rafmacalaba/datause-encoder-data huggingface dataset:
human capital, thereby lowering the rate of return to schooling _._ The results for Venezuela in the first two years of the new century are in line with what happened in other middle-income countries in Latin America in the 1990s – such as Mexico, Brazil and Chile (see for example Blom and others 2001; Lachler 1998) – where the returns to secondary and tertiary education increased over time, and where the overall rate of return to schooling also increased. It signals that the demand for educated labor was not increasing in Venezuela in the 1990s, most likely due to the economic downturn, but that in the last two years education has become more profitable.

# **2. Data**

We use consistent cross-sections from the _Encuesta de Hogares por Muestro_ conducted by the National Statistical Office of Venezuela (OCEI). The data used are for survey years 1992, 1995, 1996, 1997, 1998, 1999 and 2000. The survey instrument for 1992 involved a shorter questionnaire compared to post-1992 questionnaires but a much larger sample (over 300,000 observations for 1992 compared to about 65,000-80,000 in later surveys).

The working sub-sample used in this study in deriving returns to an additional year of schooling and education levels, as well as returns by quantile, consists of workers aged 15-65, working for wages.

2
WPS4186 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 2</mark> A Kleptocrat’s Survival Guide: Autocratic Longevity in the Face of Civil Conflict Gary Milante<sup>1</sup> Development Economics Research Group World Bank Autocratic regimes are quite often short-lived kleptocracies formed and maintained through force and used to appropriate wealth from subjects. Some of these autocracies collapse after only a year or two of plundering while others manage to survive for fifteen or twenty years. This paper asks why some autocratic regimes survive while others fail. A database of political regimes from 1960 to 2003 is introduced and accompanies the paper in an appendix. A model of political survival suggests that autocrats exchange constraints on their executive power for their continued survival. The relationship between payouts from successful rebellion and ease of rebellion determines how willing kleptocrats are to extend the political franchise and protect their power. Results show that extremely oppressive regimes and great expenditures on security are likely to accompany the most difficult environments for defense of the state. The model is used to identify the costs of pervasive political conflict and to decompose the “civil peace dividend” enjoyed by inclusive democracies that do not suffer from the malady of kleptocratic rule. Finally, the model suggests that slow democratization pushed by the autocratic elites themselves to guarantee their survival, accompanied by stable development, may be the best path toward a democratic future for many fragile states.

# World Bank Policy Research Working Paper 4186, April 2007

_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._

> 1 This research was completed with funding from the World Bank/Knowledge for Change research project, “Political Institutions, Development and a Domestic Civil Peace.”  The author is thankful to advice from Stergios Skaperdas, Ibrahim Elbadawi, Håvard Hegre and discussants at workshops at the Center for the Study of African Economies (CSAE) at Oxford and the International Peace Research Institute of Oslo (PRIO).

1
Table 3: Targeted taxpayers before and after being assigned POS machines Panel A: West Manggarai

||_Restaurant_||
|---|---|---|
|**Treatment**|**Intervention Timeline**|**N Treatment**|
|RPOS<br>TMD<br>WS|Oct. 2019, Nov. 2019, May 2021, March 2022<br>October 2019<br>Dec. 2020, June 2022, and Nov. 2022|14<br>9<br>7|
||_Hotel_||
|**Treatment**|**Intervention Timeline**|**N Treatment**|
|WS|June 2022 and Nov. 2022|6|
||Panel B: Gorontalo||
||_Restaurant_||
|**Treatment**|**Intervention Timeline**|**N Treatment**|
|RPOS<br>RPOS-<br>Grab<br>TMD|November 2020, February 2021, March 2021,<br>April 2021, May 2021, June 2021, Aug. 2021,<br>Oct. 2021, Nov. 2021, Feb. 2022, March 2022,<br>June 2022, and Aug. 2022<br>November 2020 and February 2021<br>January 2021, February 2021, June 2021, July<br>2021, August 2021, September 2021, January<br>2022 and June 2022|92<br>6<br>14|
||_Hotel_||
|**Treatment**|**Intervention Timeline**|**N Treatment**|
|PAC Hotel|March 2021 and May 2021|2|
|TMD|March 2021, June 2021, July 2021, June 2022|3|

