rafmacalaba/datause-encoder-data huggingface dataset:
barriers exist in their path. In this context, then, we review the recent empirical management capability and organizational (personnel) economics fields.

The literature has also identified external conditions positively linked to OJT provision at the firm level. Favorable external conditions include advancing competition, improving regulatory environment, influencing firm conditions by providing incentives to adopt new technologies, and improving management and providing OJT. We review this evidence to understand what type of conditions would facilitate boosting the demand for OJT.<sup>16</sup>

# 3.1 Factors within firms: Information asymmetries, low management quality

How do firms decide on human resource policies? What steps are involved in accessing market information about new practices and training? Who accesses this information, and who does the cost-benefit analysis of such training? How accurate are their expectations? How adequate are their cognitive skills for information processing? How quickly do they act on it?

These are the types of questions addressed in this section.

There is a small but growing literature with robust field evidence about the link between management capability and firm productivity. We draw on this literature to provide suggestive evidence regarding the barriers to effective policy-making within firms—which in turn impacts decisions regarding the provision of OJT (Atkin and others 2015; Bandiera, Barankay, and Rasul 2011; Bloom and others 2013).

More often than not, management teams - within firms - access information about training and decide whether to provide it. They also tend to decide based on their internal cost-benefit analysis. The literature reveals that, across countries, substantial variations exist in management practices across organizations and sectors (Bloom and Van Reenen 2007; Bloom and others, 2012).<sup>17</sup> In developing countries like Brazil, China and India, management scores are lower than in the United Kingdom or United States, reflecting lower management capabilities in general. Among management practices, those pertaining to human resource management are the most relevant for our purposes: policies on evaluating worker performance, designing incentives for worker performance, training provision and so on.

> 16 In a sense, the provision of OJT could be likened to the adoption of other productivity-enhancing activities such as information and communications technology (ICT) or R&D. In the case R&D and OJT, the ability of a firm to appropriate rents may be more limited than in the case of ICT.

> 17 They have been using double-blind survey techniques and randomized sampling methods to gather information on management practices for more than 10,000 organizations across 20 countries. See Annex 2 for the survey instrument.

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empirical implications for the impact of decentralization and how this impact interacts with asymmetry in political influence.

The centerpiece of our analysis of Pakistan’s irrigation reform is an administrative database maintained by the Punjab Irrigation Department and consisting of readings taken from water discharge gauges installed at the head and tail of each channel of the entire system. These data arguably provide an objective measure of water theft along a channel. Moreover, water discharge data are available over the years 2006-2014, a period encompassing significant devolution of irrigation management to FOs. Importantly, we are also able to match villages along each irrigation channel back to unit record landownership data from recent Agricultural Censuses. This allows us to construct measures of _differences_ in political power, proxied by landholdings, between head and tail villages and thus establish whether irrigation reform has had heterogeneous impacts along this dimension.

In a companion paper (Jacoby and Mansuri 2018), we study the allocation of canal water in the presence of rent-seeking farmers and corruptible irrigation officials with career concerns. Using data from several hundred distributaries in Punjab that were _not_ subject to irrigation reform, we find that, under bureaucratic control, the extent of water theft is substantially affected by the distribution of political power along a channel: where political influence is relatively concentrated at the head of a channel, water allocations are more favorable toward the head as reflected in both the canal discharge and land value differential between head and tail. In this paper, by contrast, we focus on how inequality interacts with decentralization. Although the literature recognizes that local governance is more likely to serve the interests of elites where economic and political power is more asymmetric, empirical support for this proposition remains thin (see Mansuri and Rao, 2013).

We adopt two strategies for constructing a control group against which we compare the changes in canal water allocations following decentralization. Since FOs were phased-in starting in 2005, our first strategy is to look at variation in outcomes across distributaries 3
# **II. DATA**

This paper uses the latest National Survey of Employment, Wages, Technology and Training (ENESTYC), fielded in 2001. The ENESTYC is a periodic establishment survey fielded by INEGI ( _Instituto Nacional de Estadística Geografía e Informática_ ) on behalf of the Ministry of Labor and Social Welfare. The ENESTYC’s universe is the manufacturing establishments numbered in the Economic Census updated with the EIM – _Encuesta Industrial Mensual_ – and with information from the petrochemical industries. The sampling design is random and stratified for each of the 54 activity branches of the _Clasificación Mexicana de Actividades y Productos_ (CMAP) 1994 and by firm size. The sample size for 2001 was 8,179 firms, which represents 333,647 firms. As its name suggests, ENESTYC contains detailed information about the firm – production, output and input cost, capital assets, size, location, division of activity, ownership, technology<sup>3</sup> , quality control methods and ISO 9000 certification, and research and development – and attributes of the workforce – gender, wage and non-wage compensation by occupation and gender, and training, both in-house training and external formal training.

