rafmacalaba/datause-encoder-data huggingface dataset:
ranges from direct measures of technology to correlated changes across countries (see Berman and Machin (2000) for details and a summary).

The SBTC hypothesis requires that technology drives shifts in skill demand. Because of this one should see skill-use shifts occurring where employers have more to gain from the introduction of new technology. Consequently there should be systematic differences in the extent of relative demand shifts within particular workplaces, firms and industries, each of whom is likely to differ in their demand for and the extent of their use of new technologies. One test of relevance to the SBTC argument therefore comes from a decomposition of aggregate changes in skills demand (usually measured by wage bill or employment shares of skilled workers), say Δ Sn, for _i_ =1,...N industries as follows: The decomposition breaks the overall shift in skill demand into two components. The first is the within-industry component of skill upgrading (weighted by P, the relative size of industry _i_ , where a bar is a time mean). The second measures the contribution of between-industry shifts, namely how much bigger or smaller an industry is becoming over time (weighted by the time averaged skill demand).

A number of studies using this kind of decomposition have systematically found that the bulk of the aggregate changes have occurred within, rather than between industries or workplaces. These studies cover different countries, levels of aggregation, time period and skill measures. It is clear the bulk of the skill upgrading that has occurred in OECD manufacturing since the start of the 1970s has been within, rather than between, industries.<sup>13</sup> Moreover, the same is true in the developing world. Table 2 reported within-between decompositions for high, middle and low income countries. The within-industry component is 12
The following section describes our data. We then turn to the questions raised above. Section 3 describes our information intervention, while Section 4 reports on its impacts, both on average and for selected strata. Section 5 concludes.

# **2. The survey data and summary statistics**

We collected two rounds of data from 150 villages spread across rural Bihar. The first round (R1) was implemented between May and July of 2009 and the second (R2) during the same months one year later. These periods were chosen for being lean periods for agricultural work, and were thus expected to be peak periods for BREGS. The first of our survey periods witnessed severe floods during the monsoon (July – August of 2008) in some districts falling in the catchment area of the Kosi river. In contrast, rainfall was scanty during the 2009 monsoons and drought was declared in many districts.

A two-stage sampling design was followed, based on the 2001 Census list of villages. In the first stage, 150 villages were randomly selected from two strata, classified by high and low BREGS coverage based on administrative data for 2008/9. In the second stage, 20 households per village were randomly selected, drawing from three strata based on an initial listing of all village members and a few selected attributes. With numbers in parenthesis, the three strata were those with at least one member who had done public works in the last year (7), those with a member who had engaged in other (non-public works) casual work (7) and all other households (6). This stratified approach ensured that the sample included both scheme participants and households with likely participants. All results reported in the study are weighted with appropriate sample weights to reflect the sampling design and are representative for the state.

A household level survey collected information on a range of household level characteristics including demographics, asset ownership, consumption, employment and wages, political participation and social networks, as well as information on BREGS participation and process-related issues.

Individual level surveys were also administered to one male and one female member of each household, who were interviewed about their participation in BREGS, experience at the most recent BREGS worksite and knowledge and perceptions of the program, the village labor market and the role of women. We also collected data to calibrate a simplified individualspecific version of the Pearlin Mastery scale, which is a measure of the extent to which 6
bracket (SISBEN level 1), corresponding nearly to the population in extreme poverty, are deemed eligible to benefit from the FA program.

The program was initially piloted in 22 municipalities in 2001 but it has been continuously expanded since then. The first major expansion took place in the second half of 2002, targeting initially 400 municipalities with fewer than 100,000 inhabitants amongst other conditions.<sup>7</sup> In 2005, the program was extended to include displaced families and municipalities which either became able to offer the required education and health services or with services accessible in nearby towns. In 2007, the program was further expanded to large urban centers and municipalities not covered before. By 2010 the program reached almost national coverage, benefiting nearly 2.5 million households in 1,093 municipalities (Acci ́on Social, 2010; Attanasio et al, 2010). In 2012 the program became a law, so that all people in _SISBEN_ level 1 are eligible for the transfer, regardless of the place of residence.<sup>8</sup> Figure 1 depicts the cumulative enrollment in the program over time, where the vertical lines indicate the timing of the presidential elections.

# **3 Data**

This paper uses three administrative data sets to identify the effects of FA on voting behavior: (1) an electoral census, (2) the FA’s information system of beneficiaries (SIFA) and, (3) the _SISBEN_ . The first one has information on the most recent date of registration and voting place (municipality, polling station and voting booth) for all adults who were registered to vote in the 2010 Presidential election.<sup>9</sup> Once registered in a specific polling station, citizens are assigned to a voting booth within the polling station at the time of the elections.

> 7Municipalities could not be departmental capitals, had to have at least one bank branch in the municipality (to deliver the transfers), and had access to education and health facilities that allowed for the implementation of the program.

