rafmacalaba/datause-encoder-data huggingface dataset:
| POLICY RESEARCH WORKING PAPER 2928

# Abstract

It is well known that there is a strong positive correlation between per capita incomes and the quality of governance across countries. Kaufmann and Kraay propose an empirical strategy that allows separation of this correlation into (1) a strong positive causal effect running from better governance to higher per capita incomes, and, perhaps surprisingly at first, (2) a weak and even _negative_ causal effect running in the opposite direction from per capita incomes to governance.

The first result confirms existing evidence on the importance of good governance for economic development. The second result is new and suggests the absence of a "virtuous circle" in which higher incomes lead to further improvements in governance. This motivates the authors' choice of title, "Growth Without Governance." They document this evidence using a newly updated set of worldwide governance-indicators covering 175 countries for the period 2000-01, and use the results to interpret the relationship between incomes and governance focusing on the Latin America and Caribbean region-within a worldwide empirical context. Finally, the authors speculate about the potential importance of elite influence and state capture in accounting for the surprising negative effects of per capita incomes on governance, present some evidence on such capture in some Latin American countries, and suggest priorities for actions to improve governance when such pernicious elite influence shapes public policy.

This paper-a joint product of the World Bank Institute and the Development Research Group-is part of a larger effort in the Bank to generate and analyze worldwide governance indicators, assessing the manifestations and consequences of governance. The full governance indicators dataset is available interactively at http://www.worldbank.org/wbi/governance/ govdata2001.htm. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kevin Morgan, room J3-273, telephone 202-473-7798, fax 202-676-9874, email address kmorgan@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dkaufmann@worldbank.org or akraay@worldbank.org. November 2002. (50 pages) _The Policy Research Working Paper Senes disseminates the findings of work in progress to encourage the exchange of ideas about developmient 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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Research Advisory Staff
# **TABLE 6.1: Household Level - Univariate Probit Regressions**

Source of Data: Households' Survey Dependent variable: Household discouraged in at least one institution (0,1) Marginal Effects evaluated at mean values are reported

|Middle Income<br>0.061<br>[2.08]**|||||||||
|---|---|---|---|---|---|---|---|---|
|High Income<br>-0.021<br>[0.33]<br>|||||||||
|Secondary Education|0.156<br>[3.87]***||||||||
|University Education|0.221||||||||
|<br>|[5.46]***<br>||||||||
|female|-0.072||||||||
||[3.04]**|*<br>|||||||
|age||0.001|0.022||||||
|||[0.73]|[3.55]***||||||
|age2<br>|||0<br>[3.48]***<br>||||||
|Dishonesty of 42 institutions - Number|||0.006<br>[4.92]**|*|||||
|Dishonesty of 42 institutions - Average||||-0.018<br>[1.51]|||||
|Trust in State Institutions<br>|||||-0.018<br>[1.78]*||||
|Knowledge report corruption||||||0.074<br>[3.13]*|**||
|Member of association|||||||0.054<br>[5.40]*|**|
|Member of at least one association||||||||0105|
|||||||||.<br>[4.47]***|
|Observations<br>1558|1696<br>1696|1696|1696<br>1696|1696|1696|1696|1696|1696|
|Pseudo-Rsq<br>0|0.01<br>0|0|0.01<br>0.01|0|0|0|0.01|0.01|
|Absolute value of z statistics in brackets|||||||||

* significant at 10%; ** significant at 5%; *** significant at 1%

31
Policy Research Working Paper 5829

# **Abstract**

This paper takes a bibliometric tour of the past 40 years of health economics using bibliographic “metadata” from EconLit supplemented by citation data from Google Scholar and the authors’ topical classifications. The authors report the growth of health economics (33,000 publications since 1969—12,000 more than in the economics of education) and list the 300 mostcited publications broken down by topic. They report the changing topical and geographic focus of health economics (the topics ‘Determinants of health and illhealth’ and ‘Health statistics and econometrics’ both show an upward trend, and the field has expanded appreciably into the developing world). They also compare authors, countries, institutions, and journals in terms of the volume of publications and their influence as measured through various citation-based indices (Grossman, the US, Harvard and the JHE emerge close to or at the top on a variety of measures).

