rafmacalaba/datause-encoder-data huggingface dataset:
# **C. Business environment**

<u>Insolvency and creditor rights framework: Weak insolvency and creditor rights frameworks make</u> loan restructuring, contract enforcement, and collateral recovery more expensive and time consuming. For example, if banks cannot efficiently recuperate collateral – because, for example ownership cannot be ascertained or courts act too slowly -- then banks will not extent credit or only at high interest rates. Insolvency regimes are also important for the effective management of non-performing loans which weigh on intermediation efficiency. This has a direct impact on the loan recovery ratio (LGD) when a borrower defaults, and therefore the expected losses which determine lending rates and spreads. A 2012 study of Brazil’s 2005 bankruptcy law reform found a 10-17 percent increase in debt finance at the firm level, and an 8-17 percent reduction in the cost of credit, resulting from a reform in insolvency and creditor rights frameworks (Araujo et al, 2012). A 2012 study on Italy’s bankruptcy reform yielded similar conclusions (Rodano, et al, 2012). Reforms to creditor rights frameworks in Romania and other jurisdictions have also been shown to yield reductions in the cost of credit and increased access (Safavian, 2006).

<u>Information environment: Asymmetric information creates agency frictions which increases the</u> cost of credit intermediation. Well-functioning and regulated credit registries and private credit bureaus can reduce information asymmetries. Similarly, strong secured transaction laws and collateral registries allow borrowers to leverage their assets as collateral to reduce agency costs. Open banking initiatives and Application Programming Interfaces (APIs) democratize the flow of information and help to further alleviate these frictions and increase competition.

<u>Direct policy interventions: These include directed lending and interest subsidy programs,</u> interest rate restrictions, and high (unremunerated) reserve or liquidity requirements. These interventions are distortionary and thus affect interest rates and spreads (see also Common Policy Interventions).

48
Observational studies provide insights into determinants of service payment behavior such as households strategically delaying payment as a form of credit (Violette, 2020) or the perceived ease of payment and social pressure being associated with prompt payment (Mugabi et al., 2010). While experimental work on improving utility revenue collection has started to grow, these papers mostly rely on administrative billing and basic demographic – data to assess impacts and typically limit their attention to two dimensions payment and consumption behavior (Allcott, 2011; Jack and Smith, 2020; Szabó and Ujhelyi, 2015; Rockenbach et al., 2019). Our study combines five years of daily administrative billing data with rich primary survey data from property owners and tenants over the course of three years on a range of outcomes that helps present a more comprehensive assessment of the potential welfare implications of utility interventions.

Our study also contributes to the high-stakes contract enforcement approaches to improving regulatory compliance. The evidence on high-stakes contract enforcement is particularly limited, especially when compared to lighter-touch information/engagement interventions. The existing evidence on enforcement is mostly limited to developed country settings, most prominently in the tax evasion literature (Slemrod et al., 2001; Kleven et al., 2011), and to a lesser extent in environmental protection (Telle, 2013; Duflo et al., 2018). The small number of studies exploring high-stakes enforcement in developing countries find significant impacts. In Brazil, de Andrade et al. (2016) randomize inspections and fine firms if they are found to be operating without a business license and find that the intervention increases business registrations. In Costa Rica, Brockmeyer et al. (2019) find significant increases in tax payments from credible enforcement emails. In Kenya, Bedoya et al. (2021) randomize a regime of high-intensive inspections of health facilities with enforcement of warnings and sanctions, including the risk of closure if they do not have a license to operate. These inspections successfully increase compliance with minimum patient safety standards for all types of facilities without increasing patients’ payments or reducing facility use.

