rafmacalaba/datause-encoder-data huggingface dataset:
agricultural technologies to spread during the first year. The threshold for the incentive payment is set at 20 percentage point increase in knowledge scores among interviewed farmers, as measured by an exam administered during the midline that tested farmer knowledge of the details of how to implement the technology (see Appendix B). Communicators are given no prior information about the nature of the exam or the sampling frame for the survey, and therefore have no opportunity to teach others to the test. Furthermore, we rotated the set of households who were sampled in each round, so that there is not a perfect overlap of households across survey rounds. Not surveying the same households across rounds is costly because it creates imperfect panels, but we did it to make it more difficult for the communicators to target a minority of households in order to win the incentive payment.

Endline payouts are indexed on gains in adoption within the community. In order to avoid relying solely on respondent self-reports, we monitor adoption through an on-farm monitoring (OFM) survey administered between planting and harvest. Enumerators trained in the maize farming process visited the farms of 1,400 households to directly observe any evidence of technology use. These farm visits were timed to the agricultural cycle when that evidence would be visible. Gains in adoption of 20 percentage points and higher trigger a payment. The OFM surveys were also conducted on a rolling sample of farmers to ensure that communicators could not easily target effort on a narrow set.

Payouts were given in kind, in the form of demonstration kits and production inputs. The incentives are gender-neutral in nature since maize farms are equally cultivated by men and women in our sample. MoAFS financed the intervention and legislated their value, and project staff ensured that payouts across all villages were roughly equal in value (KW 12,000, or USD 80 per community per year).

# **2.7 Technology**

We promoted one yield-enhancing conservation farming technology in each village. AEDOs in four arid districts were trained on “pit planting”.<sup>10</sup> “Changu composting” was promoted in villages in the other four districts.<sup>11</sup> These conservation agriculture technologies

> 10 These are Rumphi, Neno, Chikwawa, and Balaka districts.

> 11 There are Zomba, Mchinji, Dedza, and Mzimba districts.

11
Policy Research Working Paper 8575

# **Abstract**

The quality of the public procurement system of an economy can have far-reaching effects on the private sector. This paper empirically explores several of these effects using two rich data sets. An overall indicator of public procurement quality is created from the World Bank’s Benchmarking Public Procurement project that is then combined with firm-level data from the World Bank Enterprise Surveys. The analysis includes more than 59,000 firms spanning more than 109 economies. The paper finds that firms in economies with good public procurement systems are more likely to participate in public procurement, face lower losses from shipping to domestic markets, and experience lower incidence of bribery than economies with poor public procurement systems. Similarly, better public procurement systems are positively correlated with more engagement in innovation, research and development, international certification, foreign technology adoption, and online connectivity.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at aislam@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**9**

the conversion factor is determined **by** interacting supply and demand for foreign exchange. It is shifts in these supply and demand schedules that give rise to price (i.e., exchange rate) changes. Thus it should be recognized that the exchange rate is a price that balances the desire to exchange one currency for another in order to effect trade in goods and services. The introduction of restrictions into the price system prevents the price from fulfilling a stabilizing function of equalizing supply and demand, thus affecting relative prices, the profitability of individual activities, and thus resulting in distortions in resource allocations.

# Conversion Factor and Price Levels of Traded/NonTraded Goods

> **13.** The imposition of price measures (tariffs, specific taxes, import surcharges, advance deposits on imports, export taxes and subsidies, and multiple exchange rates) and non-price measures (quotas, licencing **)** alter relative price levels between sectors, e.g., traded goods and nontraded goods. Here we are confronted with the issue of the distinction between "traded goods (sector)" and "nontraded goods (sector)" as well as between "tradables" and "nontiadables." In very general terms, traded goods can be regarded as those that can be imported (and exported), while nontraded goods are those without close, foreign-produced

> substitutes and must therefore be supplied **by** domestic producers. More specifically, traded goods can be regarded as "goods that are sufficiently substitutable with goods produced

> abroad that there exists an international market; nontraded goods are those that are not

> **highly** substitutable with foreign-produced goods so that international trade in these goods does not exist".' **A** very narrow dichotomy is that given in the foreign trade statistics in the

> **6** George **A.** MacKenzie and Susan M. Schadler, "Exchange Rate Policies and Diversification in Oil Exporting Countries," (Unpublished; International Monetary Fund, May **9, 1980) pg.3.**
Following existing international banking studies, we capture the potential for _scale economies and profit opportunities_ in two ways. First, we include the log of constant dollar GDP. Second, we include the growth of GDP as a measure of growth opportunities in the country. Economic growth may also be interpreted as a measure of default risk, since in countries that do not grow, borrowers might have a harder time repaying their obligations to banks. Data on both of these variables come from the World Bank _World Development Indicators_ or the IMF _International Financial Statistics_ , depending on the country.

