rafmacalaba/datause-encoder-data huggingface dataset:
# 1. INTRODUCTION

Africa faces increasingly critical resource constraints in its effort to<sup>extendwater</sup> services of acceptable quality to the vast majority of its people.<sup>(e.g.,seePouliquen,2000;</sup> Sandelin, 1994; Snell, 1998; World Bank, 1999). The inefficiency of<sup>waterutilitiesisoften</sup> identified as one of the major factors. in explaining the slow progress<sup>and themany setbacks</sup> in improving access to water and water distribution (e.g., see Schuebeler,<sup>1995;WorldBank,</sup> 1999). Yet, there is a surprising dearth of literature attempting to<sup>measuretheefficiencyof</sup> operators in a way that would allow economic regulators to introduce<sup>explicitly performance</sup> incentives in the regulation of the operators in African countries.<sup>Perhapsbecausethepartial</sup> productivity indicators, such as water losses or number of employees<sup>perconnection, have</sup> generally been so poor that radical operational reforms were easy<sup>topropose,muchof the</sup> attention of policymakers, donors and researchers seems to have<sup>focusedontheinstitutional</sup> and financing aspects of water sector reforms. The need to mobilize<sup>additionalresourcesfor</sup> water through fees and other modalities of financing and the potential<sup>for an increasedpublic-</sup> private partnership in the sector were particularly emphasized<sup>(e.g.,World Bank1999;</sup> UNDP-World Bank Water and Sanitation Program -West and<sup>CentralAfricaOffice,1998</sup> and Snell, 1998) The main purpose of this paper is to show that it is worth assessing<sup>morecarefullythe</sup> potential efficiency improvements that should result from the much<sup>emphasizedreforms.This</sup> would allow quantitative assessments of the potential improvements<sup>intheoverall useof</sup> inputs and a more analytical discussion of the optimal scale of<sup>operation.Bothpotential</sup> sources of efficiency gains could be set as targets for the restructured<sup>sectorsandgoa long</sup> way in cutting the financing requirements. This, in<sup>turn,impliesthatthe needfortariff</sup> increases may not be as high as sometimes argued for some of<sup>themarkets. Moreover,it</sup> implies that there is scope for regulatory supervision which ensures<sup>thattheefficiencygains</sup> are not simply turned into pure rent for the new operators but<sup>areeventuallysharedwith the</sup> consumers.

We assess the potential for efficiency improvements and the importance of<sup>the scaleof</sup> operation by estimating a production frontier for the<sup>region.Forlackofbetterdata,we</sup> estimate this production function from an unbalanced panel of<sup>data fora sample of 21African</sup> water utilities covering the 1995-1997 period. To contribute to<sup>thedesignofreforms,the</sup> 2
Table 4

# Regressions **of Monthly** Earnings **(ln). by Age Group.** Selected Coefficients: **Full TiMe.** Male. Salaried Uorkers Coopleters of General & Vocational Schools-Israeli Census. 1983

||Age Group:<br>**25-29**|Age Group:<br>**30-34**|Age Group:<br>**35-39**|Age Group:<br>**40-44**|Age Group:<br>**45-49**|
|---|---|---|---|---|---|
|P.CERT|-0.00|0.032|0.037|-0.132|0.001|
||(0.20)|(0.73)|(0.77)|(0.193)|(0.01)|
|S.CERT|0.088|0.057|0.120|-0.055|0.066|
||(2.64)|(1.40)|(2.65)|(0.85)|(1.11)|
|BAG|0.172|0.102|0.142|0.024|0.106|
||(4.35)|(2.06)|(2.45)|(0.31)|(1.43)|
|VOC|0.002|-0.006|0.046|0.021|0.072|
||(0.10)|(0.25)|(1.92)|(0.64)|(2.20)|
|N|4,500|3,776|2,778|1,492|1,333|
|R2|0.083|0.096|0.145|0.186|0.193|

t values in parentheses

- 20 -
A key factor that helps determine both firm productivity, and the ability of firms to export, is the management practices used by firms. Bloom et al. (2016) estimate that differences in management can account for 30 percent of cross-country productivity differences. Better management is also strongly associated with export performance, with Bloom et al. (2021) finding that better managed firms are more likely to export, sell more products to more destinations, and have higher export revenues. However, Colombian firms have low levels of management practices according to global surveys like the World Management Survey, with levels similar to countries like India and Kenya that have much lower per-capita incomes. A prior pilot experiment in Colombia (Iacovone et al., 2022) had worked with a single industry, the autoparts sector, and found that individual and groupbased consulting had improved management practices, but was not focused on exporting or export-oriented firms.

