rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Actual embezzlement Suspected embezzlement<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>RANDOM/ RANDOM/ ELECTIONS/ ELECTIONS/<br>PRIVATE INFO TRANSPARENCY PRIVATE INFO TRANSPARENCY<br>Fraction Embezzled (Mean)<br><!-- End of picture text -->

**Figure 5.** Citizens underestimate embezzlement by elected decision makers. The bars represent, by treatment condition, unweighted means (with 95% CI) of actual and suspected embezzlement.

_DV: Difference between the expected and the actual fraction embezzled_

|Population-weighted<br>(1)|Unweighted<br>(2)|
|---|---|
|Intercept<br>0.0023|0.0041|
|(0.015)|(0.019)|
|Elections<br>-0.055<sup>_∗∗_</sup>|-0.063<sup>_∗∗_</sup>|
|(0.023)|(0.026)|
|Transparency<br>-0.037<sup>_∗∗_</sup>|-0.051<sup>_∗∗_</sup>|
|(0.021)|(0.025)|
|Elections_×_Transparency<br>0.059<sup>_∗_</sup>|0.089<sup>_∗∗_</sup>|
|(0.031)|(0.035)|
|Obs.<br>1884|1884|

Standard errors in parentheses (adjusted for clustering by group).<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

**Table 3.** Elections and transparency bias citizens’ expectations. The table shows OLS coefficents,

the dependent variable being the difference between suspected and actual embezzlement.

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6 above 50 cents per piece.<sup>7</sup> This may help to explain why the United States only mandated universal home delivery in 1958, when letter volumes were above 300 per person per year (Campbell, 2004).

**Figure 2:  Estimated Relationship Between Costs per Letter and Volumes**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>0 50 100 150 200 250 300 350 400 450 500<br>pieces per capita<br>cost per piece<br><!-- End of picture text --> Table 3 suggests the breakdown of costs of US-quality delivery per piece of mail in a system of universal service in an economy where postal volumes per capita are low, based on data from the Cohen et. al. study and the World Bank. As seen, delivery would be expensive—over $0.76 per letter in a country with mail volumes of 10 per capita. Even without delivery to the door, the costs are high—over $0.60 per letter.<sup>8</sup> Again, this data only suggests orders of magnitude, as it is based on United States’ postal data adjusting for input costs—and the USPS is an operation in a very different (broadly more positive) environment from most developing countries. Nonetheless, the point is clear that letter delivery becomes considerably more expensive in lowvolume environments, not merely because of rising costs of home delivery, but also because the other fixed costs of the network are divided up amongst far fewer stamp purchases. And the reality for a great majority of poor countries is that total mail volumes will be small enough to suggest severe scale diseconomies—mail delivery will be comparatively very expensive to provide to all, let alone the difficult-to-serve.

> 7 Cohen et. al. Provide data on prices for US quality USO at United States’ input costs for various levels of letter per capita.  To adjust (at a high level of approximation) for differing input costs (primarily labor), the author used a regression of GDP PPP per capita on letters/capita to obtain estimates for GDP per capita at various levels of letters/capita.  I then multiplied the “unadjusted” Cohen estimates by (predicted GDP)/(United States GDP) in order to get output cost estimates adjusted for input costs.

> 8 The procedure to adjust the Cohen et., al. estimates was similar to the above except that GDP per capita at a given level of letters per capita was calculated from a regression of GDP on letters per capita for countries with letters/capita values within 50 percent of those listed in the table (0.5 to 1.5 letters, 2.5 to 7.5 letters and 5 to 15 letters per capita respectively).
speaking countries may also reflect the much stronger growth performance enjoyed by countries in this group, hence, the possibility of endogeneity.

