rafmacalaba/datause-encoder-data huggingface dataset:
x6 = Predictable career path x7 = Policy consistency x8 = Policy communication x9 = Policy support xlO = Political interference/micro-management xl 1 = Resource predictability

# A 6.2. Dealing with data and estimation problems: the example of Bolivia

# _Number ofobservations_

Having 1 1 independent variables (and a constant term), and only 15 organizations, i.e., data points was a problem. In order to calculate IRRs for different organizations and different groups of public officials, responses from each public official were utilized in running regressions. That basically gave (after some data cleaning) 690 observation points for the entire Bolivia; and 240 for central administration, and so on.

# _Collinearity among independent variables_

Rather than doing nothing or changing the functional form, explanatory variables were transformed into binary responses: an indicator which is less than 5 out of 10 points were converted into zero, otherwise into one. This transformation also allowed the interpretation of the estimated coefficients in a more sensible way. Instead of referring to increasing policy credibility by one or by a certain amount, it was possible to refer to making policies credible. Similarly, instead of referring to two public officials who believe that rules in place 72.4% or 39.8%, it is possible to refer to distinguish between public officials who believe and don't believe respectively that rules are in place.

**Correlation coefficients for the (converted) institutional environment indicators (Bolivia)**

||_xl_|_x2_|_x3_|_x4_|_x5_|_x6_|_x7_|_x8_|_x9_|_xIO_|_xlI_|
|---|---|---|---|---|---|---|---|---|---|---|---|
|xl|1.00|||||||||||
|x2|0.13|1.00||||||||||
|x3|0.05|0.03|1.00|||||||||
|x4|0.09|0.05|0.05|1.00||||||||
|x5|0.04|0.10|0.07|0.05|1.00|||||||
|x6|0.21|0.08|0.09|0.12|0.20|1.00||||||
|x7|0.12|0.06|0.03|0.07|0.03|0.14|1.00|||||
|x8|-0.09|0.00|0.00|-0.03|0.01|-0.03|-0.02|1.00||||
|x9|0.11|0.05|0.13|0.09|0.06|0.19|0.13|-0.04|1.00|||
|xlO|0.31|0.10|0.08|0.12|0.14|0.47|0.09|-0.07|0.23|1.00||
|xl1|0.14|-0.01|0.07|0.03|0.03|0.10|0.13|-0.04|0.03|0.09|1.00|

60
17 from one source (USEPA, 1990) (which may not match well with data from MOBILE4) for average U.S. tailpipe emissions coefficients for heavy-duty (HDD) and light duty diesel trucks (;DD) and for passenger and light-duty gasoline vehicles with and without catalytic converters.

The most comparable figures are between gasoline vehicles with catalytic converters and LDD. Emissions per mile traveled varies significantly by whether gasoline or diesel fuel is being burned. Of the conventional pollutants, sulfur oxides are over five times larger and total particulates are 30 times higher for the LDDs, with sulfates about six times larger (mostly in the form of sulfuric acid), while CO, NOX, and HC (a slightly broader measure of hydrocarbons than VCCs) are substantially lower. Lead emissions are zero for diesel vehicles.

The comparison for particulates is even more unfavorable for diesel than it looks, since nearly all of the diesel particulates are very fine (and therefore can penetrate deeply into the lung)<sup>10</sup> and some carcinogens such as benzo(a)pyrene, are also far more heavily represented in diesel emissions than in gasoline emissions.

HC emissions are even larger for gasoline vehicles than is apparent from the table. Gasoline vehicles have substantial evaporative (and running loss) emissicns, in addition to the tailpipe emissions estimates on the table. EPA (1989) estimates that current model gasoline vehicles generate only 41 percent of their total VOCs from the tailpipe, the rest coming from evaporative (16 percent), refueling (11 percent), and running 10. According to another source (WHO, 1988), diesel engines generate ten times more respirable particulates than gasoline engines per kilometer traveled; even allowing for this, diesel buses are less polluting than gasoline passenger vehicles per person-trip (Rallis, 1988).
# **Table 1: Summary Statistics by Regions (mean)**

||**Rem/GDP**<br>**(%)**|**Mig/Pop**<br>**(%)**|**Difference in**<br>**ratio of**<br>**tertiary edu.**<br>**(%)**|**Bank**<br>**deposit/GDP**<br>**(%)**|**Bank**<br>**credit/GDP**<br>**(%)**|**GDP per**<br>**capita (US$)**|**GDP growth**<br>**rate (%)**|
|---|---|---|---|---|---|---|---|
|**Americas**|1.44|2.41|25.5|25.1|24.9|3191|2.09|
|**Western Europe**|0.48|3.60|15.7|75.0|98.1|21432|3.23|
|**Eastern and Central Europe**|0.88|1.55|27.5|25.2|17.7|2931|3.83|
|**Middle East and North Africa**|3.37|2.09|21.0|53.2|38.7|4827|3.84|
|**Sub-Saharan Africa**|2.94|0.39|51.9|22.1|21.3|940|3.03|
|**South Asia**|2.47|0.21|50.9|39.6|25.5|463|5.39|
|**East Asia and Pacific**|0.59|0.36|43.3|101.9|97.7|7376|5.50|
|**Total**|1.46|0.92|40.3|64.0|59.3|7374|4.64|