In both 1993 and 1999, INEGI selected a sub-sample of the establishments interviewed in the ENESTYC. From each of those firms, a randomly selected sample of 10 workers from different occupational categories was interviewed to elicit the National Survey of Employment to Workers in the Manufacturing Sector (ENTRAM). This is a worker level dataset which includes information on basic data of the worker, educational level, job conditions, remunerations, training, and work history. Though the linked ENESTYC-ENTRAM yields an employee-employer dataset with very rich information at the worker level, it was not suitable for our analysis because the methodology in Section IV requires data for the entire workforce and the ENESTYC-ENTRAM only has only a small sample of workers by firm, especially for the larger firms, which would produce an incomplete characterization of the firms’ workforce. The excellent quality of the ENESTYC and the detail of the information elicited allow us to estimate plant-level

> 3 Such as use of different types of technology (manual equipment to robots) and ICT, including workplace practices.

4
They are significantly more likely to know about the CCMA (93%) and take their employer to court (95%), are more likely to hold a have a written contract (33%) and have negotiated wages (52.4%, not significant). The next section will explore whether the results can be explained by different _expectations_ about the productivity of native domestic workers.

# **5.2 Statistical vs. Taste**

To shed light on the nature of discrimination, I build on insights by Farber and Gibbons (1996) and Altonji and Pierret (2001): if firms use nationality as a proxy for productivity, then the importance of nationality should decrease as other predictors of productivity become available and are factored into employer beliefs. By contrast, if employers have the same productivity expectation of South Africans and foreigners but prefer hiring locals due to _taste_ -based discrimination, we would expect the revelation of additional information to have no effect on the foreign-national gap in hiring decisions. (For a formal employer learning model see Appendix D.)<sup>25</sup> I first predict the number of clicks (ˆ _yi_ ) in the pooled sample using covariate vector _Xi_ . The residualized numbrt of profile clicks ( _yi − y_ ˆ _i_ ) is regressed on nationality dummies interacted with a measure of the number of relevant information ( _I_ ) that the search result includes<sup>26</sup> : The intuition for this test is as follows: imagine there are two binary characteristics ( _X_ 1 and _X_ 2) that equally determine productivity. Individual A has a positive signal for _X_ 1 and a negative for _X_ 2 while person B does not provide any information on _X_ 1 and _X_ 2. While both may have the same predicted productivity, person A’s profile is more informative. If employers statistically discriminate we would expect employer demand to converge as more information ( _I_ ) becomes available. Given that the previous analysis found that foreigners receive fewer clicks than South Africans, we would

> 25Similar tests of the nature of discrimination have been conducted through audit studies, e.g. by Baert and De Pauw (2014). Neumark (2016) points out that it is unclear whether the information content that is varied is relevant to employers.

> 26All variables are categorized to be relevant that have a an absolute t-value>1 in specification (1). Relevant information includes gender, age, experience, references, pictures, workpermit, drivers license, and whether a person is willing to sleep in. The average profile has information on 2.9 of these variables. While applicants who do not provide information on their nationality reveal on average 2.6 characteristics, more information is revealed by South Africans (3.3), Zimbabweans (3.4) and Malawians (3.1).

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_Herfindahl index_ is the sum of the squared market share of each bank.<sup>12</sup> In both cases, higher values indicate more concentration.

Our final dataset is limited to countries which have both firm-level data on access to finance from the Enterprise Surveys and bank-level data from Bankscope to calculate the Lerner index and the concentration measures. This dataset contains information on 53 countries for the period 2002-2010.<sup>13</sup> Table A1 gives a list of the countries and years included in the final dataset. Some countries have had only two surveys in our time frame, while others have had three or more, with a maximum of five surveys for Bulgaria. The coverage of firms varies by country. For example, India has more than 4,000 firms covered in two surveys, while Malawi has fewer than 300 firms covered in two surveys. Because of this variation, we test the robustness of our results to weighting our regressions by the inverse of the square root of the number of firms in the survey, so that each country carries the same importance in our estimations. Our bank-level sample contains data on 3,409 banks and over 16,000 bank-year observations. Table A2 gives a list of countries in our sample along with the value of the Lerner index for each country over time.<sup>14</sup> Finally, we supplement our dataset with annual country-level data from several sources. We obtain data on private credit to GDP and inflation from the World Bank _World Development Indicators_ database. Data on the quality of credit information come from the World Bank _Doing Business_ dataset<sup>15</sup> and information on the share of assets held by government-owned banks comes from the World Bank Survey of Bank Regulation and Supervision.<sup>16</sup> Table 1, Panel B reports basic summary statistics for the firm survey variables and country-level variables. More than half of the firms in our sample have access to finance; in other words, they use at least one of three credit products such as loans, lines of credit, or

> 12 The Herfindahl index, calculated as gives a greater weight to larger banks. 13 At the firm-level, we have over 68,000 observations.

