rafmacalaba/datause-encoder-data huggingface dataset:
These results have important implications for enhancing the development effectiveness of public spending. Simply increasing public spending on health and education is less likely to lead to better outcomes if countries have poor govemance. These findings are particularly relevant for developing countries, where, on average, the state of governance is quite poor. Two of the seven International Development Goals are: (i) Achieving universal primary education in all countries by 2015; and (ii) lowering child mortality by three-fourths and infant mortality by two-thirds by 2015. 1 Increasing public spending on health and education is an easier option than improving governance, but as our findings suggest the easier option may not lead to achievement of these goals.

# Appendix A.

This appendix provides details on the data used for this research.

# **I.** Data on health and education variables

The data on child mortality, infant mortality, adult illiteracy, public health spending, and public primary-school education spending were obtained from the World Bank Database (various years). The data on the percentages of females aged 15 and above with primary education are from Barro and Lee (2000). The education attainment variable in the regressions was computed using data on primary school intake rates as well as rates of completion of Grade 5, as explained below. These data were taken from UNESCO (various years).

The expected completion rate measure _ECompRO ,_ used to compute the "failure rate" in the education regressions (see Section 5.2) is taken from UNESCO data. UNESCO using the "Reconstructed Cohort Method" computes it. The method is explained in detail in Fredriksen (1991); it can be summarized by the following formula: _ECompPSOt5' = (Compo_<sup>_1_</sup> ).(Complf-2' ).(Comp2-3 ).(COmp3,4 _).(Comp45 )_

> The International Development Goals measure progress from 1990 and lookto whatcanbe achievedby 2015. They set targets for reductions in poverty, improvements in health and education, and protection of the environment, and have been adopted by the World Bank, the International Monetary Fund, and members of the Development Assistance Conmmittee of the OECD. They were supported during the Millennium Declaration of the United Nations, adopted by the General Assembly in September 2000.

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<!-- Start of picture text --> 20<br>— Low-cost (Unadjusted)<br>— Regulatory<br>15<br>160<br>125<br>=<br>oODa2 00<br>=<br>5<br>Pa<br>0<br>2023-06 2023-09 2023-12<br>Date<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>— Low-cost (Jaffe Calibration)<br>— Regulatory<br>15<br>160<br>125<br>=<br>Da2<br>oO 00<br>=<br>5<br>Pa<br>0 AA, pia bry nol I<br>2023-06 2023-09 2023-12<br>Date<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>— Low-cost (Local Calibration)<br>— Regulatory<br>15<br>160<br>125<br>=<br>iQ=NnDa se<br>a5<br>‘<br>2023-06 2023-09 2023-12<br>Date<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>— Low-cost (Local Calibration)<br>— Regulatory<br>15<br>160<br>125<br>=<br>Da<br>NniQ 00<br>a 5 SX<br>‘ RAH<br>0<br>2023-06 2023-09 2023-12<br>Date<br><!-- End of picture text -->
1. **Introduction**

An efficient selection of investment projects is essential for sustained economic growth. In centrally planned economies this selection process is biased towards large-scale investment projects and disregards private incentives. Whereas in a centrally planned economy the decision process about investment can be characterized as top-down, in the (ideal-type) market economy the opposite holds. Investment is carried out by the individual firm and the firm itself decides whether to invest or not. In general the firm is not able or willing to finance the entire investment with internal funds. Hence, banks and capital markets are called upon to provide external funds and thereby take part in the selection process of investment projects.

Given the economic importance of investment, it is clear that in the transition process of the formerly socialist countries in Central and Eastern Europe (CEE) the introduction of market incentives is crucial. One way to stimulate this is to have a financial system that allocates financial resources efficiently (Berglof and Roland, 1998, p. 18). It is therefore relevant to know whether the reforms have been successful (yet) in this respect. Roughly speaking two methods can be distinguished for the investigation of the success of the process of transition to market economy. The first is to conduct a survey among managers of industrial firms and financial institutions. Such a survey can investigate whether or not managers in transition economies still behave in line with the incentives typical of the centrally planned economy or whether they have changed their modes of behaviour and act nowadays more in accordance with the incentives of market economy. Examples of these survey studies are Pinto, Belka and Krajewski (1993) and Pinto and van Wijnbergen (1994).

