rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 14. LAC: Policies to raise potential output growth

A. Potential growth under reform scenarios <!-- Start of picture text --> Percent Social benefit reform<br>Labor market reforms<br>6 Education and health improvements<br>Investment surge<br>Baseline<br>4<br>2<br>0<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE LAC<br>C. Labor market flexibility<br>Index<br>LAC EMDEs<br>4.4<br>4.2<br>4.0<br>3.8<br>3.6<br>3.4<br>2007-13 2013-18<br>E. Research and development<br>Percent of GDP<br>LAC EMDEs<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>2000-08 2011-18<br><!-- End of picture text --> B. Potential growth effects from infrastructure investment and climate disasters <!-- Start of picture text --> Percent Investment boost (climate change)<br>6 Disasters (climate change)<br>Baseline<br>4<br>2<br>0<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE LAC<br>D. Government consumption<br>Percent of GDP LAC EMDEs<br>16<br>15<br>14<br>13<br>2000-10 2011-20<br>F. Transparency of policy making<br>Index<br>LAC EMDEs<br>4.2<br>4.0<br>3.8<br>3.6<br>3.4<br>3.2<br>2007-13 2013-18<br><!-- End of picture text --> Sources: Haver Analytics; Penn World Tables; UN Population Prospects; World Economic Forum, Global Competitiveness Index; World Bank.

Note: EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.B. Period averages of annual GDP-weighted averages. Potential growth estimates based on production function approach. Sample includes 53 EMDEs (16 from LAC).

C.-F. Cross-period simple averages of annual GDP-weighted averages. Samples include: for C, 112 EMDEs (23 from LAC); for D, 53 EMDEs (11 from LAC); for E, 101 EMDEs (18 from LAC); for F, 112 EMDEs (23 from LAC).
The land share allocated to tobacco is positively affected by club membership, but only marginally so. Further, even though clubs make farmers more productive and allow them to earn more income via sales, members do not fetch better prices: the unit values from tobacco sales do not show significant differences between members and non-members. This explains why the difference in sales per acre is roughly comparable to the difference in output per acre. This result is not really surprising because there are only a few buyers at the auction floors and tobacco clubs are not big enough to counterbalance their market power.

# **3.2 IV Results**

Matching methods rely on the strong assumption of selection based on observables. In order to accommodate cases where club membership depends on unobservable characteristics, our second empirical strategy to estimate _β_ in (1) adopts an instrumental variables approach. Our instrument is an indicator of default risk pertaining to a specific group of unmarried women. These individuals, especially younger ones, are risky for tobacco clubs because of a higher probability of marriage. Our Tobacco Club Survey (TCS) advances three major threats that arise from unexpected marriages of women. First, if the marriage occurs in the middle of the cropping season, there is a high risk that the bride would move away with her husband and leave the village, thus reneging her debt with the tobacco club.<sup>15</sup> In this case, the remaining members must still honor the whole loan, including the fraction under the name of the leaving bride. Second, there is a high risk that the husband overturns the farming decision of his bride or that he disapproves of her membership in clubs. This ordinarily induces brides to abandon participation in the club and leads to debt default. Finally, there is a higher risk of pregnancy.<sup>16</sup>

> 15According to the TCS this is not only true for patrilineal districts but also it is quite common in matrilineal districts, especially in the Central Region. This panorama would probably not be so in many district of the Southern Region where the matrilineal tradition is more often enforced. However, the TCS did not cover those districts because of their small tobacco production and number of clubs.

> 16Notice that, from the perspective of the clubs, its much riskier if an unmarried woman gets pregnant immediately after marriage than if a male member impregnates his partner. Although in both situations the labor supply could be negatively affected, in the first case it is the club member herself who is directly affected. The club member is generally the family member that assumes responsibility for the harder part of tobacco farming activities. Therefore, if she is not able to handle the hard work because of her state of pregnancy and his new husband is not willing to overcome the tobacco farming activities, the tobacco

22
and GSP rates that Turkey will be required to apply. We take a simple average of the GSP and MFN rates in each sector, reflecting the _aggregate_ share of each group of countries in Turkish imports in recent years. Note that agricultural import tariffs do not change after the CU.