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at awagstaff@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
identifies households that report receiving transfers (e.g. gifts or loans) from other households.<sup>9</sup> The magnitude of the increase is particularly strong after Cyclone Jokwe. The point estimates, presented in Table 6, indicate that treated households were almost twice as likely to receive transfers compared to the levels recorded in baseline. This corresponds to an increase of 11.2 percentage points, relative to a baseline value of 12.4%. The receipt of transfers also rose (by approximately 50%) after the drought – the coefficient of interest shows an increase of 6.6 percentage points.

# _3.4 Food crop prices_

We use data on food prices from markets across the country to investigate both the degree to which prices respond to weather shocks and the extent of the impacts. Agriculture in Mozambique is largely traditional, practiced mostly by smallholders that rely on rainfall for water and have low input utilization and technology adoption. Hence, extreme weather is expected to disrupt crop yields, triggering supply- and demand-side effects in food markets. Moreover, the livelihoods of rural households are largely linked to crop performance either for self-consumption or market transactions. Food price volatility also matters for urban dwellers, the majority of which are net buyers. Even in rural areas most farmers tend to be net buyers of food (Baez et al 2018). While the overall perception is that weather shocks are inflationary, the direction of the change is an empirical matter since the supply and demand effects are expected to push food prices in opposite directions.

We focus on maize prices for several reasons. First, maize is the staple most widely produced and marketed. Data from 2015 show that about 72.5% of farmers cultivated maize. Maize production represented 77% of the domestic production of cereals during the period 2010-2015. Second, maize yields are highly determined by weather conditions. The total production in a given year is a function of the timing and quantity of rain. Third, maize constitutes a significant part of the Mozambican diet. While an average household in Mozambique spends nearly 25% of its food budget in maize, this budget share is higher among rural households. Because of the limited availability of storage (within season and inter-annually) and other inventory smoothing

> 9 The survey does not ask any information (geographic location, family or ethnic ties, etc.) about the households and the motives for the transfers.

16
# Anticipation, Disappointment and Adaptation: Evidence from a Home Upgrading Project<sup>_∗_</sup>

Xavier Giné<sup>1</sup> , Meritxell Martínez<sup>2</sup> , and Virginia Melo<sup>3</sup>

> 3Gabinete de Coordinación de Políticas Sociales, Dominican Republic

Originally published in the Policy Research Working Paper Series on _August 2023._ This version is updated on _October 2023._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** anticipation effects, disappointment, subjective well-being. **JEL Codes:** D84, I31, I38.

> _∗_ Giné: `xgine@worldbank.org` . Martínez: `mmartinezruiz@worldbank.org` . Melo: `virginia.melo@ gabsocial.gob.do` . Funding from the World Bank and ProSoli is gratefully appreciated. We would like to thank Dan Silverman, Tavneet Suri and Daan Van Soest for very helpful comments and Miriam Montenegro and Alejandro De la Fuente for constant encouragement. MKT and a team of enumerators from SIUBEN contributed to baseline data collection. Views expressed in this paper are those of the authors, and do not necessarily reflect either the opinions of the World Bank, its executive directors, the countries they represent, or the views of the federal government of the Dominican Republic.
# **8.  The Spatial Distribution of S5P CH4 Intensities for Irrigated Rice Production**

Local S5P CH4 anomalies also provide new information about the spatial distribution of CH4 intensity for irrigated rice production, which we index as the difference between the CH4 percentile and the rice production percentile from IFPRI (2019). This can be interpreted as an index of the methane produced per unit of food value. Figure 8 displays the spatial distribution, which features an unbalanced incidence of positive and negative outlier clusters (with high and low methane intensities, respectively). Large clusters of the former are concentrated almost entirely in northern Pakistan and northern India. On the other hand, significant clusters of the latter are identifiable in southeastern Australia, southwestern and far western China, the Nile Delta and river valley, northeast Tanzania, northern Côte d’Ivoire, southwestern Spain, Uruguay, western coastal Peru, western Honduras and California.

