rafmacalaba/datause-encoder-data huggingface dataset:
Overall, the descriptive analysis suggests the following three findings: (i) firms in Kenya are those with larger ICT use; (ii) the differences in ICT use between manufacturing and services are not significant; and (iii) larger firms show significantly larger levels of ICT use than small firms.

_<u>Table 13 ICT Index by Country, Sector and Size</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|88.77|82.78|66.83|72.78|106.67|136.45|100.00|
|**Main Sectors**||||||||
|Manufacturing|91.05|82.39|72.84|71.92|124.63|134.99|102.33|
|Service|86.39|82.91|64.59|73.06|101.68|137.10|97.70|
|**Size**||||||||
|Small|70.61|73.00|60.26|70.17|98.31|114.76|75.85|
|Medium|116.54|120.12|86.39|66.72|111.73|160.99|127.69|
|Large|159.93|185.25|106.92|162.53|201.37|183.41|173.10|

Source: Author’s own elaboration from enterprise surveys

# **6. ICT use, innovation and productivity**

## **6.1 Are ICT adopters more productive?**

A critical challenge when examining the relationship between ICT adoption, innovation and productivity, is reverse causality; while ICT use can increase productivity, it is also possible that only more productive firms adopt ICT. In what follows, we start exploring this question by looking at basic distribution plots between labor and multifactor productivity and ICT adoption. Specifically, we focus in two categories of ICT adoption: computer users and internet users. We use the relative distribution of productivity between adopters and non-adopters as a first approximation for stochastically dominance, which provides a better measure of productivity differences between these groups than evaluating it by simple averages.

_Computer users are more productive than non-users._ Figure 1 shows the relative distribution of labor productivity for all firms in all countries pooled together, measured by sales per worker, and of total factor productivity (TFP) estimated at the sector level.<sup>14</sup> As observed, differences between computer users and non-computer users are wider in labor productivity compared to TFP. Computer users show higher productivity level throughout the whole relative distribution of labor productivity. Looking at their averages, computer users are four times more productive than non-users. Regarding TFP, productivity differences between computer users and non-users are larger for low productivity firms than for most productive firms. Evaluating at their mean, computer users are 26.2% more productive than non-users in terms of TFP.

_Internet users are also more productive than non-users._ Observing the distribution of internet users versus non-users (Figure 2) shows similar results as for computer use; internet users tend to be more productive than non-internet users regarding labor productivity. For TFP, there are no productivity differences for very

> 14 Figures using value added per employee as labor productivity and TFP measures using sector as a dummy are available in the Appendix.

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_24_ groups. This presents an opportunity to estimate the potentially disparate impacts across not only temperature zones, but also income groups. A central question in the development discourse is how climate change affects the development trajectory of poorer countries and whether the effects are disproportionate across income groups. To check this, we estimate the model for subsamples based on income groups. We estimate nonlinear impacts for two sub-samples, one including only low-income economies and another including middle and high-income economies. Table 6 presents the estimation results, which indicate that hotter temperatures have a significant impact on firm productivity.

Table 6—: Estimation of Log Sales per Worker: By Income Group

||Low income|Middle and high income|
|---|---|---|
|WBT Deviation_×_Zone<sup>_Q_2</sup><br>(23_._7<sup>0</sup>_C,_24_._9<sup>0</sup>_C_]|0.091<sup>_∗_</sup><br>(0.054)|-0.192<sup>_∗∗∗_</sup><br>(0.040)|
|WBT Deviation_×_Zone<sup>_Q_3</sup><br>(24_._9<sup>0</sup>_C,_25_._7<sup>0</sup>_C_]|-0.114<br>(0.115)|-0.211<sup>_∗∗∗_</sup><br>(0.066)|
|WBT Deviation_×_Zone<sup>_Q_4</sup><br>(25_._7<sup>0</sup>_C,max_)|-0.707<sup>_∗∗∗_</sup><br>(0.083)|0.038<br>(0.133)|
|Sector dummy|Yes|Yes|
|Country FE|Yes|Yes|
|Year FE|Yes|Yes|
|Country x year FE|Yes|Yes|
|Population (25km radius)|Yes|Yes|
|Road infrastructure (25km radius)|Yes|Yes|
|Pollution (25km radius)|Yes|Yes|
|_Observations_|43,375|42,931|
|R<sup>2</sup>|0.195|0.300|