# **3 Institutional Context**

Like many LMICs, Kenya’s constitution established the right to “reasonable standards of sanitation” and “clean and safe water in adequate quantities”, and Kenya’s Vision 2030 set a goal of universal water and sanitation coverage. Achieving this vision faces important challenges. Nairobi’s urban population has tripled over the past 25 years,<sup>8</sup> but access to safely managed water fell from 62% in 2000 to 50% in 2017.<sup>9</sup> Water supply for existing customers

> 8UN population dynamics data 2021

> 9Joint Monitoring Program data: https://washdata.org/data/household!/

6
with a rising share of pollution-intensive output will carry a heavier regulatory burden than those with a falling share. The environmental impact of changing shares is reflected in our projections.

Recent trends suggest less diversity in the movemen **t** oward concentration of production in large plants. Shares in all five cities are trending upward at about the same rate, so the projected distribution across provinces in 2020 has a spread similar to the current one. Cities in the northeast group (Beijing, Shanghai, Shenyang) are tightly clustered around one path; Guangzhou and Chongqing around another. Figures 22 (a-b) display a striking <!-- Start of picture text --> 22(a) 22(b)<br>i'Moni Share ofLaige and Stated Plaris Output Share of Large Plants<br>aG - - ------ - - - -- --- --- ----<br>as 0 .70 -<br>Q45 /0.#10 40 ., ..... xe -a = aergzh<br>a40 / . . a~~~~~~~~30 _ ____ q<br>1ses i 1994 t m0.40 02 1994 20D 201 202<br><!-- End of picture text --> increase in projected shares: For China as a whole, the large-plant share increases from 30% to 58% during the period 1988-2020. Across provinces, the minimum large-plant share rises from 22% to 49% (Guangdong), while the maximum increases from 48% to 70% (Beijing). While they forecast large changes, our projections are mostly within the range of current experience.

For state ownership, the projections illustrated in Figure 22 (a-c) show a continued sharp decline at the national level, but a more mixed experience across provinces. Extrapolation from recent trends yields a projected national decline in SOE share from 58% (22c) to 43% during the period 1988-2020. **Ou Share of Slh*fd lars** Beijing, Shanghai and Chongqing o.-...... experience asimilar decline in SOE Q + share: From around 70% in 1988 to (I , *D..-...... around 40% in 2020. Guangzhou _0.40_ preserves its status as a strong non-SOE O. ......... 'outlier,' declining from an already-low **0.10 I** SOE share of 45% in 1988 to around **1s** 1194 mm zno **z)** 15% in 2020. Shenyang, on the other hand, had an actual increase in SOE share during 1988-1994. While we do not expect this to continue, we have no evidence to support a projection of declining SOE 17 As previously noted, the 'pollution intensive' sectors in this analysis are the sectors with large 'dirty' or 'clean' residual effects after size, ownership and provincial pollution levies are accounted for.

23
Consider next the two other macroeconomic determinants of interest for each of the subsamples of data in Table 4. For the United States, the estimated IRRs for these two determinants did not change during the crisis in statistically meaningful ways away from the 1988:Q1-2008:Q3 estimates. For example, in specification (4), the IRR estimate of 1.06 on the bilateral real exchange rate is not statistically different from the pre-crisis IRR estimate of 1.03, and the IRR estimate of 1.87 on the domestic unemployment change is not statistically different from the pre-crisis IRR estimate of 2.16. The same pattern holds when we redefine the macroeconomic determinants at _t-2_ in specification (5). Furthermore, results from the European Union sample are like the United States in that the IRR estimate on the domestic unemployment rate change during 2008:Q4-2010:Q4 is not consistently different from the IRR estimate from prior to the crisis. While specification (6) presents evidence of a differential, this is not robust to redefining the macroeconomic determinants at two lags. As described earlier, the estimated IRR for the unemployment rate change appears particularly sensitive to the relatively shorter time series of available data for the EU sample.

However, there is one other robust differential between estimated IRRs from the European Union sample. The estimated IRRs for the EU’s bilateral real exchange rate for 2008:Q4-2010:Q4 are less than 1 and they are statistically less than the EU’s IRR estimate for the earlier period. Thus there is some evidence of a change in that the EU imposed fewer TTBs in response to the euro appreciation early in the crisis period (see again Figure 1), given the EU’s 1999:Q1-2008:Q3 IRR estimate which indicated that an appreciation of the euro currency historically led to more TTBs.

