rafmacalaba/datause-encoder-data huggingface dataset:
Della Porta, D. and A. Vanucci 1999, _Corrupt Exchange: Actors, resources, and mechanisms of political corruption_ . Walter de Gruyter.

Donchev, D. and G. Ujhelyi (2008) What Do Corruption Indices Measure? University of Houston Economics Department Working Paper (un-numbered).

Estache, A. 2006. A survey of recent and upcoming issues. The World Bank Annual Conference on Development Economics, ABCDE, Tokyo 2006, May 29-30.

Estache, A. 2006. PPI Partnerships vs PPI Divorces in LDCs, _Review of Industrial Organization_ 29 3-26.

Estache, A. and A. Goicoechea, 2005. A 'Research' Database on Infrastructure Economic Performance. World Bank Policy Research Working Paper No. 3643. Washington D.C.: The World Bank.

Estache, A. and D. Martimort. 1999. Politics, transaction costs, and the design of regulatory institutions. World Bank Working Paper Series 2073. Washington, D.C.: The World Bank.

Estache, A., A. Goicoechea and L. Trujillo (2006) Utilities Reforms and Corruption in Developing Countries. World Bank Policy Research Working Paper 4081, the World Bank, Washington, D.C.

Estache, A., S. Perelman, and L. Trujillo. 2005. Infrastructure Performance and Reform in Developing and Transition Economies: Evidence from a Survey of Productivity Measures. World Bank Policy Research Working Paper No. 3514: Washington D.C: The World Bank.

Ferraz, C. and F. Finan, 2007. Electoral Accountability and Corruption in Local Governments: Evidence from Audit Reports, IZA Discussion Paper Series, Los Angeles: University of California, Institute for the Study of Labor.

Glaeser, E. L. and A. Shleifer. 2003. The Rise of the Regulatory State. _Journal of Economic Literature_ , 41(2): 401-425.

Glaeser, E.L. and R.E. Saks, 2005 Corruption in America, _Journal of Public Economics_ , 90:1053-1072.

Grout, P. and S. Sonderegger, 2005. Predicting Cartels. Report for the Office of Fair Trading.

Guasch, J. L., J. J. Laffont and S. Straub. 2003. Renegotiation of concession contracts in Latin America. World Bank Policy Research Working Paper 3011. Washington D.C.: The World Bank.

Guasch, J.L. 2004. _Granting and renegotiating infrastructure concessions: doing it right?_ WBE Development Studies.

Gulati, M. and M. Rao, 2006. Checking Corruption in the Electricity Sector, mimeo, World Bank.

Gupta, S., H. Davoodi and R. Alonso-Terme, 2001. _Does corruption affect income inequality and poverty?_ Berlin: Springer.

Habib, M and L. Zurawicki, 2002. Corruption and Foreign Direct Investment, _Journal of International Business Studies_ , 33(2):291-307.

Harker, M. and C. Waddams Price. 2004. Consumers and antitrust in British deregulated energy markets. In: _The pros and cons of antitrust in deregulated markets._ Swedish Competition Authority.

20
latter is that, as unemployment rates rises, the productivity of domestic labor may fall due to scarcity of domestic capital in the form of market goods, health, education and housing. If domestic work is unproductive, then family members effectively face high domestic prices and, in response, may switch to leisure.<sup>52</sup> In some circumstances, they may have no choice – they are, in effect unemployed or underemployed at home as well as in the market place.

Table 3 presents weighted means that indicate this type of inverse relationship between work and leisure for two racial groups selected from the SSA TUS2000. Panel A reports weighted means for Africans and the Panel B, for whites, aged 18 to 59 years. These two

# **Table 3 SSA TUS2000 data means: time allocations of individuals aged 18 years and older, by gender and race** (std. dev. in parenthesis)

## **Panel A: African**

|Minutes per day|Female|Std.dev|Male|Std.dev|
|---|---|---|---|---|
|SNA work<br>|154|(238)<br>|255|(287)<br>|
|Domestic work + care<br>|300|(197)<br>|101|(124)<br>|
|Total work|454|(231)|356|(277)|
|% Unemployed|31.3||18.9||
|Household income|1140|(1370)|1201|(1411)|
|Household size|4.4|(2.6)|3.7|(2.5)|
|Sample size|4029||3589||
|**Panel B: White**|||||
|Minutes per day|Female|Std.dev|Male|Std.dev|
|SNA work|246|(262)|380|(298)|
|Domestic work + care|258|(209)|94|(141)|
|Total work|504|(232)|474|(270)|
|% Unemployed|10.2||4.0||
|Household income|5293|(3716)|5940|(3923)|
|Household size|3.1|(1.4)|3.2|(1.4)|
|Sample size|520||467||

