rafmacalaba/datause-encoder-data huggingface dataset:
Another formalization policy studied in the literature is through stricter enforcement. We experiment with the effects of improving enforcement policies in revenue neutral set-up on the aggregate variables and the life-cycle of plants on average. The mean size and aggregate output increases by 63.5% and 16.4% under the revenue-neutral full enforcement scenarios, respectively. When taxes are fully enforced such that there is no option of operating informally, plants have no incentive to stay small. Therefore, the overall plant growth rate increases by 25.2% and the entrepreneurial quality increases by 68.1%.

**Background** Our paper contributes to the informality and misallocation literature. The informality literature, as explored by De Soto (1989), La Porta and Shleifer (2008), Maloney (2004) and Loayza (2016) provides a comprehensive understanding of the economic implications of informal sectors.<sup>5</sup> Our research is closely related to the works of Leal (2014), Meghir et al. (2015), Ulyssea (2018), and Tamkoç (2024b). We contribute to this body of work by demonstrating that informal establishments do not growth as much as formal establishments do throughout their life-cycle and by developing a model to quantify the impact of informality on aggregate output.

In addition, our paper engages with the misallocation literature, including seminal works by Hsieh and Klenow (2009), Restuccia and Rogerson (2008) and Bartelsman et al. (2013).<sup>6</sup> We specifically draw on literature examining the growth of plants over their life cycle, such as Hsieh and Klenow (2014), Guner et al. (2018), Garcia-Macia et al. (2019) and Eslava et al. (2022). Our unique contribution lies in constructing a model where formal and informal plants coexist and grow over their life cycle, thereby providing new insights into the dynamics of plant growth and the role of informality in economic development.

# **2. Establishment-Level Data**

Our quantitative exercises exploit data from the Mexican establishment census.<sup>7</sup> The census is administered by Mexico’s statistics agency INEGI and targets the universe

> 5For a detailed survey of this literature, see La Porta and Shleifer (2014) and Ulyssea (2020)

> 6Please see Restuccia and Rogerson (2017) and Hopenhayn (2014) for an overview of the misallocation literature.

> 7Access to the census data is restricted and requires clearance from INEGI. Data for this paper were processed at INEGI’s data lab in Mexico City.

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# **Executive Summary**

Services account for over 60 percent of GDP in Turkey and are critical drivers of employment and exports. Yet, there are signs that services may not be living up to their potential in Turkey, for instance when looking at productivity and the growth of services value‐added.

Services are also an increasingly important component of manufacturing and global value chains. Services _inputs_ as well as services activities _within_ manufacturing firms account for over half of the value of manufacturing exports. This global trend toward more use of services within manufacturing firms is known as servicification. For countries to be competitive, appropriately regulated and productive services are necessary, both for the services sector itself and for manufacturing.

Thanks to a unique firm‐level data set from the Turkish Ministry of Science, Industry, and Technology, this paper looks at a previously unexplored area—the production of services by manufacturing firms and links to productivity. While servicification has the potential to increase firm performance, this does not appear to be happening in Turkey: manufacturing firms with services tend to be less productive. For instance, non‐ exporter manufacturing firms with a services affiliate are 18 percent less productive than firms without a services affiliate. Exporters with a services affiliate are 9 percent less productive than those without one.

To better understand the lower productivity, the paper breaks down services by stages of the production process. Productivity differences between manufacturing firms that provide services in each stage are then highlighted. For instance, firms that have post‐manufacturing (e.g. transport and distribution) service affiliates are found to be particularly less productive in Turkey.

Regulatory restrictions are explored as a potential explanatory factor, given that removing regulatory restrictions on services has increased their contribution to productivity growth in other countries. Three regulatory areas are briefly examined: i) FDI restrictions in services; ii) domestic regulatory barriers in services that affect both Turkish and foreign firms; and iii) discriminatory services trade barriers that prevent foreign service providers from entering and operating in the Turkish economy.