The second approach for investigating the relationship between investment and finance in the transition process is to estimate an investment function. These studies use firm-level data to investigate whether investment is financially or liquidity constrained and whether the empirical relationship between investment and liquidity constraints corresponds with that found in similar studies of market economies. Examples of this type of studies are Lizal (1996), Anderson and Kegels (1997), and Lensink and Sterken (1998).

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from about one hour to two hours. In rural area, this travel time to reach the clinic goes from 64 to 160 minutes on average, and in the capital from 29 to 49 minutes.

In addition to traveling longer and farther, patients who bypass facilities nearer to their residence to seek care better adapted to their health needs, also accept frequently to incur higher user direct costs of care. Indeed, bypassing patients spend on average more than 2.5 times on consultation costs than do patients who use health centers closer to their home. When total costs of visits, including tests and prescription drugs, are considered, bypassers spend on average 2.3 times more.

While evidence seems to indicate that bypassing is mainly motivated by a quest for greater quality of care in more distant centers, there are also indications that bypassing can partly be explained as an attempt to reduce the cost of health. In our data, this is particularly true for patients who bypass a modern provider to visit an NGO. Indeed, those patients have spent on average 71% and 32% less than those who attended the closest clinic in rural areas and the capital city respectively. Also, patients using private clinics in the capital and that have bypassed a modern provider have spent on average 28% less than patients in private clinics that have chosen the closest provider (12 279 FCFA vs. 17 053 FCFA respectively) (not illustrated).

Table 11: Cost Incurred by Bypassing the Formal Health Sector

|||T|YPE|||LOCATIO<br>Other|N||
|---|---|---|---|---|---|---|---|---|
||Public|Private|Religious|NGO|Capital|Urban|Rural|Total|
|**Distance from Residence (Km)**|||||||||
|Without bypassing|7.7|3.2|5.6|3.1|3.2|3.4|8.6|6.8|
|With bypassing|14.5|8.7|21.3|27.1|11.2|15.8|18.0|14.3|
|**Travel time (Min)**|||||||||
|Without bypassing|57.2|64.8|54.9|42.1|29.4|58.0|63.7|57.5|
|With bypassing|186.7|43.2|159.3|51.1|48.6|258.2|161.0|123.8|
|**Consultation costs (FCFA)**|||||||||
|Without bypassing|173|614|300|285|569|265|167|245|
|With bypassing|211|1348|512|105|1041|292|243|608|
|**Total costs of visits (FCFA)**|||||||||
|Without bypassing|1491|3794|1697|1472|4603|1285|1378|1795|
|With bypassing|1835|8551|3163|1333|6642|2011|2374|4031|

Source: Chad 2004 QSDS and authors’ calculations.

The next section presents a simple model of bypassing which will then be tested empirically.

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Interestingly, the sequence of reform matters: mainline penetration is lower if competition is introduced after privatization, rather than at the same time (Figure 8). This result suggests that delays in the introduction of competition – for example due to market exclusivity guarantees granted to newly privatized entities – may adversely affect performance even after competition is eventually introduced. This could happen for three reasons. First, the importance of location-specific sunk costs in basic telecommunications suggests that allowing one provider privileged access may have durable consequences because sunk costs have commitment value and can be used strategically. Second, allowing privileged access creates vested interests that may then resist further reform or seek to dilute its impact.

<!-- Start of picture text --> Fig. 8. Sequencing of telecom reform and outcomes<br>30.00%<br>25.50%<br>25.00%<br>20.00%<br>15.00%<br>12.40%<br>10.00%<br>5.00%<br>0.00%<br>Privatization before Competition before<br>competition privatization<br><!-- End of picture text --> _Source_ : World Bank/ITU Telecommunications Policy Database & Fink, Mattoo, Rathindran (2003).

Sequences matter because of the implied changes in the regulatory environment: in one case, the incumbent is a relatively inefficient public operator and the regulator is well informed about the cost structure; in the other case, the incumbent is a relatively efficient private operator and the regulator is less well informed. It could be argued that new entry is easier to accomplish in the former situation.

