rafmacalaba/datause-encoder-data huggingface dataset:
reducing/deterring opportunistic renegotiation, and (c) on its effects on productivity, quality of service, coverage and prices. That is done respectively in Sections 2, 3 and 4 respectively.

The analysis uses an extensive data set of about 1000 concessions granted in Latin America from the late 1980s to the early 2000s compiled by Guasch (2004).

# **2. Testing the Impact of Regulation on Aligning Costs with Tariffs-firms Profitability**

Unlike normal competitive business sectors, the profitability of concessions is not simply a reflection of market conditions and managerial competence, but is to a considerable extent determined ⎯ or at least circumscribed ⎯ by regulatory decisions. Infrastructure companies operate mostly under a monopoly regime and thus are subject to regulation of tariffs and other aspects of enterprise performance. Thus, the observed profitability of these concessions in part should reflect the quality of the regulatory framework and the performance of the regulators that oversee them.

# **2.1 Theoretical framework**

Regulation aims to protect consumers from abuse of monopoly power and investors from opportunistic behavior by the government, given the politically sensitive nature of infrastructure tariffs and the large sunk cost characteristics of the companies’ investments. In consequence, regulatory decisions have a substantial impact on the profitability of companies. Ideally, the regulator’s objective should be to maintain alignment between a company’s rate of return and its cost of capital. This is because a rate of return in excess of the cost of capital inappropriately 4
Panama, 32% in Ecuador, and 15% in Colombia. A similar pattern arises for place of birth: poverty is less spatially concentrated than economic opportunity: 70% of the poor in Brazil live in the North or Northeast (as compared with 100% of the opportunitydeprived being born there). 65% of the poor live in Colombia’s peripheral departments, while 99% of the opportunity-deprived were born there. And so on. Family backgrounds are also more heterogeneous among the poor than among the opportunity deprived, although the share of children of agricultural workers is still very high at 80% in Ecuador and Panama, and 75% in Guatemala.

The last row in Table 12, analogously to Table 11, provides the income share of the poor in each country. They are 0.7% in Brazil (using income per capita), 1.5% in Colombia and Panama, 1.8% in Guatemala and Peru, and 1.9% in Ecuador. The ratio of the income share of the opportunity-deprived to the income share of the poor is 1.80 in Panama; 1.94 in Guatemala; 2.31 in Ecuador; 2.66 in Peru, 3.33; in Colombia; and 4.14 in Brazil.<sup>41</sup> Since the income share of the opportunity-deprived is larger when some among them succeed in escaping poverty, these ratios are suggestive indicators of “mobility”. The higher the ratio, the less opportunity-deprivation would seem to amount to a sentence of life in poverty, delivered at birth. Nevertheless, more confident statements on the relationship between opportunity-deprivation profiles, poverty profiles, and more standard measures of mobility (which largely rely on the association between outcomes and one particular circumstance, such as father’s wage or education), would require further work.<sup>42</sup>

# **8. Conclusions**

This paper has proposed a simple conceptual framework for the measurement of inequality of opportunity, which derives two empirical tools directly from John Roemer’s theory of equal opportunities. The first tool is a class of scalar indices that measure

> 41 The number for Brazil is _not_ comparable to those of the other countries, since it is built on an income, rather than consumption expenditure, distribution.

> 42 Van de Gaer et al. (2001) contain a pioneering theoretical discussion of the relationship between mobility and equality of opportunity. See also Gaviria (2007) for a recent survey of intergenerational mobility in Latin America, with some discussion of attitudes to redistribution. Fields et al. (2007) provide a survey of the evidence on intra-generational income mobility in the region.