The Colombia Productivity and Export Improvement Program (PEIP)<sup>3</sup> was designed by the Programa de Transformación Productiva (Program of Productive Transformation) (PTP) with the explicit objective of “improving the productivity and export capacity of the participating firms”. It aimed to achieve these goals through providing consulting services to firms to improve their management practices (described in detail below). The program was launched in November 2017. To be eligible for the program, firms had to have existed for at least two years, be formally registered, belong to one of thirteen selected sectors (transport manufacturing, construction, textiles, fruits and fruit products, specialty coffees and coffee products, beef, aquaculture, cocoa products, processed food, cosmetics, pharmaceuticals, plastics and paint products, and basic chemicals), provide financial statements, and complete an online application process. Along with the general call for firms to apply, PTP specifically reached out to a list of firms that had exported at least once in the past five years to see if they were interested in applying. The closing date for applications was March 23, 2018 (see timeline in Appendix A).

# **2.2 Random Assignment and Firm Characteristics**

A total of 200 firms met the eligibility conditions for the program after applying. These 200 firms were then randomly assigned to two groups of 100 each on April 11, 2018. The application form data were used to stratify firms by size (small, medium, or large), and whether or not the firms had exported at all in the last 3 years. An additional two strata were added: one stratum of 19 export outlier firms (defined in terms of having export values, the number of destinations exported to, or the number of different products exported above the 95th percentile in the self-reported export data on the application form), and one stratum of 1 firm that was missing firm size information. We then formed

> 3This is a pseudonym, which we use at the request of PTP.

6
# **Table 9 SME data from CGAP financial access database**

||**Agency**|**monitors t**<br>**to S**|**he level of**<br>**MEs**|**lending**|**Promoti**<br>**access to**|**ng SME**<br>**finance**||**SME definitio**|**n**|**SME**<br>**lending**|
|---|---|---|---|---|---|---|---|---|---|---|
||Yes,<br>regularly<br>(Yes/No)<br>|Yes,<br>irregularly<br>(Yes/No)|No<br>(Yes/No)|No, but<br>another<br>agency<br>does<br>(Yes/No)|Agency is<br>responsible<br>(Yes/No)<br>|Dedicated<br>team or<br>unit<br>(Yes/No)<br>|Number of<br>employees<br>(max.)|Sales|Loan size|Loan<br>value (%<br>of GDP)|
|**Malaysia**|√||||√|√|150.00|7,093,199.00|..|17.43|
|**OIC**<br>**countries**<br>**average**|72.97%|8.11%|10.81%|13.51%|62.16%|48.65%|151.00|4,008,095.95|284,592.68|7.43|
|**Developing**<br>**countries**<br>**average**|47.52%|9.90%|26.73%|17.82%|48.51%|37.62%|141.19|9,150,287.27|168,779.94|7.44|
|**OIC-GCC**<br>**average**|79.41%|8.82%|8.82%|14.71%|61.76%|52.94%|155.33|3,837,600.94|138,565.15|7.43|
|**Low-income**<br>**countries**<br>**average **|58.62%|3.45%|20.69%|6.90%|72.41%|51.72%|66.43|1,018,377.27|72,356.47|5.58|
|**OECD**|30.00%|86.67%|53.33%|93.33%|33.33%|50.00%|274.00|56,203,661.64|2,080,697.75|24.47|

Source: CGAP financial access report data 2010 Note: The CGAP Financial Access Database covers questionnaires sent to 151 economies: 13 in East Asia and the Pacific, 27 in Europe and Central Asia, 20 in Latin America and the Caribbean, 14 in the Middle East and North Africa, 6 in South Asia, 40 in Sub- Saharan Africa, and 23 in the high-income OECD countries. Among them, 37 OIC countries are covered.

Selected questions are presented in the first and second rows of the table. The row for OIC countries means the percentage of OIC countries that replied yes to the questions (for Y/N questions), or means the average value for OIC countries. The same calculation applied to the groups of developing countries, OIC-GCC, and low incoming countries. OIC-GCC means OIC countries excluding the GCC countries covered by the CGAP database.

55
l. **Introduction** The countries of Eastern Europe and the Fonner Soviet Union embarked on two transitions: from a command to a market economy (economic transition), and from an authoritarian political regime to a democratic one (political transition). They have had varying degrees of success on both fronts. Some countries, e.g. Hungary and Poland, have had remarkable success liberalizing their economies and their political systems. Others, e.g. Belarus and Uzbekistan, continue to have economic and political systems reminiscent of the Soviet era. This paper tries to explore the links between economic and political transitions using panel data from 25 post-communist countries in Europe and Central Asia.' Economic theory does not give a clear answer on whether political freedoms help or hinder economic transiltion. Surveys presented in Rodrik (1996) and in Williamson (1994) present arguments for both sides of the debate. Balcerowicz (1997), and Balcerowicz and Gelb (1994), discussing the interplay between economic policy and democratization, argue for a "'big bang" approach to economic transition, but leave open the question of whether democratic governments are better able to implement the "big bang" than authoritarian ones. The analysis in Roland (1992, 1994 and 1997) and in Dewatripont and Roland (1992) indicates that, under majority rule, time consistency and adverse selection problems generate a slow and gradualist economic transition. On the other hand, Aslund et al. (1996) argue that rent-seeking authoritarian governments slow-down stabilization policies needed in the first stage of transition.