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# **QUANTIFYING INSTITUTIONAL IMPACTS AND DEVELOPMENT SYNERGIES IN WATER RESOURCE PROGRAMS: A Methodology with Application to the Kala Oya Basin, Sri Lanka***

## **R. Maria Saleth and Ariel Dinar** **

**_Keywords:_** _Development Programs, Econometric Analysis, Food Security, Impact Pathways, Impact Synergy, Institutional Analysis, Institution-Impact Matrix, Kala Oya Basin, Millennium Development Goals, Perception Data, Sri Lanka, Stakeholder Evaluation._

> ∗This paper is a product of the study “ _The Institutional Matrix of the Millennium Development Goals: An Empirical Study of Food Security Goals in Kala Oya Basin, Sri Lanka_ ” funded by the World Bank Research Committee and the International Water Management Institute. Earlier versions of this paper were presented at the International Seminar on _Water Management “Technology, Economics and the Environment”_ , Fundacion Ramon Areces in Madrid, Spain, January 19-20, 2007 and in a seminar at the International Food Policy Research Institute, Washington DC, November 29, 2007.  We thank the participants in the two seminars for their comments.  We would like to thank Susanne Neubert, Bandi Kamaiah, Sarath Abayawardana, Seenithamby Manoharan, Ranjtih Ariyaratne, and Shyamalie de Silva for their support in data collection and analysis, and Carlos Espina and Mahfuz Rahman for their help with the MAPLE and MATHEMATICA software application used for derivative analysis.  We also thank the 67 respondents for their valuable time and insightful information without which this study would not have been possible.  This version of the paper benefitted greatly from comments provided by Daniel Bromley, K. William Easter, Elinor Ostrom, and Vernon Ruttan.

∗∗The authors are respectively Principal Researcher, International Water Management Institute, Colombo and Lead Economist, World Bank, Washington, DC.
44.5%, 8.4% and 47.1% of the airports in our dataset operate under increasing, constant and decreasing returns to scale, respectively.<sup>7</sup> LAC airports appear to be the ones that suffer the most from a suboptimal scale operation. Scale inefficiency is close to 20% (SE = 0.801), mainly concentrated in the increasing returns to scale area (70.5% of observations). This means that on average, airports in LAC could improve their efficiency 20% if they were to increase its scale of operation to the optimal scale. On the contrary, nearly 70% of Canadian and US airports operate in the decreasing returns to scale region. The results of return to scale diagnosis coincided with the intuition: airports in LAC are smaller and given that the production technology of airports is characterized by large fixed investments (runways, terminals) it is logical to expect that smaller airports are still in the increasing return to scale zone of the production function<sup>8</sup> .

As is the case whenever international comparisons among airports are made, in particular when efficiency is compared, care must be exercised when interpreting the results (UK Civil Aviation Authority, 2000). Airports operate under very different environments and subject to different sets of regulations. For instance, European airports tend to employ more personnel than US airports as the later usually lease terminals to airlines. Difference in the operational approaches followed by airports could influence the results. These differences are well explained in ATRS’ reports which are the most widely used data source for international airport comparisons, and the one used in this paper.

**Table 2: Average TE scores and scale efficiency by region (2005-2006 average)**

|**World**<br>**Region**|**Tec**<br>|**hnical efficie**<br>|**ncy**<br>|**Retur**<br>(%<br>|**ns to scale dia**<br>of observatio<br>|**gnosis**<br>ns)<br>|
|---|---|---|---|---|---|---|
||**TE-CRS**|**TE-VRS**|**SE**|**IRS**|**CRS**|**DRS**|
|**Latin America**|0.532|0.690|0.801|70.5|9.1|20.5|
|**Asia**|0.670|0.771|0.869|84.6|12.8|2.6|
|**Europe**|0.490|0.530|0.927|43.9|6.1|50.0|
|**Canada & US**|0.540|0.616|0.875|23.2|8.0|68.8|
|**All**|0.545|0.629|0.875|44.5|8.4|47.1|

Table A3 in the Appendix replicates Table 2 but adds the results of computing average TE scores using a model with two inputs (leaving runways and staff and taking out boarding bridges). Investment in boarding bridges show a significant underinvestment in LAC (569,000

> 7 The results presented here were confirmed using the bootstrapping method proposed by Simar and Wilson (1998) and the FEAR software developed by Wilson (2007). The Spearman correlation between DEA TE scores computed with the original sample and those computed using the data generated by a smooth bootstrap re-sampling (400 replications) procedure were in all cases higher than 0.90.

