rafmacalaba/datause-encoder-data huggingface dataset:
in thin fertilizer markets and high transaction costs and we include average rainfall (climate), price variability, and the distance to fertilizer distribution centers to take this into account. While credit and insurance markets are weak in Ethiopia, some farmers do gain access to credit and farmers can take additional meliorative actions before abandoning fertilizer-using techniques. Therefore we include access to credit and a set of arguments (literacy, age and participation in extension programs) that indicate the capacity of the household to overcome weak credit and insurance markets. For the same reason, we also include a measure of wealth: the ownership of a house with a corrugated iron roof. Moreover, the dominant role of state actors in rural Ethiopian fertilizer and credit markets is an important aspect that likely shapes outcomes since fertilizer distribution and credit provision are often handled at a local administrative level. In the face of weak governance and communication systems, holding a position in local government can result in better information or preferential treatment. To test for this, we include a dummy variable to indicate whether the head of the household is a member of the village council.

Input demand is measured in kilos per hectare and we include as determinants farmed area and the relative price of fertilizer. We also include an indicator of whether organic fertilizer is used. Soil moisture is import to the efficacy of fertilizer use and farmers have some scope for adjusting its use as the growing season progresses (Larson and Plessmann, 2009). We therefore include weather outcomes measured relative to long-term averages. In the face of imperfect markets, consumption and production are inseparable and endogenous shadow prices enter into the input demand functions (de Janvry et al., 1991). Nevertheless, the input demand equations can be estimated indirectly by using instruments for the shadow prices (Fafchamps and Quisumbing, 1999). For this reason, we also include a set of household characteristics: household size, wealth, age and education. To account for the cumulative effects of technology adoption, we include a time effect.<sup>17</sup>

# **Data Sources and Descriptive Statistics**

The household data used in our analysis comes from a countrywide panel household survey conducted in 2004 and 2006 by the Ethiopian Economic Association (EEA) and the World Bank. The survey consists of about 2,300 randomly selected households in 115 villages (kebeles) stratified by agro-ecological zone and region to ensure coverage of all the agricultural production systems of the country. After adjusting for missing data, there are data on 2,140 matching households. The first round focused on extension services while the core section was the land certification program during the second round, so there are some differences in the

> 17 For a discussion of how adoption rates are expected to spread with time, see Feder and Umali (1993).

7
# **V. Discussion**

The PACI reflects a narrow definition of corruption: the propensity of public officials to accept bribes from foreign firms.<sup>15</sup> In denominating the index as a general “public administration” measure of corruption, the study implicitly assumes that what is observed by means of crosscountry corruption statistics is also informative of the level of corruption in the public sector as a whole. Obviously, this assumption may be put to question, especially since levels of corruption may vary sensibly across public institutions within a given country. This article, however, argues that adopting narrow definitions of a phenomenon is desirable because it facilitates the testability of the assumptions on which hinges any extensive interpretation of the resulting measures.

For this reason, the PACI represents a welcome departure from most indices of governance currently available, where “sometimes it is not clear what precisely is being measured, rendering questionable the validity of at least some of the proxies” (Klitgaard and Light 1998). Such a state of affairs may reflect a situation “experienced in many ‘new areas’ of the social sciences: an explosion of measures, with little progress toward theoretical clarity or practical utility” (Klitgaard et al. 2005, 414). Quite likely, as “open data” become more widely available, more measures of governance, based on hard data, and narrowly defined like the PACI, would be ushered (see the discussion in Picci 2011, 117–119). Such a development, in turn, will facilitate greater theoretical clarity.

Data availability, as seen, poses certain constraints to the usefulness of the PACI. This limitation is the reason why this paper opted to demonstrate its application using a 15-year period.

> 15 A Bribe Payer’s Corruption Index ( _BPCI_ ) may also be computed along the same lines as the _PACI_ , but using cases first enforced in the foreign country. However, the scarcity of data proves to be an obstacle towards its computation.