> 8 `http://www.eltiempo.com/politica/ARTICULO-WEB-NEW_NOTA_INTERIOR-11929202.html`

> 9In Colombia citizens become eligible to vote at age 18. Colombia has very high registration rates. According to LAPOP, a Latin American political survey, registration rates between 2004 and 2010 were approximately 90%. Yet, actual turnout rates are much lower, usually in the order of 50% percent

8
receives smaller projects.

## **2. Roads and economic activities 3**

To our knowledge, the impact of roads on the location of economic activities in Mexico has not been studied yet. A set of studies, however, have investigated the link between infrastructure and productivity. For instance, Becerril-Torres et al. (2010) study the effect of total infrastructure (measured with an index that includes roads, ports, airports, and telecommunications) on the convergence across states in technical efficiency. They find that their infrastructure index was associated with greater regional productivity, but mainly during the Import Substitution Industrialization (isi) period (1970-1985), before Mexico entered the North American Free Trade Agreement (nafta) in 1994. Similarly, Brock and German-Soto (2013) find that lower levels of infrastructure investments had a lower effect on regional productivity during the nafta period, and conclude that continued investment in transportation will be necessary to boost industrial sector growth. Focusing specifically on the effect of road infrastructure on productivity growth in the manufacturing sector in Mexico, Duran-Fernandez and Santos (2014a and 2014b) conclude that road infrastructure had positive effects on productivity and on the average product of labor.

# **3 Data and descriptives statistics**

## **.1 3 Firms and employment**

We use panel data for employment and firm locations from two different sources published by the Mexican National Institute of Statistics (inegi).<sup>3</sup> The first source is the Economic Census for the years 1986, 1989, 1994, 1999, 2004, 2009, and 2014, which provides employment figures and breakdown by broad sector of activity, as well as total income at the level of municipalities and for formal businesses. The second source of data is the Directory of Economic Units (denue)<sup>4</sup> for the years 2004, 2009 and 2014. It is an exhaustive dataset which contains detailed microgeographic information on all formal establishments, including the 6-digit naics classification, the number of employees of the establishment, turnover, the municipality identifier, and exact geographic coordinates. For the year 2014, the database contains information on approximately 3,000,000 establishments throughout the country. Note that although the denue database has finer geographic identifiers than the Economic Census (actual coordinates vs. municipality of location) and a finer industry identifier (6-digit naisc vs. broad sectors of activity), it covers less years than the Economic Census. In the analysis, we use either one, as appropriate.

> 3Instituto Nacional de Estadísticas y Geografía.

> 4Directorio Estadístico Nacional de Unidades Económicas.

5
**_Energy demand models for policy_** **_<u>formulation</u>_**

its utilization decision to determine demand. Despite its appeal this method found limited econometric use due to lack of capital stock data. The development of multinomial logit model represented a break-through in this regard.

In the 1980s the assumption of stationarity of economic variables assumed in the standard ordinary least square (OLS) method of estimation was questioned. It was suggested that many variables used in energy demand analysis (e.g. energy consumption, real energy prices, and real income) have a root close to unity and are integrated of degree 1. Using non-stationary data in regression results spurious results unless the variables are cointegrated.<sup>14</sup> The problems include non-stationary error process, autocorrelation of explanatory variables, non standard distribution of coefficient estimates and endogeneity problem. As a solution to the problem, tests for co-integration and estimation of vector error correction models have emerged. This development in the econometric analysis has significantly influenced the energy demand studies in the 1990s and brought the “unitroot revolution”. It is now a routine exercise in the academic literature to test for stationarity and co-integration of variables before undertaking any forecasting exercise econometrically.

However, Harvey (1997) criticized this over-reliance on co-integration as “unnecessary or misleading or both”. Harvey (1997) argued that “The use of autoregressive models and associated unit root tests forces the researcher into a specific way of modeling which effectively excludes forecasting and decomposition procedures which may be more effective and have a more natural interpretation”. He also suggests that the statistical properties of the method are poor and consequently, there is limited justification for such a systematic use of this method. Instead, he has proposed an alternative method called the Structural Time Series models, which have been applied to the energy demand, among others, by Hunt et al (2003) and Adeyemi and Hunt (2007).

> 14 See Engsted and Bentzen (1997) for a non-technical review of these developments. See also Bentzen and Engsted (1993).