> 8 Airports identified as operating at the optimal scale (CRS) in our database handle between 20 to 30 millions of passenger each year. This result differs from previous estimations that found that the segment of constant returns to scale starts around the 3 million mark (Doganis, 1993).

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**Figure 1: Output growth volatility and crisis volatility, 1970-2006**

<!-- Start of picture text --> Output Growth Volatility Crisis Volatility<br>9% 30%<br>8%<br>25%<br>7%<br>6% 20%<br>5%<br>15%<br>4%<br>3% 10%<br>2%<br>5%<br>1%<br>0% 0%<br>1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006<br>EAP NON-OECD NON-EAP NON-OECD OECD EAP NON-OECD NON-EAP NON-OECD OECD<br><!-- End of picture text --> Source: author’s own calculations using WDI data. Volatility is measured as the standard deviation GDP per capita growth using a ten year window. Crisis Volatility is measured the portion of the standard deviation of per capita GDP growth that corresponds to downward deviations below the world average volatility threshold (see Box 1). Average volatilities are weighted by GDP.

**Nonetheless, volatility in East Asia remains well above OECD levels, and the current financial crisis is bound to exert an upwards pressure.** The current financial crisis demonstrates that East Asian economies remain sensitive to external shocks. Terms of trade in low and middle-income countries in the region are rapidly worsening, in particular in commodity exporting countries, and both domestic and foreign direct investments are drying up, limiting the potential for domestic growth. Recent evidence and forecasts suggest that the downturn in East Asia will be significant and that in many countries recovery will be slow. Overall GDP growth in East Asia is projected to fall to 5.3 percent in 2009 (against 8 percent in 2008), and some countries such as Malaysia, Thailand, Singapore, and Cambodia are facing negative expected growth rates. Moreover, while recessions tend to last on average one year, recessions such as the current one linked to Banking crises and equity busts are likely to last longer.<sup>3</sup>

**This study investigates factors that drove the long decline in output growth volatility in East Asia and the rest of the world, and derives lessons about ways to mitigate the impact of a more volatile international environment.** Our objective is to discover the overriding factors that contributed to lower macroeconomic volatility in the past decades, and to investigate factors that could reduce it further (or at least manage the current pressure of a more volatile international environment). We consider both domestic factors under countries’

> 3 World Bank East Asia and Pacific update, April 2009.

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<!-- Start of picture text --> Baseline scenario Policy scenario<br>Consumption: Consumption:<br>100t Import prices: $50/t 90t Import prices: $90/t<br>Import volume: 70t Import volume: 35t<br>State State B<br>B<br><!-- End of picture text --> <!-- Start of picture text --> Import prices: $70/t Import prices: $75/t<br>Import volume: 30t Import volume: 55t StateC<br><!-- End of picture text -->
Preface Adjustment with growth is the dominant objective of reform programs supported by the World Bank in the past few years. Within this context, African countries, particularly those with high indebtedness, aim at achieving structural adjustment while pursuing stabilization goals. In many instances, stabilizing domestic and external debt as a ratio of GDP is a prerequisite for a successful medium-term structural adjustment strategy.

This paper brings into sharp focus the relationship between the government deficit and key macroeconomic parameters. It discusses the consistency of fiscal policy with the stabilization objectives of government, both in a historical context and as a background for evaluating proposed policy actions. By calculating the "operational" deficit, which best reflects the government's claims on resources, and combining the accounts of the Central Bank with the fiscal balance, the paper presents an integrated framework for the assessment of the fiscal stance and its consistency with the inflation, the growth, the interest and the real exchange rates.

