states, where about 90% of the SMEs are located. The surveys contain 76 questions subdivided into 7 parts. The first part collects information about the firm: its location, the economic activity it is involved in, its ownership type, and age. The second part contains questions on the number of banking relations the firm has and whether and why the firm has intensified or reduced its operations with the banks identified in this section. The third section asks information about the type and number of banking products used by the SMEs. In particular, the survey looks into specific types of deposit and saving products, financing products, and banking services. The remaining sections ask detailed questions regarding how firms finance foreign trade, whether they use internet banking, and what forms of payment they accept.

# **3. SMEs as Bank Clients**

This section shows that the overall data from the surveys of banks within and across countries suggest a very similar and consistent pattern—that SMEs have become a strategic sector for most banks. Put differently, even if SMEs were ever a niche bank business, they no longer are. In fact, foreign banks and large banks have become very aggressive in targeting SMEs. Moreover, the market structure is competitive; that is, 16
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