28
# **Table 9: Overview of returns to education estimates**

|||Men →|||||Women →||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
||OLS|Linear<br>estimates<br>after<br>controlling<br>for<br>endogeneity<br>and selection|Non‐linear<br>returns|Non‐linear<br>returns<br>after<br>controlling<br>for<br>endogeneity|Non‐linear<br>returns after<br>controlling<br>for<br>endogeneity<br>and selection<br>Heckman|Non‐linear<br>returns after<br>controlling<br>for<br>endogeneity<br>and selection<br>Heckman ‐<br>Hotz|OLS|Linear<br>estimates<br>after<br>controlling for<br>endogeneity<br>and selection|Non‐linear<br>returns|Non‐linear<br>returns after<br>controlling<br>for<br>endogeneity|Non‐linear returns<br>after controlling<br>for endogeneity<br>and selection<br>Heckman|Non‐linear<br>returns after<br>controlling<br>for<br>endogeneity<br>and selection<br>Heckman‐<br>Hotz|
||Table 4|Table A.3|Not reported|Table 5|Table 7|Table 7|Table 4|Table A.3 Col 2|Not|Table 5|Table 7|Table 7|
||Col 3|Col 1||Col 3|Col 1|Col 2|Col 6||reported|Col 6|Col 7|Col 7|
||||‐0.002|0.12|0.12|0.12|||‐0.01|0.01|0.01|0.02|
|Detailed self|0.04**|0.16*|0.03|0.15|0.14|0.15|0.08**|0.05|0.15***|0.17*|0.29**|0.21|
||||0.05***|0.18*|0.14|0.18|||0.16***|0.13|0.29*|0.18|
||||0.05|0.18|0.18|0.18|||0.13**|0.16|0.15|0.16|
|Short|0.10***|0.22**|0.07**|0.20*|0.19*|0.20*|0.13***|0.04|0.12***|0.15|0.26**|0.17**|
||||0.11***|0.24**|0.19|0.24*|||0.17***|0.19*|0.37**|0.26**|
|Difference short ‐|||0.05|0.06|0.06|0.06|||0.14**|0.15**|0.14**|0.14**|
|detailed self|0.06**|0.06**|0.04|0.05|0.05|0.05|0.05|‐0.01|‐0.03|‐0.02|‐0.03|‐0.04|
||||0.06**|0.06**|0.05*|0.06**|||0.01|0.06**|0.08***|0.08**|

Standard errors, clustered at the household level, in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

35
371 students (32 percent of the original sample) had completed Standard 8 and thus taken the KCPE Exam. KCPE results from 2005 through 2009 were copied from official statistics from the Ministry of Education. We matched 328 out of these 371 students (88 percent) of pupils with KCPE results from the MoE. We examined KCPE scores with the same school officials – at the school – who had helped us track the students, to minimize any errors due to a failure to match children’s names.

Our principal regression sample consists of 1,152 children who are identified in the initial Randomization data set, the Recipient 2002 data set, and the Pupil Attendance data set. Table 2 compares children who won the lottery to those who did not across a set of basic characteristics – gender, age, school dummies, and age dummies, for which data are available for virtually all the children. There are no significant differences across any of these characteristics. For a smaller sample, the subset of children who were present when enumerators arrived at school for the baseline census, we have a wide array of variables. Table 3 Panel A compares children who won the lottery to children who did not win the lottery for the 780 children in school on the day of the 2002 Pupil Questionnaire. Even in this smaller, more selected sample, we observe few significant differences (e.g., winners have slightly fewer meals at home per day, have fewer pens, were slightly more likely to have breathing trouble, and were less likely to cook regularly at home or work on the farm). Table 3 Panel B compares children who received uniforms in June 2002 to those who did not, again for the 780 children in school on the day of the questionnaire. We see few significant differences – uniform recipients are slightly less likely to be female, have slightly higher pre-program attendance, and eat slightly fewer meals at home per day than non-recipients. There are no discernable patterns of difference between groups. Once we include school-grade-gender fixed effects, treatment and comparison groups (both intended and actual) are balanced in advance of the program (results not reported). Eighty percent of children registered in the program received uniforms; 78 percent of children who received uniforms had initially won the lottery.

11
# Appendix Table A4: Individual Financial Numeracy Skills Indicators

||(1)|(2)|(3)|
|---|---|---|---|
||||Income<br>|
||Cheaper Loan<br>Option: Correct|Annual Fee:<br>Correct|Calculation:<br>Correct|
|Migrant-Only Training (A)|0.018|0.003|0.028|
||(0.041)|(0.042)|(0.034)|
|Family-Only Training (B)|0.039|0.062|0.010|
||(0.042)|(0.039)|(0.033)|
|Migrant and Family Training (C)|0.016|0.019|0.090**|
||(0.043)|(0.041)|(0.036)|
|R-squared|0.021|0.009|0.044|
|Sample Size|1133|1133|1133|
|Number of Clusters|394|394|394|
|Mean of Dependent Variable in Control Group|0.317|0.704|0.153|
|Test: A-B = 0 (p-value)|0.615|0.151|0.623|
|**Test: A** **C = 0 (p** **value)**<br>-<br>-|**0** **961**<br>.|**0** **707**<br>.|**0** **109**<br>.|
|Test: B-C = 0 (p-value)|0.596|0.286|0.036|
|Test: A = B = C = 0(p-value)|0.829|0.369|0.077|