Table 5 provides a summary view for states/provinces that have more than 10 observations. The table displays top-20 positive and negative outliers in descending order. Among the countries with positive outliers, Brazil and Nigeria are represented by 4 areas; three countries by 2 areas (India, Myanmar, Bolivia); and six countries by 1 (China, Cambodia, Pakistan, the Islamic Republic of Iran, Zambia, Paraguay). Among countries with negative outliers, Indonesia leads with 7 areas, followed by Mali (4), Uruguay (3), Thailand (2) and four countries with 1 area (China, Egypt, Senegal and the República Bolivariana de Venezuela). In summary, Brazil and Nigeria dominate the positive outlier countries and Indonesia, Mali and Uruguay dominate among negative outliers. Full results are available from the authors on request.

20
**Table 11. Community monitoring**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||How would you<br>rate the<br>performance of<br>the CPC overall?|How would<br>you<br>rate the<br>performance<br>of<br>the CPMC<br>overall?|How would<br>you<br>rate the<br>performance<br>of<br>the CPMC<br>overall?|How likely<br>would<br>you be to choose<br>the same<br>subproject<br>management<br>committee again?|It is easy to get<br>many<br>members of the<br>community to come<br>together to solve<br>issues in the<br>community|It can be hard to get<br>members of the<br>community to come<br>together to solve<br>issues in the<br>community|
|Training|0.013|-0.005|0.036|-0.015|0.094*|-0.037|
||[0.037]|[0.036]|[0.047]|[0.065]|[0.057]|[0.059]|
|Scorecard|-0.066|-0.047|-0.094|-0.022|-0.028|0.023|
||[0.043]|[0.041]|[0.063]|[0.077]|[0.072]|[0.074]|
|Training x scorecard|0.048|0.020|0.008|-0.047|0.016|-0.095|
||[0.055]|[0.053]|[0.074]|[0.094]|[0.088]|[0.091]|
|Training + Scorecard + interaction=0|0.892|0.381|0.294|0.210|0.152|0.068|
|Training=Scorecard|0.035|0.259|0.013|0.901|0.048|0.352|
|Control means|3.441|3.488|3.512|3.469|3.046|2.402|
|N|3,613|3,710|3,852|3,845|3,851|3,848|
|R-squared|0.148|0.144|0.176|0.094|0.119|0.117|

Notes: This table reports the OLS regression results for the treatment effect on individual-level outcomes at the final endline survey using the training and scorecard information samples. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

47
Table 3: Transport costs for 1 quintal of paddy, _kharif_ 2009

|Variable|Coef.|Std.<br>Err.|_t_|_P > |t|_|[ 95% Conf.|Interv.]|
|---|---|---|---|---|---|---|
|1_/QijT_|_−_1.89|22.46|_−_0.08|0.933|_−_46.36|42.58|
|ownhold (ha.)|_−_0.45|0.81|_−_0.56|0.574|_−_2.05|1.14|
|sc|_−_1.96|9.43|_−_0.21|0.836|_−_20.63|16.72|
|obc|_−_5.33|8.14|_−_0.65|0.514|_−_21.44|10.78|
|totarea (ha.)|_−_0.18|0.04|_−_4.42|0.000|_−_0.26|_−_0.10|
|popdens|0.34|2.44|0.14|0.891|_−_4.50|5.18|
|block2|_−_21.22|14.45|_−_1.47|0.145|_−_49.83|7.04|
|block3|_−_11.73|10.48|_−_1.12|0.265|_−_32.48|9.03|
|block4|_−_32.80|12.87|_−_2.55|0.012|_−_58.29|_−_7.32|
|block5|9.82|9.17|1.07|0.286|_−_8.34|27.98|
|d0 (km.)|12.32|1.90|6.48|0.000|8.55|16.08|
|d1 (km.)|4.69|1.02|4.61|0.000|2.68|7.61|
|d2 (km.)|_−_0.85|2.88|_−_0.30|0.768|_−_6.55|4.85|
|d2c3*d2|18.38|7.75|2.37|0.019|3.03|33.73|
|d3 (km.)|_−_0.02|0.69|_−_0.03|0.977|_−_1.39|1.35|
|d3c3*d3|27.89|5.18|5.39|0.000|17.64|38.14|
|const.|29.96|16.00|1.87|0.064|_−_1.73|61.64|
|_σε_|24.33|2.31|||19.76|28.90|

