rafmacalaba/datause-encoder-data huggingface dataset:
3 stabilize the domestic value of the EUV (see Gelb t19743). For example, through Its effects on export earnings and the money supply, a reduction In the dollar value of the EUV of an Important export may lead to a devaluation of the currency which will tend to stabilize the EUV valued In local units.

The mapping of EUV In local currency to the average producer price Is primarily affected by three factors. The first Is the share of production sold In the domestic market or carried forward In stocks, especlally If there are qu_llty differentials between the domestic and export markets. Second, government Intervention in the form of export taxes, attempts at price stabilizatlon, or other Interventlons In the domestic market induce less than perfectly correlated movements between the domestic price and the EUV. The third factor Is the size and temporal behavior of marketing and processing margins retained by market intermediaries.

In this succession of price relationships, there Is a further transformatlon from average producer prices to the prices received by Individual farmers which depends upon farmspecific productlon and marketing characterlstics, such as time of sale. However, this relationship Is not pursued In this paper; the analysis Is instead confined to the available data on average price movements.

In tracking the relationship between fluctuations In world prices and the average prices that producers recelve In Individual countries, we attempt to Identify the stabilizing or
**Table 10. Firm Efficiency and Government Facilitation: The RZ-style Estimates**

|**Panel A. Pooled sample**|Industry chara|cteristics:<br>||||||||
|---|---|---|---|---|---|---|---|---|---|
||exportind|Large_mktind|Innovateind|||||||
|Loan helpc* industry charind|12.733***|3.666**|6.212***|||||||
||(4.467)|(1.543)|(2.278)|||||||
|R squared|0.353|0.353|0.353|||||||
|Info facilitationc* industry charind|0.867|1.299**|1.961**|||||||
||(3.164)|(0.636)|(0.942)|||||||
|R squared|0.351|0.352|0.352|||||||
|observations|6,739|6,739|6,739|||||||
|**Panel B. By ownership:**|**state**|||**Domestic p**|**rivate**||**Foreign**|||
||exportind|Large_mktind|Innovateind|exportind|Large_mktind|Innovateind|exportind|Large_mktind|Innovateind|
|Loan helpc* industry charind|12.279|5.056*|10.551**|8.132|5.612**|6.440**|15.376***|2.381|4.085|
||(9.324)|(2.780)|(4.449)|(5.113)|(2.296)|(3.096)|(5.413)|(1.862)|(2.630)|
|R squared|0.361|0.363|0.366|0.283|0.287|0.285|0.421|0.419|0.419|
|Info facilitationc* industry charind|-1.370|1.869|3.089|5.193**|2.101***|3.060***|-3.126|0.739|0.833|
||(6.419)|(1.721)|(2.470)|(2.413)|(0.688)|(1.037)|(3.248)|(0.743)|(1.026)|
|R squared|0.359|0.361|0.362|0.284|0.288|0.288|0.419|0.419|0.418|
|observations|1,694|1,694|1,694|2,188|2,188|2,188|3,139|3,139|3,139|
|**Panel C. By income level**|**poor**|||**rich**||||||
||exportind|Large_mktind|Innovateind|exportind|Large_mktind|Innovateind||||
|Loan helpc* industry charind|21.818***|8.944***|20.226***|4.416|-0.096|0.159||||
||(4.652)|(3.207)|(5.604)|(4.130)|(1.499)|(2.141)||||
|R squared|0.310|0.311|0.314|0.368|0.368|0.368||||
|Info facilitationc* industry charind|0.450|6.108***|11.300***|3.187|0.947*|1.541*||||
||(7.698)|(1.630)|(2.559)|(2.333)|(0.564)|(0.809)||||
|R squared|0.304|0.314|0.316|0.369|0.369|0.369||||
|observations|3,158|3,158|3,158|3,581|3,581|3,581||||

Note. In each column within two lines, we report the results on the term “government facilitation * industry characteristics”, with government facilitation being either loan facilitation or information facilitation, and industry characteristics being either export tendency, large domestic market tendency, or innovation tendency. **We also control for** firm characteristics (Ln(firm age), ln(L), ownership), the interaction term of industry dummies with log(K/L) and log(L), CEO characteristics, **industry dummies and city dummies** .