This table presents pooled regression analysis across three follow-up survey rounds for Individual components of the aggregate measure "Financial Numeracy Skills", presented in Table 3. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
- 6 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

by Whyte (1968). Based on these characteristics, the authors differentiate between natural/organic and planned/regular/artificial/geometric cities. Planned cities are characterized by straight streets forming a grid, which can include some radiality with the incorporation of streets that symmetrically converge to a point in the city. They also show a clear segregation of uses and are the result of large-scale planning processes. They also tend to have circular borders. Organic cities grow slower than those that are planned, and their geometry is the result of individual decisions and follows more closely the natural landscape. It has curved roads and open spaces randomly located. The pattern shows no evidence of planned growth and it is radially concentric in structure (Batty, 2008).

Our contribution to the literature consists of using the multidimensional definition of urban form from urban planning to attain an integral understanding of the influence of urban form on city productivity. We also contribute with new empirical evidence of this relationship using a large set of Latin American cities.

# **3 Models**

To examine the effect of urban form on productivity, we estimate the following model: where Y�� measures productivity of city _i_ at time _t_ ; S is a vector of urban shape variables; T is a vector of urban texture variables; L represents the land use pattern within the city; and X is a vector of control variables, included the intercept; θ are the city fixed effects; and ε is the error term.

One of the main challenges to be tackled in this paper is the endogeneity of urban form when estimating the relationship between productivity and city shape; city form in fact, can be thought of as being the result of the interaction between decisions taken by firms, households, and government, and hence both urban form and productivity result from the interplay between agglomeration economies and congestion forces. In the simplest example, cities that grow dense (or in a concentric pattern around a central business district) can facilitate agglomeration economies by easing proximity of firms and, hence, fostering productivity.
# Annex 3: Robustness tests econometric results

## Panel A. Dependent variable: Nominal lending interest rate

|||No|minal lendi|ng interest r|ate||
|---|---|---|---|---|---|---|
|VARIABLES|(14)|(15)|(16)|(17)|(18)|(19)|
|Inflation (%)|0.066*|0.070**|0.053|0.053|0.065*|0.066*|
||(1.800)|(2.007)|(1.504)|(1.511)|(1.779)|(1.872)|
|St. Deviation Inflation (%)<br>|0.123**|0.093|||0.133**|0.107*|
|ons|(1.993)|(1.599)|||(2.096)|(1.812)|
|5-year St. Deviation Inflation (%)<br>diti|||0.249***|0.235***|||
|on|||(5.813)|(5.474)|||
|Public Debt (%GDP)<br>al c|0.032*|0.037**|0.033**|0.024|0.035*|0.043**|
|fisc|(1.756)|(2.325)|(1.979)|(1.485)|(1.877)|(2.469)|
|GDP percapita<br>ro-|-0.000|-0.000**|-0.000|-0.000*|-0.000|-0.000*|
|ac|(-0.331)|(-2.051)|(-0.728)|(-1.933)|(-0.395)|(-1.738)|
|Policy/money market rate (%)<br>M|0.191***|0.205***|0.197***|0.197***|0.196***|0.198***|
||(4.335)|(4.859)|(4.820)|(4.833)|(4.394)|(4.593)|
|Devaluation rate (%)|||||-0.007|-0.011|
||||||(-0.721)|(-1.181)|
|Overhead costs (%Total Assets)|0.173|0.244**|0.153|0.199*|0.171|0.214*|
||(1.436)|(2.126)|(1.355)|(1.769)|(1.413)|(1.853)|
|Three bank concentration (% Total<br>bank system assets)<br>ristics|0.012|0.026*|0.007|0.016|0.011|0.024|
|acte|(0.744)|(1.659)|(0.448)|(1.046)|(0.677)|(1.489)|
|Non-performing loans (% Total<br>Gross Loans)<br>char|0.083|0.051|0.043|0.056|0.085*|0.065|
|ctor|(1.618)|(1.135)|(0.915)|(1.232)|(1.653)|(1.381)|
|Non-interest income (% Total<br>Income)<br>ing se|-0.024*|-0.023*|-0.016|-0.016|-0.022*|-0.017|
|ank|(-1.883)|(-1.901)|(-1.337)|(-1.334)|(-1.750)|(-1.357)|
|Private credit (% GDP)<br>B|0.034*|0.014|0.049**|0.053***|0.036*|0.024|
||(1.706)|(0.764)|(2.580)|(2.793)|(1.788)|(1.238)|
|Credit Bureau Coverage (% adults)<br>ent|-0.015*||-0.018**||-0.015*||
|nm|(-1.809)||(-2.289)||(-1.803)||
|Time to resolve insolvency (years)<br>viro|0.473***||0.509***||0.463**||
|s en|(2.623)||(3.018)||(2.564)||
|Rule of Law (Kaufmann et al.,2010)<br>ines||-1.327||-2.112**||-1.997*|
|Bus||(-1.272)||(-2.087)||(-1.859)|
|Constant|6.438***|8.995***|6.139***|7.306***|6.370***|7.914***|
||(5.320)|(8.685)|(4.944)|(6.021)|(5.239)|(7.395)|
|Observations|445|491|455|463|445|473|
|Number of country_code_|50|51|51|52|50|51|