� _n_ = 136; 74 left-censored obs. LR: _χ_<sup>2</sup> (16 _d.f._ ) = 159 _._ 91. Pseudo _R_<sup>2</sup> = 0 _._ 2093 _. εijT N_ (0 _, σε_ ). sc: dummy variable for scheduled caste; obc: other backward caste; reference group, scheduled tribe. d0: trip segment on a track or _kutcha_ road; d1, d2 and d3 analogously defined for segments of village all-weather road, district road and state/national highways, respectively; d2c3 is a dummy for a d2 segment in deplorable condition; d3c3 analogously defined.

interval).<sup>3</sup> The corresponding cost on a good PMGSY road is Rs. 4.69 per quintal-km., also rather precisely estimated, with a confidence interval that does not overlap that of the coefficient on d0. Replacing a track with a nice new PMGSY road therefore yields an estimated saving of Rs. 7.63 per quintal-km. The estimated unit cost per km. on a district road in good condition is essentially zero (see the associated confidence interval). If, however, this segment is in poor condition (d2c3 = 1), the unit cost increases by no less than Rs. 18.38 ( _t_ = 2 _._ 37) for each km. so travelled. The same qualitative finding holds for highway segments, whereby the estimated trip-cost per quintal-km. is even larger when the road is in poor condition.

> 3Since the question of primary interest concerns the effects of the road network on the entire population rather than just those who used it at the time of survey, the relevant marginal effects are those pertaining to _τ_<sup>_∗_</sup> rather than _τ_ . In the latter case, an adjustment must be made to reflect the condition _τi >_ 0, conventionally by evaluating the derivative at the mean value of _Xij_ for non-zero observations.

11
We then tested the validity of these findings by studying the association between our measure of relative labor deprivation and subjective responses to questions about social status and migration. We found a strong association between our measure and these variables whereas we found no association between our measure and subjective variables that are not expected to be associated with labor deprivation such as satisfaction with voice in society or with satisfaction with participation in the life of the community. Moreover, we also found that our findings strongly match the findings of a study conducted with focus groups in Morocco.

These are encouraging results in the context of there being very few instruments to understand the relation between objective and subjective deprivation. The shock generated by the Arab spring among observers worldwide has underlined how inadequate our understanding of this relation is. This paper has provided a first set of tools to better understand how objective deprivation may turn into ‘feelings’ of deprivation.

These findings have important policy implications. Policy makers should have an interest in understanding the gap between objective and subjective deprivation. In theory, policy should be guided by objective conditions of deprivation, not by subjective feelings. Economic policies should be concerned about reducing unemployment rather than distributing jobs to louder groups who may in fact be less objectively deprived. In practice, however, politicians or governments understandably respond to manifested social discontent and they frequently succumb to the temptation of implementing shortterm policies. This has resulted in a wave of populist measures such as increases in public employment and public wages. These policies, however, are often not directed toward groups that face poorer outcomes such as women or the youth. Our methodology highlights the objective-subjective deprivation divide. An understanding of this divide can help policy makers make better choices by designing policies that are responsive to the objective conditions of deprivation in the country as well as be cognizant of peoples’ sentiments.