*, ** and *** represent statistical significance at the 10, 5 and 1 percent, respectively.  White-heteroskedasticity-corrected standard errors, clustered at the city-industry level, in parentheses.

41
a dummy for urban residence, as defined by the DHS,<sup>7</sup> as population density of the child’s sub-national region, or with both in the same regression.

Table 2 presents estimates of the descriptive regression in equation 1. As the literature suggests, column 1 finds that averaging over the combined dataset, children in urban places, as defined by the DHS, are 16 per 1,000 more likely to survive their first year of life than children in rural places. Part of this apparently large urban advantage reflects the better sanitation environment in urban areas. Column 2 adds local open defecation, and an interaction with the urban indicator. The coefficient on urban in this regression declines in absolute magnitude by almost two-thirds relative to the magnitude of its coefficient in column 1, which does not include sanitation. As expected, open defecation and urban residence interact, such that open defecation is more steeply associated with mortality in urban rather than rural places. The results predict that the average urban child is only 2.4 per 1,000 less likely to die in infancy in places where everyone defecates in the open, compared with 8.0 per – – 1,000 or more than triple the advantage in places where nobody defecates in the open. This suggests that, on average, urban places combine the advantages of available resources and better sanitation with the disadvantages of population density.

To check this interpretation, column 3 documents that infant mortality is lower, on average, in more densely populated places, and column 4 adds the interaction of population density with sanitation. We find that open defecation is more steeply associated with mortality in more densely populated places, and that once differences in sanitation are accounted for, population density itself is associated with neither a mortality advantage nor a mortality disadvantage at the average level of open defecation.

Finally, the regression results in column 5 include the interactions of both population density and local sanitation, as well as urban residence and local sanitation. We find that infant mortality is higher where open defecation is more common, and lower, on average, in urban places. However, the urban advantage documented in column 5 is only one-third as 7The DHS defines urban residence based on the definitions used by countries’ national statistical offices.

11
Our dataset provides enough variation to allow us to obtain very precise estimates on whether products with relatively large preferences have been liberalized or protected to the same extent as other products. It is also helpful that the ASEAN members generally set their applied MFN tariffs well below their bound rates at the World Trade Organization, so we do not need to worry with this potential institutional constraint.

In line with recent analyses of regionalism in developing countries, our results imply that AFTA is a “building bloc” to free trade. There is strong evidence that preferences induce a faster decline in external tariffs than otherwise would occur. The results are both statistically and economically significant. For example, in a country where imports of a certain product from outsiders faced a 10% MFN tariff but were granted duty free access if stemming from other ASEAN members, the member would subsequently tend to reduce its MFN tariff on that product by between 21⁄2 and 41⁄2 percentage points.

While the correlation between changes in external and preferential tariffs is unquestionable, determining causality is trickier. For example, it may be that some products are easier to liberalize than others, and trade in those products tends to be liberalized both regionally and multilaterally. We use three main distinct strategies to determine if this is a causal effect and find evidence that it is. First, evidence of “tariff complementarity” remains strong if we use lagged changes in preferential tariffs (or preferential margins) as our main regressor. Second, we look for and find differential effects precisely when either the theory or the practice tells us we should find them. Specifically, no tariff complementarity arises when the margin of preferences is too small to be meaningful for exporters. Furthermore, stronger tariff complementarity is obtained in sectors where the margin of preferences is meaningful and the share of intra-bloc imports is higher, as theory suggests.