11
- 1.42 -

Figure I.C.2 COCOA ACREAGE ADJUSTMENT TO PRICE UNDER FIXED RESOURCES <!-- Start of picture text --> 3.8<br>3.2 - / \ _ _ _ _ _ _ _ _ _ _ _<br>Ij 2.6-<br>AK1//N~~~~~~~~~~~~ \\ =\<br>4167 1907 2007 2017 2027 20:37<br>PERIOD MIDPOINTS<br>0 120 * 1 o g _0<br>q ~<br><!-- End of picture text -->
the selection of inappropriate species and poor site selection. Mangroves were often planted in lower intertidal or subtidal zones, where mangroves do not naturally occur, because more suitable land was not available (Lange et al. 2010). Project failures in the past will offer insight into what to avoid in the future. In general, site‐specific design improves the likelihood of successful mangrove interventions (Forbes and Broadhead 2007).<sup>20</sup> See Box 3 for an example.

# **Box 3. Mangrove afforestation and coastal protection in Bangladesh: the importance of siting**

A study to design the optimal combination of mangrove forest size and polder height was conducted by the Institute of Water Modeling for protecting _Hatia Island_ from cyclones in Bangladesh. The study used simulation modeling to (a) identify the relationship between storm‐surge height and forest parameters such as species, density, tree girth and forest width; and (b) based on this information, determined the necessary forest area for a given height of embankment.

The authors derived the function showing the relationship between surge height and forest width up to 600 meters wide for different parts of the island. The study found that storm‐ surge attenuation varied not only by forest width, but also by location on the island. At the southern end of _Hatia_ island, a mangrove forest 600m wide reduced the surge height by 0.45m, from about 6.20 m to 5.75m. For a forest width of 133 m, the reduction in surge height was 0.18m. However, no appreciable (>0.1 m) reduction in surge height from mangroves was observed at the southeastern or southwestern sides of the island. The results indicate that the forest site must be planned carefully with the consideration of mangroves in combination with "hard" infrastructure, because site‐specific characteristics greatly influence the extent of storm protection.

Source: Coast, Port and Estuary Division, Institute of Water Modeling, Bangladesh 2000 The costs of afforestation and replanting mangroves can also vary significantly. For example, Primavera and Esteban (2008) report average planting costs in the Philippines that are over

> 20 For a list of mangrove resilience factors that inform site selection, see McLeod and Salm 2006, pp 20‐ 21.

28
# **Appendix A: Data Construction**

|**Variable**|**Description**|**Source**|
|---|---|---|
|**Firm Types**|||
|_Ben Ali Firm_|Dummy variable taking the value 1 if the firms is owned, fully or in part, by<br>a member of the Ben Ali clan.|CC and MoF|
|_Offshore Firm_|A dummy variable taking the value 1 if a firm is privately owned by entities<br>other than the Ben Ali clan and operates in the tax regime ‘totalement<br>exportatrice’, commonly referred to as the “offshore” sector. Firms in this<br>tax regime do not have to pay output and import taxes, provided they export<br>at least 70% of their output or sell it to other “offshore” firms.|INS|
|_Onshore Firm_|A dummy variable taking the value 1 if a firm is privately owned and<br>neitheroperates in the ‘totalement exportatrice’ tax regime nor is owned by<br>the Ben Ali clan.|INS|
|_Public Firm_|A dummy variable taking the value 1 if a firm is state owned.|INS|
|**Firm Characteristics**|||
|_Y_|Output as reported in firm’s annual tax declaration.|MoF|
|_L_|Number of salaried employees (annual average over 4 quarters).|INS|
|_Age_|The age of the firm defined as the difference between the current year minus<br>the year in which it first registered.|INS|
|_Sector_|Classification of a firm’s main activity based on the Nomenclature d’Activités<br>Tunisienne (NAT) 1996 5 digit classification, the most disaggregated sector<br>classification available in Tunisia.|INS|
|_Post Privatization_|Dummy value that takes the value 1 after a firm has been privatized and zero<br>while the firm is still public or is never privatized.|MoF|
|_Post-Privatization*Ben Ali_<br>_Owned_|Dummy value that takes the value 1 after a firm has been privatized and is<br>owned by the Ben Ali family, and zero while the firm is still public, is never<br>privatized, or privatized to owners not belonging to the Ben Ali clan.|MoF|
|**Trade policy data**|||
|_Tariff_|Bilateral tariff between Tunisia and a partner country at year t for a certain<br>product (HS6); note that for years for which such information is missing, the<br>last year for which such information is available is used. Tariff data are<br>missing altogether for 2007 and 2009; for these years we use the values from<br>2006 and 2008 respectively.|WITS|
|_Non-Tariff Barrier (NTB)_|This variable measures the sum of all import-related non-tariff barriers at the<br>product-year level. The raw data contain the year of creation of these non-<br>tariff measures, but not the year of their removal.|WITS|

