rafmacalaba/datause-encoder-data huggingface dataset:
In what follows we explore the impact of financial and infrastructure services policy reforms on growth using time-series data for a panel of 24 transition economies covering the 1990-2004 period. Annex Tables 2, 3, and 4 describe the data and the sample, and provide descriptive statistics and pair-wise correlation coefficients. We exclude three countries for which the coverage of macroeconomic data over time is poor (Turkmenistan, Serbia/Montenegro, Bosnia/Herzegovina). We start with simple OLS country fixed effects regressions<sup>9</sup> (Table 5). The dependant variable is the growth rate of per-capita GDP. In most of the literature, the main factor driving growth is assumed to be investment (e.g., Levine and Renelt, 1992). Transition economies experienced large swings in investment in the first half of the 1990s, with the collapse of central planning and the initial lack of market institutions leading to sharp reductions in investment (Roland, 2000; Falcetti et al, 2002). Subsequently, a gradual buildup of a domestically and externally financed private capital stock occurred. This well-known U-shaped pattern of output and investment collapse and recovery suggests that the _change_ in the investment ratio be used as an alternative to the investment-GDP ratio as a measure of investment.<sup>10</sup> The reduced form models (1) and (2) test the alternative hypotheses of a linear vs. nonlinear relationship between investment and growth. Investment/GDP is statistically insignificant (model 1), while the change in the ratio is significant. Thus, the initial collapse and the subsequent recovery in GDP growth was associated with changes in the rate of change in investment at a fast pace: first falling and subsequently rapidly growing investment ratios. We therefore use the change in investment in the regressions. In model (3) we add variables to account for crises and inflation. Inflation, a measure of macroeconomic stability, is expected to have a negative impact on growth. Crisis is a dummy variable that equals one in years when countries experienced armed conflict or a major financial crisis. It captures conflicts affecting Georgia, Armenia, Azerbaijan, and Tajikistan and the financial crises in Russia and Albania. These events will be captured in part by other variables, but not completely, and we want to

> 9 The fixed effects model allows to some extent for heterogeneity across countries.

> 10 In the empirical analysis we do not use several variables that are often used in growth regressions. These include measures of human capital, trade openness and initial per-capita income. The reason is that our country sample is quite specific in the sense that all experienced a sharp fall in output in the first half of the 1990s, notwithstanding relatively high levels of human capital. Also, trade volumes during the early transition do not reflect integration with world markets but rather traditional COMECON barter trade relations. As a result of these factors, conditional convergence is not observed in the data, and including these variables yields rather counterproductive results.

13
# **Table 11. Complementarity, substitutability, and equality of public and private preschools**

|||**Work Pa**<br>|**rticipation**<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Public * Private * Eligible||||-0.003*<br>(0.001)|
|Public * Private||||0.000<br>(0.001)|
|Public * Eligible|0.074***||0.071***|0.092***|
||(0.016)||(0.017)|(0.022)|
|Public Preschools|-0.015||-0.013|-0.014|
||(0.012)||(0.013)|(0.018)|
|Private * Eligible||0.002|0.001|0.002|
|||(0.001)|(0.001)|(0.002)|
|Private Preschools||-0.001|-0.001|-0.001|
|||(0.002)|(0.002)|(0.002)|
|Eligible Child|-0.034***<br>(0.005)|-0.032***<br>(0.008)|-0.040***<br>(0.008)|-0.043***<br>(0.008)|
|Public = Private (p-value)|||0.000|0.000|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses work participation on the density of public and private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3- 6 in each district, a dummy for having a preschool-aged eligible child, and the respective double and triple interactions. Comparison group includes all mothers of children of the wrong ages and nonmothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. The last row tests the equality of ‘Public * Eligible’ and ‘Private * Eligible’ coefficients and reports the associated p-value. There are 226,400 female-year observations.