23
# **1. Summary of Findings**

In 2009, the G20 embarked on an ambitious financial regulatory reform agenda to address the fault lines that caused the global financial crisis. While the global benefits are expected to outweigh the costs, the impact may differ across countries, particularly for emerging markets and developing economies (EMDEs) that have sizeable financing gaps to fund development needs and that rely on commercial financing sources since their socio-economic progress remains constrained by shallow, bank-centric financial systems, limited fiscal space, and weak institutional capacity.

Specifically, the reforms may produce cross-border spillover effects to individual EMDEs that are not required to implement the reforms themselves (specifically non-G20/FSB EMDEs), but are affected by their implementation elsewhere. For example, such an EMDE may face a disproportionate tightening or outright reduction of a range of local and cross-border financial activities provided by a foreign bank whose home jurisdiction is implementing the reforms.

To improve the evidence base on these potential spillovers, the World Bank has undertaken surveys of senior officials at regulatory agencies, local banks and global banks that are active in seven non-G20/FSB EMDEs. In particular, the surveys focused on the impact of the regulatory reforms relating to bank resilience (Basel III), the additional requirements on global systemically important banks (G-SIBs), and “over the counter” (OTC) derivatives.

Overall, the findings from the surveys should be interpreted with caution and regarded as preliminary, as important caveats apply related both to the survey (e.g. sample size and lack of quantitative evidence) and to the difficulty in interpreting results, including separating the unintended and intended consequences of the G20 regulatory reforms; disentangling the impact of regulatory reform from other factors; distinguishing between short-term adjustment costs and longer-term stability benefits; and interpreting response variation between the impact of spillover effects rather than the impact of the local implementation of G20 regulatory reforms.

With these caveats in mind, the surveys’ findings show that regulators broadly expect the effects of regulatory reforms to be positive over the longer term while recognizing they may be negative during the transition phase. Respondents, however, anecdotally report exceptions in some of their jurisdictions, including upward pressures on borrowing costs for non-financial 3
# **Table 2: Baseline Regressions**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration. _Log firm size_ is the logarithm of the firm’s number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|||Acc<br>|ess to finance<br>||
|---|---|---|---|---|
|Variables|(a)|(b)|(c)|(d)|
|Lerner index|-0.615**|-0.749**|||
||[0.269]|[0.335]|||
|Concentration 3|||-0.129<br>[0.142]||
|Herfindahl index||||-0.033<br>[0.148]|
|Log firm size|0.086***|0.088***|0.087***|0.087***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.028**|0.028**|0.031***|0.032***|
||[0.011]|[0.011]|[0.011]|[0.011]|
|Exporter|0.032***|0.028***|0.032***|0.031***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign-owned|-0.080***|-0.092***|-0.083***|-0.081***|
||[0.011]|[0.012]|[0.011]|[0.011]|
|Government-owned|-0.136***|-0.164***|-0.137***|-0.141***|
||[0.031]|[0.035]|[0.031]|[0.032]|
|Log firm age|0.013***|0.016***|0.013***|0.012***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Financial development|0.315***|0.331***|0.219**|0.273***|
||[0.090]|[0.061]|[0.105]|[0.087]|
|Inflation rate|-0.357**|-0.450***|-0.347**|-0.359**|
||[0.138]|[0.158]|[0.136]|[0.140]|
|Constant|0.486***|0.538***|0.525**|0.324**|
||[0.153]|[0.162]|[0.202]|[0.148]|
|Observations|68,353|68,353|67,270|68,353|
|R-squared|0.211|0.187|0.213|0.209|

21
# WPS3781

## **Policy and Planning for Large Infrastructure Projects:**

## **Problems, Causes, Cures**

By Bent Flyvbjerg

### World Bank Policy Research Working Paper 3781, December 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._