rafmacalaba/datause-encoder-data huggingface dataset:
alternative energy sources might have some background in the environmental concerns, it also reflects the interest in the broadening of alternative energy sources in a context of future energy supply constraint.

# **3 THE SOCIAL ACCOUNTING MATRIX INCLUDING BIOFUEL SECTORS**

The basic data for the model are obtained from a social accounting matrix (SAM) that in this case also isolates sectors related with biodiesel and bioethanol production from the other accounts.

Here we summarize the most critical aspects of data collection and treatment. The initial matrix of intermediate purchases is based on the 1997 data (INDEC, 2001); it was updated in Chisari _et al._ (2009). For this study, we used sectoral information to update it as of 2006. The distribution of the factor income across income groups is based on the distribution observed in Argentina in 2006 according to household income surveys. The distribution of the consumption basket per type of goods and services is based on aggregates from a new household consumption survey for 2005.

In both the input and output matrix and the household consumption, consistent data on consumption and production were obtained through the cross-entropy method (Robinson, Cattaneo y El-Said, 2001). As for the government expenses, distribution between goods and services data are available for 2006 for the national and provincial governments. Municipal expenditures are assumed to be distributed in the same proportion as the average for the two other government levels.

Aggregate demand and supply in the SAM are consistent with national accounts.

The model includes 29 production sectors, four for agriculture, one for petroleum and mining, 16 for goods and eight for services. In addition to the usual activities, the SAM identifies sectors related to the production of biofuels as separate sectors: soy, corn, sugar cane, soy oil, industrial sugar, refined gasoline, diesel, biodiesel and bioethanol.

Four factors of productions are accounted for: labor, land, physical capital and financial capital. Both labor and financial capital are mobile across sectors while physical capital is sector specific. Land is mobile within various agricultural sub-sectors.

Table 1 presents participation of each sector in terms of value added, expenses in inputs and gross output. The sectors “Textiles and others”, “Other manufacturing industries” and “Services” are disaggregated in our complete SAM and account for 13 sub-sectors.

6
|RefineryModification and LPG Recoveryand Distribution Project|Kariyawasam Wijetilleke|
|---|---|
|RefineryModification and LPG Recoveryand Distribution Project|Kariyawasam Wijetilleke|
|RefineryModification and LPG Recoveryand Distribution Project|Kariyawasam Wijetilleke|
|RefineryModification and LPG Recoveryand Distribution Project|Kariyawasam Wijetilleke|
|RefineryModification and LPG Recoveryand Distribution Project|Kariyawasam Wijetilleke|
|RefineryModification and LPG Recoveryand Distribution Project|Kariyawasam Wijetilleke|
|Industrial Sector Credit Project||
|Industrial Sector Credit Project||
|Population Project(03)||
|Population Project(03)||
|Population Project(03)||
|Population Project(03)||
|Population Project(03)||
|Population Project(03)||
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|Road Rehabilitation and Maintenance Project|Stein Lundebye|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|BWDB Systems Rehabilitation Project|S. A. M. Rafiquzzaman|
|Liquefied Petroleum Gas Transport and Distribution Project|A. S. M. Bashirul Huq|
|Liquefied Petroleum Gas Transport and Distribution Project|A. S. M. Bashirul Huq|
|Liquefied Petroleum Gas Transport and Distribution Project|A. S. M. Bashirul Huq|
|Liquefied Petroleum Gas Transport and Distribution Project|A. S. M. Bashirul Huq|
|Liquefied Petroleum Gas Transport and Distribution Project|A. S. M. Bashirul Huq|
|Liquefied Petroleum Gas Transport and Distribution Project|A. S. M. Bashirul Huq|
|Industrial EnergyEfficiencyProject||
|Industrial EnergyEfficiencyProject||
|Industrial EnergyEfficiencyProject||
|Industrial EnergyEfficiencyProject||
|Public Resource Management Adjustment Credit Project|Nadaraja Ramachandran|
|Public Resource Management Adjustment Credit Project|Nadaraja Ramachandran|
|Public Resource Management Adjustment Credit Project|Nadaraja Ramachandran|
|Public Resource Management Adjustment Credit Project|Nadaraja Ramachandran|
17 consistently had to revise downwards their trade forecasts but the projections may still be too high.

The explanation provided in this paper is that the lower long-run elasticity of trade with respect to GDP has contributed to the lower than expected performance of world trade in recent years. Specifically, the analysis of the previous subsection indicates that approximately half of the slowdown in 2013 is structural and is due to the lower elasticity of trade to income that prevailed in the 2000s. Since in the coming years we are likely to continue to live in a world where trade is not as responsive to income as it used to be in the long 1990s or in the pre-crisis period of the 2000s, the growth of world trade is going to be moderate, even in the event that cyclical factors improve.