27
# **Table 4:  Regression results - Determinants of common environmental perceptions**

Dependent variable: Inter-individual correlation coefficient

|<br>Individuals|Environm<br>to Li|ental Threats<br>velihood|<br>|General E<br>Con|nvironmental<br>ditions||
|---|---|---|---|---|---|---|
|Have Common:|OLS|Robust|GLS|OLS<br>|Robust|GLS|
|Locality|0.136|0.136|0.137|0.089|0.089|0.094|
||(29.76)**|(28.32)**|(5.49)**|(27.25)**|(26.99)**|(4.21)**|
|High SES|-0.002|-0.002|-0.018|-0.080|-0.080|-0.074|
||(0.10)|(0.09)|(0.54)|(3.95)**|(4.22)**|(2.59)*|
|Low SES|0.029|0.029|0.027|0.040|0.040|0.031|
||(1.96)|(2.08)*|(1.41)|(3.22)**|(3.33)**|(2.15)*|
|Ethnicity|0.005|0.005|0.004|0.006|0.006|0.004|
||(0.94)|(0.91)|(0.23)|(1.88)|(1.88)|(0.13)|
|Constant|0.095|0.095|0.093|0.277|0.277|0.285|
||(28.86)**|(33.29)**|(7.84)**|(123.40)**|(123.36)**|(9.84)**|
|Obs|23,871|23,871|20,503|109,278|109,278|96,141|
|R-squared|0.04|0.04|0.04|0.01|0.01|0.01|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% Tables 5 and 6 display predicted correlations from the GLS regressions by locality of residence and SES.<sup>5</sup> Middle SES is assigned to paired status for individuals who are neither High SES nor Low SES. Both tables display the highest correlations for paired individuals with Low SES in a common locality. Conversely, paired individuals with High SES in different localities have the lowest correlations. Typical correlations are higher for general environmental changes than for livelihood-related changes, but livelihood-related changes have greater relative effects. From highest to lowest case, the correlation falls by 69% for livelihood-related changes and 49% for changes in general environmental conditions.

> 5 Ethnicity has no meaningful effect on these results; the table entries are calculated for paired individuals who do not have common ethnicity.

10
GDP growth, effectively limiting the disproportionate influence of very large swings in quarterly growth.

For the level of economic development, we rely on the World Development Indicators (WDI) database from the World Bank and use purchasing power parity-adjusted GDP per capita in constant 2017 international dollars. For the income status of countries, we use the IMF’s definition of advanced economies (AEs), emerging markets and middle-income countries (EMs), and low-income developing countries (LIDCs) to divide countries into mutually exclusive groups. For the main analysis, we combine the last two into a single group: emerging market and developing economies (EMDEs).

For the shares of agriculture, manufacturing, and services in each economy, we also rely on the WDI database.

# _Statistical Capacity and Voice and Accountability_

We use the World Bank’s Statistical Capacity Indicator, which measures a nation’s ability to collect, analyze, and disseminate high-quality data about its population and economy. It is a composite score assessing the capacity of a country’s statistical system based on the methodologies and data used, as well as the periodicity and timeliness of data releases. It uses publicly available information and country inputs to score countries against 25 criteria.

From the World Bank’s Worldwide Governance Indicators, we obtain a measure for voice and accountability. It captures perceptions about citizens’ ability to select their government, the extent of freedom of expression and association, as well as the presence of a free media.

# _Cloud Cover, Subnational Population, and Spatial Aggregation_

The Climatic Research Unit at the University of East Anglia provides monthly highresolution gridded data sets on cloud cover (Harris, Osborn, Jones, and Lister, 2020). For the analysis of elasticities, we take the area-weighted average of cloud cover (CRU TS version 4.03) within each country and across time between 1901-2018.

For our subnational analysis, we use population data from the fourth version of the Gridded Population of the World from the Socioeconomic Data and Applications Center.

Throughout, when aggregating variables by country boundaries, we use the simplified version of Global Administrative Unit Layers 2015 from the Food and Agriculture Organization of the United Nations. When aggregating variables by the first adminis8
14 As was also noted in the discussion of Barro's results, without this one cannot be confident that the regression provides a satisfactory estimate of the effect of education on growth.

Nevertheless, an important aspect of Mankiw, Romer and Weil's results is that the restriction which they test and impose, that the coefficients of the investment ratio, SCHOOL and the sum of population growth, technical progress and depreciation add to zero, has the implication that there are no spatial externalities linking education to economic growth. This is discussed further in section 4.

It has been argued that the _World Development Report, 1991_ regression is inconclusive as to whether there is an externality present or not, but one should also ask whether of Barro's figures, which include both primary and secondary education in the regression, suggest an extemality. His figures can be compared with the results found by Weale (1992) in the developed world. In this study an increase of one year in the average number of years of education of the workforce raised the rate of growth _ofper capita_ GDP by 0.14%. Since the average number of years of education of the workforce in the sample of countries considered was just over 9, this is equivalent to an increase of 11% in the fraction of the population attending a 10-year schooling program. Alternatively, a 10% increase in secondary-school throughput would be associated with an increase in the growth rate of 0.127% p.a.