56
2 having their endangerment reduced to levels that would allow 'them to be removed from the list. Overall, the relevant government agencies face difficult problems of, first, deciding which species to place on the endangered species list and, second, deciding how much to spend on the recovery of each listed species. In the sections that follow, we examine these two decisions in detail. We believe this subject deserves serious attention from economists because the direct and indirect costs of this type of environmental protection are already substantial, and such expenditure is growing more rapidly than almost any other comparably sized item in the national economy.' The remainder of the paper is organized as follows: Section 2 contains a discussion of various normative justifications for the preservation of biodiversity and of the difficulties of constructing a single objective function that the government might be expected to follow. We then identify a subset of these normative justifications that can be defined operationally and quantified, and we describe the data that we use for them. This subset includes 'scientific' characterstics such as "degree of endangerment" and 'taxonomic uniqueness" as well as more 'visceral' characteristics such as "physical size" and the degree to which a species is perceived as a "higher form of life". In Section 3, we describe the Endangered Species Act and the listing process in more detail, and then estimate a regression to determine the relative importance of these species characteristics in the listing decision. We find that both 'scientfic' and 'visceral' elements play an important role in determining whether a species becomes listed. In Section 4, we focus on the government's direct spending to improve the condition of listed species. First, we describe the available spending data and the method by whiich it was collected. Then, using the same independent variables as in Section 3, we estimate a regression with "species-by-species" spending as the dependent variable. We find that the 'visceral' characteristics play a highly significant role in explaining the observed spending patterns, while the more 'scientific' characteristics appear to have little influence. Next, in Section _5,_ we extend the analysis to include explanatory variables of a more openly bureaucratic nature. The goal here is to determine how closely 'm **illustration** of tdis growth is the **drmatic rise** in direct expendiues on **species-by-species** presevation. **These figures are** studied in Section 4.
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World Bank. 2003. Rwanda Tea Sector Reform: Analysis of Poverty and Social Impacts ― Stakeholder and Institutional Analysis. Washington, DC. The World Bank (mimeo) 22
Winters, Paul, Alain de Janvry and Elisabeth Sadoulet (2001) “Family and Community Networks in Mexico-US Migration”, _Journal of Human Resources_ 36(1): 159-84.

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22
We therefore run regressions using weighted averages of the mean score over the three EITs, 2008-2009-2010, using the students participating in each year as weights. We use these weights both for the dependent and the explanatory variables<sup>33</sup> .

# _e. Using Weighted Averages over 3 Years_

Table 11a and 11b present the regression results for the participation ratio and the mean Ukrainian score.

Given that we take out part of the random variation by taking weighted averages, it comes to no surprise that the explanatory power of our regressions increases, adding roughly 10 percentage points to the adjusted R<sup>2</sup> .

In general, the results are similar to what we found above when just using 2010 data, albeit that the sizes of the class and school size effects are slightly smaller.

> 33 An implicit assumption here is that the production functions are stable over time- this is likely to be a harmless assumption, even though there were slight changes in the organizational procedure of the tests ( f.e. some very specific groups of students were not required to participate) from  year to year.

50
finn by its corresponding industry median market-to-sales ratio. The imputed value of the firm is obtained by summing the multiples across all of its segments.

The financial and segment data of the Worldscope database allow the computation of the market performance measure. Similar to the ownership data, historical segment data for many of the firms are, however, missing from Worldscope. In such cases, we collected the missing segment data from various issues of the Asian Company Handbook and the Japan Company Handbook. All financial data are converted to US dollars using end-year exchange rates. We exclude a small number of firms from the sample because they do not report segment sales. For the remaining firms with complete segment data, we determine the industrial sector(s) to which they belong and group a firm's reported segments according to the two-digit Standard Industry Classification (SIC) system.<sup>4</sup> Table 3 provides mean and median statistics of EXV for the whole sample and subgroups by types of ownership. By construction the median of EXV is (about) 1. We find that a higher degree of cash-flow rights is associated with somewhat higher mean and median EXV for all type of owners combined (Panel A, column 2). The relationship does appear to taper off, however, as the mean and median EXV for the fourth quartile are only marginally higher than those for the third quartile (1.237 and 1.021 versus 1.231 and 1.012). Splitting the sample by the type of owners, we find that for family ownership the relationship between cash-flow rights and 4 This procedure involves two steps. First, we assign the four-digit SIC codes reported by Worldscope to appropriate segments. In the majority of cases we are able to obtain one-to-one matches between SIC codes and segments. For some companies, the number of reported SIC codes is not the same as the number of reported segments. If a segment can not be associated with a reported SIC code, we determine the segment's SIC code according to its business description. If a segment is associated with multiple SIC codes, it is broken down equally so that each segment is associated with one SIC code. In the second step, we redefine segments at the two-digit SIC level and aggregate segment sales to that level. Second, we classify finms as single-segment if at least 90 percent of their total sales are derived from one two-digit SIC segment. Firms are classified as multi-segment if they operate in 15
Tversky and Kahneman (1985)). We argue that the MPL format has the potential to introduce framing effects because the two options are often presented in the same order both within MPL (e.g. with the value difference ∆ _t_ always ascending) and within each binary choice (e.g. with the same option always shown on the left).

