rafmacalaba/datause-encoder-data huggingface dataset:
This paper uses the case of Uganda to explore three issues in more detail. First, we are interested in the prevalence and incidence of corruption across different government departments as assessed by the private and the public sector, and the extent of knowledge on how to respond to corruption and hold officials accountable. Second, we are interested in factors that affect the extent to which households are exposed to bribery, hypothesizing in particular that greater ability to hold officials accountable will decrease the likelihood of such bribery. Finally, we expect that such accountability will affect not only corruption but also the quality with which public services will be provided.

The paper is structured as follows: Section two links the topic to the broader literature and discusses the conceptual framework as well as the strategy to implement econometric tests using the data at hand. Section three describes the data from Uganda’s second integrity survey and uses them to provide descriptive evidence on the incidence of corruption across institutions, households’ exposure to corrupt practices and their knowledge of procedures to report corruption, and the quality of public service delivery. Section four provides econometric evidence to identify the impact of greater accountability on the incidence of bribe taking and the quality of public service delivery. Section five concludes by summarizing the evidence and translating it into a number of recommendations for policy.

# **2. Conceptual model and relation to the literature**

Issues of governance have long been one of the neglected areas in development economics (Bardhan 2000). Recently, however, there has been increasing interest in quantifying the levels of corruption, transparency, and accountability across countries to improve understanding of their impact and identify measures that can help to reduce undesirable consequences. This has brought to the fore the fact that corruption is widespread and constitutes an important obstacle to economic growth, in particular through its impact on the nature and quality of spending on public service provision and the investment decisions by foreign and domestic private entrepreneurs that, in return, will affect the size of a country’s tax base. As developing countries share many characteristics that make them particularly prone to high levels of corruption, a consensus that actions to improve governance would be warranted and needed in addition to growth-enhancing policies is now emerging among international financial institutions. While this has resulted in the establishment of institutions that can help combat corruption, less attention has been devoted to the need to also enhance accountability in order to make these institutions work. To illustrate this point and provide a basis for subsequent empirical investigation, we develop a very simple model of the way in which greater accountability affects the incidence of corruption and the quality of public service delivery and discuss how this can be implemented econometrically using data from Uganda’s second integrity survey.

# **2.1 Insights from the literature**

2
between-group inequality, while the Salter-Swan model with heterogeneous firms predicts a fall in withingroup inequality. We formally evaluate this prediction in the next section.

The simulation exercise also generates other interesting results. Figure 8, for example, shows the predicted and actual share of employment in the traded sector. The share of employment in _a_ is predicted to fall as the relative price of tradables falls. Figure 8 shows that the model over-predicts the actual shift between sectors, but it is worth pointing out that the model has no adjustment costs. Estimates of the cost to workers from moving between sectors are very high (Kaplan, Lederman, and Robertson 2013), and therefore it is not surprising that the actual shifts are smaller.

Finally, the “between industry” inequality is measured by the ratio of average wages of sector _a_ to average wages in sector _b_ . There is a slight increase in the prediction of between-industry inequality during the GATT period, and a much larger increase after 2000. Note that the overall change in between industry inequality is less than 2% over the entire sample, which is a result that can be compared using Mexican data.

The structural model relies on the assumption that workers are homogenous and that changes in labor-income inequality is driven by inequality within the sector with heterogeneous firms. Although the model is tractable, it is worth assessing if these features of the model are broadly consistent with Mexican labor market data. <mark>We now compare the model’s predictions to Mexican labor force data.</mark>