Third, we employ an instrumental variables approach that takes advantage of a unique feature of our dataset: the _agreed_ speed and depth of internal liberalization of the six original members in their 1992 negotiations. As it turns out, observed changes in preferences have not corresponded to the planned ones in 1992. Numerous reasons may have caused this discrepancy. For us, this is especially valuable because the 4
escalate. Despite the numerous ways in which projects can trigger conflict, we found that KDPrelated conflicts almost never become violent. There was only one minor violent dispute relating to KDP between 2001 and 2003 in the research areas<sup>34</sup> ; in contrast, there were 36 violent disputes related to other government development programs and provision of government services (Table 2). We argue that the difference is largely because (a) KDP projects, by virtue of emerging from a process whereby communities define their needs, are less likely to clash with local priorities, and hence conflicts are less likely to emerge; and (b) KDP has a battery of inbuilt mechanisms (people and procedures) that allow tensions to be addressed as they arise. Resolution success rates of KDP-related problems are also very high. This is in marked contrast to many other development projects, which do not have such mechanisms for channeling complaints and quickly addressing inevitable conflicts as they arise; as a result, conflicts relating to other governmental and non-governmental projects are far more likely to turn violent.

**_-_** **_<u>Table 2: Development Conflicts and Their Impacts (2001 2003)</u>_**

|**Type of P**|<br>**rogram **|<br>**# Conflicts**|<br>**# Violent Conflicts**|<br>**% Violent**|<br>**# Killed**|<br>**# Injured**|**# Properties**<br>**Damaged**|
|---|---|---|---|---|---|---|---|
|**KDP**||**42**|**1**|**2%**|**0**|**2**|**0**|
||East Java|10|0|0%|0|0|0|
||NTT|32|1|3%|0|2|0|
|**Other Gov**|**ernment**|**713**|**36**|**5%**|**4**|**38**|**11**|
||East Java|619|20|3%|2|23|6|
||NTT|94|16|17%|2|15|5|
|**Other Pro**|**gram **|**19**|**1**|**5%**|**0**|**1**|**0**|
||East Java|17|0|0%|0|0|0|
||NTT|2|1|50%|0|1|0|
|**_Total _**||**776**|**38**|**5%**|**4**|**41**|**11**|

Source: KDP & CCN dataset<sup>35</sup> Indeed, KDP in-built forums and facilitators are commonly used for dealing with KDPrelated conflicts/problems: 92% of survey respondents in East Java reported the use of KDP forums for addressing KDP-related problems; 96% did so in NTT.<sup>36</sup> Forums at the sub-district level were more likely to be used for solving KDP conflicts than those at the village level (95% compared to 85%), reflecting the fact that more conflicts emerge at this level.<sup>37</sup> Further, respondents reported very high success rates for the forums at solving KDP-related conflicts: 84.1% of informants in East Java reported that KDP conflicts were successfully solved in KDP

> 34 The one case that did occur was relatively minor.  It involved a KDP facilitator being beaten up for reporting a case of corruption to the police.

> 35 The numbers include all conflicts reported in local newspapers in fourteen districts: the four qualitative research districts, plus surrounding ones.  See Barron and Sharpe (2005) for more analysis.

> 36 Sample sizes: East Java (n = 119; NTT (n = 130).

> 37 In East Java, 91.9% reported use at the village level, and 95% use at the sub-district level (sample sizes: 99 and 20).  In NTT, 96.4% reported use at the village level, and 94.8% at the sub-district level (sample sizes: 111 and 19).

20
dimensions. MENA did worse in all of them during the considered period. MENA even had a lower score in these last two dimensions than the much poorer Africa region.

# 4. Household budget surveys in the MENA region

# <u>Availability, timeliness, and access</u>

## _Regional picture_

Household budget surveys (HBSs) collect detailed information about individual/household expenditure, consumption, and income, and also collect household demographic and socio-economic characteristics. This information is used to measure monetary poverty. Most HBS surveys last one full year to allow accounting for seasonal variations in consumption/income. Once primary data collection is finished, data cleaning and poverty measurement can take one year to be completed. It is advisable to conduct HBSs regularly at least once every three to five years (Serajuddin et al., 2015). Given rapid changes in the MENA region, every three-years is a more advisable benchmark, but we use the more conservative five-year gap below.