Export subsidies are notoriously difficult to estimate in Turkey, because of the complex way in which they are implemented as reductions in other tax obligations. We employ World Bank estimates of these rates for 1993 (see Arslan [19951), although subsequent government decisions indicate that these rates will be much lower in 1995.

The VAT rates reported in Table A3 reflect actual collections, using data for 1993 obtained from the State Planning Organization (SPO). There are several reasons for these rates to vary more than one would expect after the CU. Turkey has only employed a VAT for a few years, and administrative problems in uniformly implementing and enforcing the statutory rates cause collection rates to vary across sectors even if statutory rates do not. Some sectors are easier to audit than others, by the nature of their activities (e.g., urban-based _versus_ rural-based, large invoiced transactions _versus_ smaller cash-based transactions, more computerized accounting systems _versus_ more traditional accounting systems).

Production subsidies are calculated using data from the 1990 10 table. For tobacco and alcoholic beverages we assume a production subsidy equal to the benchmark consumption tax4 applies. We compute these subsidies as _ad valorem_ rates from the 10 data and transfer these rates to our SAM, re-balancing domestic supply in each sector so as to ensure zero profits. This re-balancing also takes into account our use of data on tariffs, VAT rates and export subsidies from "outside" the original, balanced SAM.

4 These taxes are embodied in our benchmark VAT rates.

A-11
at which firms leave the market and under what conditions. Although partial, these studies can provide comprehensive insight. Such studies often employ in-depth, firm-level panels, with detailed information on firm characteristics and cover sectors of the economy beyond manufacturing (for instance, Frazer (2005) and Söderbom et al. (2006) utilize survey data in Ghana, Tanzania, and Kenya). While the exit of less productive firms from the market can proxy for creative destruction, the persistence of less productive firms may likewise indicate market distortions, possibly dampening overall economic output.

Within this body of literature, the beginnings of a consensus have started to emerge. Older firms tend to survive longer in the market, as do more productive firms, all else equal. Similarly, larger firms tend to have a lower likelihood of exit (see for instance, Aldrich and Auster (1986); Fackler et al (2013; Shiferaw (2009)). Myriad market effects and shocks, including crises and trade liberalization, have also been shown to have significant effects on firm survival (for instance Pavcnik (2002) or Hallward-Driemeier and Rijkers (2013)). Yet these efforts rely on limited-scope studies, and little has been said about how widely these results hold: broad-reaching, comparable analysis of exit rates in several economies – and their regulatory and policy environments – is currently missing in the literature (see Cirmizi et al 2010 for a discussion).

The current paper follows such an approach by utilizing broad, comparable, panel data, in an attempt to describe patterns and determinants of firm exit. Its contribution is three-fold. First, we provide estimates of the magnitude of firm exit for a large set of developing economies using unique, comparable data. We use a unique variable in the World Bank’s Enterprise Survey (ES) where firms interviewed in previous rounds are each contacted several years later to ascertain whether the firm is still operating (more on this in section II). This estimate facilitates cross-country comparison of the size of firm exit and thus may provide a partial glimpse into the extent of creative destruction for these developing economies. Secondly, we study the key predictors of firm exit to see if exiting firms are indeed less productive as well as analyzing the effects of several firm- and country-level attributes that predict exit. Importantly, by using a wide range of economies, we also provide analysis for comparative, macro-level factors. Finally, we provide an estimate of the magnitude of job loss due to the exit of these firms. For each of these areas, we believe, such analysis has not been previously presented over such a wide set of developing economies.

3
l POLICY RESEARCH WORKING PAPER 1796

# Summary findings

Narayan and Pritchett construct a measure of "social capital" in rural Tanzania, using data from the Tanzania Social Capital and Poverty Survey (SCPS), a large-scale survey that asked individuals about the extent and characteristics of their associational activity and their trust in various institutions and individuals.

They match this measure of social capital with data on household income in the same villages (both from the SCPS and from an earlier household survey, the Human Resources Development Survey). In doing so, they show that "social capital" is indeed both capital (in that it raises incomes) and social (in that household incomes depend on village, not just household, social capital).

The magnitude of social capital's effect on incomes is impressive: a one standard deviation increase in village social capital increases a household proxy for income by at least 20 to 30 percent.