# IV. Variance Decomposition 1990-2010 Census Data

We begin by using data from the Mexican Population Census. We employ the 10% samples from the 1990, 2000, and 2010 Mexican censuses (Minnesota Population Center 2014). Our main variable of interest is the hourly wage, which is computed by dividing monthly earnings by reported hours worked during a typical week times 4.33. All earnings measures are converted into 1990 US dollars. First, we converted Mexican wages into dollars by using the nominal exchange rate from the Banco de Mexico. We then deflated the wages to 1990 dollars using the US Consumer Price Index (CPI) obtained from the Bureau 16
rely on cross-sectional survey data due to the constraints of data availability (Adger, 1999; Datt and Hoogeveen, 2003; Carter _et al._ , 2007; Lal _et al._ , 2009). Cross-sectional studies observe a set of units (e.g., households or individuals) at a single point in time, allowing researchers to make comparisons and draw conclusions about prevalence of poverty and inequality. However, cross-sectional data comes with inherent limitations. It only provides a snapshot view of poverty across units at one point in time but does not offer insights into changes over time or the dynamics that drive these changes. This lack of temporal perspective can lead to omitted variable bias and severely limit causal inferences. Recognizing these limitations, many studies have turned to panel data, which observes the same units across multiple time points (Giesbert and Schindler, 2012; Rodriguez-Oreggia _et al._ , 2012; Yamamura, 2015; Keerthiratne and Tol, 2018; Baez _et al._ , 2020; Sedova _et al._ , 2020; Sohnesen, 2020; Cappelli _et al._ , 2021). Panel data allows researchers to track changes over time and explore the temporal dynamics of poverty and inequality; consequently, it also offers a more in-depth understanding of the effects of climate change and natural disasters, which often have a longitudinal nature themselves.

However, the use of panel data is subject to several barriers. In particular, tracking the same households (individuals) over a long period is expensive and poses logistical challenges, especially where households migrate or live in remote locations. In particular, attrition—where households drop out of the panel for various reasons—is a well-known issue affecting the quality of panel data and would require considerable resources investment and advanced survey strategies to address (Schoeni _et al._ , 2013). As an example, the attrition rate in the panel Russia Longitudinal Monitoring Survey (RLMS) is already 40 percent in the first 10 years after it was fielded, but sharply increases to more than 70 percent after another 10 years (Kozyreva _et al._ , 2016). These challenges can be amplified in poorer countries (or conflict situations), where both resources and data collection capacity are limited. Given these challenges, alternative data imputation methods have been proposed to construct synthetic panels from repeated cross 13
children are incorporated into the preschool system. This is true for both groups of children. However the data reveal a significant catching up among children of low-educated mothers starting with the 1992 cohort, i.e. the cohort supposedly entering pre-primary school (at age 5) in 1997. Notice that this is exactly the first cohort who should have benefited from the infrastructure expansion. The same pattern is found when we condition on household fixed effects, although differences between the two groups are generally smaller.

It is important to point out that the data in Table 3 are based on retrospective information on preschool attendance. One might be concerned that the acceleration in pre-school attendance across cohorts in Table 3 is a statistical artifact of the data, stemming from older cohorts being more likely to underreport preschool attendance due to (systematic) recall error. To get a grasp of this in Table 4 we compare the retrospective preschool data (from 2001-2005) with contemporaneous statistics computed from preschool attendance reported by parents of children age 3, 4 and 5 in several waves of the ECH before 2001. The statistics presented in this table are for all children and for the cohorts covered by the paper (1986-1998). In columns (1) to (3), we report the mean level of preschool attendance by cohort for children age 3, 4 and 5 respectively based on 1989-2000 data. Assuming that the children who enter preschool do not leave it before enrolling in primary school and that every child enrolls in primary school at age 6, these statistics provide unbiased estimates of the number of preschool years attended by each cohort. In practice, the share of each cohort enrolled at age 3 will be an unbiased estimate of the proportion of children having attended at least 3 years of preschool. Similarly, the proportions at age 4 and 5 provide estimates of the share attending at least 2 years and 1 year of preprimary education respectively. The sum of these proportions, reported in column (4), gives an estimate of the number of years of pre-school completed.<sup>5</sup> We report the same statistics based on retrospective information from the 2001 to 2005 data in columns (5) to (8). If

> 4 In the regression exercises that follow we address this problem by conditioning on month of the survey.

> 5 Let P(A=j) be the probability of attending preschool at age j and P(Y=k) the probability of having attended k years of preschool. Under the assumptions in the text (no drop out and primary starting age equal 6) average preschool years by

10
owned, reverse causality is only a concern if the effect of investment on mobile money adoption is not the same for both female-owned and male-owned firms. If the simultaneity between mobile money and investment is the same for both female-owned and male-owned firms, then this is less of a concern if we find a positive effect of mobile money on investment for female-owned firms but not for male-owned firms. Our strategy is to account for as many firm-level factors as possible to limit the issue of omitted variable bias. These are described in detail below.