Going forward, the performance of world trade will depend on how far the post-crisis scenario is structural. The World Economic Outlook (WEO) of April 2014 projects world trade volumes to grow at an average annual rate of 5.2 percent in the period 2014-2019. We compare these estimates with the predictions that we would obtain using the estimates obtained in Section III from our regression analysis for the periods 2000-2013 and 20082013 (Figure 7 and Table 4). WEO projections are lower than our predictions of trade growth using the implied trade elasticity for the longer period and are higher when we use the estimates for 2008-2013. If the latter estimates capture a further structural (and, hence, more permanent) change in the trade-income relationship rather than a transitional phenomenon, current trade projections could be overly optimistic.

Which scenario is more likely? As discussed in Section III.b, reaching a firm conclusion on the extent of the trade slowdown may be difficult at this stage. Formal tests indicate that there is a structural break in the pre- and post-Great Recession trade-income relationship, lending support to the more pessimistic scenario. However, the estimates for 2008-2013 are based on a period that may be too short to rule out cyclical factors (albeit a long cycle) and to properly capture changes in the long-term association between trade and income.
Table 5. Country risk rankings, March 1999

||**Risk r**|**ankings**<br>**_The Institutional_**|**Access to capital**<br>**markets'**|
|---|---|---|---|
|**Country**|**_Euromoney_**|**_Investor_**|**_Euromnoney_**|
|_West_**_and Central Africa_**||||
|Benin|144|115|0.13|
|Burkina Faso|105|106|0.13|
|Cameroon|135|110|0.13|
|Central African Republic|158|..|0.00|
|Chad|157|..|0.00|
|Congo, Rep. of|140|128|0.00|
|Cote d'Lvoire|121|96|0.17|
|Equatorial Guinea|155|..|0.00|
|Gabon|99|98|0.00|
|Guinea-Bissau|163|..|0.00|
|Mali|108|119|0.00|
|Niger|129|..|0.00|
|Senegal|90|100|0.33|
|Togo|119|114|0.00|
|Gambia, The|103|..|0.00|
|Ghana|86|78|0.00|
|Guinea|133|118|0.00|
|Liberia|173|131|0.00|
|Mauritania|154|..|0.00|
|Nigeria|128|113|1.00|
|Sierra Leone|170|134|0.00|
|**_East and Southern Africa_**||||
|Angola|149|124|0.00|
|<br>Botswana|61|40|0.75|
|Burundi||||
|Congo, Dem. Rep.|168|136|0.00|
|<br>Eritrea||||
|Ethiopia|148|116|0.00|
|<br>Kenya|97|97|0.17|
|Lesotho|116|..|0.00|
|Madagascar|162|...|0.00|
|Malawi|131|102|0.00|
|Mauritius|46|39|2.50|
|Mozambique|150|111|0.00|
|<br>Namibia|151|66|0.50|
|Rwanda|165|..|0.00|
|Somalia|172|..|0.00|
|South Africa|56|50|2.67|
|<br>Sudan|160|132|0.00|
|Tanzania|145|109|0.00|
|Uganda|95|103|0.00|
|<br>Zambia|147|117|0.00|
|Zimbabwe|101|91|0.50|
|_Sub-Saharan Africa_|**130**|**103**|**0.22**|

(unweighted average) a The maximum score is 5.00, which is obtained by most OECD countries. _Source: Euromoney_ and _The Institutional Investor_ 12
# **1. Introduction**

Surveys remain a primary approach to empirical data collection in economics and the social sciences, yet variation exists in the design and protocols for implementing these surveys across study contexts. These discrepancies in survey design may induce different sources of non‐ random measurement error, but the potential biases are often left unquantified, which is particularly relevant when estimating structural parameters. This paper investigates whether survey methods matter for estimating returns to education, a common parameter in labor, development and public economics. Using a survey field experiment the study randomly allocates two common types of surveys, referred to as a long and a short questionnaire, and two common methods of response: response by the respondent him or herself, or by a proxy respondent. The results indicate that estimated returns depend on whether using a detailed or short questionnaire, but not on whether the respondent was interviewed herself or by proxy. The effect of questionnaire in our randomized control trial varies considerably by gender and level of education which underscores the potential heterogeneous effects of survey design bias across populations.