In international banking, pricing risks materialize when the value of the claims or return on the claims held by foreign banks declines as a result of exchange rate or other price changes. We measure price risk by controlling for inflation in our estimations.<sup>14</sup> Data on inflation comes from the _World Development Indicators._ While both cross-border and local foreign claims are exposed to price and default risks, local claims are also subject to expropriation risks. We capture the extent of expropriation risks by including a measure of property rights in the host countries. This measure is an index compiled by the Heritage Foundation and reported as part of the _Index of Economic Freedom_ . Higher levels of this index indicate worse property rights in the country. In some estimations, we replace the measure of default, price, and expropriation risks for one composite country risk measure as collected by the International Country Risk Guide (ICRG) in its _country risk index._<sup>_15_</sup> We estimate the following equation to investigate the implications for financial stability of the mix (cross-border versus local) of foreign claims: _Foreign claims volatilityi,j_ =γ0 + γ1 _Share of local claimsi,j_ + γ2 _Growth volatility,j_ + γ3 _Price volatilityj_ + γ4 _Volatility of claims to other countriesi,j_ + γ5 _Banking crisis indicatorj_ + μ _i,j_ (2)

> 14 Results remain unchanged if we used the change in the exchange rate instead.

> 15 This index is defined by ICRG so that higher values mean less risk, but in our estimations we reverse the sign on this variable to give it the more intuitive interpretation that higher values mean more risk.

18
social development outcomes. For example, Agenor and colleagues (Agenor, Bayraktarb and El Aynaoui 2005; Agenor, Bayraktarb and Pinto 2005) and Pinto and Bayraktarb (2005) model the real economy through a single representative sector with a parameterized elasticity on poverty.

The Maquette for Millennium Development Goals Simulations (MAMS) model originally developed by Bourguignon and colleagues (2004) was novel for its decomposition of government sectors, emphasizing interactions between labor markets, infrastructure, and the achievement of outcome targets for poverty, education, health and water and sanitation. Its major contribution is the ability to show the evolution of intermediate outcomes en route to internationally-agreed development goals and highlight the implications of sequencing investments among sectors (Bourguignon and Sundberg 2006a, 2006b). The original MAMS model had a single representative productive sector, which did not permit evaluation of subsistence dynamics, although more recent applications have incorporated the core Lofgren et al. (2002) framework as the basis for evaluating more detailed analysis of productive sectors (Lofgren et al. 2013).

Some previous models have integrated the biophysical aspects of agricultural productivity into a developing country CGE framework. For example, Alfsen and colleagues (1997) use Aune and Lal’s (1995) Tropical Soil Productivity Calculator in a 17-sector closed public sector model to show the contribution of soil nutrients to the growth of gross domestic product (GDP). A limitation of that model is that it does not include a ceiling for possible soil nutrient accumulation and does not allow for the practical reality of zero fertilizer use among large numbers of small-holder farmers, because the fertilizer term enters as a simple input in a Cobb-Douglas production function and zero input implies zero output. Wiig and colleagues (2001) pursued a comparable strategy to introduce soil degradation as a time-dependent Hicks6
- 3 -

countries in Central Europe and Latin America, the number of institutions operating in the second pillar is even smaller, as shown in Table 2.

**Table 2:  Number of Second Pillar Funds in Latin America and Central Europe, 2004**

|**Central Europe **|**Latin America**||
|---|---|---|
|**Hungary**<br>**18**|Argentina|12|
|Bulgaria<br>8|Bolivia|2|
|Croatia<br>7|Chile|6|
|Estonia<br>6|Colombia|6|
|Kazakhstan<br>13|Costa Rica|8|
|Latvia<br>5|El Salvador|2|
|Poland<br>16|Mexico|13|
||Peru|4|
||Uruguay|4|

Source: HFSA, AIOS, FIAP 10. What are possibly unique in Hungary are the segmented nature of the second pillar and the continued existence of smaller institutions. Table 3 shows the large difference between the smallest and the largest institution in the Hungarian second pillar, whether the difference is measured in assets, members, or contributions.