Larger firms may have different investment needs than smaller firms and thus firm size can influence investment (Cull and Xu, 2005). Larger firms may also have more access to resources and networks to carry out investments than small firms. The same argument can be employed for productive firms, and multi-establishment firms as opposed to single-establishment firms. Thus, we account for firm size ( ), whether the firm is part of an establishment (M ), and labor productivity ( ) in equation (1). We also we account for firm age ( ) given technology SSMMSSII MMIIMM upgrades are likely to be influenced by the age of the firm (Cull and Xu, 2005). Several studies LLM LLM LL AAMMII have uncovered relationships between a firm’s performance and its age and size (Biesebroeck 2005; Bigsten and Gebreyeesus 2007; Haltiwanger et al. 2013). We also account for power infrastructure quality and ownership of generators that are important for firm performance (Cole et al., 2018).

Manager characteristics may be positively related to investment (McMillan and Woodruff, 2002). Optimistic and more experienced managers may be more likely to invest, and thus manager experience (M ) is captured in the empirical specification. Also following the literature, we capture whether firms offer formal training ( ) given that it improves human capital capacity IIM IIMM and thus raises productivity and investments (Kinda et al, 2015). We also include variables such TTM MMII as exporter status (E ) and foreign ownership ( ) given that outward orientation of the firm is correlated with innovation and productivity, and therefore, with potentially higher level of EEMMII FFM II investment (Seker, 2012; Lopez 2005; Bernard et al. 2007; Dimelis and Louri, 2002; Guadalupe et al., 2012). Finally, the gender of the manager of the firm may affect the productivity of the firm and the propensity to invest due to a variety of factors (Schubert et al. 1999).

Access to finance influences the investment decisions of firms. Access to finance has also been found to improve firm productivity (Gatti and Love, 2008; Rajan and Zingales, 1998). This is 13
# **ASSESSING BANK PERFORMANCE AND THE IMPACT OF FINANCIAL RESTRUCTURING** IN **A MACRO-ECONOMIC FRAMEWORK**