49
Table 8. Militarization and Montalvo and Reynal-Queirol’s measures of ethnic and religious polarization.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Military<br>Expend.|Military<br>Expend.|Military Pers.|Military Pers.|Arms Imports|Arms<br>Imports|
|Ethpol (Montalvo|0.112|0.081|0.179|0.175|-1.600|-1.581|
|& R-Q.)|||||||
||(0.11)|(0.08)|(0.26)|(0.26)|(0.82)|(0.81)|
|Relpol (Montalvo|0.331|0.071|0.074|0.053|0.099|0.114|
|& R-Q.)|||||||
||(0.52)|(0.10)|(0.21)|(0.16)|(0.08)|(0.09)|
|GNI p.c. (ln)|0.183|0.062|0.252**|0.244**|-0.095|-0.097|
||(0.76)|(0.20)|(2.17)|(2.10)|(0.26)|(0.26)|
|Economic growth|0.825|0.833|-0.976|-0.976|-4.506|-4.381|
||(1.19)|(1.19)|(0.88)|(0.88)|(1.15)|(1.13)|
|Polity|-0.035*|-0.031*|-0.005|-0.005|-0.157**|-0.157**|
||(1.91)|(1.74)|(0.76)|(0.74)|(2.08)|(2.11)|
|Government<br>|<br>0.134***|<br>0.128***|<br>0.016**|<br>0.016**|<br>0.204***|<br>0.200**|
|expenditures|||||||
||(4.98)|(4.81)|(1.99)|(1.99)|(2.59)|(2.56)|
|Population (ln)|-0.153|-0.251|-0.236***|-0.246***|0.274|0.264|
||(1.08)|(1.46)|(3.07)|(3.06)|(1.05)|(1.01)|
|Contiguous<br>militarization|0.103**|0.089*|0.579**|0.574**|0.120|0.116|
||(1.97)|(1.77)|(2.15)|(2.14)|(1.38)|(1.34)|
|Peace years (civil|-0.023***|-0.022***|-0.006***|-0.006***|-0.042**|-0.041**|
|war)|||||||
||(3.86)|(3.82)|(2.85)|(2.91)|(2.35)|(2.40)|
|Peace years|0.010|0.012|-0.007|-0.007|-0.035**|-0.035**|
|(intern. confl.)|||||||
||(1.20)|(1.38)|(1.21)|(1.19)|(2.24)|(2.28)|
|Civil war|0.597***|0.602***|0.067|0.067|2.178**|2.187**|
||(2.90)|(2.95)|(0.90)|(0.92)|(2.18)|(2.21)|
|International|0.253|0.231|0.298*|0.296*|1.865**|1.887**|
|conflict|||||||
||(0.86)|(0.81)|(1.74)|(1.74)|(2.10)|(2.16)|
|Observations|1307|1307|1161|1161|1164|1164|
|Countries|102|102|109|109|109|109|

Notes: Absolute t-statistics in brackets. Constant and year-specific time-dummies included, but coefficients not reported. *, **, *** significant at .1, .05 and .01 level, respectively.