31
Figure C.3: Correlation conditional to PD characteristics <u>(2009-2015)</u> <!-- Start of picture text --> 0.15<br>0.13<br>0.13<br>0.11 0.10<br>0.09<br>0.07<br>0.05<br>0.03<br>0.01<br>No PD PD<br>0.18 0.17<br>0.18 0.18<br>0.16 0.16<br>0.16<br>0.14 0.13<br>0.14<br>0.12<br>0.12<br>0.10<br>0.10<br>0.08<br>0.08<br>0.06<br>0.06<br>0.04<br>0.04<br>0.02<br>0.02<br>0.00<br>No PD with autonomous regions 0.00<br>No PD with local governments elected<br>0.30 0.28<br>0.18 0.17<br>0.16<br>0.16 0.25<br>0.14<br>0.20<br>0.12<br>0.15<br>0.10 0.15<br>0.08<br>0.10<br>0.06<br>0.05<br>0.04<br>0.02 0.00<br>0.00 No PD with state fiscal & legislative<br>authority<br>No PD with state governments elected<br>0.15<br>0.12<br>0.10<br>0.05<br>0.00<br>No PD with state constituencies in the<br>upper house<br>‐0.02<br>‐0.05<br><!-- End of picture text --> 44
# **Pronatal Property Rights over Land and Fertility Outcomes: Evidence from a Natural Experiment in Ethiopia**<sup>**¶**</sup>

## **Daniel Ayalew Ali**<sup>*****</sup> **, Klaus Deininger**<sup>*****</sup> **, Niels Kemper**<sup>**†**</sup>

*Development Research Group, The World Bank **†** University of Mannheim, Department of Economics

_JEL classification codes:_ J13, N50 _Keywords:_ Property rights, Fertility, Ethiopia, Natural Experiment

> ¶ We are indebted to Gebeyehu Belay, Solomon Haile, Zerfu Hailu, Seid Nuru, and Dessalegn Rahmato for in-depth discussions on land reforms in Ethiopia. We thank conference participants at the World Bank's 15th Annual Research Conference on Land and Poverty and the Brown Bag Seminar in Development Economics at the Goethe University in Frankfurt. We thank Alexandra Avdeenko, Albrecht Bohne, Markus Frölich, Dany Jaimovich, Heiner Schumacher, Matthias Schündeln, and Pia Unte for valuable comments.
7

**Figure 2: Retail prices of regular gasoline in March 2015** (US$/liter)

<!-- Start of picture text --> Uruguay<br>Malawi<br>Turkey<br>Mozambique<br>Argentina<br>Senegal<br>Bangladesh<br>Madagascar<br>Mali<br>Rwanda<br>Uganda<br>Fiji<br>Burkina Faso<br>Côte d'Ivoire<br>Dominican Republic<br>Nepal<br>Colombia<br>Chile<br>Cameroon<br>Brazil<br>Cape Verde<br>Morocco<br>Lao PDR<br>Costa Rica<br>China<br>Nicaragua<br>Kenya<br>India<br>Togo<br>Peru<br>Honduras<br>South Africa<br>Tanzania<br>Niger<br>Philippines<br>Gabon<br>Sri Lanka<br>Mexico<br>Thailand<br>Ethiopia<br>Tunisia<br>Guatemala<br>Angola<br>Ghana<br>Jordan<br>Liberia<br>Vietnam<br>Equatorial Guinea<br>Namibia<br>El Salvador<br>Bolivia<br>Pakistan<br>Benin<br>United States<br>Russia<br>Kazakhstan<br>Malaysia<br>Indonesia<br>United Arab Emirates<br>Nigeria<br>Ecuador<br>Egypt, Arab Rep. of<br>Turkmenistan<br>Iran, Islamic Rep. of<br>Algeria<br>Kuwait<br>Venezuela, RB<br>0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80<br><!-- End of picture text --> _Sources:_ Government websites, local news articles.

_Note_ : The research octane number (RON) is 90–93 for regular gasoline but some countries have only higher octane grades, typically 95 (Algeria, Malaysia, Tunisia). The RON is 88 in Indonesia, and 80 in Bangladesh.
|PovertyReduction Support Credit 2(PRSC 2)|PhilipS. Goldman|
|---|---|
|ET-PRSC 2 DPL(FY05)|Trina S. Haque|
|ET-PRSC 2 DPL(FY05)|Trina S. Haque|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|SECOND SHANDONG ENVIRONMENT PROJECT|GuangmingYan|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|GUATEMALA Maternal and Infant Health and Nutrition|Christine Lao Pena|
|CUNDINAMARCA EDUCATIONQUALITY IMPROVEMENT|Martha Laverde|
|CUNDINAMARCA EDUCATIONQUALITY IMPROVEMENT|Martha Laverde|
|CUNDINAMARCA EDUCATIONQUALITY IMPROVEMENT|Martha Laverde|
|CUNDINAMARCA EDUCATIONQUALITY IMPROVEMENT|Martha Laverde|
|CUNDINAMARCA EDUCATIONQUALITY IMPROVEMENT|Martha Laverde|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
|Grenada: OECS Education Development Project|Harriet Nannyonjo|
and the National Nigerian Petroleum Company (NNPC)—and their inability to effectively share data and information.

