rafmacalaba/datause-encoder-data huggingface dataset:
# _Fiscal Accounts_

## **Following the 2002 economic crisis, Uruguay made significant progress in improving its fiscal framework**

**and outcomes** . Starting in the mid-20th century, fiscal imbalances and the monetization of deficits resulted in chronically high inflation and nominal volatility, while Uruguay’s economy diverged from the frontier. Inflation returned to single-digit figures only in the 1990s, as fiscal imbalances moderated and the monetization of deficits was phased out.<sup>3</sup> Fiscal outcomes improved after the 2002 crisis, underpinned by prudent macroeconomic and fiscal policies, sound debt management, and robust economic growth.<sup>4</sup> The gross debt of the non-financial public sector, which spiked to 96.1 percent of GDP in 2003 following the depreciation of the Uruguayan peso, fell to a low of 53.8 percent in 2012 (Figure 3). The improvement in fiscal accounts stalled with the deceleration of growth in 2015, and the COVID-19 pandemic added to the challenges. Debt increased to 67.6 percent in 2020, driven also by the depreciation of the Uruguayan peso (about half of public debt is nominated in US dollars).

**Despite the growth slowdown since 2015 and COVID-19 challenges, Uruguay emerged from the pandemic as the country with the lowest sovereign spreads of the region** . Uruguay implemented a reform agenda to enhance the macro-fiscal framework, which included a structural balance fiscal rule, an expenditure ceiling linked to potential GDP growth, the establishment of an independent committee of experts to provide inputs to estimate the structural balance, and an independent advisory fiscal council. At the same time, it rationalized spending not directly linked to the pandemic, such as public wages, to buffer the negative impact on fiscal accounts. In addition, a reform to improve the long-term sustainability of the pension system was approved in 2023. As the post-pandemic recovery went underway, Uruguay found itself as the country with the lowest sovereign spreads of the region, and improved credit ratings from the main credit agencies.

**Figure 3: Uruguay's fiscal outcomes**

a) Fiscal Balance of the non-monetary public sector b) Debt-to-GDP ratio of the non-monetary public sector In percent of GDP In percent of GDP <!-- Start of picture text --> 0%<br>-1%<br>-2%<br>-3%<br>-4%<br>-5%<br>-6%<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> Source: World Bank database, based on BCU and MEF 3 Marandino and Oddone (2022).

> 4 See World Bank Group (2022) for more details.

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BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 12. Burkina Faso has made progress on improved water supply and sanitation**

<!-- Start of picture text --> a. Water b. Sanitation<br>90 100<br>80 90<br>70 80<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>1993 1999 2003 2007 2009 1993 1999 2003 2007 2009<br>Improved Unimproved Improved Unimproved<br>Percentage of population Percentage of population<br><!-- End of picture text --> _Source:_ WHO Joint Monitoring Program 2010a and 2010b, from Demographic and Health Surveys for 1993, 1999, and 2003; Questionnaire a Indecateur de Base du Bien-Etre for 2007 and 2009.

Most of the progress in water supply is to be found in urban areas. In 2001 the Office national de l’Eau et l’Assainissement (ONEA), the corporatized public water utility serving urban areas, implemented a performance-based service contract for its commercial and financial activities—without transferring management control to the private sector—linking a portion of the service contractor’s revenue to measurable efficiency improvements (Fall and others 2009). The results were impressive. Under a costrecovery tariff, Burkina Faso’s ONEA essentially recovered full costs with collection rates. At around $0.76 per cubic meter (m<sup>3</sup> ) for residential consumers and $2.15 per m<sup>3</sup> for nonresidential customers, as of 2003 Burkina Faso’s water tariffs were substantially higher than those found in other African countries with scarce water resources, yet they were affordable for most of the customers.