28
new equilibrium within the factor markets. Demand for the factors used by agriculture is likely to increase, while factor demand by industrial sectors is expected to decline. The ultimate result will be determined by the calibrated model parameters.

The change in the allocation of factors among activities is given in Table 4.6. Labor employment increases in all activities, except in tobacco production. Increases are as high as 44 percent in maize and 32.5 percent in pulses; which are the sectors most affected by the designated world price change. Land and water employment in maize also increases significantly, at the cost of employment of these factors in the other sectors.

**Table 4.6. Percentage change in factor employment**

||**La**<br>|**bor**<br>|**Rainfe**|**d Land**<br>|**Irrigat**|**ed Land**<br>|**Wa**|**ter**<br>|
|---|---|---|---|---|---|---|---|---|
||**Base**<sup>*****</sup>|**Sc. 1**<sup>******</sup>|**Base**†|**Sc. 1**<sup>******</sup>|**Base**†|**Sc. 1**<sup>******</sup>|**Base**‡|**Sc. 1**<sup>******</sup>|
|**Wheat**|2,662|12.51|8,267|1.95|1,898|3.36|5,133|4.43|
|**Barley**|734|7.30|3,165|-3.47|||||
|**Maize**|344|44.02|458|29.74|128|33.45|738|33.46|
|**Oth. grains**|1,367|4.99|629|-5.51|140|-2.28|437|-3.42|
|**Pulses**|678|32.50|1,535|18.84|||||
|**Tobacco**|734|-1.65|219|-11.01|||||
|**Sugar beet**|1,225|0.24|||426|-6.56|3,177|-6.43|
|**Cotton**|377|11.19|||646|2.85|5,667|2.66|
|**Oil seeds**|391|10.53|505|1.02|108|2.54|381|3.36|
|**Potatoes**|985|1.68|||215|-5.25|999|-4.80|
|**Vegetables**|5,731|1.63|3,687|-8.08|826|-6.52|4,187|-5.72|
|**Fruits**|4,065|1.79|1,154|-6.65|439|-6.49|2,134|-5.69|
|**Nuts**|1,295|2.34|418|-7.30|||||
|**Oth. crops**|2,242|0.62|202|-9.07|39|-7.43|122|-8.22|
|**Total Agr.**|22,831|4.94|20,238|0.00|4,866|0.00|22,975|0.00|

_Source: Results from TACOGEM-W._ *** **** 3 In terms of labor VA, Million TRY, % Change, † 1000 Ha., ‡ Million m .

By comparing Table 4.6 with Table 4.4 an important fact is revealed. Labor employment increases despite the fall in production quantities. An important account of factors shifts to the agricultural sector to compensate for the loss in total supply as a result of the increase in import prices.

Since total supply of land and water is fixed in the model, the change reflects the reallocation process as various sectors compete for resources. Factors are shifted to the activities where prices increase as a result of the world price shock. Water use increases in wheat, maize, cotton and oil seeds at the cost of water usage in other sectors.

40
level to allow for arbitrary correlation in the error structure of individuals within a state. These historic rates can be argued to be the result of the pattern of arrival of the railroad system in Mexico coupled with changes in U.S. demand conditions for agricultural labor. As migration networks lower the cost of migration for future migrants, they become self-perpetuating, and as a result, continue to influence the migration decisions of households today.

Our identifying assumption is then that historic state migration rates do not affect education outcomes over 70 years later, apart from their influence through current migration. Instrumental variables estimation relies on this exogeneity assumption, and so it is important to consider and counteract potential threats to its validity. One potential threat is that historic levels of inequality and historic schooling levels helped determine migration rates in response to the railroad expansion, and also influence current levels of schooling due to intergenerational transmission of schooling. To allow for this possibility we control for a number of historic variables at around the same time period as our historic migration measure. The controls are the proportion of rural households owning land by state in 1910 taken from McBride (1923)<sup>11</sup> ; and the number of schools per 1000 population by state in 1930, and male and female school attendance for 6 to 10 year-olds by state for 1930, both taken from DGE (1941).