Nevertheless, a final contributing explanation for the lack of a major import protection response by the United States or the European Union during 2008:Q4-2010:Q4 is related to the movements in each economy’s bilateral real exchange rates over the period. The historical evidence for the EU and US is that real currency appreciations led to more import protection through TTBs. As the grey line in Figure 1 again illustrates for the EU, for much of the 2008-2010 period the euro is depreciating. Furthermore, shortly after the sharp appreciation of the US dollar in 2009:Q1, the dollar _depreciated_ by a nearly identical amount, and then continued a period of weakening throughout 2009-2010. These real depreciations suggest another dampening effect on new EU and US import protection.

relationship, although the IRR is less precisely estimated. When using real GDP growth at the annual frequency in data extended through 2009, the point estimate for the IRR is no longer precisely estimated.

23
water consumption accounts on average for 5.5% of the mean income of a family in the lowest income decile nationwide. However, there is sharp regional disparity, and of course within each region not all first-decile families receive the average income. Reducing regional disparities should not be very complicated, but at the individual level it could be more difficult. One possibility would be to differentiate subsidy rates according to a family's SEF point score.

**Table 2: Water consumption subsidy: beneficiaries and expenditure 1990-1998**

|YEAR|ESTIMATED<br>POTENTIAL<br>BENEFICIARIES|ACTUAL<br>SUBSIDIES<br>GRANTED|COVERAGE<br>%|EXPENDITURE<br>Ch$ million at<br>current prices|EXPENDITURE<br>Ch$ million at<br>1998 prices|
|---|---|---|---|---|---|
|1990|424,625|21,842|_5.1_|3.5|**_7,980_**|
|1991|424,625|177,719|41.9|263.0|**_503,893_**|
|1992|441,040|315,901|71.6|2,255.7|**_3,654,189_**|
|1993|443,068|351,925|79.4|4,437.2|**_6,506,373_**|
|1994|454,038|389,712|85.8|7,129.8|**_9,268,727_**|
|1995|461,508|399,205|86.5|9,305.8|**_11,166,935_**|
|1996|466,508|442,524|94.6|12,053.5|**_13,505,565_**|
|1997|466,508|443,953|95.2|14,339.,0|**_15,055,950_**|
|1998|466,508|||115,479.1*|**_15,479,065_**|

**Source: Ministry of Economics**

*Allocated in national budget

13
Legal Origin and Latitude explain 2.16% of the total variation in firms’ responses that amounts to about 13% of the cross-country variation although Legal Origin has a more significant drop in explanatory power when compared to the full sample of 80 countries (-44%) than Latitude (-3.6%).

# **Insert Table 4**

Taken together, all the institutional factors explain approximately 52% of the cross-country variation in this reduced sub-sample, while they explained 50% of the cross-country variation in the full sample that included the former Socialist countries.

At the firm level (not shown in the table), legal organization of the firm explains 0.94%<sup>20</sup> which is more than ownership structure (0.44%). Size explains 0.28% and there is very little variation explained by the industry sector in which the firm operates (-0.03%). When we look at the marginal contribution of the firm effects in Table 4 (second row in each panel), the variables with the maximum explanatory power across models is legal organization. The strongest country-firm interactive effect is that of Latitude-Legal Organization (0.93%).<sup>21</sup>

# **_5.1.2. Using Settler Mortality_**

Given the strong performance of the latitude variable, we use another measure of geographical endowments, **Settler Mortality** , as stressed by Acemoglu, Johnson and Robinson (2001). Table 5 shows the results for a sub-set of countries for which data on settler mortality is available. The sample is now restricted to 40 countries with 3222 observations. This sample of countries is similar to the sample of non-socialist economies in Table 4.