**Table 9. Evolution of operational indicators associated with ONEA**

|Year|Water delivered<br>(million m<sup>3</sup>/year)|System losses<br>(%)|Collection ratio<br>(%)|Average cost<br>($/m<sup>3</sup>)|Average<br>effective tariff<br>($/m<sup>3</sup>)|Total hidden<br>costs<br>($ million/year)|Total hidden<br>costs<br>(% revenues)|
|---|---|---|---|---|---|---|---|
|2001|26|15.9|85|0.89|0.86|4|33|
|2002|27|14.0|83|0.91|1.02|5|32|
|2003|28|15.2|78|1.09|1.12|7|31|
|2004|31|17.0|88|1.14|1.16|4|17|
|2005|34|18.3|93|1.15|1.20|3|13|
|2006|37|18.0|95|1.15|1.23|3|8|
|2007|40|18.0|95|1.15|1.23|3|6|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

Distribution losses, at 18 percent of produced water, are almost half of what is observed in utilities in other LICs (34 percent), despite a small increase in unaccounted-for water when the system was expanded (table 8). During the first few years (2001–07) of the service contract implementation in the ONEA service area, urban access to piped water increased from 53 percent to 63 percent, water became available 23
ownership of subsidiaries or on the expansion of the local branch network. Figure 4 (LHS graph) shows that in the banking sector while a little more than one-third of countries maintain an open regime in both cross-border trade and FDI, only about one-quarter of countries impose major restrictions on commercial establishment. In the insurance sector (RHS graph), only about one-fifth of the countries have an open regime in both cross-border trade and FDI, and about one-third of the countries maintain significant restrictions on commercial establishment. How far these alternative combinations of policy matter for policy is an issue to which we return in the penultimate section.

**Figure 5: STRI in financial services by region and sub-sector (Mode 1 and Mode 3)**

<!-- Start of picture text --> GCC SAR MENA EAP AFR LAC ECA OECD<br> Banking Auto insurance<br> Life insurance Reinsurance<br>Note: 103 countries included.<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Certain regional patterns emerge in terms of the policy restrictions on entry, ownership, and operation (Figure 5). GCC members and other MENA countries have among the most restrictive policies and generally do not allow majority ownership and control in a foreign invested financial institution.<sup>19</sup> Also, most of Asia‘s emerging and large economies restrict 19 Iran does not allow FDI in banking and insurance; and Qatar does not allow FDI anywhere outside Qatar‘s Financial Center. In Bahrain, the foreign ownership limit is 49%, for Kuwait 40%, and Saudi Arabia 40%. In addition, the licensing requirements are not predefined, which increases the discretion of the regulators significantly. In Yemen and Morocco, if foreign ownership is greater than 10% of the equity, approval of the Governor of the Central Bank is required. In Egypt, entry through subsidiary is not allowed, and for expansion of existing banks, the (not-predefined) requirements specified by the Central Bank should be fulfilled.

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their main activity for at least nine years. Conversely, entrepreneurs in the lowest quartile of experience exhibit decreased revenues after being exposed to the basic program. These results are confirmed by several robustness checks. We can rule out factors that may be associated with firm longevity such as education, asset accumulation and networks as explanations for this heterogeneity.

The implications from these findings are twofold. First, the delivery method affects the effectiveness of a training program, and individualized support can be especially helpful. Second, this kind of training program is most effective for experienced entrepreneurs. This could be because their experience complements the managerial skills provided by training programs. It could also be that these entrepreneurs have already overcome other binding constraints that still limit the ability of less mature entrepreneurs to grow their businesses.

The rest of the paper is organized as follows. Section 2 provides a review of the related literature. The third section provides further details of the MKUBWA program. Section 4 discusses our data and identification strategy and section 5 presents the results. Section 6 concludes.