A second possible threat to validity is that the development of the railroads in certain states and communities ushered in the subsequent development of other infrastructure, such as school facilities, and led to changes in the income distribution which themselves influenced the incentives and ability to invest in schooling. We include the following state-level controls for this possibility, all calculated from the

> 11 Land ownership data were kindly provided by Ernesto López-Córdoba.

11
# **Executive Summary**

A majority of World Bank supported projects in education focus on improvements in public education. This, however, is not the case in all projects. This paper traces trends in private sector development in 11 World Bank education projects spanning a three-year period (1995-97). The paper explores the issue of how the Bank is supporting and might support an environment for private financing and provision of educational services to flourish alongside public education. The review of projects includes examples of Bank financing in some countries in Africa, Latin America and South East Asia. Evidence indicates that justification for Bank support in the sample countries is based on a reasonably balanced analysis of the respective roles of the private and public sectors in education.

The 11 examples reveal answers to two questions: (1) What have been the rationales for Bank lending in education (sectoral/sub-sectoral) in some of the client countries? (2) In countries where private sector presence exists alongside publicly financed and provided education, how has the Bank encouraged the private sector to thrive? Project Staff Appraisal Reports (SARs) and Project Appraisal Documents (PADs) constitute the basic information sources, along with interviews with task team leaders who were responsible for leading project design and preparation. Analysis of the 11 case studies provides glimpses into the Bank's rationales for supporting private sector development in education. The rationales are multiple and reveal emerging trends in Bank lending. The project examples are not presented as the proven best policies but rather as illustrative approaches, among others, for the particular circumstances confronted by the Bank.

The 11 country case studies reveal that: (a) the role of the private sector in education, both from demand and supply perspectives, has been growing over the years; (b) in the context of countryspecific project design, there is a growing need to carry out comprehensive assessments (at the macro or country level, as well as at the micro level) about the respective roles of public and private education to better understand the responsibilities of all parties before projects are designed; (c) in those instances where the role of the private sector is analyzed, there is an absence of a country-specific definition of the "private sector" in education. Often the private sector is a term utilized to encompass out-of-pocket expenses of households rather than for-profit or not-for-profit (religious or otherwise) sectors. In many countries the use of the term "non-governmental sector" encompasses the private sector; and (d) within iv
41 In general, an increase in the dispersion of the distribution of income in the early phases of economic modernization is not unusual – and is often described in terms of movements along the upward-sloping section of the “Kuznetz curve”. This is often assumed to reflect the distributional consequences of the emergence and expansion of a high-productivity, modern sector of firms alongside traditional low-productivity production units.<sup>75</sup> When trying to explain the rise in income inequality in China, we have to add that the modernization process took place simultaneously with a shift to an economic system that relies, much more than the previous one, on economic incentives. It should, however, be added that the increased reliance on economic incentives in agriculture in the late 1970s and early 1980s, for a while actually contributed to _reduce_ the relative income gap in the country – not only between urban and rural areas but also across households in the country as a whole. This illustrates the obvious but important point that the relation between economic growth and income inequality depends critically on the sector composition of aggregate economic growth. Similarly, the distributional consequences of increased reliance on economic incentives depend on which population groups actually experience such an increase.

Specific policy measures have also contributed to increase the income gaps during the reform period. For instance, income differences across provinces were accentuated by the opening up of the country to foreign trade and investment in the mid-1980s, since this released the (potential) absolute and comparative advantage of coastal (eastern) provinces with relatively good locations for such activities. The differential effects of these policies across provinces were accentuated by the _selective_ opening of the country to the outside world, as well as the favoring of these areas as regards the allocation of infrastructure investment.<sup>76</sup> The distribution of income has become more uneven also _within_ provinces, rural areas and urban areas. According to a study by the Chinese Academy of Social Sciences (2002), the emergence and increase of entrepreneurial income has contributed to this development in

> 75 As often pointed out, however, South Korea and Taiwan/China experienced rapid economic growth during several decades without much (if any) widening of the overall distribution of income (Maddison, 2003).

> 76 According to Jones et al. (2003), areas enjoying status as Special Economic Zones grew by 5 percentage points faster per year than the average for provinces.