# **A New Application**

by Yavuz Boray and Hector Sierra

||Table of Contents||
|---|---|---|
|I.|Introduction|2|
|II.|Methodology|3|
|Ill.|Description of the Model|_5_|
|IV.|An Application of the Model|23|
|V.|Model Simulations|35|
|VI.|Conclusions|40|
|Appe|ndix||
||A: Monetary Sector Indicators|42|
||B: Simulation Results|49|
||C: Equation and Closure List|52|
|P089743|2004|2008||0 Closed|Vietnam|
|---|---|---|---|---|---|
|P089743|2004|2009||0 Closed|Vietnam|
|P089743|2004|2010||0 Closed|Vietnam|
|P089743|2004|2011||0 Closed|Vietnam|
|P089751|2005|2005|5/25/2005|1 Closed|Azerbaijan|
|P089751|2005|2006|3/10/2006|2 Closed|Azerbaijan|
|P089751|2005|2006|6/25/2006|3 Closed|Azerbaijan|
|P089751|2005|2007|6/26/2007|4 Closed|Azerbaijan|
|P089751|2005|2008|1/19/2008|5 Closed|Azerbaijan|
|P089751|2005|2008|5/12/2008|6 Closed|Azerbaijan|
|P089751|2005|2009|11/12/2008|7 Closed|Azerbaijan|
|P089751|2005|2009|4/30/2009|8 Closed|Azerbaijan|
|P089751|2005|2010|1/9/2010|9 Closed|Azerbaijan|
|P089751|2005|2010|5/28/2010|10 Closed|Azerbaijan|
|P089751|2005|2011|3/7/2011|11 Closed|Azerbaijan|
|P089751|2005|2012|11/14/2011|11 Closed|Azerbaijan|
|P089751|2005|2012|11/14/2011|12 Closed|Azerbaijan|
|P089761|2008|2008|6/30/2008|1 Closed|Yemen,Republic of|
|P089761|2008|2009|12/23/2008|2 Closed|Yemen,Republic of|
|P089761|2008|2009|4/7/2009|3 Closed|Yemen,Republic of|
|P089761|2008|2010|12/29/2009|4 Closed|Yemen,Republic of|
|P089761|2008|2010|4/7/2010<br>|5 Closed|Yemen,Republic of|
|P089761|2008|2011|1/8/2011|6 Closed|Yemen,Republic of|
|P089761|2008|2012|8/26/2011|7 Closed|Yemen,Republic of|
|P089761|2008|2012|4/2/2012|8 Closed|Yemen,Republic of|
|P089761|2008|2013|9/19/2012|9 Closed|Yemen,Republic of|
|P089761|2008|2013|6/26/2013|10 Closed|Yemen,Republic of|
|P089761|2008|2014|1/11/2014|11 Closed|Yemen,Republic of|
|P089761|2008|2014|6/23/2014|12 Closed|Yemen,Republic of|
|P089761|2008|2015|12/22/2014|13 Closed|Yemen,Republic of|
|P089761|2008|2015|5/31/2015|14 Closed|Yemen,Republic of|
|P089774|2003|2008||0 Closed|Mozambique|
|P089793|2007|2007|4/19/2007|1 Closed|Brazil|
|P089793|2007|2008|7/16/2007|2 Closed|Brazil|
|P089793|2007|2008|6/27/2008|3 Closed|Brazil|
|P089793|2007|2009|12/19/2008|4 Closed|Brazil|
|P089793|2007|2009|6/23/2009|5 Closed|Brazil|
|P089793|2007|2010|12/14/2009|6 Closed|Brazil|
|P089793|2007|2010|6/24/2010|7 Closed|Brazil|
|P089793|2007|2011|2/14/2011|8 Closed|Brazil|
|P089793|2007|2011|5/10/2011|9 Closed|Brazil|
|P089793|2007|2012|11/2/2011|10 Closed|Brazil|
|P089793|2007|2012|1/1/2012|11 Closed|Brazil|
|P089816|2007|2007|3/22/2007|1 Closed|Nicaragua|
|P089826|2006|2006||0 Closed|Peru|
|P089826|2006|2019|5/9/2019|1 Closed|Peru|
|P089839|2007|2007|4/16/2007|1 Closed|Haiti|
income in the past month, we assign zero to the wage income for these individuals to make use of all the data. To match this categorical income variable in the LFS, we convert the continuous per capita income variable in the HEIS into a categorical variable with the same income categories.

We provide in Tables 5 and 6 a comparison of the distributions of the common variables across the two surveys for their overlapping years in 2008 and 2010, and test for their differences taking into account the complex survey design.<sup>26</sup> Given the different survey design, it is unsurprising that the means of the variables in the LFS are mostly statistically different from those in the HEIS. For example, households in the LFS generally have younger but more educated heads, a smaller share of young household members (ages 0-14 and 15-24), but higher shares of both younger and working age members (ages 25-59), are less likely to have selfemployed members, and more likely to live in urban areas. These differences help emphasize the need to benchmark the variables in the LFS against those in the HEIS.

# **IV.2.2. Estimation Results**

Using Proposition 3(i), we start first with transforming some positive variables including household size and age in the HEIS and LFS to normality using the Box-Cox method, then standardizing the variables in the LFS according to the distributions of the corresponding variables in the HEIS respectively for 2008 and 2010. As a result, t-tests (not shown) indicate that the distributions of the standardized LFS variables are not statistically different from those in the HEIS, which satisfies Assumption 1. To ensure that Assumption 2 is satisfied, we use the closest version of Model 6 in all the following estimation, where the income variable is in a categorical format as earlier discussed.

> 26 We implement this test by pooling data from the two surveys, setting the data to incorporate the complex sampling design, and running a (complex survey adjusted) regression of the variable of interest on a dummy variable indicating the survey round.

31
Jamasb, T., Mota, R., Newbery, D., M. Pollitt, 2004. Electricity Sector Reform in Developing Countries: A Survey of Empirical Evidence on Determinants and Performance. CMI Working Paper 47. Cambridge, The Cambridge-MIT Institute Kwoka, John E. (2002). ― _Vertical economies in electric power: evidence on integration and its alternatives_ ‖, International Journal of Industrial Organization, 20 653-671.

van Koten, Silvester and Andreas Ortmann _(_ 2007) ― _The Unbundling Regime for Electricity Utilities in the EU: A Case of Legislative and Regulatory Capture?_ ", mimeo.