# **2. Related literature**

The literature on business training programs has grown substantially in the past five years, documenting the results of multiple studies designed to test the effectiveness of these programs. McKenzie and Woodruff (2013) reviewed 16 randomized control trials and one regression discontinuity study evaluating the impact of business skills training. They observed that while some programs successfully increase knowledge or promote the adoption of new business practices among entrepreneurs, the vast majority of these experiments do not detect any effect on revenues or profits. Yet, we should not conclude from these results that managerial capital is not relevant to firm performance. A subsequent paper by the same authors (2015), building upon work by Bloom and Van Reenen (2010), and drawing on data collected in six countries (Bangladesh, Chile, Ghana, Kenya, Nigeria, Sri Lanka), showed that even for small informal firms, business practices matter and are associated with larger profits and higher growth. In this later paper, McKenzie and Woodruff indicated that if most business training programs do not detect any effect on business outcomes, it is most likely because their effect on business practices, while significant, is too small.

Moreover, a key insight from McKenzie and Woodruff’s 2013 review article is that there are substantial differences among the programs that were evaluated, and that program characteristics, particularly in terms of content, significantly affect their effectiveness. Most training programs focus on general business skills applicable to a wide range of activities. These programs seek to encourage practices such as keeping records, separating the household and the business budgets, or expanding marketing efforts. However, it appears that the interventions that 3
developing world. These findings have helped establish a correlation between innovation and growth. In a host of categories, different measures of knowledge inputs to production continue to increase, and measures of the returns to these inputs outstrip those to less knowledge-intensive production. The evidence confirning the positive returns to investments in knowledge is vast. Recent meta-analyses summarized 57 published studies of rates of return to industrial R&D at both the firm and the industry level [OECD 2001] and 292 published studies of agricultural R&D demonstrated consistent high double digit returns [Pardey 2001]. Moreover, as the recent flagship report on closing the gap in education and technology [LAC World Bank 2002] confirmed, within the context of the knowledge economy framework, the "interaction between technology and skill is critical in determining growth, productivity and the distribution of earnings." Third, and perhaps most important from the perspective of the World Bank, technological capacity appears to be contributing to accelerated growth in some large developing countries (e.g., China, India, Brazil, Mexico, Philippines, Thailand, Malaysia). High tech manufacturing exports have grown faster than all other categories and developing countries are gaining a larger share of this expanding trade. These developing countries, which are now benefiting from a combination of technological capacity, openness to trade and other comparative advantages (such as lower wages), are also experiencing accelerated rates of economic growth. What is more, the countries benefiting are home to over half the world's population, and more than half of the world's poor. Additional evidence [Dollar and Kraay, 2001] shows that incomes of poor people are rising proportionally with growth rates.

Table 4a and Figures 4b and 4c on the following page present the quantitative evidence for the correlation between increased technology content of exports and GDP growth.<sup>9</sup>

> 9 For an annotated listing of recommended readmg relevant to S&T and economic growth as well as S&T

> and each of the sectors covered in the preceding sections, consult the annotated bibliography in _Annex 6 of Volume Two._

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compares the change in outcomes in the treatment group to the change in outcomes in the comparison group. By comparing changes, we control for observed and unobserved timeinvariant characteristics as well as for time-varying factors that are common to the treatment and comparison groups. The change in the comparison group is an estimate of the true counterfactual – i.e. what would have happened to the treatment group if there had been no intervention.

We estimate a multivariate regression specification of the difference-in-difference model where an individual’s outcome is regressed against a dummy variable indicating whether the facility was receiving P4P that year, an individual fixed effect, a year indicator, and a series of individual and household characteristics. We compute robust standard errors clustered at the district by year level to correct for correlation of the error terms across facilities within districts. The models were estimated using Stata version 10.

# **_Covariates_**

In all of the estimated models, we control for the number of family members, the number of family members under six years old, family assets, the distance of the household from the facility, and whether the family is enrolled in health insurance. Assets are measured as the value of owned houses, durables in the house, farm animals, farm equipment and microenterprise equipment. We also include a variable for if the household owned land. In the prenatal care and delivery analyses, we also control for maternal years of schooling, marital status, whether the partner currently lives in the household, the number of prior pregnancies, and age defined in years. In the prenatal care quality analysis, we also include a variable indicating whether the observation was from the facility exit interview or household survey. In the children’s utilization 8
positive (negative) number means that the factor concerned raised (lowered) the debt ratio during that sub-period.