- The use of suitable and secure computer-based information technology systems to provide consistent data to various users and to analyze this data is limited or often nonexistent.

- The NNPC monthly returns on crude oil sales do not indicate the volume of crude, the applicable price, the exchange rate used, and the method used for computing the proceeds.

- In the determination of the profits tax liability, all the joint venture companies fail to apply the 2000 MOU regarding the use of realizable price. Misapplication of the MOU provisions revealed significant differences in tax liability leading to additional assessments.

- Some companies claimed capital allowances on construction in progress. The argument was that paragraph 15 of the second schedule to the Petroleum Profits Tax Act allows for the claiming of capital allowances from the moment the expenditure is incurred, rather than when the asset is first put to use.

- The NEITI audit indicated potentially significant petroleum profits tax under assessments in relation to operating expenses, intangible drilling and development costs, investment tax allowance, and capital allowance on non-associated gas. In several areas, the legislation is unclear as to how the liabilities and allowances should be computed.

# **Spending Resource Wealth**

88. Nigeria‘s fiscal system, in particular the arrangements for sharing federally collected revenue among the three tiers of government, has undergone several changes since independence (for details, see World Bank 2007). Fiscal federalism was revived in 1999 at the start of the Obasanjo administration. In principle, all oil revenues collected at the federal level are paid into the CRF and then shared among the three tiers of government. Under the budget reform program of 2003, however, the government began using an oil-price-based fiscal rule. A reference price for oil is set annually and oil revenues accrue to the CRF in line with this oil price. Revenue derived from oil prices that are higher than the reference price is put into the excess crude account (ECA). The oilprice-based fiscal rule delinks expenditures from current oil revenues and is meant to create a contingent stabilization fund. If implemented properly and in combination with prudent budgeting and expenditure programs, the rule is expected to promote good governance and sound fiscal policy in the oil-dependent country. The new fiscal institution has faced stiff challenges but held up throughout the fierce political storms.

31
- 1.80 -

assesshg long run hnvostment prospects. As a practical matter, the worst case<sup>is</sup> vacillating government pollcy that arbitrarily<sup>varles the real buying price (or real tax) since</sup> Investors will shorten Investment horizons<sup>(which Is the kiss of death forlong-lived</sup> investments like cocoa) until they have<sup>formed stable prior distrlbutlons. This might take</sup> long tine if government Is sufficiently arbitrary.

# Pollcy Implications of Simulation Experlments

It Is now appropriate to sort out the lmpications<sup>of the analytical work on cocoa</sup> producer supply response. First and foremost, It Is clear that<sup>specification of a</sup> producer buying price (even In real<sup>terms) Is not sufficient Information to permit</sup> unqualifled estimates of cocoa supply.<sup>Analytical methods which attempt to do this are</sup> really saying that the future will be like the<sup>past. In many contexts, this Is an acceptable</sup> approach; but givon Ghana's economic history<sup>of recent decades, such an approach can</sup> be misleading. Who could have predicted<sup>the exuberant cocoa Investment resDonse of</sup> Ghanaian farmers to Improved price Incentives,<sup>given the way that previous policy had</sup> worked against the Interests of cocoa<sup>farmers? Yet the evidence from the Survey for</sup> Cocoa Producer Pricing shows that 33 percent<sup>of existing cocoa acreage In trees have</sup> been planted In the past four years.

The results from the simulatlon experiments<sup>are less obvious since they are</sup> concerned with medium- and long-term<sup>horizons. The Inertia of the past weighs heavily</sup> on the adjustment path of long gestation,<sup>long-lived Investments. Thus, the differences</sup> among the several scenarios with<sup>respect to cocoa production and acreage are not</sup> great for the first ten years of the<sup>multieriod experhnents. By the tkme 40 years have</sup> elapsed, the differences can be huge.

Perhaps the real Issue of judgment<sup>iswhich scenario Is the relevant one for</sup> policy planning purposes? Since no<sup>one can predict the future, this Is not a question</sup> that responds to economic expertise.<sup>For that reason, several varlant scenarios have</sup> been shmuiated. However, discounting<sup>sconarios with zero population growth or with Income</sup>
## Figure: E.10.7

# PANAMA LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE TOTAL EXPORTS<br>25000<br>5 20000 5<br>20000<br>15000<br>o 10o000o<br>0~ 5000<br>0 1970 1980 1990<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1980<br>NA (5.24%)<br>MsEX (1.05%)_<br>CA (19.81%)<br>,~~~~~~~~~~~~O<br>ROW (73.89%)<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1970<br>_NA (2.50%)<br>| ~~~~~~~~~~~~~~~MEX (0 50%)<br>ROW (51 42%) CA (45 58%)<br>LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>(3 61W366%)<br>NA (47.42%)<br>CA (14.62%) -MX(1 35%)<br><!-- End of picture text -->