# **Insert Table 5 here**

We construct the table using the five point categorization applied to settler mortality. The results of Table 5 once again dramatically change the order of explanatory

> 20 Legal organization is still the dominant firm level variable in smaller sub-samples if we collapse the legal organization variable into four categories: sole proprietorships, partnerships, corporations and others instead of the usual six categories: sole proprietorships, partnerships, cooperatives, unlisted corporations, listed corporations, and others.

> 21 The regression coefficients reveal that firms organized as sole proprietorships in countries close to the equator are more likely to perceive property rights protection to be poor whereas incorporated firms in countries in the fourth quantile of countries (when countries are ranked on their distance from the equator) respond most favorably to the question on the extent of property rights protection.

18
The private sector accounts for about 88 percent of total employment in Mexico, a much larger share than that prevalent in other OECD countries. The Mexican private sector shows a growing duality: a large traditional sector coexisting alongside an expanding modem sector. The former, which consists primarily of micro-enterprises and small and medium-size enterprises (defined to include firms with up to 250 workers), employs a large fraction of the labor force but accounts for a small portion of output and exports. Roughly, these enterprises account for 71 percent of total employment, 53 percent of employment in manufacturing, 95 percent of employment in the retail sector, and 73 percent of employment in services.

From 1988 to 1996, annual output per worker was low in the service sector. While some studies have shown that the manufacturing sector has become more efficient as a result of trade liberalization, with gross labor productivity increasing at an annual rate of 3.1 percent during the 1988 to 1996 period, this rate was still low compared with that in other developing countries, and was about the same as in the United States (World Bank 1998).

One plausible explanation for this slow growth in labor productivity is the lower educational level of Mexican workers and the resulting deficiency in the on-the-job human capital accumulation compared to elsewhere. The increase in wages associated with an additional year of work experience for Mexican men is low compared to the increase for men with similar educational attainment in other countries (3.8 percent in Mexico compared with 8.1 percent in United States, 8.4 percent in Japan, and 9.1 percent in France). This rate is low even when compared with the rate in countries at a similar level of development and with comparable education indicators, such as Brazil (6.2 percent) and Colombia (5.8 percent). Given the welldocumented correlation between wage growth, on-the-job training, and productivity observed in 9
> - 34 _-_

the **_Rlv.._** If these rules stress a high productiont (a, is large) it is likely that a high wage will be the outcome. Unemployment is in this model correlated with the size of the labor force, m, and will tend to lower the wage in the R2E. Finally, if the Workers have good income possibilities in the private sector their threat point will be large leading to upward wage pressure in the state sector.

However, the R2E regime raises a host of other interesting questions which we are unable to address adequately using the above model. Among other issues that need to be tackled will be the differences between the preferences of the union/worker group as bargainer with private employers and as an employer in its own right. The outcomes of that bargaining with respect to the wage and employment are not however obvious, particularly given the overlay of structural change imposed on the system by the transition to an R2E regime.