**Note:** Figures are weighted by the population. They are based on the mean values for the period between 1998-2002 except for logarithm of migrants abroad, the ratio of migrants to population, and the difference between the ratio of people with tertiary education among migrants and the ratio among the working population in home country, which are the figures for the year 2000

18
International development institutions and almost all developing countries track poverty measures, and the results are watched keenly. But how do we judge performance? When do we say it is “good” or “bad”? Little attention has been given to these questions.<sup>2</sup> A closely related issue is how to set goals for future performance. Setting goals can motivate achieving them. The _Millennium Development Goals_ were clearly conceived with that aim in mind.<sup>3</sup> The first Millennium Development Goal (MDG1) was to halve the developing world’s 1990 “extreme poverty rate” by 2015. Using the $1.25 a day poverty line for defining “extreme poverty,” MDG1 was attained in 2010, five year ahead of the goal (Chen and Ravallion, 2012). Yet, it is not clear exactly how the MDGs were determined. At the time MDG1 was set, there were very few time series observations to draw on for benchmarking performance. That has changed dramatically.

This paper draws on recent research on the measurement of poverty and on global economic prospects to try to identify measures and seemingly defensible benchmarks for judging progress going forward. The measures proposed span both absolute and relative poverty. The benchmarks for each rest on explicit assumptions about future growth and distributional change, though they are assumptions that are informed by knowledge of recent past performance against poverty and current expectations about growth prospects across the developing world. While there must (inevitably) be a large dose of optimistic conjecture in setting such targets, it is hoped that this effort will help inform public discussions on development goals, guide assessments of performance over the coming decade, and help mobilize efforts against poverty.

Two benchmarks over the next 10 years are proposed. The first is dubbed the “optimistic” target. This assumes that the recent trajectory of success against poverty can be maintained; this can be thought of as the counterfactual in the absence of extra effort but with a measure of good luck. The second is an “ambitious trajectory”—a “high case” path that would be a real challenge, requiring substantial extra effort from the whole world. These benchmarks are not (at least now) dreams beyond imaginable reach; they stem from scenarios that are within the

> 2 To my knowledge, the only careful attempt to benchmark performance at country level is found in Newman et al. (2010) who draw on time series evidence across developing countries. They use the empirical distribution of absolute changes in the poverty rate as data for informing the setting of performance benchmarks for countries in Latin America and the Caribbean.

> 3 As Hume (2009, p.4) puts it the MDGs aimed to “stretch ambitions and mobilize political commitment and public support.” The MDGs were not the first such effort at goal setting; Hume identifies a number of antecedents including the UN Declaration of Human Rights.

2
second highest incidence of non-neutral and non-humanitarian interventions – the average prevalence of foreign military interventions was less than half of that observed in MENA. By contrast, neutral and humanitarian interventions appear evenly distributed across regions. Appendix Table 1 provides a complete list of military interventions that have been classified as non-neutral and non-humanitarian in the IMI database and that have been implemented around the world following the end of World War II.

The data suggest that nearly all MENA countries have been the target of military interventions, but the case of Lebanon – a multi-sectarian state – stands out. It illustrates the dynamics between external interventions, the onset of the Lebanese civil war and its intensification. Prior to the war, interventions occurred because following the ‘Black September’ 1970,<sup>9</sup> the Palestine Liberation Organization (PLO) was pushed out of Jordan and established presence in Lebanon, disturbing the balance among different sects in the country. After 1970 interventions occurred in Lebanon in support of the Shia minority, which was pushed out of Southern Lebanon into the urban peripheries of Beirut.<sup>10</sup> These interventions, occurring in the context of shifting population weights, led to increases in sectarian polarization and a struggle for political power, which resulted in a split into pro-Nasser Sunni Muslim camp and pro-Western Christian camp. Eventually, a confrontation erupted between the Lebanese Forces (LF)<sup>11</sup> and the Lebanese National Movement (LNM) and sectarian violence escalated, leading to further interventions in a vicious cycle that grew into a large-scale conflict.

> 9 During the ‘Black September’ conflict, the Jordanian Armed Forces fought with the Palestine Liberation Organization (PLO) and forced it to relocate from Jordan to Lebanon. 10 The ruling Alawite minority in Syria viewed the Shia minority in Lebanon as a counterweight against the Sunni majority of Syria and the Palestinians.