Standard errors are in parentheses. All models control for firm age, ownership, export status, firm size (employment), proportion of permanent workers, proportion of skilled workers, 55 dummy variables indicating a narrow industry classification, and dummies indicating World Bank regions. All WBT categories are included separately in the model, in addition to the interaction terms. The reference category for climate zone is the bottom quartile in the distribution of WBT which is less than or equal to 23.7<sup>0</sup> _C_ . The minimum WBT in our sample is 14.5<sup>0</sup> C. FE = fixed effect; km = kilometers; OLS = ordinary least squares; WBT = wet-bulb temperature. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 In low-income countries, firms in the highest WBT category, _Zone_<sup>_Q_</sup> (25<sup>4</sup> _._ 7<sup>0</sup> _C,max_ )<sup>or</sup> the hottest climate zone experience productivity losses of about 50.7 percent, the
# **V. Conclusions**

The global trend of school-based management follows the principle of decentralized decision-making, putting power in the hands of the frontline providers and parents to improve their schools, aiming at improving education beyond providing more classrooms, more teachers, and more textbooks. The idea behind decentralized decision making and management is that the frontline providers and clients know best what they want, and what goes on in schools and other public service provision facilities.

This paper examines the key aspects of the practices of school-based management currently in Indonesia, and its effect on education quality. Using a conceptual framework of an accountability system of public service delivery, this paper explores the relations among parents, school committees, schools, and government education supervisory bodies from three tenets: participation and voice; autonomy; and accountability. The paper’s empirical approach allows a close examination of how these aspects affect school decision-making on key educational inputs, which significantly affect student learning outcomes.

Using the data from a nationally representative survey of about 400 public primary schools in Indonesia, the paper finds that the level of parental participation and voice in school management is quite low in Indonesia. While the role of school committees is still limited to community relations, school facilities, and other administrative areas of schools, the school principal, together with teachers, is very much empowered to assert professional control of the school. The most important finding of this paper is that the accountability system is very weak in Indonesia’s school system, which is reflected in inadequate information flow to parents, as well as seemingly low parental awareness of holding schools accountable. The accountability arrangement of the Indonesian school system currently puts more emphasis on top-down supervision and monitoring. Even though the interactions between district or subdistrct education offices and schools are generally frequent, the lack of rewards and sanctions for good or bad performance also leaves the system weak.

The empirical work of this paper shows that although the scope of school-based management in Indonesia is limited today, it has begun to help schools make the right decisions on allocation of resources and hiring additional (non-civil servant) teachers, and 24
# **<mark>Excess Poverty: Is the effect on poverty driven by more than “the size of the pie?”</mark>**

<mark>As has been shown in the literature (e.g. Borenzstein and Panizza, 2009) and confirmed in this paper, sovereign defaults coincide with significant output collapses. The question is then: Does a sovereign default worsen the impact of an output decline on a society’s poorest?</mark> The change of poverty headcounts around default appears as a key metric to turn to when trying to answer this question. However, much of the dynamics of poverty headcounts that we presented in the previous section are driven by changes in aggregate income because of two factors. First, mechanically, for a given shift of the income distribution, an output collapse will increase the poverty headcount. Second, while the World Bank’s Poverty and Inequality Platform, where we take our data from, often presents time series data on poverty, most of the observations available are imputations derived from income growth dynamics and an assumed log normal income distribution. In other words, it assumes the impact of an output collapse to be distribution-neutral, which ties both poverty and growth outcomes together for most crosscountry analyses. As is well established in the literature (Clementi and Gallegati, 2005, Souma 2001, McDonald 2008), income size distributions are typically lognormal, with the density