Table 1 demonstrates that the vast majority of MENA countries fall short of this benchmark. It summarizes for every region the number of countries that conducted HBSs suitable for constructing a monetary poverty metric over the past 15 years.<sup>8</sup> The first panel of Table 1 reports the number of countries in the region that conducted no surveys, one to two surveys, and three and more. The second panel reports the share of countries by number of surveys conducted. The third panel reports the share of the population in the respective region covered by the number of surveys conducted (as opposed to share of countries). Countries with fewer than three surveys are considered “data deprived” according to the benchmark of conducting a survey at least every five years.

Between 2004 and 2018, seven of 13 MENA countries, or more than half, conducted fewer than three surveys. This falls short of areas suitable for socio-economic comparison to MENA, such as Latin America and the Caribbean (LAC) and Europe and Central Asia (ECA). The gap between MENA, LAC, and ECA widens once the population size of each country is considered: 46 percent of MENA’s population live in data deprived countries compared to 5, 1, and 0 percent in LAC, EAP and ECA, respectively.

> 8 For a comprehensive assessment of available household surveys suitable to measure poverty, we use the number of poverty statistics reported in the World Development Indicators (WDI) database for the last 15 years, 2004 to 2018 (we use both poverty headcount ratio at $1.90 a day 2011 PPP and national poverty rates including noncomparable ones). In the MENA region, we manually added information about existing household budget surveys in the Islamic Republic of Iran for each year between 2004 to 2018, Jordan for 2017 , Libya for 2007, and Egypt for 2017,  countries for which we either have access to microdata or know can be, or were, used to measure monetary poverty but not included in the WDI.

7
# **From Theory to Practice: Open Government Data, Accountability, and Service Delivery**

# **Michael Christopher Jelenic**<sup>**1**</sup>

**_Keywords_ :** Open Data, Open Government Data, Transparency, Accountability, Service Delivery **_JEL Classification:_** H11, H51, H52, H83, L38, O55

1 Michael Christopher Jelenic is a Public Sector Specialist in the World Bank’s Governance Global Practice. This working paper was prepared for Open Data Research Symposium, which took in Buenos Aires, Argentina as part of the 2018 International Open Data Conference (IODC).

This research has benefitted from the comments provided by the participants of the IODC conference as well as the detailed feedback provided by members of the World Bank’s Open and Transparent Government Community of Practice (CoP), including Stephen Davenport (Senior Governance Specialist, CoP Chair), Tiago Peixoto (Senior Public Sector Specialist), Jonathan Mellon, and Fredrik Sjoberg. Additional internal review and comments were provided by Timothy Herzog (Senior Data Scientist, DEC), Craig Hammer (Program Manager, DEC), as well as the World Bank Africa Region’s Chief Economist’s office (AFRCE). External peer review was provided by Stefaan Verhulst (Co-Founder and Chief Research and Development Officer, Governance Laboratory at NYU), by Cédric Dupont (Professor, International Relations/Political Science, _Institut de Hautes Études Internationales et du Développement, IHEID_ ), as well as through a double-blind review from the Centre for Open Data Research (School of Media and Communication of the Pan-Atlantic University in Lagos, Nigeria). Special thanks to Renaud Seligmann (Practice Manager, Governance Global Practice), Haishan Fu (Director, Development Data Group), and the World Bank Policy Research Working Paper publications team for supporting this work as well as to Augustin Velasquez (Research Assistant, _IHEID_ ) and Elizabeth Boomer (Research Assistant, _IHEID_ ) for their invaluable feedback.

The findings, interpretations, and conclusions expressed in this paper are those of the author alone, and do not represent or otherwise reflect 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. Any errors, omissions, and typos are unintentional and will be corrected in subsequent iterations of this working paper.
> - 24 -

# 2. Gender of the Extension Agent: Does it Matter?

3.06 Whether the gender of the extension agent matters in transmitting information to women farmers varies enormously depending on the sociocultural context. In certain Muslim areas it is impossible for a male extension agent to work with a woman farmer; in other areas, women farmers are so eager for extension advice that the agent's gender is irrelevant.