|**Table 1 : Factors Exp**|1998-2004<br>**laining Governm**|2005<br>**ent Debt to**|2006-08<br>**GDP Path**<sup>**a/**</sup>|2009-2010|
|---|---|---|---|---|
|Change in Government Debt / GDP|2.76|-11.72|-3.08|0.28|
|Underlying factors:|||||
|Primary balance|2.17|2.13|1.95|2.55|
|Real GPD growth|-3.79|-6.01|-5.62|-4.57|
|_Real Interest Rate_|_1.68_|_-1.36_|_-1.11_|_2.49_|
|_Real Exchange Rate_|_0.81_|_-3.92_|_-1.98_|_-2.48_|
|Privatization|-0.3|-0.04|0|0|
|Other factors|2.19|-2.84|3.58|2.30|

> a/ The numbers in the table are the _average annual_ impact of each factor for each sub-period of time in percentage points of GDP.

Source: CBSL Annual Reports, Bank staff calculations.

If we compare the debt build-up phase, 1998-2004 (when debt rose sharply and stayed above 100 percent of GDP until 2004) with the subsequent rapid decline over 2005-2010:<sup>7</sup>

- The primary fiscal deficit hovered around 2 percent of GDP—there was not significant change here

- Faster growth over 2005-2010 resulted in an _additional_ erosion in the debt ratio equal to 1.5 percentage points of GDP per year (-3.8 over 1998-2004 compared to -5.3 over 2005-2010)

- Combined real interest rate and real exchange rate impact went from +2.5 percentage points of GDP over 1998-2004 to -2.4 percentage points over 2005-2010, a swing of close to 5 percentage points per year.  This was the biggest factor by far and explicable largely by a pick-up in inflation, as we shall see below.

Almost half of the 24 percentage point reduction in the debt ratio between 2002 and 2010 occurred that year, which followed the devastating tsunami of December 2004. Nominal interest rates dropped significantly and the currency appreciated in nominal and real terms as aid inflows

> 7 These observations are based on comparing the numbers for 1998-2004 with those for 2005-2010.  The reason for splitting up the latter period in Table 1 will become apparent below.

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# _(B) Uzbekistan_

<!-- Start of picture text --> Self-Reported Vaccination Intentions in Uzbekistan<br>Apr-22<br>Mar-22<br>Feb-22<br>Jan-22<br>Dec-21<br>Nov-21<br>Oct-21<br>Sep-21<br>Aug-21<br>Jul-21<br>Jun-21<br>May-21<br>Apr-21<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Definite yes Lean yes Lean no Definite no<br><!-- End of picture text -->

# _(C) Tajikistan_

<!-- Start of picture text --> Self-Reported Vaccination Intentions in Tajikistan<br>Mar-22<br>Feb-22<br>Jan-22<br>Dec-21<br>Nov-21<br>Oct-21<br>Sep-21<br>Aug-21<br>Jul-21<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Definite or Already Lean yes Lean no Definite no<br><!-- End of picture text --> _Source: World Bank “Listening to Central Asia” Question: “If any vaccine to prevent COVID-19 were available today, would you...” Options included: a) Definitely get the vaccine, b) Probably get the vaccine, c) Probably NOT get the vaccine, d) Definitely NOT get the vaccine, e) Already got the vaccine, f) Do not know._ As each of the three surveys uses a panel design, individual responses can be tracked over time to identify transitions in responses. Identifying these changes is particularly useful to describe changes in views among previously vaccine hesitant respondents. Table 1 panel (A) shows the panel transition matrix of reasons for vaccination refusal from April 2021 to May 2022 in Kazakhstan, panel (B) shows the same for Uzbekistan from April 2021 to April 2022, and panel (C) for Tajikistan from July 2021 to March 2022. In each, the leftmost column can be thought of as the “starting” response and moving from left to right reports the share of respondent observations that shifted into one of the other response categories (or stayed the same) in later rounds of the survey.