The conceptual framework is applied in Ghana and Kenya, and the empirical results are compared with critical developments in the past and with projections for the period through 1991. Apart from the analytical conclusions, the authors point out the need for more accurate information on the stock and flow statistics of Government and the Central Bank for the effective design and successful implementation of an adjustment strategy.
# **5.2 Informal firms have lower productivity, with consistently lower levels of management and technological capabilities across all types of informal firms**

Consistent with the finding in the literature that informal firms are highly inefficient (La Porta and Shleifer, 2014), the BPS data for South Asia shows that informal firms are largely present in the first two terciles of pre-pandemic labor productivity (Figure 3, panel a)<sup>15</sup> and that there is a clear negative relationship between the degree of informality and productivity (Figure 3, panel b).<sup>16</sup> Figure 3: Informality according to normalized polychoric **PCA 5** vs. labor productivity <!-- Start of picture text --> (a) Proportion of formal and informal by tercile of labor (b) Labor productivity vs. Informality<br>productivity<br>.4 Formal .4<br>Informal<br>.3 .2<br>.2 0<br>.1 -.2<br>0 -.4<br>1 2 3 -.2 0 .2 .4 .6 .8<br>Terciles of initial labor productivity PCA Informality<br>Proportion of firms<br>Labor productivity standardized at country level<br><!-- End of picture text -->

**Notes:** Panel (a) classifies as informal firms those with a value above the median in our continuous measure of informality ( _PCA 5_ ) Panel (b) controls for size, sector, age, country, and severity of the crisis. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

These results hold consistently for other predictors of firms’ total factor productivity, such as managerial capabilities (see Bloom et al., 2019, 2022; Grover and Torre, 2019) and technological readiness.<sup>17</sup> Management and digital readiness are negatively correlated with the degree of informality (Figure 4, panels a and b), and especially so for parasite firms. While fully formal firms apply, on average, 54% of structured management practices, parasite firms implement around one-third (4, panel c). The results for digital readiness are very similar (Figure 4, panel d) in line with the management literature confirming that managerial

> 15 Informal firms are defined as those with above median value of _PCA 5_ .

> 16These results are robust to using our other measures of informality, as shown in Figures A2 and A3.

> 17The management score is computed using three dichotomous variables that indicate whether the firm monitors targets at least once a month, advertises the business at least once every six months, and promotes staff promotions based solely on performance, in a way comparable to (for example, Bloom et al., 2019). While we present the results using _PCA 5_ as a measure of informality, they are robust to using other alternative measures ( _PCA 1_ and _PCA 2_ , as shown in Figures A4 and A5 of the Online Appendix. Technological readiness is computed based on the pre-pandemic period status of firms’ use of digital payments solutions, social media, or big data analytics for marketing and Enterprise Resource Planning (ERP) for business administration and operations.

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because (say) no one was home or the respondent was too busy—then enumerators made as many as two return visits. If the enumerator was unable to complete the interview after three attempts, a new household from the same neighborhood was selected.

During the interview, respondents were asked to show their electricity and water bills. The enumerator recorded respondents’ utility customer ID numbers on the questionnaire form. In the case of electricity, the ID number allowed us to verify that the household interviewed was the same household that had been randomly selected; we were then able to match this sample household with their EEU customer billing record. In the case of water, the customer ID number helped us identify the sample household in the AAWSA customer billing records. We were able to match 99% of the completed interviews to the electricity utility records.<sup>17</sup> Yet we could match only 70% of our observations of water private connections to the water utility database; as mentioned in note 3, for the remaining 30% we relied on self-reports and back-calculations.<sup>18</sup>

# **4. Analytical framework for calculating the distribution of household electricity and water subsidies**

# **_Basic subsidy incidence calculation_**

This section describes how we estimated (a) the electricity and water subsidy received by each of the 366 households with both private electricity and private water connections and (b) the incidence of subsidies across these 366 households. For each household _i_ , we calculated the subsidy it received for

> 17 Only nine electricity customer ID numbers could not be matched to the electricity utility records. These observations were not included in our final sample.

> 18 The remaining sample households had either shared connections (394) or no water connection (211 households). Most households with shared connections were not able to show a water bill to the enumerator, which prevented us from obtaining the billing ID that would have allowed for matching with the water utility’s billing records data set.

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