**Figure 4 – Baseline: Total contributions of males and females to growth in English and French SSA**

<!-- Start of picture text --> SSA French<br>4<br>3<br>2<br>1<br>0<br>1980-84 1985-89 1990-94 1995-99 2000-04 2005-2010 1980-2010<br>-1<br>-2<br>-3<br>GDPPCGR Combined Female h, L, T contribution to growth Combined Male h, L, T contribution to growth<br><!-- End of picture text --> <!-- Start of picture text --> SSA English<br>4<br>3<br>2<br>1<br>0<br>1980-84 1985-89 1990-94 1995-99 2000-04 2005-2010 1980-2010<br>-1<br>-2<br>-3<br>GDPPCGR Combined Female h, L, T contribution to growth Combined Male h, L, T contribution to growth<br><!-- End of picture text --> 17
**Contents**

|I.|Intro|duction|**...........**<br>I|
|---|---|---|---|
|H.|Stru|ctural Adjustment and Aid Flows<br>..|3|
||H. 1|Overview of the Literature.|**3**|
||11.2|Empirical Evidence.|**4**|
|III.|<br>Data|Deficiencies and Adjustments.|8|
||111.|1 Data Deficiencis .8||
||I1.2|Adjustmens to the Data .10||
|||**111.2.1**<br>Foreign Assistance .10||
|||mI.2.2<br>Exports .11||
||**111.3**|<br> Results of tne Adjustments .12||
|IV.|The|Structural Adjustmint Program in Tanzania<br>.1|3|
|V.|Cros|s-Country Policy and Performance Assessment<br>.<br>.|.<br>.15|
||V. 1|Methodology<br>.<br>.<br>.15||
||V.2|<br>Policy Indicators<br>.<br>.<br>.16||
|||V.2.1<br>Real Effective Exchange Rate<br>..|16|
|||V.2.2<br>Current Account Deficit .....<br>......|.........<br>.<br>16|
|||V.2.3<br>Fiscal Deficit .................................|18|
|||V.2.4<br>Inflation Rate<br>..|19|
||V.3|Performance Indicators ..<br>.|20|
|||V.3.1<br>GDP Growth Rate ......<br>......<br>...............|.<br>20|
|||V.3.2<br>Investment ......................|1......<br>..<br>l.l|
|||V.3.3<br>Domestic Savings ...|2.3......<br>1|
|||<br>V.3.4<br>Export Growth ...|2'|
||V.4|<br>Overall Assessment ..||
|VI.|Summ|ary and Conclusions ...................................|26|
|Bibli|ograph|y .....................<br>..|2..........<br>..|
<!-- Start of picture text --> Tax Frontier and Actual Collections in Indonesia<br>S<br>&<br>io<br>ow<br>on<br>3<br>=e*ac<br>:<br>ce.<br>a4<br>2<br>]<br>1990 2000 Year 2010 220<br>—— Frontier ——— Actual<br><!-- End of picture text --> <!-- Start of picture text --> 3000 60% 2<br>2500 50% 2=<br>@ a<br>2 2000 40% fc]2<br>==== 15001000 30%2% ==2a55on<br>ze<br>500 3°a<br>o wt ool ox10% &28<br>oNSSSSESSSSSSSSSSSESSESSEANNMO NNNTM eRNNNOTMNNNNM TtTHVveeaonNNN ON ONO wa5—<br>2<br>aeMS CentralProvinceGovernmentGovernments ExpenditureExpenditure(LHS)(LHS) aE<br>meee District Governments Expenditure (LH)<br>——Portion of SNG Expenditures to Total Government Expenditures (RH5)<br><!-- End of picture text -->
Ihe deviation of productivity from best-practice first increases then decreases as the age of the firm increases. Very young firms and old firms exhibit the widest deviation from the most efficient firm within the industry. The elasticities with respect to age are, however, quite small.

As the Herflndahl index -which measures concentration or scale effects at the three-digit industry level- increases, the dispersion of productivity, as well as the level of productivity, first increases and then decreases. This might show that for low levels of concentration, firms may not have yet achieved their economies of scale and therefore exhibit low productivity levels, while for high levels of concentration it is the low degree of competition which causes low levels of productivity.

As firms are more geographically concentrated (i.e. as GEODISP increases), they sbow a greater deviation from the most efficient firm within a sector (i.e. DTFP decreases) and a lower level of productivity. Therefore, being more concentrated geographically is not increasing the level of competiton but rather is crowding out on the use of limited infrastructure and services. As noted earlier, this surely seems the case of Casablanca. In fact, the Government is putting effort into encouraging firms to **move** out of the condensed areas. As expected, the less firms specialize in production (the greater the product diversification) tho lower the productivity.