Empirical estimation of Mincerian returns to education using earnings functions (Mincer 1958) has become standard practice, and frequently involves comparison of returns over time, across countries and across sub‐groups within countries (see for example Psacharopoulos and Patrinos (2004), Shultz (2004), or World Bank (2012), among others). This analysis often relies on data generated from different surveys. The comparisons commonly find differences that are in some cases large, and in other cases only a few percentage points apart. Much attention has been given to limitations of comparability due to differences in data sample coverage (selection issues) and method of analysis (non‐linearity). A body of work also analyzes how to get structurally accurate (‘true’) estimates for returns to education, taking endogeneity into account.<sup>3</sup> These estimations provide important inputs to policy debates and decisions, especially in low and middle income countries where education attracts tremendous attention as a

> 3 Alternative ways to estimate returns to education include instrumental variable techniques (Angrist and Kruger (1991), Card (2001), Cruz and Moreira (2005), among others), twin studies (Behrman et al. (1994, 1996), Miller et al. (1995), Bound and Solon (1999), among others) or randomized control trials.  Our deliberate focus on cross section survey data arises from this being still the most common approach, especially in international comparisons, developing country settings, and education policy debates, as cross‐country and time varying estimates remain costly to obtain.

2
(Table 9), and they did not affect parenting behaviors (Appendix Tables B6 and B7).

Neither the program as a whole, nor the D2D and HBP interventions, seems to have affected the way parents perceived education. While the program did have an effect on the reported optimal age for starting preschool, these impacts are not larger in T2 and T3 (Table B8). In addition, there are no impacts on the perceived return to school, maybe because parents already considered the return to primary and secondary school to be high (6.8% per school year in primary and 8.5% for secondary in the control group) and even higher than those reported by Humphreys (2015) based on Cambodia’s Socio-Economic Survey (CSES) in 2010. Unlike other contexts (for example in Madagascar (Nguyen, 2008) or in Dominican Republic (Jensen, 2010)), Cambodian caregivers did not appear to have under-estimated the return to education. We cannot rule out the possibility that a more intensive set of programs (e.g., more frequent home visits, more intensive HBP) might have had bigger impacts. But it is also possible that, in the context of these small rural villages, the construction of the CPS itself had a demand-side “information” component in the sense that it was likely an important village event that households would have known about. In such a context, it could have been the case that the additional demand-side programs did not convey any information over and above the construction itself given what parents already believed about the return to education.

# **6 Conclusion**

Consistent with findings in other studies, our results show that a relatively wellimplemented preschool program—90% of treatment villages received a community preschool (CPS)—can initially increase the cognitive and socio-emotional performance of young children. Even though the midline ITT impacts are low in magnitude (+0.04 SD), the complex substitution patterns make these results consistent with moderate-to-large impacts on children who would have stayed at home absent the program. Our analysis also shows that the impacts on children who would have attended another preschool absent the CPS construction are small and insignificant at midline. These suggest that the midline ITT impacts are driven by additional enrollments rather than improvement in the quality of the available preschool provision. This is consistent with our findings related to preschool quality: While the CPS program considerably improved the quality of infrastructure, we did not find significant difference between CPS and the informal preschool program in terms of teaching quality.

Our results also show no significant overall treatment effect at endline. The absence of effect at endline can in part be explained by the fact that overall exposure to preschool remains equivalent at midline and endline. This is due to many children 33
_0.56 0.60 0.78_ Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p-values for a test for whether there is a difference between the fixed effects between that year and the previous year. Values are shown in the prices of 2007. Boldfaced numbers are statistically different from the previous year at the 5% level; yellow cells indicate lower real expenditure per capita in 2009 than in 2008.

The first point to note is that there are no strong differences in the experience of men and boys compared with women and girls; both may have had falling real spending in 2008, but higher levels of real consumption in 2009. Children and young adolescents were not particularly hard hit by the recession; although they did not see the per capita real spending levels of their families rise in 2008, they did experience improvements in the recession year of 2009.

Young adults, defined as those aged 20 to 29, saw a large improvement in their material living standards in 2008, followed by a real drop from this level in 2009, before a strong rebound in 2010. This is the group that was most clearly hit by the 2008-09 recession, at least relative to the level of material wellbeing achieved on the eve of the recession.

Most prime-age adults (30-59) saw their living standards take a hit in 2008 and 2010, with a modest improvement in 2009. Those at or near retirement (60-64) did very well in 2008 but were hurt (relatively) in 2009; some may have opted for earlier retirement, or been vulnerable to reductions in working hours. Those aged 65 and older did not see any significant changes in their material standard of living through this period.

The remaining results are set out in Table 5, and mainly examine whether some groups, traditionally considered to be vulnerable, were hurt by the recession. The answer is by and large no. Widows, and those who had separated from their spouses, were not differentially affected. The ill and disabled did not become worse off.

The average income of households with children at school is relatively low, but this group was, if anything, better off during 2009. For those aged 15-19 who were at school (Table 5) or in general (Table 4), real consumption levels did not fall during this period, so one would not have expected any generalized effect on school dropout rates. Of all the educational categories, the one group that gained in 2008, and then saw its standard of living fall in 2009, were those with a vocational education. This group may have been affected disproportionately by industrial retrenchment.