> 11 The LF included the Maronite Christians and the LNM represented a coalition between Druze, Shia, Arab Nationalists, Socialists, Communists and Sunni Militias. The LNM had the support of the PLO.

8
Holden, S. T. and H. Ghebru. 2011. Household Welfare Effects of Low-cost Land Certification in Ethiopia. Ås, Norway: Norwegian University of Life Sciences. Mimeo.

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33
areas: Czech Republic, Estonia, Hungary and Slovenia. South Caucasus countries (Armenia, Azerbaijan, Georgia) and Albania, however, invest significantly less in R&D.

**Figure 14 – There is Significant Variation Within ECA in Terms of Innovation; Yet The Region is Well Behind the Top Performer, Switzerland**

ECA innovation index, 2011 <!-- Start of picture text --> 60<br>SUI<br>55<br>50<br>45 HUN<br>EST<br>CZE<br>40<br>MDA SVN<br>35 SRB<br>RUS<br>UKR BGR LVA<br>TUR ROM HRV<br>30 SVK<br>ARM<br>POL<br>MKD LTU<br>25 KYR GEO<br>ALB<br>KAZ<br>20 AZE BIH<br>15<br>10<br>30 40 50 60 70<br>Innovation Input Sub-Index<br>Innovation Output Sub-Index<br><!-- End of picture text --> Source: INSEAD, the Global Innovation Index 2011 Note: The chart compares ECA countries with each other in terms of Innovation Input Sub-Index (covers the Institutions, Human Capital and Research, Infrastructure, Market Sophistication, and Business Sophistication in a given country) and Innovation Output Sub-Index (covers Science Outputs and Creative Outputs in a given country). Switzerland (SUI) is also included in the scatter plot for benchmarking as the highest performer in the Global Innovation Index There are several ways in which governments can anticipate the need to shift away from energy-intensive industries by enacting forward-looking skills, training and innovation policies. In particular, countries may: (i) delay the transition to vocational specialization by extending general education, as Poland did; (ii) align vocational and job training programs to future demand from clean industries; (iii) assess and invest in the development of relevant non-cognitive skills; and (iv) create an enabling environment that fosters innovation.

As discussed above, investing early in the flexibility and portability of workers’ skills will be of utmost importance for meeting the challenges of a possible industrial transition. High quality basic education that endows children with strong cognitive and non cognitive skills, therefore, must be at the center of any renewed skills policies. Moreover, an emphasis on building general competencies can be achieved by delaying vocational specialization to the upper secondary or post-secondary phase, while increasing the number of years devoted to comprehensive general education. For example, a recent education sector 28
unemployment wages. Blau and Robins (1986) and Kiefer and Neumann (1989) find a positive but statistically insignificant relationship between benefits and earnings. Addison and Blackburn (2000) find weak evidence of improved earnings, and Classen (1977) finds no effect. Meyer (1995) finds that re-employment bonuses shorten the duration of compensated unemployment without affecting post-unemployment wages. Note that empirical testing of the effects of unemployment insurance on postunemployment earnings is complicated by the fact that it is hard to isolate the effects of changes in unemployment benefits on wages, because the factors responsible for variations in benefit generosity among recipients may also affect post-unemployment wages of recipients. Of course this also holds for other characteristics that determine the quality of job matches, i.e. the type of contract offered and the duration of postunemployment jobs.

Several studies have investigated the effects of unemployment insurance on the duration of post-unemployment jobs. One recent study finds that while the occurrence of unemployment severely reduces the duration of subsequent job tenure, the – duration of unemployment has no deleterious impact in fact, longer durations of unemployment are rewarded by longer job tenures, presumably because a longer period of job search improved worker-job match (B ̈oheim and Taylor, 2002). But Portugal and Addison (2003) find no evidence that unemployment benefits facilitated entry into stable jobs in Portugal. Belzil (2001) using Canadian data finds a negative correlation between unemployment duration and subsequent job duration, but this correlation is mostly explained through the negative correlation between individual heterogeneity of the job finding rate and the job separation rate. Other studies include Jurajda (2002), who uses U.S. data and finds that eligibility for UI reduces workers employment durations, and Tatsiramos (2006), who uses data for ten European countries and finds that post-unemployment jobs last longer in countries with relatively generous UI.

Our paper investigates the impact of unemployment benefits on several postunemployment job characteristics and takes advantage of a change in Slovenia’s UI benefit law to isolate the effects of unemployment benefits. The exceptionally rich administrative data sets on both unemployed and employed workers allow us to focus not only on post-unemployment wages, but also on the duration of subsequent employment as well as the type of contract obtained. The reform of Slovenia’s unemployment benefit allows us to compare pre- and post-reform outcomes for treatment 4
associated with firm’s age. Conversely, Adrjan (2018) finds that young firms pay a small premium for new hires. This positive young-firm wage premium is more recently supported by Babina et al. (2019), who show a positive and significant young-firm pay premium for the United States that only appears when one controls for the necessary worker (sorting) and firm fixed effects (employer heterogeneity) (Abowd et al., 1999).