**Table 3: Market Structure of the Hungarian Second Pillar, 2004**

||**Assets**<br>**(HUF 000,000)**|**Members**<br>**(unit)**|**Contributions**<br>**(HUF 000,000)**|
|---|---|---|---|
|Smallest MPF|1,003|2,840|320.3|
|Largest MPF|224,161|681,231|57,267.8|
|Average MPF|48,997|133,456|11,851.4|
|Market Total|881,944|2,402,210|213,324.9|
|Share of Largest Six MPFs|83%|87%|83%|
|Herfindahl Index|1,491|1,638|1,510|

Source: HFSA raw data.

11. The co-existence of large and small institutions is to a large extent due to the diversified nature MPF sponsors. Table 4 shows how the ten MPFs sponsored by financial institutions tend to be much larger. Average assets, contributions and members in this first group are much higher than for the other two groups.<sup>4</sup> The five MPFs sponsored by large employers have between 8,000 and 20,000 members, mostly employees of the sponsoring company or the group of companies in the sector (i.e., the electricity sector). The three MPFs in the third group have on average 60,000 members, although there is a large difference between the smallest and the largest MPF in this group.

12. The MPFs in the first group account for 87 percent of the market, however measured, reflecting the profit oriented nature of the sponsor and the more aggressive marketing tactics. The MPFs sponsored by employers or considered to be independent represent only around 5 and 8 percent of the market, respectively. These MPFs are open to members who are not employees of the sponsor(s), and most of them would actually welcome an increase in membership as a means to reduce average operating costs.

> 4 However, the smallest MPF belongs to this group.
Figure 3: Average Firm Size across Countries under Idiosyncratic Distortions Only <!-- Start of picture text -->
1.5<br>1.0 Finland France<br>Spain<br>Italy<br>0.5 Latvia Malaysia<br>Bulgaria Romania Hungary<br>Salvador Peru Belgium<br>Portugal Ghana Colombia Chile Kenya<br>Ethiopia India<br>0.0<br>0.0 0.5 1.0 1.5<br>Data/USA<br>Misallocation/Undistorted<br><!-- End of picture text -->

Note: The left panel of the figure illustrates the average size in the model with idiosyncratic distortions only relative to the undistorted one, against the ratio of the average size in each country relative to the US. Each country’s stationary equilibrium is solved under the distortion elasticity reported in Figure 2. The right panel illustrates the model’s relative average size with respect to the undistorted allocation and the data’s average size relative the US, against the log of GDP per capita on the horizontal axis. Fitted regression lines for _AvSizeRatio_ = _α_ + _βlog_ ( _GDPpc_ ) are added for both the model and the data average size ratios.

omy features a productivity elasticity of distortions of 0.17 (recall this is net of the US degree of correlated distortions). According to the model, had the idiosyncratic distortions been the sole friction, the average firm size in Chile would have been brought down to 30 percent the size in the US. Accounting for the 80 to 30 percent gap then requires a complementary distortion that counteracts the correlated misallocation. We argue that this countervailing force reflects entry barriers in the economy.

# **5.4 Model-Based Estimates of Entry Barriers**

Convinced about the need of a complementary force to account for the crosscountry distribution of average firm size, this section implements a strategy to identify such force as a barrier to firm entry. Since these barriers increase the average firm size for any given value of the distortion productivity elasticity, the strategy involves iterating on the space of _τi_<sup>_E_,taking</sup><sup>_γi_asgiven,untilthe</sup> 35
Policy Research Working Paper 6853

# **Abstract**

Educators believe that they are adequately preparing youth for the labor market while employers lament the lack of skills. A possible source of the mismatch in perceptions is that employers and educators have different understandings of the types of skills valued in the labor market. This paper uses economics and psychology literature to define four skills sets: socioemotional, higher-order cognitive, basic cognitive, and technical skills. The paper reviews the literature that quantitatively measures employer skill demand, as reported in preference surveys. A sample of 28 studies reveals remarkable consistency across the world in the skills demanded by employers. Although employers value all skill sets, there is a greater demand for socioemotional and higher-order cognitive skills than for basic cognitive or technical skills. These results are robust across economy size and level of development, sector, export-orientation, and occupations. Employers perceive that the greatest skills gaps are in socio-emotional and technical skills. These findings suggest the need to reconceptualize education and training systems. Taking into consideration the developmental process to acquire the skills identified by employers, this implies the need to recognize that (a) the job-skills development process necessarily begins at birth and continues throughout the life cycle so skills policy should, as well; (b) schools play a relevant, but limited, role in skills development and the role of parents, mentors, and the work place must be defined and enhanced; and (c) the skills most demanded by employers—higher-order cognitive and socio-emotional skills—are largely taught (the former) or refined in secondary school, which argues for a general education until these skills are formed.