If one assumes that fertility effects are of no great importance in the developed world, Weale's figure appears to be below Barro's estimate of 0.23% p.a. But the nature of the effects identified by Barro merits some discussi6n. In fact his figures represent two effects, a stock effect and a flow effect. It is clear from his dala that school enrollments have been rising during the period of his sample. As the number of educated people rises, so will the level of output. Denison (1967) suggests a rule of thumb that one extra year of education adds 5% to labor productivity up to the eighth year of education. Beyond that, one extra year adds 8% to productivity. Barro's data indicate a sharp increase in the fraction of the school-age group enrolled at school between 1950 and 1985, and it is therefore reasonable to assume that the fraction of the workforce with primary and secondary-school training was rising rapidly during the period studied (1960-85). It is likely that the increase in the fraction of the work-force qualified is positively correlated with the fraction of the school-age cohort enrolled in 1960, because all the developing countries probably built up education from a low base in the 1930s and 40s. This means that at least a part of the effect of education on growth identified by Barro is a consequence of the direct effect of increased education on earning power. On the other hand Weale's figures were intended to represent only an external effect whereby education raises the rate at which innovation is taken on
Are the low rates of female labor force participation in the North and East larger for married women? Figure 10 suggests that the answer differs in the North and the East. In the East, married women are much less likely to work than in other parts of Sri Lanka. But in the North, there is a much smaller difference between married and never married women, as both are relatively unlikely to work. It is not clear what lies behind the low employment rates of married women in the East.

**Figure 10: Married women are less likely to participate in labor market than never‐married women in the Northern and Eastern provinces (Source: LFS 2015)**

<!-- Start of picture text --> Employment rate of women >24 years)<br>55%<br>47%<br>43%<br>41%<br>33% 33%<br>25%<br>20%<br>Married Never married<br>Northern Province Eastern Province Western Province Other Provinces<br><!-- End of picture text --> What kind of jobs are missing among women in the Northern and Eastern provinces? Fewer women in these provinces are employed in agriculture and industry, elementary occupations, and private sector jobs more generally. Table 3 describes the sector, occupation, and industry composition of jobs that men and women are engaged in different parts of Sri Lanka. The rows add up to 100 and the universe is all employed individuals.<sup>2</sup> The second and fifth rows of Table 3 show that very few women in the Northern and Eastern provinces are employed in agriculture and industry. The services sector is the largest employer of women in the Northern and Eastern provinces, unlike provinces other than Western, where agriculture is. Because of the size of the service sector, a smaller share of women is employed in elementary occupations in the Northern and Eastern provinces than in provinces other than Western. Unlike men, women in the North and East are more likely to be employed in the public sector than in the private sector. In other words, women in the Northern and Eastern provinces are passing up elementary occupations and private sector jobs, leaving most female workers in public sector jobs in the service economy.

> 2 Appendix Table 2 also includes a row of not working adults to facilitate comparisons among all working‐age individuals.

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to a module, there were 13 replacements due to refusals. Three households that started a diary were dropped because they did not complete their final interview. This, in addition to dropping the eighth experimental arm as explained above, yields a final sample size of 3,525 households.<sup>9</sup> The basic characteristics of the sampled households generally match those from the nationally representative 2006-07 Household Budget Survey (the comparison results are not presented here but are available from the authors upon request). The randomized assignment of households to the eight different questionnaire variants appears successful in terms of balance across characteristics relevant for consumption and consumption measurement when examining a set of core household characteristics (Beegle et al. 2012).

In regards to issues discussed in the previous section on sources of error, there are several points to note about the survey experiment. The recall modules administered in the survey experiment ask the respondent about consumption and not acquisition of food. These questionnaires record details on meals consumed outside the home by household members as well as meals within the household that were shared with non-household members. The diaries are acquisition diaries which account for food given to animals (e.g. scraps, or left-overs), food taken from stocks and food brought into the household by children (individual diary only). At the end of each week, there is a review of the main meals the household ate each day and additional information is recorded if any components for these meals were not captured in the diaries.

The survey was administered on paper. To minimize data entry errors, all questionnaires were entered twice and discrepancies were adjudicated. As non-standard units are common in Tanzania, the experiment collected conversion factors during a community price survey conducted by the field supervisors in each sample community. Supervisors used a food weighing scale to obtain a metric value of food-specific non-standard unit combinations. Median district-level metric conversion rates were used to convert non-metric units into kilograms or liters. Where district-level conversion rates were not available, the sample median was used and where this was not available, measurements at the survey’s

> 9 We have almost no item non-response (in that the respondent does not answer whether the household consumed the specific item in the specified period) in the recall modules. We do not observe any distinct patterns in non-response across our survey designs, or within a recall design by the location of the item on the list.