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Equations (1) and (3) are jointly estimated using the generalized method of moments estimator derived in Kapoor, Kelejian, and Prucha (2007), and implemented in R statistical software (Millo and Piras 2012).

# Results and Robustness Checks

This section describes the results of hypotheses testing based on the estimation of regression equations (1) and (3). Table 1 reports the generalized method of moments (GMM) estimates of these equations, with the explanatory variable cumulative values calculated over the 3-month window before elections to the election date _._ The first-stage coefficient estimates of the marginal effect of the instrumental variable, I are positive and statistically significant. This is consistent with our expectation that potholes are ββ more likely to appear in the municipalities with a larger number of daily temperature switches and more precipitation during the heating season. The Wald test confirms that the constructed instrumental variable cannot be excluded from the first stage without loss of statistical significance, rejecting the null hypothesis of weak instruments. The estimated spatial lag coefficient of the pothole complaints, , indicates negative spatial dependence. Turning to the second stage estimates, we see that the estimated marginal effect of digital public XX ρρ engagement measured by pothole complaints, , is positive and statistically significant in equations with total votes for incumbent and the margin of victory as dependent variables. Moreover, in both ββ equations, the magnitude of the estimated coefficient increases as the election date gets closer. These results indicate a sizeable positive effect of digital public engagement on the electoral support for the incumbent. Conditional on the municipal- and election year- fixed effects, each pothole complaint is associated with additional 29 to 44 votes for the incumbent and 18 to 27 votes increase in the margin of victory. The last column of Table 1 shows that an increase in digital public engagement also leads to a slightly lower turnout. However, this effect is not statistically significant. The estimated spatial lag coefficient of electoral competitiveness indicators, , indicates positive spillovers from neighboring municipalities. This coefficient, however, is not statistically significant. ρρ 17
and street address can cause them to fall out of the sample. There are two factors that may account for the different degrees of success in identifying treatment and control schools in the 2011-12 census. First, having received consistent financing for several years, and knowing that they could count on continued transfers through the TWP for the next few years, participating schools may have been more likely to remain open, in the same location and with the same “branding”, than control schools. Second, because treatment schools had more contact with the Ministry of Education through the TWP, they may have been more readily locatable when it came time to conduct the census. However, because the TWP and census are run by separate departments within the Ministry and one has no implications for the other (for example, no data from the census is used for the TWP), this seems unlikely to be an important factor.

**<u>Table 1: School sample and identification rates</u>**

||<br>Number of<br>applicant sch<br>Treatment|<br>qualifying<br>ools in 2008<br>Control|<br>% identified<br>school<br>Treatment|in 2011-12<br>census<br>Control|% matched<br>school<br>Treatment|to 2002-3<br>census<br>Control|
|---|---|---|---|---|---|---|
|Nord|99|114|84%|66%|55%|56%|
|Nord Est|107|139|76%|28%|56%|33%|
|Centre|110|59|65%|53%|44%|74%|
|Grand Anse|113|58|80%|55%|49%|44%|
|Nord Ouest|110|115|75%|58%|63%|69%|
|**Total**|**539**|**485**|**76%**|**50%**|**53%**|**57%**|

The low rates of school identification in both census waves are likely driven by both random and non-random attrition. The lack of consistent school identifiers and the poor quality of the school census data contribute to the limited number of schools matched, and these issues are believed to impact treatment and control schools equally. As discussed above, treatment also impacts attrition – there is a 26 percentage point difference in the identification rate between the treatment and control schools with the 2011-12 census. This differential survival may have implications for the measures of the program’s impact on school characteristics – as in all randomized experiments, attrition that is correlated with the treatment can bias impact estimates. In this context, survival depends in large part on a school’s ability to attract students and operate in a financially sustainable manner. If attriting schools are those that would have had the fewest students, then the impact estimates may be understated.

Using the 2002-3 school census, a test for balance between the treatment and control schools that are identified in both census waves also suggests that even after selection (only nonattritors are observed) the groups appear observationally equivalent prior to the program.

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