52 In general, household income can be expected to increase, and female leisure hours to decline, as female hours of market work rise. Ilahi (1999b) reports that women in the bottom two deciles of per capita consumption work longer than those in higher deciles, and therefore have less leisure. However, the analysis does not control for household size. We would not expect to find a negative relationship between female hours of work and household income, and therefore a positive relationship between leisure and household income, other than at high male wage rates.

49
is unable to pay for a minimum basket of food. In urban areas, where 73 percent of the population resides, the incidence of poverty is 33.5 percent. Similarly, 26 percent of the population older than age 15 is still illiterate and the infant mortality rate is still high at 48.7 per 1,000 live births—much higher than Brazil’s average but slightly better than the Northeast average.

# **3. Data Sources**

We apply municipality level data for 150 municipalities in Rio Grande do Norte. The different sources of data are cited below.

## **GDP**

The source of municipality GDP data (1970, 1975, 1980, 1985, 1990 and 1996) is <u>www.ipea.gov.br. The data are in US dollars of 1998. More information about</u> methodological procedures, etc. used to generate the data can be obtained at IPEA’s web site.

## **Population**

The data on population of the municipalities are from IBGE Censuses of 1970, 1980, and 1991 and IBGE _Contagem_ in 1996. One part of this data is available at www.ibge.gov.br and another part was tabulated by the authors using the software “WinR+ 2000 Lite”<sup>4</sup> and the CD-Rom bases of IBGE. For the years 1975, 1985 and 1990, data are interpolated using the geometric mean rate of growth of the respective decade.

# **4. Overall Trend and Location Structure of Growth**

– Brazil was the fastest-growing country in the world during 1930 1995,<sup>5</sup> with an average growth rate of 6.1 percent per year, with much of the growth occurring prior to the 1980s. Although the Northeast also grew, the region remains much poorer than the national average. In 1998, the Northeast’s GDP of R$119 billion was about 13 percent of Brazil’s GDP of R$914 billion, while its population was 29 percent of Brazil’s population. Thus the per capita GDP of the Northeast was only 47 percent of per capita GDP in Brazil.

RN’s per-capita income has hovered around 45 percent of Brazil’s per capita GDP over the last five decades (Figure 4.1). The state’s annual per capita growth averaged 3.6 percent from 1947 to 1998, higher than for Brazil (2.9 percent) or the Northeast (3.0

> basket” equal to the Food and Agriculture Organization (FAO) minimum caloric intake of 2,288 per day. This corresponds to a monthly per-capita income of about R$73 in 1999, the most recent year for which household information is available.

> 4

> It is a freeware software available for download in the ECLAC web site: www.eclac.org/software/ 5 See Abreu and Verner (1997).

6
# Table 8. Who Initiates the Incidents?

|||Post-Elec|tion Perio|d||Pre-Elect|ion Perio|d|
|---|---|---|---|---|---|---|---|---|
||Casu|alties|Violent I|ncidents|Casu|alties|Violent|Incidents|
||AFP|Rebel|AFP|Rebel|AFP|Rebel|AFP|Rebel|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incumbent victory|0.24*|0.30**|0.16***|0.15**|0.010|0.017|–0.025|–0.036|
||(0.12)|(0.14)|(0.053)|(0.075)|(0.34)|(0.19)|(0.16)|(0.12)|
|Margin|–4.12|–0.60|–2.92**|–1.79|11.8|–3.84|3.60|–0.94|
||(2.60)|(2.32)|(1.37)|(1.45)|(12.9)|(3.68)|(6.51)|(3.20)|
|Margin _×_ incumbent victory|–11.6|–3.62|–1.45|0.19|–26.6*|–5.37|–7.95|–2.91|
||(7.21)|(3.81)|(3.38)|(2.90)|(15.7)|(10.4)|(7.21)|(5.23)|
|Mean of dep. var.|0.25|0.25|0.11|0.12|0.42|0.30|0.18|0.18|
|No. of municipalities|153|153|153|153|153|153|153|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ The running variable of the RD design is the incumbent margin of victory. All regressions are weighted by a triangular kernel with a bandwidth of 0.05 and include the same control variables as in Table 3. Reported values are marginal effects, calculated at the sample means. The dependent variables in columns marked “AFP” are based on incidents initiated by the Armed Forces of the Philippines. The dependent variables in columns marked “Rebel” are based on incidents initiated by insurgent groups. Standard errors are in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