> <sup>σ2</sup> mean e(μ + 2 ~~)~~ and median eμ. With knowledge of the parameters μ and σ, one can arrive at the poverty headcount, _x_ , using the cumulative distribution function:

where Φ is the cumulative distribution function of the standard normal distribution. Using two moments of the distribution prior to default, e.g. the median and the mean, we derive the parameters μ and σ before the default. Because distribution neutral growth implies that each member of the society is affected the same, relative to their prior income, we can estimate the impact on the share of the population falling below the poverty line, _x_ , by shifting the 26
Portugal, Martins (2011) finds that workers who move from a domestic to a foreign firm receive a considerable pay increase. However, few studies trace the same individuals in developing countries,<sup>36</sup> perhaps due to data constraints.

This section examines how wages of _identical_ individuals would have changed as they “switch” between domestic and foreign sectors by exploiting a panel of individuals in the VHLSS 2002 and 2004.

I estimate a worker-specific fixed-effects model of the type used by Korenman and Neumark (1991) and Lemieux, MacLeod and Parent (2009). I start with equation (1) and introduce the time invariant individual specific characteristics _ηi_ which may be correlated with independent variables.

_Ln Wit =_ **_α Ownership_** _it +_ **_β X_** _it +_ **_γ ES_** _it+_ **_δ Location_** _i + ηi +_ εit (2) where _i_ and _t_ index individuals and years (2002 and 2004) respectively. _Wit_ is an individual’s wage; **_Ownership_** _it_ are a series of dummy variables reflecting ownership (foreign, state, private, and informal wage); **_X_** _it_ is a vector of individual characteristics; **_ES_** _it_ and **_Location_** _i_ are a set of industry and location dummy variables defined in equation (1) and _ηi_ is an unobservable timeinvariant individual characteristic of individual _i_ .

In order to remove the individual-specific effect _ηi_ , a first-difference is taken between the years 2002 and 2004, and then the parameters will be estimated via Ordinary Least Squares (OLS).

> 36 The results of some studies based on _firm-level_ panel data suggest that impacts of foreign ownership in labor markets may differ between developed and developing countries (Almeida, 2007). For instance, using the panel of Indonesian manufacturing data to control for unobserved plant level characteristics, Lipsey and Sjöholm (2006) find that acquisition of Indonesian plant leads to substantially higher average wages of employees in acquired firms. Their results contrast with those for European countries which tend to find much smaller wage effects of foreign acquisition on employees (e.g., Almeida (2007) for Portugal, Conyon, Girma, Thompson and Wright (2002) for the United Kingdom).

20
2. Trade Linkages between B&R Economies: Gross Trade To help visualize the strength of the production sharing network, we provide a complete overview of sourcing patterns using network analysis tools. From the analysis, it turns out that China, and to a lesser degree Russia, are the main economic centers of the B&R network. Given the importance of China, we proceed to quantify its sourcing patterns as well as the extent of vertical trade.

We conclude the study by calculating the expected impact of GVC trade flows of a reduction of costs in the B&R. Because of the cascading effect of trade costs, which increase with each time crossing a border, GVC trade is more elastic to these changes. We estimate a regression model where GVC trade depends on these costs.

The rest of the paper is structured as follows. Section 2 presents stylized facts on the B&R Economies based on gross exports. Sections 3 and 4 introduce the economic methodology to decompose gross exports into several value-added components. Section 5 uses network analysis tools to visualize the strongest production linkages between the B&R Economies. Section 6 discusses the integration of China in GVCS. Section 7 discusses the implications of further integration within B&R and section 8 concludes.

# **2. Trade Linkages between B&R Economies: Gross Trade**

# **_2.1 Gross Trade Linkages between B&R Economies Revealed by UN Comtrade Data_**

The section presents stylized facts based on the evolution of trade patterns of B&R Economies using the UN Comtrade database, which covers bilateral trade of 182 economies in total. The database only contains product level information, so it can inform us only on gross trade flows of intermediate and final goods. The UN Comtrade database has the greatest country coverage of all the databases at disposal for scrutiny. For 182 countries, it covers all bilateral gross trade figures. These are exports and imports of final and intermediate goods that are recorded by customs agencies.