This paper is a product of the Human Development Department, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wcunningham@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
positive interactions (0.24 s.d.) with their local community (Table 2, column 3). Table 6 (row 5) reveals that participants that served in Hausa states report a statistically significant lower quality of stay compared to other non-Yoruba regions.

# **6.2 Internal Migration and Knowledge of Other States**

A key aspect of national integration is the free movement of people across the country. From inception, one of the stated objectives of NYSC has been “to encourage members of the service corps to seek, at the end of their corps service, career employment all over the country thus promoting free movement of labour” (NYSC 2015b). I examine the extent to which participants who served in a different ethnic region become more likely to make their homes outside their ethnic homeland.

I find that interethnic exposure during NYSC results in a fivefold increase in the likelihood that an individual lives outside their ethnic region seven years later. The interethnic migration rate for control group subjects is 3.3 percent. This is similar to the overall rate among the Yoruba: only about 5 percent of Yorubas live outside their ethnic homeland (estimate calculated from NDHS 2008).<sup>21</sup> Table 3 (column 1) shows that, using the IV regression framework, the treatment effect is 14.4 percentage points.

Living outside one’s ethnic region after being exposed is not simply due to individuals remaining in (or returning to) the NYSC location, for example, by obtaining a permanent position with the NYSC employer or finding a job through a network of contacts. Only 20 percent of the 15.4 percent of treated participants living outside their ethnic region are living in their NYSC service state. The remaining 80 percent are living in non-coethnic states other than their NYSC state.

> 21There is significant variation in interethnic migration rates across ethnic groups; for example, over 20 percent of Igbos live outside their ethnic homeland. The rate for Hausas is similar to Yorubas at about 4 percent.

24
# **Arithmetics and Politics of Domestic Resource Mobilization**

Kimberly B. Bolch,<sup>_∗_</sup> Lidia Ceriani,<sup>_†_</sup> and Luis F. Lopez-Calva<sup>_‡_</sup> _The World Bank_

**Keywords:** domestic resource mobilization; poverty gap; fiscal capacity; taxation; political

**JEL Codes:** D63; I32; P16

> _∗_ e-mail: `kbolch@worldbank.org`

> _†_ e-mail: `lceriani@worldbank.org` ; Corresponding Author

> _‡_ e-mail: `lflopezcalva@worldbank.org`
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# _Correlations to District Traits_

Table 7 documents multivariate analyses of the district traits that associate with specialization and diversity levels, as well as changes in these values over time.<sup>9</sup> Outcome variables are indicated by column headers. These dependent variables are expressed in a tenpoint scale for the levels metrics, representing deciles of the raw index values, and in unit standard deviation for the change metrics. District traits are taken primarily from the 2001 Population Census, with some manufacturing specific covariates also calculated directly from the ASI and NSS. We transform non-logarithm explanatory variables to have unit standard deviation for interpretation. Estimations include state fixed effects, weight districts by their log 2001 population, and report robust standard errors.

Several important observations can be made from Table 7. First and most important, this substantial battery of district traits has pretty weak predictive power for industrial specialization and diversity. This weakness is observed in the limited number of regressors that pick up a statistically significant coefficient (often only two out of the 14 regressors modeled) and the low R-Squared value overall. To an important degree, this is the outcome of the metric design that makes these traits more or less independent of factors like district size. Given this independence, we can expect to find broad stability in our upcoming regressions with and without district covariates being modeled (which will hold true).

Second, there are two traits of districts that consistently link to these industrial traits. The literacy rate of places is closely linked to their initial levels and subsequent changes. Districts with high literacy rates in 2000 start with both a more specialized and diversified industrial base. It is important to recognize that these partial correlations are conditional on other district traits like education shares, population levels, and consumption per capita. Places with high literacy rates also shift towards less specialization over time.<sup>10</sup> Second, the manufacturing share of the 9 Appendix Tables 1 and 2 report univariate correlations between the index values and district and industry traits, respectively. Caution should be exercised about over-emphasizing any single correlation, given the lack of other controls.

10 This connection to literacy rates is also intriguing given that Ghani et al. (2013b) find in contemporaneous work a connection between local literacy rates and greater use of a wider variety of distinct material inputs in Indian plants (overall and relative to plant size). This connection to literacy rates is similarly robust to controlling for many other traits of districts.

16