32
<!-- Start of picture text --> Figure 6. External CreditLocal Credit 1990-2000<br>120 140<br>100 ~ 120<br>ad r<br>_ a‘+ = = 80<br>5<br>o© 60 == 7 .<br>~¢ ~7 “= AO<br>- = ¢ we +<br>40Be .<br>/ee :<br>in O<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>= @ = Bankstlist+ ST/ Local Credit l= Ganks + Inst. + 57+Bonds/Credit Local<br><!-- End of picture text -->
_Table 28: Probability of having recovered after two years, as a function of various housing characteristics, magnitude of losses. N.B. Logistic regression is run with the original sample of affected households (no matching)._

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Did not lose more than 5% of house-|||||||
|hold expenditure due to the flood|||||||
|Lost more than 5% of household ex-|||||||
|penditure due to the flood|-0.354|-0.360|-0.433|-0.436|-0.458|-0.527|
||(0.376)|(0.379)|(0.385)|(0.386)|(0.382)|(0.381)|
|Log of real per capita expenditure||-0.130||-0.0363|-0.0851||
|||(0.236)||(0.227)|(0.237)||
|Main income source: Monthly salary|||||||
|Main income source: Casual labor|||-1.244**|-1.239**||-1.126**|
||||(0.493)|(0.492)||(0.502)|
|Main income source: Hawking|||-0.0798|-0.0900||-0.165|
||||(0.516)|(0.520)||(0.598)|
|Main income source: Remittances|||-0.0635|-0.0681||-0.678|
||||(0.418)|(0.418)||(0.657)|
|Main income source: Stable business|||-0.159|-0.165||-0.146|
||||(0.292)|(0.292)||(0.298)|
|Main income source: Other|||-0.216|-0.224||-0.592|
||||(0.856)|(0.856)||(0.809)|
|Does not anyone to borrow money fro|m in case of|emergency|||||
|Can borrow money from at least one<br>or multiple persons in case of emer-|||||||
|gency|||||0.675**|0.551*|
||||||(0.304)|(0.306)|
|Did not receive remittances in the past|year||||||
|Received remittances in the past year|||||0.883***|1.000**|
||||||(0.318)|(0.431)|
|Constant|0.875***|1.983|1.173***|1.485|0.935|0.593|
||(0.160)|(1.995)|(0.322)|(1.946)|(2.043)|(0.381)|
|Observations|393|393|393|393|393|393|
|Prob > F|0.354|0.549|0.252|0.368|0.00269|0.0119|

53
effects. This section also contains a brief overview of some of the livestock-related responses, in terms of adaptation responses via risk management and climate proofing (shortand long-term, respectively), and mitigation responses such as management, breeding, and policy options to reduce greenhouse gas emissions in livestock production.

The paper concludes with an attempt to draw the various elements together into a short synthesis. The section also includes some observations on the highly diverse roles of livestock in different production systems, key knowledge and information gaps, and the need for differentiated policy action, with some examples by system type.

# **2  Changing demand for livestock products, and production response**

# **_2.1  The increasing demand for livestock products_**

Growth in population is an important driver of demand for food and livestock products. There are several estimates of human population growth to 2050, and these continue to be revised, mostly downwards in recent years. The UNPD (2008) revision of 2005 puts total population at 9.19 billion in 2050, and FAOSTAT's current estimate is just over 9 billion in 2050 (Figure 1). The Rosegrant et al. (2009) "reference" (baseline) run uses a population of just over 8.2 billion in 2050. Globally, the population growth rate peaked towards the end of the 1960s at about 2.04 percent per year, and by the late 1990s this had fallen to about 1.35 percent per year. The growth rate may be around 0.33 percent per year by the late 2040s. Most of these population increases are projected to take place in developing countries.

Even within developing countries, there will be marked differentiation. For example, East Asia will have shifted to negative population growth by the late 2040s (FAO, 2006). In contrast, Sub-Saharan Africa’s population will still be growing at 1.2 percent per year. By 2050, 18 million of the 26 million people added annually to world population will be living in Sub-Saharan Africa (FAO, 2006). Population projections for some countries indicate that populations may treble between 2000 and 2050 -- Table 1 shows data for five countries of East Africa. As FAO (2006) notes, rapid population growth could continue to be an important impediment to achieving improvements in food security in some countries, even when world population as a whole ceases growing sometime during the present century.