Nagayama, Hiroaki (2007) ―Effects of regulatory reforms in the electricity supply industry on electricity prices in developing countries‖, Energy Policy, 35, 3440-3462.

Meyer, Roland (2010) “Benchmarking Economies of Vertical Integration in U.S. Electricity Supply:An Application of DEA‖, Jacobs University Bremen, mimeo.

Nemoto, J. and M. Goto (2004) ―Technological externalities and economies of vertical integration in the electric utility industry, International Journal of Industrial Organization, 22, 1, 67-81 Vagliasindi, Maria and John Besant-Jones (2012) _Revisiting Policy Options on the Market Structure in the Power Sector_ , forthcoming Direction in Development, World Bank, Washington DC.

Vagliasindi, M. and K. Izaguirre (2007) ―Private participation in infrastructure in Europe and Central Asia‖, Gridlines No. 26, Public-Private Infrastructure Advisory Facility (PPIAF) Zhang, Y.-F., Parker, D. and C. Kirkpatrick (2002) ―Electricity Sector Reform in Developing Countries: An Econometric Assessment of the Effects of Privatisation, Competition and Regulation.‖ No. RP0216. Centre on Regulation and Competition, University of Manchester, Manchester, U.K.

World Bank (2004) ―Operational Guidance for World Bank Staff: Public and Private Roles in the Supply of Electricity Services.‖ Energy and Mining Sector Board. Washington, D.C.

28
employees, the value of tangible fixed assets (thousands of 2001 U.S. dollars), and cost of materials (thousands of 2001 U.S. dollars); _INDUSTRYs_ is a vector of industry dummy variables defined at the 4-digit NACE level (1510 to 3663); _LOCATIONs_ is a vector of location dummy variables including a capital city dummy variable (equal to 1 if the firm is located in a capital city—that is, Belgrade, Bucharest, Kyiv, Prague, Sofia, Tallinn, Warsaw, or Zagreb—and 0 otherwise) and a large city dummy variable (equal to 1 if the firm is located in a city with a population of 250,000 or greater, and 0 otherwise); and _COUNTRY c_ is a vector of country dummy variables for Bulgaria, Croatia, the Czech Republic, Estonia, Poland, Romania, Serbia, and Ukraine.

Lastly,  _COMPETITION tm_  1, _c_ is the change in the level of competition in each industry _m_ of country _c_ from 2001 to 2003 and is equal to 1 minus the change in the four-firm concentration ratio for industries defined at the 4-digit NACE level (so that positive changes indicate higher levels of competition). The four-firm concentration of an industry is equal to the market share as measured by operating revenues of the four largest firms in each 4-digit NACE level industry and is country-specific.11 Given the lower data requirements, a much larger AMADEUS sample is used to calculate the competition indicator: 69,116 firms in 1,935 4-digit NACE industries from the 2001 sample and 77,265 firms in 1,970 4-digit NACE industries from the 2003 sample. Table 2 presents descriptive statistics of the competition indicator.

The above specification of the model addresses a number of econometric concerns. Given that the objective of the paper is to capture the effect of changes in the business environment on productivity growth of the average firm, the regression analysis opts for a balanced panel design, pooling observations across 296 NACE industries at the 4-digit level in eight countries with data in the years 2001 through 2004. Second, first-differencing firm characteristics and lagging business environment indicators by one year mitigates further endogeneity between unobservable firm heterogeneity and factor input choices. Third, the inclusion of lagged changes in log TFP addresses serial correlation that is not eliminated by first differencing. Given the necessary assumption made in the TFP estimation technique of Levinsohn and Petrin (2003) of a Markov process for productivity—that is, the conditional probability distribution of future states of the productivity, given the present state and all past

> 11 Market forms are often classified by their four-firm concentration ratio.  Perfect competition is associated with a very low ratio, monopolistic competition with ratios below 0.4, oligopoly with ratios above 0.4, and monopoly with a near-1 four-firm measurement.

14