The first stylized fact is that the share of B&R Economies in world trade has been increasing through time. In Figure 1, we observe that in 2015 the share of the 60 B&R Economies in world exports (i.e., exports of the 182 economies covered in the database) is 37 %, and their importance in world exports has been growing since 1995 when their share was 21 %. Figure 2 further suggests that this has been mainly driven by exports of intermediate goods, of which the share of B&R Economies grew faster than that of final goods, reflecting an increase in GVC participation. B&R Economies also have a relatively larger share of intermediate goods exports, accounting for 42 % of total intermediate exports (Figure 2, panel A), compared with 24 % for the share of final goods (Figure 2, panel B).

5
**How Vulnerable Are Arab Countries to Global Food Price Shocks?**

# **1. Introduction**

There is a belief that households in the Middle East and North Africa<sup>1</sup> (MENA) should be largely insulated from fluctuations in global food prices. The region stands out among other developing regions for its extensive use of price subsidies and controls (Ortiz _et al._ , 2011). In addition, governments in MENA employ other policies aimed at managing and regulating food consumption, production and trade.<sup>2</sup> These include production subsidies, import protection cuts, and build-up of food reserves.

At the same time, high and volatile international food prices continue to be a major concern in the MENA region, and some have even cited them as a contributing factor in the Arab Spring (Breisinger _et al._ 2011; Zurayk, 2011). Food prices rose substantially during the second half of the 2000s, and even though prices plunged during the global economic and financial crisis, they recovered rapidly in 2010 before coming down somewhat in 2011 (Figure 1). At present, the aggregate food price index is above the levels observed in the mid-2000s, and prices of certain food items remain high (World Bank 2012a).

High international food prices feature prominently in public policy discussions in the Middle East and North Africa for a number of good reasons. Dependence on imported food is substantial and likely to grow in the future. Using data from the United States Department of Agriculture (USDA) this paper shows that the region meets through imports about half of its wheat and barley consumption requirements, 40 percent of its rice consumption, and nearly 70 percent of its corn consumption (Table 1). The region is the largest wheat importer in the world, and wheat prices increased by 70 percent in the second half of the 2000s. With expected high population growth rates and climate change that is likely to raise the frequency of draughts and water scarcity, MENA‟s import dependence will only grow going

> 1 In this paper we use interchangeably the terms “MENA” and “Arab world.” The definition of MENA corresponds to the operational definition of the Middle East and North Africa region of the World Bank, which includes most members of the Arab League and Iran. We also refer to three main subgroups in MENA: the oil importing economies which include Egypt, Tunisia, Morocco, Jordan, Lebanon, Djibouti, and the West Bank and Gaza; the oil exporting economies of Algeria, Iran, Iraq, Syria, and Yemen; and the GCC economies comprised of UAE, Saudi Arabia, Qatar, Oman, Bahrain, and Kuwait.

> 2 Ortiz _et al._ (2011) have a list of policy interventions by country and developing region.

-2-
11 Figure 2. Financial Depth and Income Inequality <!-- Start of picture text --> Change in Gini<br>coefficient<br>0.03 UGA<br>NGA<br>0.02<br>ZMB<br>ROM ECU<br>GTM<br>0.01 GHA NER LSO CHL HKG<br>PAN<br>SLE ARG CIVURYNZL BRA USA<br>0 IND HUNLKAMDGPERDOMBELJAMIDNCRIEGYIRNMEXVENPHLSLVDNKCOLHNDTTOGBRBOLCANKORTUNTHAMYSSGP<br>NPL BGD CMRGRC AUS LUXJORESPPRT JPN<br>TZA ETH TUR PAK ITA NOR CHE<br>-0.01<br>IRL SWE<br>NLD<br>FIN FRA<br>SEN<br>MUS AUT<br>-0.02<br>-0.03<br>-3 -2 -1 0 1 2<br>Private credit to GDP<br><!-- End of picture text --> Source: Authors’ update on Beck, Demirgüç-Kunt, and Levine (2007). Note: The Gini coefficient is on a scale from 0 (total equality) to 1 (maximum inequality). The chart is a partial scatter plot, visually representing the regression of changes in the Gini coefficient between 1960 and 2005 on the private sector credit–to-GDP ratio (logarithm, 1960–2005 average), controlling for the initial (1960) Gini coefficient. Variables on both axes are residuals. The abbreviations next to some of the observations are the three-letter country codes as defined by the International Organization for Standardization.