The potential for framing bias in binary evaluation tasks is well known; a famous example is the Galaxy Zoo project which asked “volunteer scientists” to assess the rotational direction of images of galaxies. Inflated counts of counter-clockwise rotation in early data led the researchers among other things to vary the arrangement of classification buttons on the screen.<sup>14</sup> Anchoring effects could also arise because of the order within MPL. For example, in an MPL with decreasing ∆ _t_ , the subject may select option A for the first few choices _t_ , based on the utility difference from the options. As she is presented with repeated similar choices, her attention may decrease, or her early choices may even (temporarily) affect her subjective preferences. In both cases she will choose A more often as ∆ _t_ decreases than she would have if presented with the same choice in isolation. In this case, the error term is negative across _t_ (and possibly increasing in size), and as a result, the researcher may overestimate WTP for option A.<sup>15</sup> Some forms of framing and anchoring in MPL are discussed in the existing literature, e.g. Andersen et al. (2007) (see also section 3.4), but to our knowledge, there is no widespread acknowledgement of potential order biases. Of the 23 studies in Appendix Table B, only three randomize the order of choices within the MPL, and none randomize the order of options within choice.

# **2.3 Approaches in the literature to MSB, NSB and interval data**

Before discussing our proposal for dealing with choice error in MPL, we review the solutions that have been applied in the literature and point out some of their drawbacks. The existing research has introduced a variety of MPL design features aimed at reducing multiple switching and dominated choices _ex ante_ , at the elicitation stage. In addition, a range of approaches aim to deal with observed MSB and NSB and the related problem of

> 14Between 2007 and 2019, over 400,000 volunteers on Galaxy Zoo carried out more than 11 million classification tasks (Raddick et al., 2019). In early data, it was found that a significantly higher than 50:50 share (around 52%) of galaxies were classified as rotating counter-clockwise. In a test, volunteers were therefore asked to rate mirror images of the same galaxies, and they again classified over 51% of galaxies as rotating counter-clockwise (Land et al., 2008), suggesting that the universe has no directional preference, but humans either have such a preference or click buttons in certain screen locations more frequently.

> 15We assume here that the interest is in a reproducible measure of WTP in contexts outside the MPL.

12
The handful of studies that examine the causal relationship between foreign ownership and firm performance produce mixed results. Harris and Robinson (2003) use data from the United Kingdom and find that foreigners acquire the bestperforming domestic firms and that foreign ownership does not lead, in general, to improved performance of the acquisition targets. In contrast, Conyon et al. (2002) conclude that acquisitions have a positive effect on the labor productivity of acquired firms in the United Kingdom. A similar conclusion is reached by Girma and Görg (2007), who study food and electronics sectors in the United Kingdom, and Griffith (1999), who considers the British car industry.

The lack of consistent findings in studies focusing on industrialized countries mirrors the pattern found by studies of wage effects. It is also not surprising given that the productivity gap between multinationals and their acquisition targets is most likely not as large in the United Kingdom. One would expect the gap to be larger in the case of developing countries; thus, one would anticipate larger productivity effects of foreign acquisitions.

The only study focusing on a developing country, by Arnold and Javorcik (2009a), is based on Indonesian plant-level data from the Manufacturing Census covering the 1983–2001 period and confirms that a change from domestic to foreign ownership leads to improved performance. The study combines propensity score matching (within industry-year cells) with a difference-in-differences approach. The results suggest an increase in plant productivity reaching 14
30 and trade cooperation zones in the Arab Republic of Egypt, Ethiopia, Mauritius, Nigeria, and Zambia (China, Information Office of State Council 2010). More such initiatives are likely to happen.