> 14 Because we use one year lagged values of the Lerner index in our regression, we report bank data for years 20012009.

> 15 Data available at www.doingbusiness.org

> 16 Data available at http://go.worldbank.org/SNUSW978P0

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of some 30%. This is plausible as these data are known to exclude farms not formally registered as well as household plots. With 40, 25, 73 and 24 dt/ha for cereals, sunflower, maize and soybean, respectively, yields in 2018 were uniformly above those in 2017, a year in which precipitation was well below the long-term average.

# **3.3 Econometric approach**

Indexing crops by _j_ and village councils by _v_ , we characterize a field’s crop rotation history by the crops that precede crop _i_ to assess the impact of different predecessor crops on yield of this crop via an OLS regression of the form log i i i log i i (1) ii jj,ii−1 jjii,ii−1 ii,ii−1 ,ii−1 ii,ii−2 ,ii−2 ii ii ii (YY ) = αα+ �ββ SS + γγ MM + γγ MM + δδ (FF ) + εε jj≠ii where i is the yield of crop _i_ in village council _v_ at time _t_ , is the share of area planted with crop _i_ in _t_ ii that was under predecessor crop _j_ in _t-1_ ; i and jjii,ii−1i are the share of area with crop YY SS _i_ that had been cropped with the same crop consecutively for two or three years; ,ii−1 ,ii−2 i is the average MM MM farm size for crop _i_ in village _v_ to adjust for suitability and i is a random error term. Coefficients ii FF of interest are , the effect of having crop _i_ preceded by _j_ as well as ii and , the effect of εε planting the same crop jj,ii−1 _i_ for two and three consecutive years, respectively. Parameters are estimated ii,ii−1 ii,ii−2 ββ γγ γγ separately for 2017 and 2018. As data on crop cover are available only for 2016-2018, the effect of monocropping is estimated for two consecutive years in 2017 and for two and three years together in 2018. Indices for vegetative cover (EVI, LAI, FAPAR) and soil water content (LSWI) for the areas covered by relevant crops constructed as described above from remotely sensed data are used as alternative left-hand side variables. For both yields and these indices, (1) is estimated separately for maize, soybean, cereals (wheat and barley) and sunflower.

# **4. Results**

Results from yield regressions support the notion of continuous cultivation of the same crop having negative effects but, contrary to findings in the literature for (largely) US-based rotations, suggest that having sunflower as a predecessor crop leads to worse outcomes. For maize and sunflower, estimated marginal effects of three-year monocropping are significantly different from those of twoyear monocropping. Regressions with indices of vegetative development or soil water content (based on remotely sensed imagery) as dependent variables yield similar results, allaying concerns about our results being driven by shortcomings in statistical data and suggesting that exploration of such indices’ suitability for broader analysis may be warranted.

# **4.1 Yield effects of crop rotation**

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TASAF worked with an elected community management committee (CMC) in each village to select beneficiary households.<sup>7</sup> The CMC surveyed the poorest half of households, collecting data on eight household characteristics: roof material, light supply, water supply, type of toilet, ownership of four different assets (vehicle/motorcycle, radio, iron, poultry), number of windows on the house, household size, and number of meals eaten per day. TASAF then carried out a proxy means test to propose a ranking of households by poverty level, for CMC and village leader approval. On average, 23 percent of households became beneficiaries.

# **3 Evaluation Design and Data**

## **3.1 Evaluation design**

We evaluate the impacts of the CCT program using three waves of data on beneficiaries and would-be beneficiaries (no data were collected from those not selected to be beneficiaries). Table 1 presents the chronology of the program and impact evaluation. A baseline survey was carried out during January–May 2009 and payments began in January 2010. A midline survey was conducted during July–September 2011 (18-21 months after transfers began) and an endline survey was conducted during August–October 2012 (31–34 months after transfers began). The baseline survey included 1,764 households (a subset of beneficiary households) comprised of 6,918 individuals. The quantitative data collection was supplemented by two rounds of qualitative data collection (following the midline and endline surveys) employing focus group discussions and in-depth interviews.