The empirical literature presents a different picture: it indicates a strong positive correlation between political and economic reforms in the post-communist countries. Using data for 26 transition countries de Melo et al. (1996) found a correlation of 0.8 between their index of economic liberalization and the Freedom House index of political freedom. De Melo et al. (1997) use panel data to estimate the determinants of economic liberalization in transition countries. Political freedom appears with a

> 1 This includes all of the countries of Central and Eastern Europe (CEE) and the Former Soviet Union (FSU), except for Bosnia and Yugoslavia (Serbia and Montenegro) for which necessary data are not available.

2
coefficient of variation in TFP. That is, a convergence of TFP implies a c"ivergenze of factor returns; however, a convergence of factor returns need not result in a convergence _oL_ TFP.<sup>1</sup> 4 6.5 In order to evaluate the extent to which levels of technical efficiency among enterprises have become more equal, we construct measures of total factor productivity for 1980, 1985 and 1989 and evaluate their patterns of change over time. To do this, we assume a uniform Cobb-Douglas technology from which we develop the following expression:

**NTFP** = **exp[lnQ- aln** K- aln W- ao4ln Ml (3)

where **_a,,_** _a,,_ and a. are normalized factor output elasticities that sum to unity.1<sup>5</sup> 6.6 Note that NTFP is constructed using outputs and labor and material inp' .. measured in current prices and the net value of fixed assets. No effort is nlad& to deflate these inputs and outputs in order to achieve comparability of TFP measures over time and make inferences about the growth of total factor productivity during the period under study. Instead, we use current price values in order to allow for cross-section comparisons of enterprise TFP within each of the three years under review. To avoid confusing our measure of overall efficiency with conventional physical measures of TFP, we employ the term -iominal total factor productivity (NTFP) to represent measures calculated from Equation (3) above.

6.7 The dispersion index of NTFP for the full sample, shown in the last column of Table 2, shows that for 8 of the 10 branches, the dispersion of NTFP declined over 1980-89. In S of these 8 branches, the decline is monotonic from 1980-1985 and 1985-1989. These results indicate that an important source of gains in efficient production was the tendency for the degree of technical efficiency among enterprises within the sample to become more uniform.

6.8 Another way of distinguishing between allocative and technical efficiency is to investigate the sign of the correlation coefficients pairing measures of single factor productivity. Firrm that are aliocatively inefficient, such as A and B, should reveal a negative correlation between returns

- **"** This would be the case unless there occurred an efficiency reversal-that is, technical inefficiency gives way to allocative inefficiency (ie. D and C move to A and B) or allocative inefficiency is replaced by technical inefficiency (i.e. A and B migrate to C and D).

- 5 The weights, respectively 0.205, 0.120, and 0.675, are obtained from JRZ, who find that these estimates are stable over two cross-sections (1984 and 1987) of state-owned enterprise data aggregated to the urban level.

_Asseing Gains in Epcikt Production Among China's hndustrial Eaterprises,_ by Gary H. Jefferson and Xu Wenyi 10
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A second group of studies relies on the estimation of partial correlations based on firm-level data from developed countries to analyze the adjustment in employment, skills, and wages due to ICT adoption. They use a wide variety of firm-level ICT adoption measures such as IT capital stock, computer adoption, the number of computers, IT investment, and the number of IT workers. Their results indicate that ICT adoption is associated with a higher relative demand for skilled workers and higher wages (Caroli and Van Reenen, 2001; Greenan and Topiol-Bensaid, 2001; Bresnahan et al., 2002). A handful of more recent studies establish a causal relationship between ICT adoption - measured by IT investment and access to broadband internet - and labor outcomes at the firm-level. The results generally show that the adoption of ICT by firms does not lead to changes in overall employment but tends to be linked to increased wages and better labor outcomes for skilled workers - argued to carry out non-routine abstract and cognitive tasks - and worse outcomes for unskilled workers - argued to perform more routine tasks which are automated with the use of ICT (Bartel et al., 2007; De Stefano et al., 2014; Akerman et al., 2015; Gaggl and Wright, 2016).