This is as great an impact as an equivalent increase in nonfarming assets, or a tripling of the level of education.

Data from the two surveys make it possible to identify some of the proximate channels through which social capital affects incomes: better publicly provided services, more community activity, greater use of modern agricultural inputs, and greater use of credit in agriculture.

- This paper a joint product of Social Development, and Poverty and Human Resources, Development Research Groupis part of a larger effort in the Bank to understand the social determinants of sustainable development. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Sheila Fallon, room N8-030, telephone 202-473-8009, fax 202-522-1153, Internet address sfallon(ciworldbank.org. July 1997. (41 pages)

_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 he 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._ Produced by the Policy Research Dissemination Center
N PCM 2100, Small Farms + %Change of Ag Income | IMP_farm.D100_pcm_s PI) 0.0019 -0.0500 NI) 0.0501 -0.1000 HI 0.1001 -0.2000 HI 0.2001 -0.3000 HE 0.3001 -0.9900 [LSILSLS ~—s«*d‘ Decimal Degrees NIGGOL SEO, YALE UNIV. 0 4 8 16 24 32
# **Results**

# **_Descriptives_**

For the post-socialist transition countries in the sample, the average rate of return increased from 4.1 before the transition, to 8.0 percent after the transition. The latest evidence suggests that nontransition European countries have higher returns to schooling than post-socialist countries: 8.2 versus 8.0 percent (Montenegro and Patrinos 2021; Psacharopoulos and Patrinos 2018). For nontransition countries, the returns before 1992 were 8.0 percent, changing slightly thereafter to 8.2 percent. Over time the returns to schooling in post-socialist countries have increased significantly: from 4.1 percent in the 1980s to 8.0 percent today (see Table 2).

**Table 2: Returns** **<u>to schooling: pre-transition to transition</u>**

||Re|turns|Years of Sc|hooling|Chan|ge|
|---|---|---|---|---|---|---|
||Pre-<br>transition|Transition|Pre-<br>transition|Transition|Returns<br>(% pts)|Years of<br>Schooling|
|**Transition**|4.1|8.0|8.9|10.9|3.9|2.0|
|Bulgaria|4.1|6.5|8.5|10.0|2.5|1.5|
|Czechia|3.4|8.1|10.3|12.2|4.8|1.9|
|Estonia|1.5|6.6|8.9|11.1|5.1|2.3|
|Hungary|6.0|11.3|8.9|11.3|5.3|2.4|
|Poland|4.2|9.4|8.8|10.6|5.2|1.8|
|Romania|3.8|8.7|6.4|10.0|4.8|3.6|
|Russian Federation|3.2|4.2|9.0|10.7|1.0|1.7|
|Serbia|5.8|9.7|7.1|10.0|3.9|2.9|
|Slovak Republic|2.8|7.7|10.1|11.6|4.9|1.5|
|Slovenia|6.3|9.2|10.8|11.5|2.9|0.7|
|Ukraine|3.7|6.6|9.2|10.7|2.9|1.5|
|**Non-Transition**|8.0|8.2|7.7|9.8|0.2|2.1|
|EU in 1992|7.7|8.5|7.4|9.8|0.9|2.5|
|EU after 1992|8.7|7.1|8.0|9.7|-1.6|1.8|
|Non-EU/non-transition|8.4|8.9|8.2|9.6|0.5|1.4|

Sources: Barro and Lee 2013; Montenegro and Patrinos 2021; Psacharopoulos and Patrinos 2018 Notes: This table compares information only for those sample countries with data before and after the year of transition, namely 1992. The countries included in “EU in 1992” are Denmark, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, and Spain, while those included in “EU after 1992” are Austria, Cyprus, Finland, and Sweden, which joined the EU between 1995 and 2005. The Non-EU non-transition values refer to the average of Norway, Switzerland, and Türkiye.