## **3.2 Data and outcomes**

In each of the three survey rounds, we collected individual-level data on total health clinic visits in the last year,<sup>8</sup> ownership of protective footwear (shoes and slippers) by children,

> 7CMC elections occurred at village meetings; 10–14 members were elected, with secret ballots. To run, a candidate had to have received financial training and successfully managed a past TASAF-supported project.

> 8We lack data on whether such visits were for preventive or curative purposes, and only know the total.

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-2-

# II. The History of LSMS

# A. The State of Data on Poverty in the Third World Previous to LSMS

At the close of World War II three international organizations were founded which would, among other responsibilities, play a major role in developing data collection systems throughout the world: the United Nations, the World Bank (International Bank for Reconstruction and rev2lopment), and the International Monetary Fund (IMF). Also after the close of World War II, the European powers began to undertake the process of decolonization, so that by the 1960's most developing countries were newly independent rnations. Perhaps the first systematic attempt to ensure that data collected from both developed and developing countries were comparable was the introduction of the United Nation's System of National Accounts. 3eginning in late 1940's and early 1950's the UN developed an accounting proceduri for calculating economic aggregates such as Gross Domestic Product, which presented a picture of national economies. These prccedures were substantially revised in the early 1960's, and data collection efforts spread to other kinds of information, such as data on education (UNESCO), labor force activity (ILO), health (WHO), and agriculture (FAO).

While these steps were a major achievement in the international collection of economic and social data, the published numbers were often national averages, and as such provided little information on the distribution of income and consumption. This in turn meant that they had limited use in the measurement of poverty. To improve on these efforts data had to be collected at the household level, either in the form of a census or a household sample survey. Such types of data collection had long been in existence in many
Figure 8: The agricultural output gap (left axis) and annual inflation (right axis) (in %)

# **5. Determinants of Inflation in Ethiopia**

In this section, we develop single equation ECMs for cereal, food, non-food and overall CPI inflation. The models are estimated with OLS for the period January 1999 (including lags) to November 2008. We use general-to-specific modeling, starting with general models that include the money market and foreign sector error correction terms and the agricultural production output gap, and variables in first differences. The reduction of the general model is carried out with Autometrics, a computer-automated general-to-specific modeling approach. In principle, Autometrics tests all possible reduction paths and eliminate insignificant variables while keeping the chosen significance level constant. A great advantage of Autometrics is that it can handle models with many variables and few observations.<sup>20</sup> We reports results based on models with eight lags in the general models.21 Since the general models contain many parameters, we use the 1 percent significance level, rejecting the null hypothesis and including variables erroneously (Type I error)

> 20 The methodology is based on Hoover and Perez (1999) and Hendry and Krolzig (2001). See Doornik and Hendry (2007) for a description of Autometrics. Castle and Hendry (2009) and Ericsson and Kamin (2008) are two applications of automated general-to-specific modeling.

> 21 We first estimated models with 13 lags, implying they had over 100 parameters, but 8 lags seemed sufficient.

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# d)

<!-- Start of picture text --> Total Managed Forest Land Area (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>1.9 10<br>1.85 5<br>1.8 0<br>1.75<br>-5<br>Range of all sample paths<br>1.7 -10 average10% quantile<br>50% quantile<br>Deterministic-baseline 90% quantile<br>1.65 Deterministic-optimistic Stochastic-optimistic<br>Deterministic-pessimistic -15 Stochastic-pessimistic<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>e)<br>Natural Land Area (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>2.45 10 Range of all sample paths<br>average<br>2.4 8 10% quantile50% quantile<br>90% quantile<br>2.35 6 Stochastic-optimisticStochastic-pessimistic<br>2.3<br>4<br>2.25<br>2<br>2.2<br>2.15 0<br>2.1 -2<br>2.05<br>-4<br>2 Deterministic-baseline<br>Deterministic-optimistic -6<br>1.95 Deterministic-pessimistic<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year Year<br>billion hectares million hectares<br>billion hectares million hectares<br><!-- End of picture text -->

# f)

<!-- Start of picture text --> Protected Land Area (Deterministic) Difference between Stochastic and Deterministic-Baseline Solutions<br>0.5 5<br>Deterministic-baseline<br>Deterministic-optimistic<br>0.45 Deterministic-pessimistic<br>0.4 0<br>0.35<br>-5<br>0.3<br>Range of all sample paths<br>average<br>10% quantile<br>0.25 50% quantile<br>90% quantile<br>-10 Stochastic-optimistic<br>Stochastic-pessimistic<br>2020 2040 2060 2080 2100 2020 2040 2060 2080 2100<br>Year Year<br>billion hectares million hectares<br><!-- End of picture text --> Figure 3: Optimal Global Land Use Paths 18