# **_How Big Might the Benefits Be?_**

As China moves forward, there will be a major difference with earlier patterns of industrial upgrading: its economy is significantly larger than those of the geese that led the first round of structural transformation in Asia (table 5). China has an estimated 85 million workers in manufacturing, most of them in labor-intensive sectors. The reallocation of these workers to higher value added, more sophisticated products and tasks will open up great opportunities for labor-abundant, lower-income countries to step in and produce the labor-intensive manufacturing goods that China leaves behind. As a result, China will not be a _goose_ in the traditional leaderfollower pattern of industrialization for a few lower-income countries but a _dragon_ .

**Table 5. Comparing Manufacturing in China with That in Earlier Geese at Similar Levels of Development**

|**Country**|**Year**|**GDP pe**<br>**(consta**<br>**2000 US$**|**r capita**<br>**nt US$)**<br>**2005 US$**<br>**(PPP)**|**Share of value**<br>**added(%)**|**Manufacturing**<br>**Share of labor**<br>**(%)**|**Employment**<br>**(millions)**|
|---|---|---|---|---|---|---|
|China|2009|2,206|6,200|43|17.7|85|
|Japan|1960|5,493|6,976|35|20.0|9.7|
|Korea,Rep.|1982|3,709|6,123|25|14.6|2.3|

Sources: World Bank, World Development Indicators database; International Labour Organization, LABORSTA; China, National Bureau of Statistics (2010).

In the absence of detailed data on manufacturing employment in all African countries, one can only conjecture about the size of the potential gains for the region. Still, even back-of-theenvelope calculations suggest that the benefits would be enormous. In 2009 alone, China exported $107 billion of apparel to the world, compared with Sub-Saharan Africa‘s total apparel exports of $2 billion (2 percent of Chinese apparel exports). Let‘s assume that as a result of rising wages, 1 percent of China‘s production of apparel is shifted to lower-wage African countries. All things equal, that alone would boost African production and exports of apparel by 47 percent. A 5 percent shift of Chinese export-related investments in the industry could translate into $5.4 billion in additional exports—a 233 percent increase.

Even rough employment estimates suggest the potential gains in manufacturing jobs. Africa‘s population (north and south of the Sahara) is 1 billion, slightly less than India‘s 1.15 billion. In 2009 manufacturing value added was 16 percent of GDP in India, 13 percent in Sub-Saharan African countries, and 16 percent in North African countries such as Egypt, Morocco, and Tunisia.<sup>24</sup> India‘s employment in manufacturing was 8.7 million in 2009. So it is reasonable to assume that total manufacturing employment in Africa is at most 10 million. This suggests that relocation of even a small share of China‘s 85 million labor-intensive manufacturing jobs would

> 24 Data from the World Bank, World Development Indicators database.
- a. SEE (Albania, Bosnia and Herzegovina, Bulgaria, Macedonia, Romania, Serbia and Montenegro)

- b. Middle income CIS (Belarus, Kazakhstan, Russian Federation, Ukraine)

- V. Low income CIS (Armenia, Azerbaijan, Georgia, Kyrgyzstan, Moldova, Tajikistan, Turkmenistan, Uzbekistan)

We will sometimes refer to Groups I and II as the “pre-2001 EU” or “developed market economies” (DMEs), and groups III-V as “transition economies” (TEs).

The income classification uses the standard World Bank scheme based on GNI per capita in 1999 (see Table II.3). The division between I, II, III and IV is based on EU status as of 2005, but also matches GNI per capita in 1999 almost exactly. The division between IV and V is from the World Bank income classification. Croatia is an outlier – located in South-Eastern Europe, this country has GNI per capita that is substantially higher than that of the other countries of the region – and is therefore omitted from the analysis. East Germany as well as Turkey, Korea and Vietnam were also included in the BEEPS 200405 surveys, but these countries are too heterogeneous for aggregation; we omit them from the aggregation scheme and most of the analysis, but occasionally make use of them.

# **II. Growth and convergence**

As mentioned in the introductory section, this paper looks at the function of, and convergence in, markets and firm behavior of the transition countries with those in the developed market economies. Our focus is on competition and market structure, finance and the structure of lending to firms, and how firms respond to the economic environment by restructuring. We begin, however, by briefly reviewing the existing literature on growth and convergence in the transition countries at the micro level, assembling basic stylized facts and comparing them to the evidence from the BEEPS data. The purpose of this is two-fold: first, to set the scene for subsequent discussion, and second, to show important similarities between the previous findings in the literature and the BEEPS data. The latter will reinforce the evidence from the studies conducted in 10
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