Finally, looking at the trade variables, which we are mainly concerned about, they tum out to be the most significant of all other explanatory variables in explaining productivity and have the expected signs as stipulated by our hypotheses above.

A higher share of export in total sales increases the level of productivity of the firm, or alternatively decreases the gap between the firm's productivity and the efficiency frontier in the corresponding industry. Tbis confirms the hypothesis that firms selling in external markets are forced to increase their productivity to stand up to the high competition found abroad. This is an important result considering the effort put by the Moroccan authorities to encourage exports as part of its liberalization program. It should be noted that the direction of causality between export and productivity is not known.

19
to August. Second, the Mini-Susenas is nationally<sup>representative and perhaps the</sup> 100 villages population is, by virtue of being smaller,<sup>poorer, villages also less</sup> variable. Third, maybe the Mini-Susenas<sup>has worse inter-temporal measurement</sup> error - but we know of no way of checking that.

These estimates of vulnerability are probably<sup>too high as they do not account</sup> for measurement error. We try to address this<sup>by the admittedly crude expedient of</sup> reducing the estimated standard deviation<sup>of changes in log expenditure by the</sup> estimated measurement error from the appendix,<sup>i.e. 30 percent for Mini-Susenas and</sup> 50 percent for 100 Village Survey data. In<sup>this case the level of vulnerability of the</sup> household at mean income and with mean variability<sup>of Mini-Susenas is 0.39,</sup> roughly a 40 percent chance of an episode of<sup>poverty in three years. It is still the</sup> case that even though the poverty line is chosen<sup>so that only 20 percent are "poor,"</sup> nearly a half of the population (47 percent)<sup>are vulnerable to poverty, in the sense of</sup> facing even odds (or worse) of one episode<sup>of poverty in three years. The lower</sup> variability in 100 Village Survey, meanwhile,<sup>resulting in 30 percent headcount</sup> vulnerable rate.

These high levels of vulnerability confirm the<sup>voluminous literature based on</sup> qualitative assessments of the importance of vulnerability<sup>in the analysis of poverty.</sup> The poor at any point in time are only a fraction<sup>of those who must worry about, and</sup> struggle to avoid, falling into poverty.

expenditures. This eliminates the arbitrary nature<sup>of defining near poverty line as simply a chosen</sup> percent above the poverty line.

16
We explore the economic magnitude of effects of increasing SCI, trade openness, and primary school enrollment by one standard deviation on the log of real GDP per capita. For panel estimations, we use the within-country standard deviation of the variables. For all the estimations, the magnitude of the effect of increasing SCI by 1 standard deviation (ranging from 0.002 to 0.009) is larger than the magnitude for primary school enrollment (0.0001 to 0.001). Comparing SCI with trade openness, the magnitude of the effect of increasing SCI by 1 standard deviation is slightly larger than the magnitude of trade openness in the system GMM estimations (0.0023 vs. 0.0016) but more than twice as large using the IV estimations (0.009 vs. 0.004). The caveat with comparisons of the effects of data transparency, trade openness, and education is that they may entail different costs. Unfortunately, there is little data available on the amount invested by countries on data transparency (World Bank, 2021). However, there is some indication that data transparency is less costly than for example improving education systems.<sup>12</sup> The developing Middle East and North Africa (MENA) region is the only region in the sample that experienced a decrease in the SCI score between 2005 and 2018, thus offering another assessment of the economic magnitude of the impact of SCI on the level of GDP per capita. Within this period the average SCI score for the region fell by approximately 4.4 percent, while trade decreased by 2.7 percent (using 2018 as the base). Between 2005 and 2017, primary school enrollment rates increased by 1.5 percent.<sup>13</sup> The base OLS estimates with the cross-section data show a loss of -1.8 percent in GDP per capita between 2005 and 2018 due to the fall in SCI. This is larger than the -0.4 percent decrease in GDP per capita due to the decline in trade openness (between 2005 and 2018), and the 0.01 percent increase due to the increase primary school enrollment (between 2005 and 2017). Using the OLS with year fixed effects estimates (panel data), the loss in GDP due to the fall in SCI is slightly higher at -1.9 percent. This is still larger than the loss of -0.4 percent and gain of 0.4 percent due to the decrease in trade openness and the increase in schooling respectively.