These changes in the returns to schooling imply a difference of 3.9 points for post-socialist transition countries and only 0.2 for non-transition countries. That the returns to schooling remained almost constant in non-transition countries despite an increase in schooling is remarkable enough (Psacharopoulos 1989). Schooling levels, from an already high level in socialist economies, increased in post-socialist countries, yet the returns increase as well (see Figure 1), suggesting that the rewards to human capital were driven by an increase in skilled labor demand 8
Table 1.6 Select <u>unified tariff rates of Belarus, Kazakhstan and Russia Customs Union</u>

|_Product category_|_Tariff rate_|
|---|---|
|Tractor trailers [Euro 2 and Euro 3]|25|
|Tractor trailers [Euro 4 and above]|5|
|Tractors (small)|5|
|Tractors|15|
|Busses (diesel) > 120 pl|10|
|Busses > 120 pl|20|
|Spareparts,components|0-5|

Source: unified tariff in effect from July 1, 2011.

**Preferential access to the Russia market helped smooth the economic contraction at the beginning of the transition and to benefit from Russia’s decade of high growth on the back of favorable external environment and oil price exports** (Figure 1.3).<sup>15</sup> In 2008, manufacturing exports<sup>16</sup> to Russia accounted for more than two thirds of Belarus’s total manufacturing exports; it fell to less than 50 percent by 2011.

Figure 1.3: GDP levels in Belarus Russia and oil price dynamics <!-- Start of picture text --> 2 3.5<br>1.8<br>3<br>1.6<br>1.4 2.5<br>1.2<br>2<br>1<br>1.5<br>0.8<br>0.6 1<br>0.4<br>0.5<br>0.2<br>0 0<br>Belarus GDP Russia GDP Oil Prices<br>GDP levels (1991=1.0)<br>Oil price level (1991=1.0)<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: Rosstat, Bloomberg, Authors estimates.

**How profitable were SOEs during 1995-2010?** Providing an unambiguous answer is extremely difficult because measures of performance based on net revenue (or profits) and value of assets are based on distorted prices and do not reflect true opportunity costs.<sup>17</sup> Little can be done to surmount this problem except for insisting that statistical results ought to be interpreted with pause.  With that caveat in mind this section discusses three indicators: the association between average return on assets and ownership; the relationship between loss making enterprises and ownership; and the consistency between aggregate return on capital estimates and profits.

> 15 The benefits from preferencial access to Russia extended even during the financial crisis as Russia allowed selected Belarus SOEs to benefit from Russian fiscal stimulus measures. In April 2009, Russia included 11 Belarus industrial enterprises (Minsk Tractor Works, MAZ, Gosselmash, Bobruskagrommas, Belaz, Atlant, Minsk electro technical plant, Belkadr, BaATE, and Minsk Motor Plant) into a preferential list for state procurement. 16 Groups 5-8, excluding 68 SITC rev.3.

8
with SAT and IMSS had yes-or-no answers, with the possibility of a refusal to respond.<sup>13</sup> A third of establishments in the 2018 cross-section report registration with SAT and only 0.23% have missing values in this question. In contrast, only 6.6% of plants report registration with IMSS, but the rate of non-response is 11.5% (see Table 1 below). As we show below, rates of non-response are correlated with the size of the plant—smaller establishments are less likely to report registration with tax authorities.

Table 1: Sorting of establishments into formal and informal and share of plants and employment in each category. 2018.

|**SAT**|**IMSS**|**Formal / informal**|**Share of**<br>**plants**|**Share of**<br>**workers**|
|---|---|---|---|---|
|Yes|Yes|Formal|6_._48|40_._14|
|No|Yes|Formal|0_._11|0_._33|
|Yes|No|Formal|26_._24|30_._24|
|Yes|Non-response|Formal|0_._76|0_._33|
|Non-response|Yes|Formal|0_._00|0_._00|
|No|No|Informal|55_._71|25_._03|
|Non-response|Non-response|Informal|0_._23|0_._54|
|No|Non-response|Informal|10_._48|3_._39|
|Non-response|No|Informal|0_._01|0_._00|

**Notes:** Total in each column may not add up to 100 due to rounding error.

In our analysis, establishments registered with SAT and/or IMSS (as reported by the respondent) are formal; establishments that are not formal are informal (unregistered) as we list in Table 1.<sup>14</sup> Non-responses are thus included in the informal category. Figure 1 shows, for each size bin, the fraction of registered or formal establishments in the 2018 cross-section. The share of formal establishments according to our definition markedly increases with size. The figure also shows that the share of plants not registered with either SAT or IMSS and the rate of non-response to the two questions is higher among small plants. Under our definition, 34% of establishments are formal, accounting for

> 13In Spanish, the questions read _¿Este establecimiento cuenta con registro ante el Servicio de Administración Tributaria (SAT)?_ and _¿Cuenta con registro patronal ante el Instituto Mexicano del Seguro Social (IMSS)?_ Two follow-up questions asked the respondent to provide the identification numbers of the establishment with each tax authority. These four questions were not included in 1998-2013 waves.