_Note:_ Annual information between 2005 and 2017 for 51 EMDE countries. _Public debt_ includes debt data for general government. In case this data is not available, debt data for the central government is used. _St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y- o-y, using a one-year rolling window. _5-year St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y-o-y, using a 5-year rolling window. _Devaluation rate_ is the y-o-y change of the exchange rate vs. US dollar. Benchmark regressions in columns (14) and (15).

Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable capturing Global Financial Crisis period included.

t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 50
<!-- Start of picture text --> Polley Research<br>Agricultural Policies<br><!-- End of picture text -->

**WPS 1 140**

- This paper a product of the Agricultural Policies Division, Agriculture and Rural Development Department- is part of a larger effort in the department to analyze changes in agriculture in economies. This research was supported by a grant to the University of Minnesota from the National Council on Soviet and Eastern European Research, and by the World Bank. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cicely Spooner, room N8-039, extension 32116 (May 1993, 66 pages).

Under administered prices through the fall of 1991, Russia's food distribution system broke down, and it was feared that the food supply would be inadequate in the winter of 1992 and thereafter. In January and March 1992, price ceilings were removed on most items sold in state-owned Russian stores. Price liberalization was intended to return food to shelves and to improve the flow of food among regions through responses to price diflrentials. Privatization of the distribution system did not begin until October 1992. At the time of price liberalization the environment was still dominated by unrestructured state enteiprises.

Retail prices immediately rose sharply and fluctuated. Because food prices did not stabilize after the initial jump, many people questioned whether price liberalization accomplished anything positive. Gardner and Brooks examine data on movements in food prices and volumes between December 1991 and August 1992 to examine how retail food market, responded to liberalization. They address the following questions: Is there any evidence that after liberalization food retumed to retail outlets that were essentially bare in December 1991? Is there evidence that transactions took place in response to price differentials (did markets begin to emerge despite the lack of privatization and demonopolization)? Did city-to-city price differentials evolve to reflect a price surface explainable by transportation costs and other economic variables? if not, why not?

Gardner and Brooks conclude that progress was made toward market integration in the seven months after price liberalization. The volume of food sold in mordtored shops increased substantially. The geographic dispersion of prices declined over time. But large price differences between cities persisted that cannot be explained in tenns of available economic variables.

Large economic gains could be achieved by further market integration.