There is sizeable variation in private credit across countries. For example, averaging over 1980– 2010, private credit was less than 10 percent of GDP in Angola, Cambodia, and Yemen, while exceeding 85 percent of GDP in Austria, China, and United Kingdom. The annual average value of private credit across countries was 39 percent with a standard deviation of 36 percent.

An alternative to private credit is total banking assets to GDP, a variable that is also included in the Global Financial Development Database. Compared to private credit, this variable includes also credit to government and bank assets other than credit. It is arguably a more comprehensive measure of size, but it is available for a smaller number of countries and has been used less extensively in the literature on financial development. In any case, the two variables are rather closely correlated, with a correlation coefficient of about 0.9 (Figure 5), so private credit can provide a reasonably close approximation for total banking assets.

Despite the literature‘s focus on banks, the recent crisis has highlighted issues in non-bank financial institutions (NBFIs). The coverage of NBFIs by data is much less comprehensive than that of banks. Nonetheless, to acknowledge this point, the Global Financial Development
# Dynamics of Demand for Rainfall Index Insurance: Evidence from a Commercial Product in India

Daniel Stein (The World Bank)<sup>1</sup> JEL: D12, D14, D81 Keywords: Micro-Insurance, Rainfall Index Insurance, Demand for Insurance

> 1 dstein@worldbank.org. This work would not have been possible without the assistance of the insurance staff at BASIX, especially Sridhar Reddy, for help in obtaining and understanding their customer database. Thanks to Tim Besley, Greg Fischer, Gani Aldashev, and Xavier Gine for giving helpful advice and commenting on earlier drafts. Thanks to participants at the Munich Re 6th Annual Microinsurance Conference, the Midwestern International Economic Development Conference, seminars at the London School of Economics, University of Namur, and KU Leuven for helpful comments. All remaining errors are my own.
### SPLIT DECISIONS

McCrary (2008) nonparametric test for manipulation of the test score variable. While there is an increase in the density at the passing threshold, it is small in magnitude, not statistically significant, and well within the observed variance in score density at nearby levels. This is reassuring but does not _per se_ rule out self-selection. Second, in all of the analysis to follow, the test score we use is exclusively the test score from each worker’s _first_ attempt to pass the KLT. A small number of failers re-took the test in later rounds, and if we were to use scores from subsequent attempts, this would raise the possibility of workers self-selecting across the passing-score cutoff.<sup>17</sup> Third, the test was administered and scored by a Korean institution and we are not aware of any substantial reports of corruption or other irregularities in scoring or record-keeping.

# 5. New survey data

We conducted a new, purpose-built survey of the households of EPS-Korea job applicants who has scored near the passing threshold. Survey teams visited the households in February and March of 2010. Any knowledgeable respondent present at the time of the survey team’s visit was allowed to complete the survey.<sup>18</sup>

## 5.1. Sampling strategy

In order to ensure that the sample was as representative as possible of the sampling universe, we provided target addresses to the survey firm in stages. First, we gave only the addresses of households whose applicant was within one point of the cutoff score. Only after the firm had attempted to contact all of those households did we provide a second set of target households whose applicant was within two points of the cutoff. We proceeded in this fashion, one point at a time, until our resources for conducting

> 17Because the test-score we use is only the first-attempt score, a small number of those with failing scores are deployed to Korea. These are people who failed the first attempt but passed on subsequent attempts. This is of concern to external validity, but not internal validity.

> 18Cull and Scott (2010) show that survey responses on the financial life of Ghanaian households provided by knowledgeable respondents are as good as full household enumeration and better than responses from randomly selected respondents.

13