> 12 Source: How should governments spend on data <u>https://blogs.worldbank.org/opendata/how-much-shouldgovernments-spend-data</u> (accessed July, 25<sup>th</sup> 2021, 10:45 pm).

> 13 We use the 2017 value for primary school enrollment rate due to the sparse data in MENA for 2018. All trends are for developing economies in the MENA region (i.e. high-income economies in the region are excluded).

27
Policy Research Working Paper 8637

# **Abstract**

Inadequate infrastructure impedes the productivity of manufacturing firms, with negative consequences for the wider economy. This study examines how water infrastructure copes with severe weather fluctuations and analyzes the effect of unreliable water supplies on the productivity of manufacturing firms, focusing predominately on firms in developing economies. This is achieved using firm-level data from World Bank Enterprise Surveys covering more than 16,000 manufacturing firms in a cross-section of 103 countries between 2009 and 2015. The study finds that periods of significantly low rainfall lead to higher water outages, and that the overall impact is driven by the effects of drought on low-income and lower-middle-income economies, with upper-middle-income and high-income economies benefitting from more resilient water infrastructure. Furthermore, the study finds that incidents of water outages lead to lower firm productivity for firms in less developed economies. For the average firm located in a low-income or lower-middle-income economy, one additional water outage incident per day in a typical month can lead to losses of approximately 8.2 percent of annual sales. This finding calls for increased policy focus on water infrastructure services, particularly in poorer countries where water infrastructure and firms seem to be particularly vulnerable to the vagaries of rainfall.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at aislam@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
This paper has offered a first empirical assessment of the impact of poverty on growth. The paper’s strategy involves the estimation of a growth equation with poverty added to an otherwise standard set of growth determinants. To this basic framework we further add inequality as another explanatory variable, in order to assess if any effects of poverty on growth present in the data reflect just the effects of inequality acting through poverty. Thus, the framework is very similar to that employed in recent empirical studies of the effects of inequality on growth, but shifting the emphasis from inequality to poverty.

The resulting empirical specifications are estimated on a large panel dataset using a GMM approach to deal with the potential endogeneity of the regressors. On the whole, the results reveal a consistently negative and strongly significant impact of poverty on growth, which is also economically significant: our estimates suggest that a 10 percentage-point increase in the headcount poverty rate reduces annual per capita growth by about 1 percentage point. When we add inequality to the regressions, the sign, significance and magnitude of the poverty effect remain essentially unchanged, suggesting that it does capture a true poverty effect rather than an inequality effect.

The finding that poverty has a negative impact on growth survives a battery of robustness checks, including (i) the use of alternative poverty lines, (ii) the use of alternative poverty measures, (iii) the use of alternative sets of control variables in the regression, (iv) the use of alternative sets of instruments in the estimation, (v) the use of alternative estimation methods, and (vi) allowing for nonlinear effects of inequality on growth.

The paper has also attempted to shed light on the mechanism through which the adverse effect of poverty on growth operates. The evidence suggests that poverty deters investment, especially when the degree of financial development is limited. While still tentative, this result appears consistent with stylized theoretical models in which financial market imperfections prevent the poor from taking advantage of their investment opportunities.

The result that poverty tends to deter growth also has implications for the choice of growth-oriented policies. Specifically, our findings suggest that the biggest growth payoff is likely to result from policies that not only promote growth, but also exert an independent, direct impact on poverty – hence reducing the drag of poverty on growth.

21
<!-- Start of picture text --> 5 96<br>Ss7 90 94<br>ae 76 ee See79<br>« & Lec<br>3 66_-<br>=] igi<br>HE “7<br>NeZz _— _--7<br>‘3Aswm 7 y<br>==a5B «| {7 # 7 7 7<br>aa<br>ES<br>oO<br>2014 2015 2016 2017 2018 2019 2021<br>‘Year<br>— Poverty rate (%) —-— Extreme poverty rate (%)<br><!-- End of picture text -->
- 2.2.3  Applying the Framework in Honduras: The Community-Based Education Project (Proyecto Hondureño de Educación Comunitaria, PROHECO)

PROHECO is an education decentralization project that devolves decision-making authority over education matters to community-based school councils.<sup>21</sup> In addition to assessing the impact PROHECO has had on education outcomes (such as access to and quality of education), a recent evaluation also sought to gauge the extent to which community members have become empowered in terms of the functions devolved to them by the project. The ME framework was used to establish hypotheses and empowerment indicators and to design instruments for data collection. General hypotheses tested include (1) whether the reform empowers school councils to have greater decision-making authority, and (2) whether it empowers different community members to participate in and have a voice in school council meetings.