Losers and Winners in Recession: Thailand 2008-09 19
4 firm-level and macroeconomic variables. The paper also explores the relevance of potential conditional factors of the relationship between structural reforms and firms’ productivity and whether reforms are substitutes or complementarity in affecting firms’ labor productivity growth.

The paper constructs an index of structural reforms from the IMF Monitoring of Fund Arrangements (MONA) database. Structural reform indexes are matched with firm-level data from the World Bank Enterprise Surveys (WBES) for 37 Lower and Lower-Middle income countries over the 2006-14 period. The MONA database contains comparable information on the economic objectives and outcomes in the IMF-supported arrangement programs. It tracks the performance of countries regarding scheduled purchases and reviews, quantitative and structural conditionality, and macroeconomic indicators. To construct our structural reform indexes, we assume that conditionalities met under IMF-supported programs in developing countries could be identified as major macroeconomic structural reforms. We use the information available on structural benchmarks affecting the fiscal sector, the financial sector, the real sector, and the trade sector.<sup>4</sup> For each measure, we focus on measures for which targets have been met or met with minor delays. The WBES is a collection of firm-level surveys in developing countries based on a representative sample of the economy’s private sector. It contains cross-country information on individual firms’ characteristics and allows the calculation of productivity measures.

In addition to using the multilevel model, the paper devotes efforts to minimize endogeneity concerns. We present sample balance tables showing that the sample developing countries are not statistically different from those without the IMF’s program regarding key macroeconomic variables.<sup>5</sup> Having the pre-treatment macroeconomic variables well matched allows us to minimize the concerns that the estimated effects of reforms on productivity are driven by the pre-reforms macroeconomic environment. Moreover, the identification strategy relies on matching productivity data only with reforms within the 3 previous years. The matching strategy limits potential reverse causality issues between reforms and productivity by ensuring

> 4 Structural benchmarks are reform measures that are important to achieve program goals and are intended to assess program implementation during a review.

> 5 Current account and overall balance, debt and interest payment on external debt, inflation rate, GDP growth, and exchange rate.
tively more likely to have a post-graduate degree, to have studied abroad, and to have experience working in a multinational or a large firm.

The average pre-pandemic technology sophistication index in our sample is 1.78 with a standard deviation of 0.62. The typical business with that level of technology sophistication mostly employs computers with standard software (e.g., Excel) to perform business administration (e.g., HR processes, finance, and accounting), production and service operations planning, or sourcing and procurement.<sup>10</sup> They would obtain information from customers mostly through face-to-face interactions or basic online tools (e.g., WhatsApp). Their sales are mostly at the establishment, by phone or email, while their most prevalent payment methods are by check or debit/credit cards. They rely mostly on manual or visual process for quality control. These firms are relatively similar to the median firm (1.71) or the average in the middle 20% (Q3).

In our sample businesses use digital technologies with increasing level of sophistication as we move from Q3 to Q5 (the top 20%). On the other hand, firms in the bottom 40% of the technology readiness distribution (Q1 and Q2) mostly rely on manual pre-digital technology to perform their tasks. Although firms in the second quintile (Q2) are already using standard computers for business administration and planning, the method they use more intensively is handwritten processes.<sup>11</sup> In the early months of the COVID-19 pandemic, more relatively sophisticated businesses reported lower declines in sales and exhibited a higher probability of starting to use or increasing the use of digital technologies. The main digital adjustment was starting to use or increasing the use of digital platforms (41% of the sample) while comparatively lower fractions of businesses invested in new equipment, software, or digital solutions (14%); increased home-based work (12%); or reported higher fractions of online sales (13%). Of the firms in our sample, 47% have increased the use of digital technologies for at least one of the four dimensions previously. Still, more sophisticated businesses were relatively more likely to report each individual digital response.

> 10We describe the technologies in a firm with a level of sophistication of 1.78 using the data from the 19 businesses with an index between 1.77 and 1.79 in our sample.

> 11The technologies with increasing levels of sophistication associated with each business function are described in Figure A1.

9
WPS4149

# **The Worldwide Governance Indicators Project: Answering the Critics**

Daniel Kaufmann, Aart Kraay, and Massimo Mastruzzi<sup>1</sup> The World Bank

**Abstract:** The Worldwide Governance Indicators, reporting estimates of six dimensions of governance for over 200 countries between 1996 and 2005, have become widely used among policymakers and academics.  They have also attracted some explicit written criticisms.  In this short paper we synthesize eleven critiques offered by four recent papers.  We then refute them as either conceptually incorrect or empirically unsubstantiated.

World Bank Policy Research Working Paper 4149, March 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 1818 H Street NW, Washington, DC 20433, dkaufmann@worldbank.org, akraay@worldbank.org, mmastruzzi@worldbank.org.