9
|Health Project(Serbia)|Ana Holt|
|---|---|
|Health Project(Serbia)|Ana Holt|
|Health Project(Serbia)|Ana Holt|
|Health Project(Serbia)|Ana Holt|
|Health Project(Serbia)|Ana Holt|
|Health Project(Serbia)|Ana Holt|
|Health Project(Serbia)|Ana Holt|
|Health Project(Serbia)|Ana Holt|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|Integrated Ecosystem and Natural Resources Management(GEF)|Holger A. Kray|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|EmergencyRecoveryProject|Francis Ghesquiere|
|ST. LUCIA Education Development Project|Harriet Nannyonjo|
**Table S4.S: Results for the baseline specification with a dummy variable for location in the same town added.**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|supbuy|0.0148|-0.206|-0.341**|0.450|-0.304|-0.0872|-0.122|0.365|
||(0.351)|(0.325)|(0.141)|(0.571)|(0.230)|(0.0886)|(0.263)|(0.349)|
|samesup|-0.0800|-0.154*|-0.182***|-0.271**|-0.308**|0.000282|0.0316|-0.0175|
||(0.0740)|(0.0790)|(0.0645)|(0.109)|(0.130)|(0.0439)|(0.0844)|(0.0543)|
|samecus|<br>0.0243|<br>0.00623|<br>0.127***|<br>0.231***|<br>0.183*|<br>-0.0375|<br>0.0837**|<br>0.0282|
||(0.0221)|(0.0336)|(0.0347)|(0.0608)|(0.101)|(0.0286)|(0.0378)|(0.0704)|
|same_sec|<br>0.00459|<br>-0.00127|<br>-0.0132|<br>-0.0955***|<br>-0.0298|<br>0.00218|<br>-0.0341*|<br>-0.00860|
||(0.0101)|(0.0134)|(0.0185)|(0.0303)|(0.0311)|(0.00622)|(0.0190)|(0.0159)|
|ldist|0.00220|-0.00557|-0.0333***|-0.0303|-0.0465*|-0.0132|-0.0143|-0.0422**|
||(0.00489)|(0.00775)|(0.0107)|(0.0237)|(0.0254)|(0.00810)|(0.0121)|(0.0176)|
|ad_fmage|<br>0.000123|<br>0.000252|<br>0.000333|<br>-0.00319***|<br>-0.00105|<br>-0.000544*|<br>-0.000431|<br>0.000825|
||(0.000284)|(0.000366)|(0.00102)|(0.00120)|(0.00133)|(0.000294)|(0.000661)|(0.000970)|
|ad_meduc|0.00555|0.0413***|0.00900|0.0195|0.0683***|0.00494|0.000108|-0.0115***|
||(0.00617)|(0.0146)|(0.00923)|(0.0168)|(0.0236)|(0.00343)|(0.00594)|(0.00447)|
|ad_mexper|2.37e-06|-0.000322|-0.000497|-6.26e-05|-0.000238|0.000188|-0.00158***|0.000459|
||(0.000288)|(0.000271)|(0.00120)|(0.00223)|(0.00248)|(0.000587)|(0.000493)|(0.00104)|
|ad_mfemale|<br>0.00137|<br>0.0348*|<br>0.0950**|<br>0.434***|<br>0.237***|<br>0.0277|<br>0.0438|<br>0.0795|
||(0.00625)|(0.0189)|(0.0396)|(0.0916)|(0.0764)|(0.0178)|(0.0375)|(0.0539)|
|ad_lemp|0.0234***|0.0519***|0.0369**|0.166***|0.194***|0.0163***|0.0560***|0.0731***|
||(0.00905)|(0.0146)|(0.0174)|(0.0422)|(0.0455)|(0.00580)|(0.0172)|(0.0208)|
|sametown|<br>0.0118|<br>-0.0321|<br>-0.136**|<br>-0.0534|<br>-0.187|<br>-0.106**|<br>-0.0405|<br>-0.270**|
||(0.0297)|(0.0543)|(0.0674)|(0.143)|(0.158)|(0.0499)|(0.0729)|(0.121)|
|Constant|<br>0.448***|<br>0.405***|<br>0.464***|<br>0.557***|<br>0.992***|<br>0.396***|<br>0.418***|<br>0.361***|
||(0.0318)|(0.0568)|(0.0815)|(0.164)|(0.175)|(0.0529)|(0.0834)|(0.118)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 37
6 12. Foreign participation as a share of total developing country proceeds has been increasing over the years, mostly as a result of sales in sectors such as oil and gas, telecommunications, and banking, and largely in Latin America, Eastern Europe, and East Asia. The foreign share reached 76 percent of total proceeds and generated an estimated $32 billion in foreign exchange in 1999. Foreign direct investment accounted for 80 percent, and portfolio investment for the rest.