The evidence for emerging economies is much scarcer and our paper fills in this important gap in the literature. Studies using occupational data obtained from household or labor force surveys are generally aligned with the idea that ICT impacts more negatively occupations engaged in routine tasks and non-routine manual tasks. In particular, they show that occupations using ICT more intensively have a high demand for cognitive skills and a low demand for routine and nonroutine manual skills in developing countries (Santos et al., 2015). However, there is very weak evidence of labor market polarization in Latin America, perhaps suggesting that any impacts of ICT adoption during the 2000s were overcome by the strong commodity boom experienced by most economies which benefited primarily low-skilled workers (Cruces et al., 2017; Maloney and Molina, 2016). The evidence for Chile is, however, different. In spite of the commodity boom, there is evidence based on Chile’s household survey (the CASEN which we also use in our analysis) of some labor market polarization as the employment share of occupations with a high content of routine skills and a low content of abstract skills fell, while the employment share of occupations with a moderately high content of abstract and routine skills increased (Messina et al., 2016).<sup>3</sup> In addition, there is evidence based also on Chile’s CASEN survey of a wage premium

> 3 The study also shows indirect evidence for Brazil, Mexico, and Peru in the 2000s suggesting an increase in the share of high-wage occupations in total employment.

6
mobility of sampled volunteers, thorough tracking procedures led to relatively low non-response rates at follow-up: 88 percent of the 759 youth were tracked and completed the follow-up survey.

# SECTION 3: EMPIRICAL IDENTIFICATION STRATEGY

Identification of program impacts relies on a difference-in-difference (DiD) estimation, where outcomes are observed for the treatment and comparison groups for two time periods: at baseline, prior to the launching of the selected projects, and at follow up, one year following the implementation of the selected projects.

The DiD estimator compares the changes in outcomes over time between selected and non-selected youth. The **first difference** is the difference in before-and-after outcomes for selected youth to control for factors that are constant over time in that group. **The second difference** is the difference in before-and-after change in outcomes for non-selected youth who did not enroll in the program, but were exposed to the same set of environmental conditions. Subtracting both differences averages out any constant (time-invariant) observable & unobservable differences between both groups to attribute the impact estimate to program participation.

The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up.<sup>9</sup> We estimate the following individual-level intent-to-treat regression: where Y�� is the outcome of interest for respondent i in period t, T� is a post-treatment year binary variable, D� is a binary variable for being assigned to the treatment, and μ� is a fixed effect for NGOs. α represents the baseline average for the outcome of interest for non-selected youth. β is the difference in after-and- before intervention in outcomes for non-selected youth. β� δ is the difference in after-and- before intervention in outcomes for selected youth. γ is the difference in

> 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest.

9
While the existing literature on informal businesses’ exposure to bribery shows that business<sup>3</sup> characteristics often matter, a clear framework is lacking. A first contribution of this paper is such a framework, albeit a simple one. A second contribution is to provide evidence using a representative and business-level data set; these survey-based data use a novel sampling approach, affording indepth, micro-level information on informal businesses, which are often absent from such firm-level data sets. A third contribution is to provide empirical results of this exposure. Specifically, results show that informal-sector operators with a higher opportunity cost of formalizing are more exposed to an extortion of a bribe; a business’s ability to pay is found to have a limited effect. Two measures of the opportunity cost of formalizing are used. First is a measure of whether the informal business owner started the activity due to a lack of other income (NECESSITY); since this measure is selfreported and opinion-based, an alternative measure is used. A second measure is if any member of the owner’s household has employment under a contract (NOWAGE). Positive and significant results for NECESSITY and NOWAGE are largely robust to methods adjusting for the potential endogeneity of the regressors. These methods leverage instruments of whether the owner started in the sector of activity before or after the 2009 dollarization of Zimbabwe’s economy.

The remainder of the paper is organized as follows: Section II summarizes the literature on informality and corruption, section III discusses the background of these issues in Zimbabwe specifically. Section IV presents a stylized model. Section V describes the data used, and section VI gives the main econometric specification. Section VII includes the main results, and VIII concludes.

# II. Literature on Informality and Corruption

# _<u>Informality</u>_

A broad literature discusses the informal sector as a driving sector of economic activity and employment. While this stylized fact remains largely undisputed, the characterization of the sector itself is a matter of debate, particularly over the nature of informal employment. A first view is that informality’s prominence is a result of market segmentation. From this perspective, informality results from mostly insurmountable barriers to formal employment<sup>4</sup> or to starting a business (De Soto, 1989). In this sense, informality is not a free and willful decision, but a reflection of necessity

> 3 Stylistically the term “business” or “business activity” is used to describe informal “firms”; while the latter term “firms” is typical of some literature, it is generally avoided here, as it connotes a legal status more consistent with operating in the formal sector.

> 4 Amin (2009) summarizes several early works.

(3)