A related literature has focused on the wage premium associated to international activity and finds that multinational enterprises and exporting firms pay higher wages than their counterparts. Lipsey and Sjoholm (2003) find that wages in domestically-owned Indonesian manufacturing plants taken over by foreign firms increased sharply after the takeover. Hijzen et al. (2013) find a large and positive wage effect following a foreign acquisition of a domestic firm in Brazil, Indonesia, Germany, Portugal and the United Kingdom. The authors also find that wage effects are higher in developing countries and that effects on wages after foreign acquisition are restricted to high-skilled workers. In the case of exporting firms, an empirical literature has focused on the impacts of exporting on workers’ wages. Brambilla et al. (2017), using comparable data for 61 developing and low-income countries, show a prevalent wage premium in exporting firms compared to their non-exporting counterparts. Schank et al. (2008), using German employer-employee data, also find a wage premium in exporting firms, but argue that it is largely explained by the self-selection of firms that are already more productive and pay higher wages.

Explaining the dynamics and causality of these wage premiums is an important gap in some of the literature, especially regarding the firm size premium, which has primarily focused on identifying the premium size but not on how the premium is created. On the one hand, rent sharing models of the labor market argue that the premium is associated with firms applying more advance technology equipment, innovation projects, or other investments; thus enabling workers to capture some of the rents associated with innovation and other competitive advantages (Reenen, 1996; Dunne and Schmitz, 1995). On the other hand, efficiency-wage arguments suggest large and “better” firms will pay a premium because it is harder to monitor their workers (Krueger and Summers, 1988), provide better working conditions, reduce worker turnover and minimize the risk of their productivity advantage to be captured by their counterparts (Fosfuri et al., 2001). Understanding whether the wage premium is due to rent-sharing or the fact that some firms have already larger wages is important to understand the impact of innovation on income distribution.

A related literature to this paper has focused on measuring the premium associated with technology and innovation. Some of these studies have analyzed the impact of skill-biased technological change on wages and employment in developed economies (see Acemoglu, 2002; Acemoglu and Autor, 2011). In the skill-biased technological change framework, new tech5
Section 2 presents the data and descriptive statistics. Section 3 compares subjective and objective measures of welfare, at the individual and country level. Section 4 explores the measurement of subjective measures, their consistency and the issue of measurement error.

# **2. The data: Measures of welfare in the Life and Transition survey**

This paper uses data from two rounds of the Life in Transition Survey<sup>9</sup> . Following the first LiTS (2006), the second LiTS was conducted in 2010 simultaneously in 29 ECA countries, and in five Western European ‘comparator’ countries (Germany, Italy, UK, France, Sweden). In both surveys, the questionnaire was administered to a nationally representative sample of at least 1,000 respondents in each country<sup>10</sup> , using face-to-face interviews. Taken together, the two surveys provide a wealth of information on prevailing living standards, opinions and attitudes in 2006 and 2010. Most of the analysis in this paper is based on the second round of the survey (2010), although some comparisons are also drawn with the first round.

# **2.1. “Objective” measures of welfare: Consumption and assets**

Using recalled household expenditure data in the LiTS, we construct a consumption aggregate by summing all expenditure categories, expressed in per capita annual equivalents, and converted to US dollars according to market exchange rates (see Appendix 1 for more detail on the measurement of consumption in the LiTS and the construction of our consumption aggregate).

We also construct, overall and separately for each country, an asset index as an alternative “objective” measure of household welfare. The asset index is based on household ownership of the following items: a car, a secondary residence, a bank account, a debit card, a credit card, a mobile phone, a computer, access to internet at home, and household access to water, electricity, a fixed telephone line, central heating, public (piped) heating, and pipeline gas. Summary statistics as well as the methodology for constructing the asset index are presented in Appendix 2.

# **2.2. Subjective measures of well-being**

The LiTS provides a number of subjective well-being variables:

- (i) A 5-step scale measure of satisfaction with one’s life: _All things considered, I am satisfied with my life now_ (strongly disagree, disagree, neither agree nor disagree, agree, and strongly agree);

- (ii) A 5-step scale measures of satisfaction with one’s financial situation _: All things considered, I am satisfied with my financial situation as a whole_ (strongly disagree, disagree, neither agree nor disagree, agree, and strongly agree);

> 9 The Life in Transition Survey (LiTS) was conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank. The main purpose of LiTS I was to better understand how peoples’ lives had been affected by the events of the previous 15 years; four years later, LiTS II was carried out at a time when most countries in the ECA region were still facing the consequences of the global economic crisis of 2008-2010. 10 The only exception is Sweden, where the sample consists of 900 respondents.

4