13
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18 a negative effect of distance between cities on spatial spillovers. Thang, Pham, and Barnes (2016) estimate productivity spillovers in 61 provinces in Vietnam and find that although there is evidence of spillovers between provinces, spillovers within provinces are much larger, reflecting the positive effect of spatial proximity between MNC affiliates and local suppliers. For India, similar findings of a positive effect of proximity between the two types of firm are presented by Song and Son (2019). Merlevede and Purice (2016) report for Romania that spillovers from backward linkages within regions are larger and occur faster compared to productivity spillovers that materialize between regions.

# **3.4. Evidence from Rich Data Sets**

The evidence discussed in the previous sections is characterized by certain limitations. In particular, studies tend to examine only parts of the processes depicted in figure 1. Studies that provide detailed information on the extent of backward linkages and technology dissemination use mostly small-scale, firm-level surveys (often only containing MNC affiliates) and do not examine whether inter-firm linkages materialize as productivity spillovers among local suppliers. Quantitative studies that estimate productivity spillovers usually do not have information on the actual size and nature of linkages that exist between MNC affiliates and domestic firms and rely on national input-output tables to calculate indirect indicators of the magnitude of these linkages. Some recent studies attempt to address these shortcomings by combining various firm-level data sets and/or by using large-scale firm-level surveys that contain information on both the level and nature of linkages between MNC affiliates and local suppliers and information that allows for the estimation of productivity effects of these linkages.

A good example is the detailed study on productivity spillovers in Costa Rica by Alfaro-Urena, Manelici, and Vasquez (2019). They combine an administrative data set that tracks all firm-to-firm transactions in this host economy for the 2008–16 period with other firm-level data sets containing information on profits, revenues and costs, inputs, human capital, and exports and imports. To identify spillovers, they conduct event study estimations, comparing productivity changes among local firms that start to supply inputs to MNC affiliates with samples of local firms that do not supply to foreign firms during the period under investigation. The estimations identify positive productivity effects among the supplying local firms, clearly identifying backward linkages as the transmission channel. The authors also find evidence of other effects, including increases in the volume of sales and the number of other domestic firms that the suppliers establish business relationships with, suggesting that the
5 Even at this aggregate level, the data show a remarkable change in the composition of supply and employment. The share of industry (both in production and employment) has dramatically contracted, in favor of the service sector. Agriculture, largely dominated by small self-employed holdings, has by and large remained a reservoir of underemployment. Employment in manufacturing decreased by about 25

**Table 2** - **Indicators of Structural Adjustment**

||**1990**|**1992**|**1994**|
|---|---|---|---|
|**I. Employment (% of**||||
|**total)**||||
|Agriculture|27.9|28.6|28.7|
|Services|41.4|44.4|<br>44.9|
|Industry & Construction|30.7|27.0|<br>26.4|
|Manufacturing|...|21.9|21.8|
|**II. GDP(% of total)**||||
|Agriculture|8.3|6.9|7.0|
|Services|41.6|49.5|<br>50.3|
|Industry & Construction|_50.1_|43.6|<br>42.6|
|Manufacturing|...|28.0|27.4|
|**III. Ownership Structure (**<br>**added)**|**private sec**|**tor % in**|**value-**|
|GDP|30.9|47.1|52.2|
|Agriculture|81.9|81.1|<br>86.7|
|Services|35.1|52.1|<br>55.9|
|Industry|18.3|<br>26.5|<br>34.5|

Source: GUS, Yearly Yearbook, Various issues percent in 1990-93, whereas it increased in trade and finance, respectively, by approximately 40 and 30 percent. Within manufacturing, the response of individual industries to the new market conditions is also quite diversified. At one-digit level, it ranged from Food Processing, where employment increased by 15 percent during the whole period, to electro-engineering, where it decreased by as much as one third.<sup>4</sup> 4 One and two-digit employment and production data for the period 1990-93 are available upon request from the authors. For this sample, during the 1992-93 recovery, employment in total manufacturing decreased by 10 percent. But employment decreased by as much as 20-30 percent in one third of the industries, whereas increased in one fourth of the industries. It is difficult to argue that these discrepancies over a two-years period can be explained simply by the different cyclical frequencies of individual industries.