Indicators were constructed to measure school councils’ and community members’ degrees of empowerment as well as their assets and opportunity structure. Both quantitative and qualitative instruments were designed for data collection. The following paragraphs provide background information on PROHECO and discuss how the ME framework was used to evaluate the project. Empowerment indicators used for this evaluation are listed in table 3 of annex 2.

# _The Project_

PROHECO was launched in March 1999 with the objective of enhancing access to and quality of education as well as fostering community participation in school-related decision-making. Studies the Ministry of Education had carried out in 1997 showed that more than 14 percent of school-age children were not enrolled in schools; 85 percent of these children lived in rural areas. Building on the successful experiences in El Salvador and Guatemala, the Honduran government decided to establish new pre-schools and primary schools in remote rural villages, using a school-based management model (SBM). SBM is a type of education decentralization reform, key to which is the creation of a school council responsible for making a series of decisions on administrative, personnel, pedagogical, and budget matters. SBM envisages a redistribution of decisionmaking powers away from government agencies toward the school council, granting the school a greater degree of autonomy and empowerment in managing its services.

In Honduras, school councils comprise parents and other community members. They consist of two separate bodies: the general assembly and the board. Membership to the general assembly is granted automatically to all community members; only board membership requires a formal election process. While the board has to inform and consult with the general assembly, it has ultimate decision-making power. PROHECO devolves the following functions and responsibilities to the school councils: building and maintaining the school; buying school supplies; overseeing the school’s budget; and selecting, hiring, paying, monitoring and, if necessary, firing teachers.

> 21 This case was adapted from a draft report by Di Gropello and Heinsohn (2004).

21
Table 8: Effect of having a case and of pre-trial speed on parties’ log revenues after pre-trial start (diff-in-diff model)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||OLS|OLS|IV|RF|FS|
|After pre-trial|-0.082***|-0.115***|-0.112|-0.085***|0.419***|
|starts (_β_1)|(0.023)|(0.030)|(0.197)|(0.023)|(0.018)|
|After pre-trial||0.071*|0.064|||
|starts X Fast pre-trial (_β_2)||(0.038)|(0.443)|||
|After pre-trial||||0.007|0.108***|
|starts X After decree (_γ_1)||||(0.049)|(0.041)|
|Efect if fast pre-trial (_β_1+_β_2)||-0.044|-0.048|||
|P-value: efect if fast pre-trial||0.133|0.847|||
|FEs|partyid|partyid|partyid|partyid|partyid|
|Time FEs|yrmth|yrmth|yrmth|yrmth|yrmth|
|Pre-mean|18.737|||||
|Pre-sd|2.696|||||
|Fstat excl inst|||||6.98|
|Observations|302835|302835|302835|302835|302835|

<u>Notes:</u> The dependant variable in cols 1-4 is monthly log revenues; that in col 5 is the Fast pre-trial dummy. RF is the reduced form (col 4), FS the first stage (col 5). After case started is a dummy that takes value 1 for a case parties’ observations after its pretrial has started, and value 0 before pre-trial start and for firms in the sample that do not have a court case in the study period. Fast pre-trial is a dummy that takes value 1 when a case party’s pre-trial was completed in four months or less. After decree is a dummy that takes value 1 when a party’s case started its pre-trial after the application of the decree. All models include case party and month fixed effects and are estimated by OLS; they include 12 observations for each of the 1622 case parties (6 months before pre-trial start and 6 months after) as well as uninterrupted spells of log revenue observations of at least 12 months (average: 33.046 months) within the study period for 8,575 firms that did not have a court case. Standard errors in parentheses, clustered at the firm-level. Significance levels are denoted as follows: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Pre-mean and pre-sd refer to sample average and standard deviation respectively of case parties’ log revenues in the month before pre-trial start.

46