> 14In comparison to the definition developed by Busso et al. (2012) and Levy (2018), which focuses on the intensive margin of informality (defined by Ulyssea (2018)), our analysis looks at informality on the extensive margin.

5
- Participation in politics, through attending rallies, calling in to radio programs, attending peace festivals, and indicating that one feels that they can speak freely about politics (household survey).

Table 2 summarizes our outcomes and data sources. Summary statistics for covariates and outcome variables used in the analysis are displayed at the household and clan level in Tables 3 and 4, respectively.

In the household survey data, two variables—income and meals—exhibited substantial amounts of missingness (18% and 16% of observations, respectively). The missingness is due to a combination of non-response, “don’t know” responses, and enumerator error. Listwise deletion for missingness at those rates can result in substantial bias (King et al., 2001). We use one round of imputation to fill missing variables that exhibit less then 5% missingness, and then multiple imputation to fill missing outcome values with higher rates of missingness.<sup>18</sup>

# **6 Estimation and inference**

Estimates below are covariate-adjusted differences-in-means estimated via ordinary least squares (OLS) fixed-effects regressions. For household level outcomes, the specification used for all estimates is, where _yicm_ is an arbitrary outcome for household _i_ in clan _c_ and matching stratum _m_ ; _αm_ is a stratum-specific intercept; _Tcm_ is treatment status for clan _c_ in stratum _m_ , taking the value 1 for communities hosting a peacekeeping base and 0 for those not hosting a base; _Xcm_ is a vector of non-coarsened versions of the covariates for clan _c_ in stratum _m_ ; and _εicm_ is assumed to be a mean zero and exogenous error term. The effect of hosting a base is estimated as _β_ , and _λ_ is a

> 18We used the MICE package for R (van Buuren and Groothuis-Oudshoorn, 2010).

xvi
changes that are also projected to occur. The results are then decomposed to examine the impact of the regional migration liberalization on the world economy over time, as well as the other changes in the world economy.

The analysis takes into account the impact of the demographic changes expected to occur in Asia over the next 43 years, from 2007 to 2050. It includes population and labor force forecasts by skill that are based on the World Bank’s and the UN’s World Population Prospects 2008 Revision. The scenario also tracks actual and expected future changes in Real GDP from 2007 to 2012. Beyond 2012 calibrated technological change is assumed to persist (declining gradually) to 2050. Between 2007 and 2012 additional assumptions are made to take account of the global financial crisis, including additional adjustments to investment through a rise in errors in expectations, unemployment of labor and capital, government spending and a negative productivity shock aimed at capital, following Strutt and Walmsley (2010). After 2011 the financial crisis gradually comes to an end and unemployment falls back to pre-crisis levels.

Ignoring endogenous migration for the time being, both the domestic and foreign labor forces are assumed to grow at the projected growth rate in total labor. Increases/decreases in the domestic labor supply are implemented as changes in the natural rate of population growth, while foreign labor increases are implemented through changes in migration.<sup>10</sup> The change in foreign labor keeps the share of migrants in the total population constant.<sup>11</sup> Since these labor forecasts are meant to include migration we assume that foreign labor grows at the same rate as the domestic labor force. This means that a country is willing to exogenously increase migration (increase the quota) to keep the migrant shares constant. The impact of including forecasts is discussed below – these are referred to as the “forecast” results. Next we liberalize migration by allowing it to respond to wage changes – these results are labeled “liberal” below and are also referred to as ‘endogenous migration’ in the text.

> 10 We assume that children born to migrant workers are included in the natural population and that all new migrant workers come from abroad.

> 11 With the exception of Japan and South Korea where we assume that migrant growth is zero. Hence when combined with negative growth of incumbent labour, migrant shares increase slightly.

15