13. These trends in privatization activity contributed, along with new private entry, to a substantial decline in the relative share of SOE value added in GDP. Sheshinski and LopezCalva (1998) show that the share of SOEs in industrialized countries declined from an average of about 6 percent of GDP in 1980 to 5 percent in 1997. The decline for low-income countries is steeper, falling from a high point of 15 percent of GDP in 1980s to 3 percent in 1997, while middle-income countries also experienced significant reductions (Table 1). Since the upper and lower middle-income groups include the transition economies of Central and Eastern Europe, this decline was expected given the high levels of state ownership to begin with and the use of mass and rapid privatization techniques in a number of these countries.

**Table 1. Change in SOE's Activity as a Percentage of GDP (Decrease In percentage points of GDP)**

|**Countries**<br>**(byIncomeGroups)**|**1980**<br>(%)|**1997**<br>(%)|**Change**<br>(%)|
|---|---|---|---|
|Low IncomeCountries|15|3|-12|
|LowerMiddleIncomeCountries|11|5|-6|
|UpperMiddleIncomeCountries|10.5|5|-5.5|
|HighIncomeCountries|6|_5_|-1|

Source: Sheshinski and Lopez-Calva, 1998.

# 3. IMPACT OF **PRIVATIZATION**

14. Sufficient time has elapsed since the start of reforms to allow an initial assessment of the extent to which privatization has met its intended economic and financial benefits. There are to date well over 100 studies that do so, covering privatizations in all sectors through a variety of methods in a large number of developed, developing and transition countries (selected studies are summarized in Annex A). The impacts, and supporting studies, can be grouped into five categories:

- First, most assessments of privatization have looked at financial and operational performance at the enterprise level, comparing productivity and profitability before and after sale, and changes in output, investments, capacity utilization, and the like. These studies provide ample evidence that, when done right, privatization improves performance in many different settings in many different ways.

- Second, there is a limited but growing body of work about the fiscal and macroeconomic effects of privatization showing positive fiscal benefits and a high correlation between privatization and growth.
China’s selective educational system restricts access to higher levels of schooling to high ability students, so that _u_ ijt is likely to lead to upward bias in the estimated returns to schooling. Identifying the causal relationship between schooling and earnings requires a valid instrument that is correlated with **S** ijt but uncorrelated with _u_ ijt. To implement the strategy described above, we first generate predicted probabilities for high school and college enrollment from the probit models (results in Table 2) and then use these predicted probabilities as instruments.<sup>13</sup> Note that the models shown in Table 2 include the same rich set of covariates proxying for dimensions of unobserved ability that we include in the second stage of our IV models, and that identification comes from the interactions of cohortspecific education shocks with father’s education and administrative status.

We first present three panels of results for OLS models (Table 3). In column (1) we estimate equation (3), regressing log wage on schooling, age, age-squared, and city and birth cohort dummy variables. In column (2) we estimate equation (4), including interactions of three-year birth cohort dummy variables and city dummy variables. This turns out not to have a significant effect on the coefficient on schooling in any of the models estimated. In column (3), we add the additional covariates other than school quality. The estimated return to college falls from 50 to 47 percent, and the return to a year of education falls from 9.6 to 8.9 percent. In column (4) we add school quality and parent occupation and administrative status variables. The return to college education falls from 47 to 42 percent, while there is little appreciable change in the return to high school over middle school. The return to a year of schooling falls from 8.9 to 8.3 percent (Panel B). Finally, using piecewise linear spline models, we find that the return to a year of post-secondary schooling falls from 12.9 to 11.9 percent, but we do not observe an economically significant change in the return to schooling

> 13See Wooldridge (2002) pages 139-141 for a general discussion of the use of generated instruments and Chapter 18 for use of predicted probabilities from a probit model as instruments.

18