3.07 Evidence from a wide range of African countries demonstrates that communication with women farmers is generally enhanced when female extension agents are used (Evans 1989). This is true even in countries with relatively few social barriers to male-female interaction. In Zimbabwe, for example, where women are legally equal in status to men, more women were found to participate in extension when female agents were used (Skapa 1988). It is always, however, best to enlist the support of husbands and male leaders before embarking on women's agricultural programs of any sort. Otherwise, males may resist supporting programs for their wives until they see direct benefits, as happened in Malawi. (Evans 1989).

3.08 Using more women extension agents does present difficulties. First, how can the supply of women extension agents be quickly increased? Second, if more women extension agents are available, how are they to be intecrated into the extension system? How do you get male colleagues to accept anid support them, for example? How do you deal with conflicting family responsibilities? How do you ensure adequate logistical support (including transportation) for them?

# 3. Female Extension Agents

# (a) Training and Recruitment

3.09 A 1983/84 study of trained manpower in Africa revealed that only 3% of agriculturally trained personnel working in the public sector in Africa were women and only 13% of students enrolled in agricultural schools were female (FAO 1987). To increase female agricultural enrollment two strategies are proposed: (1) enroll more girls in secondary school since this is the factor most highly associated with female participation in intermediate agricultural training, and (2) implement targeted growth rates for female enrollment in agricultural training institutions by which to set policies, establish guidelines, and determine benchmarks for progress (Sigman 1985). Admission policies of intermediate agricultural institutions should be reviewed to ensure that enough places are available for women and that women are encouraged to enroll. Boarding facilities for female students should be made available, and parents of secondary school girls should be informed of opportunities in agriculture. Entry requirements that are irrelevant to the job but that may constitute barriers to female enrollment should be eliminated. And once in agricultural training, women extension agents must be offered the same curriculum as men.

3.10 Agricultural extension systems ought to accommodate married women extension agents. The practice--common in some African countries--of expelling women extension agents once they marry or become pregnant
25 effects post-transition. The profile for post-communist experience is similar to the profile for the “average” (market-oriented) developing economy.<sup>44</sup>

# **5.4. Are Returns to Experience Decreasing with its Stocks?**

While returns to education correlate negatively with the stock of education (e.g., Card and Lemieux, 2001; Psacharopoulos and Patrinos, 2018b), there is less evidence on the correlation between the returns to experience and the stock of experience. Table 4 replicates Table 3 with the exception that the variables of interest considered are measures of stocks of education and experience. For the long-difference specifications (col. (1) of Panels A-B), we indeed find a negative correlation between the returns to education and the mean number of years of education in the same year. However, the five-year specification does not show a statistically significant negative correlation (Panel C). In addition, developed countries do not have significantly lower returns to education despite the fact that workers are far more educated on average. In the simulation exercises below, we will thus ignore any possible feedback effect of increasing the quantity of schooling on the returns to education. The returns to experience are not particularly correlated with the mean number of years of experience (col. (2)), again suggesting the lack of a feedback effect.<sup>45</sup>

# **5.5. Are Experience and Education Substitutes?**

Returns to experience may decrease with the stock of education or the returns to education, possibly indicating that work experience and education are substitutable. However, if education and experience are positively related or unrelated, this may imply that experience is important and the lack of it might not be compensated by investments in education. We find evidence in line with the latter. Indeed, across 44With most transitions taking place c. 1990 and the latest surveys taking place c. 2010, we do not have enough countries with a post-communist experience of 25 years or more. We also do not have enough observations with pre-communist experience in bin 35. We do not report estimates for these bins.

45The distinct long-difference results between education and experience warrants some consideration. First, experience may not have the same signaling role as education, and thus returns to experience may not decrease as much when the stock of experience increases. Second, education and experience may provide different skills for which the price elasticity of demand may also differ. Education systems may teach students skills that are not as valued by current labor markets. As the quantity of skills taught by education systems increases, prices may fall. Conversely, firms teach skills that are directly relevant for their operations and may also be updated to reflect new advancements in the field. Third, education systems focus on teaching hard basic skills. Firms also “teach” workers soft skills. If soft skills are deeply valued and scarce, increasing their quantity via more experience may not reduce their “price” as much.