# SectLmi 6: Some Preliminary Emixical ReIsults

# 6.1 Introduction

We now present in very preliminary form results from estimations of the equation derived from the cooperative game setting for the RlE regime (see Section 4.2). To that end we use quarterly data from both Hungary and Poland covering the periods 1982(1)-1989(2) and 1982(l)-1989(4) respectively 47. Over that time both systems could be stylized as RlE economies. The expression with which we work is repeated below; W+ I (+) M X0 L) - fn(a,, o%, P, n, **Um,** Uw) The parameter, a,, relates to the incentive for enhanced production. A rough approximation for this incentive could be planned or realized productivity. _a2_ covers the wage bill tax. P is producer prices. In section 4 the Workers' and the Manager's expectations to the consumer prices were ignored -- we choose now to include consumer prices in our empirical analysis; n is employment. The manager's threat point UM depends on the manager's alternative income sources (see also section 4.2), and the workers' threat point Uw relates to the workers income possibilities for example in the private sector. For the set-up presented below, however, current data limitations have forced us to drop a number of terms and concentrate in effect on the association between wages, prices and productivity. We also incorporate simple error correction terms in the estimated equations. The following notation is used throughout; 47 Data are taken from the IMF International Financial Statistics and are more fully described in Appendix 6.
On average, approximately 62 percent of Tunisian firms engaged in either internal or external innovation between 1997 and 2007. During this period, about 36 percent of firms engaged in internal innovation, and about 58 percent in external innovation. Among those firms that invested in external innovation, most paid for consulting services, and only a few for royalties. This ratio of firms that engage in innovation is relatively large and similar to the ratios in developed economies, and similar to the result of a different survey in Tunisia.<sup>10</sup> Table A.1 in appendix A provides innovation variables by two-digit industry level. There is large heterogeneity in the amount of innovation investment across industries, and even within manufacturing. Comparing innovators’ share in entire firms (innovator’s ratio) across the two-digit industry level, I found that innovators’ ratios are not high in so-called “hightech” industries, such as pharmaceutical or automobile industries, which are typically considered innovative. In fact, a large number of previous studies on innovation focused largely on these sectors. Meanwhile, those industries that are generally considered less technologically sophisticated, such as tobacco, furniture, apparel, and printing, have some of the highest innovator ratios, together with refined petroleum, chemicals and their related products (like rubber and plastic products), which are typically capital intensive and also Tunisia’s main export items. This finding contradicts most of the previous studies, which consider high-tech industries to be technological leaders and innovation-intensive, even in developing countries.

The low level of innovators’ share in high-tech industries might be explained by the fact that Tunisian firms might avoid investing in innovation activities in sectors where the technology gap with the more technologically advanced countries is large. Another possible explanation could be that those sectors are not necessarily tech-intensive in developing countries. In fact, I visited firms that are in high-tech industries, such as automobile and aerospace, and found that they produce only certain parts of automobiles or airplanes, such as seats and plastic

> 10 . Around 55 percent of respondents responded affirmatively to innovation in Tunisian innovation survey 2005. Eurostat reveals that innovation existed in 51 percent of manufacturing firms and in 40 percent of services firms in the period 1994–96 in Europe (Pianta 2005). In the CIS survey, about 62 percent of firms claimed to innovate in 1993 in Belgium (Cassiman and Veugelers 2006).

15
effects are therefore estimated with post-program single differences between treated and control children.<sup>21</sup> Matching methods are used in the comparison of outcomes to adjust for potential biases due to nonrandom targeting and selection into the program. The standard underlying assumption for this approach is that matching on the propensity score (i.e. the estimated probability of participation in the program) eliminates any bias generated by pre-treatment differences between the two groups as long as there are no differences in unobservable variables that jointly influence program participation and the outcomes under analysis. The availability in the baseline survey of relevant pretreatment information to model program targeting and participation, in principle, makes the application of the matching methodology suitable for the evaluation of FA.

Although careful procedures were followed in the early evaluation to select comparable control areas, a comparison of baseline characteristics between treated and control children in our sample using standardized t-tests and normalized differences, presented in Table 1, reveal a number of differences that are statistical significant. In order to balance the distribution of covariates between the two groups and assess the sensitivity of the results, we matched children on the basis of three different model specifications to predict the probability of treatment. Table 2 presents the group of variables included in the three models used to predict participation into the program. The first model (Model 1) includes standard individual and household pre-treatment socioeconomic and demographic characteristics such as age and order of the child, dummy variables for married and participating in the labor force head of the household, age, education and gender of household head, urban location, the number of children in the household ages 7 to 11 and 12 to 18, and monthly expenditures. The second model (Model 2) extends Model 1 by adding a number of municipality-level covariates that proxy for measures of educational supply and demand at baseline (for instance, pupil/teacher ratio and access to schools). In addition, the third model (Model 3) includes all but the health variables used by Attanasio et al. (2005) in their participation models

> 21 Only a very small fraction of children were found to have taken the test more than once. However, although the test can be taken several times, it is administered only to students who have achieved grade 11.

16