**The Policy Research** Working **PaperSePresdissedbinates tP e indings of work under way** in **tSeBank. Anobjectiveof the series** is **to get these findings out quickly, even if presentations are less than fully polished.** .'he **findings. interpretations. and** conclusions in **these papers do not necessarily represent ofrficial Bank policy.**

**Produced by the Policy Research Dissemination Center**
**<u>Table 5 Health insurance fund expenditures by level of care, percentage, 2002–04</u>**

||**Inpatient**|**Outpatient**|**Prescription Drugs**|**Capital**<br>**Investments**|
|---|---|---|---|---|
|Bosnia and Herzegovina|||||
|- Federation|38|31|7|5|
|- Republika Srpska|51|27|7|2|
|Macedonia, FYR|50|19|13|0.36|
|Montenegro|39|30|12|4|
|Serbia|41|24|12|0.30|
|Croatia|45|16|13|n.a.|
|Slovenia|n.a.|n.a.|n.a.|n.a.|
|EU-15|38<sup>a</sup>|31|n.a.|n.a.|

Sources: Original data from Health in Transition Reports, OECD health data, and Bank estimates Note: EU-15 figure refers to expenditures on inpatient care as a percentage of total public spending on health, and not as a percentage of heath insurance fund expenditure.

# _<u>D1.5 Expenditures have to be better managed to avoid the accumulation of deficits</u>_

While there has been some improvement in recent years, in most countries in the sub-region, health insurance funds continue to incur repeated deficits which, over the years, have resulted in large arrears for the public health sector. Many healthcare institutions, too, are in arrears, with substantial accumulated debts to their suppliers.

For example, in Bosnia and Herzegovina, arrears in the health sector amounted to 0.9% of GDP in 2004. Arrears were equivalent to 0.5% of GDP in the Federation, but more acute in Republika Srpska where, at 1.9% of GDP, they were equivalent to almost one-third of public health spending. By far, the major share of arrears was incurred by hospitals.

In the FYR of Macedonia there has been a substantial reduction in arrears owing to the introduction of measures such as monthly financial reporting by the health insurance fund and the health care institutions; competitive bidding to improve efficiency in the procurement of pharmaceuticals; and hard budget ceilings for health care institutions. By the end of July 2006, total arrears were approximately US$78 million, which is a 23% reduction on the figures for the preceding year. Of the total still owed, 66% is due to debts of the health care institutions and the rest to debts of the health insurance fund.

Serbia, too, had persistent health sector deficits, with the arrears of the HIF growing from 7.9 billion dinars in 2002 to 9.5 billion dinars by the end of 2004. During this period, debts to health care institutions increased until they constituted almost half of all debts of the health insurance fund. Other arrears of the health insurance fund include accumulated debts in the form of delayed sick leave payments to beneficiaries and interest payments (equivalent to 3% of total HIF expenditures). In 2006, all arrears were paid off – in a once-off transfer – with the proceeds of privatization. Such solutions, although providing temporary deficit relief, do not address the underlying causes of the problem.

Montenegro is the one country that has increasingly managed to bring annual health insurance fund deficits under control. It has progressively improved the financial position of its health insurance fund from a deficit of over 8% at the end of 2001 to a 1% surplus in 2004.

20
our consumption and wealth findings.

The results we present here should be taken as one piece of information, rather than thorough evidence on the economic effects of IDPs on host communities, because of the data limitations described above. Given these limitations, we recommend that policy-makers and program-implementers allocate more resources toward collecting comprehensive data on IDPs, as well as on economic indicators, in Mali and other countries with IDPs. Doing so can 1) further our collective understanding of the relationships between IDPs and their host communities; and 2) provide knowledge to inform policy making for both IPDs and their host communities. We specifically recommend improving on the types of data collected. First, finding ways of quantifying and obtaining data on those IDPs who are not served by aid organizations as they, for instance, temporarily live with relatives or in slums in urban areas would provide a more comprehensive view of what happens to host communities’ economies when IDPs arrive in a large numbers. Second, following up on the pioneering work by Hoogeveen et al. (2019), tracking individual IDP households over time, for example every month for a year or over multiple years, to generate data on how they cope with displacement, and interact with their host communities. Such data can shed light both on the results presented here and on general social cohesion in the host communities. Third, providing support to the Malian statistical authority to collect more frequent price data at a granular geographical level will help researchers understand the macroeconomic effects of IDPs. An effort to collect these types of data ultimately will help policy-makers and programimplementers design better projects that support both IDPs and host communities.

33