The analysis finds that productivity gaps appear in the same areas where services are more restricted, e.g. in post‐manufacturing services. Restrictions on competition in services, particularly from foreign firms, may lead Turkish manufacturing firms to provide more services in‐house than would be optimal from a productivity perspective. Removing restrictions could boost the productivity of Turkish manufacturing firms. Professional services and transport appear particularly ripe for reform, and reforms could lead to increased firm entry, competition, growth, and, productivity.

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distinct patterns of behavior in this setting (see, for example, Philipson and Posner, 1995). Certain individuals (and families) may be more likely to suffer an AIDS death. For example, poverty is often cited as a key driver of transmission of HIV/AIDS (Fenton 2004), although the evidence of this link tends to show the opposite association (as noted by Gillespie _et al._ 2007; Wojcicki 2005; Shelton _et al._ 2005; and Glick 2007). In their study of eight Sub-Saharan African countries, Mishra et al. (2007) find that wealthier adults are more likely to be infected with HIV, although this association is very weak after controlling for urban/rural residence and education. Other traits, such as risk aversion, may be inversely correlated with prime-age mortality. In turn, these household/family characteristics associated with prime-age mortality may also be correlated with our outcomes of interest. While we partially address this by including baseline (pre-death) covariates, we cannot entirely control for unobserved covariates which may bias the results.

One strategy to address the endogeneity caused by unobserved correlated behaviors, preferences, and discount factors within the household exploits the fact that most households branch out over the relatively long time span of the panel (see, for example, Beegle _et al_ . 2007). Two individuals in the same household at baseline (in 1991) could split off into separate households at some point during the panel, and thus experience a different history of household mortality over the course of the panel’s 13 year time span. Thus, if we condition on this “dynastic” (otherwise referred to as “initial household”) fixed effect, we would be eliminating the correlated behavior and preferences of individuals within these initial households. We would identify the effects of adult death using variation in the history of mortality within dynasties/families. However, our sample of older individuals, unlike the sample of all household members in Beegle _et al_ . (2007), is small, and thus the dynastic fixed effects model would lack statistical power. Among the 613 older adults we follow from baseline to 2004, only 274 of them are co-residing with at least one other older adult at baseline. Restricting the sample to these individuals would likely lead to selection bias: those individuals whose initial household structure permitted or required them to live together may react very differently to mortality shocks. Thus, we do not attempt to compare outcomes across members within the same dynasty/family; instead we partially address endogeneity by 13
usual Cobb-Douglas specification estimated on cross-sectional data then provides estimates of coefficients biased unless the returns to scale are constant or the unadjusted _Ui_ and _Vi_ are uncorrelated with the district size. The direction of the bias of _β(_ γ _)_ is negative if _Ui_ ( _Vi)_ is positively correlated with _Li_ , (measure of district size) which is the most common case, and matching displays increasing returns to scale. Either decreasing returns to scale or negative correlation (but not both) in turn lead to positive bias. Therefore, if the matching process does not exhibit constant returns, the bias is likely to cause an incorrect acceptance of the constant returns hypothesis. The bias is greater, the greater is the portion of the correlation of _Ui_ and _Vi_ with _Li_ that is due to pure differences in the size of the district labor force. As we show in Appendix A, the bias is specific case of classical omitted variable problem. In what follows, we call this phenomenon the _spurious scale effect_ .<sup>20</sup> It can be shown that the spurious scale effect is avoided if one estimates a Cobb-Douglas function with panel data and accounts properly for the presence of fixed effects, as district size represents one of them. In that case, the within transformation removes the spurious scale effects together with all other unobserved district-specific time-invariant effects captured by ® _i_ ’s.

# **4. The Data and Variable Definitions**

In order to produce the best possible parameter estimates, we have assembled an extensive panel of data on 74 Czech, 38(79) Slovak, 20 Hungarian, 49(16) Polish **,** 34 East German and 140 West German districts. The data for all countries except Hungary cover the period from January 1991<sup>21</sup> - July 2005,

> 14 An interesting question for future research is whether the size of districts and regions, the usual units of observation in the matching function studies, tends to be determined by an arbitrary administrative fiat or an endogenous optimization process of population settlements, based on historical economic forces that are in principle similar to an optimization process determining the size of firms.

> 21 In January 1997, three new Czech districts were formed from two original districts. These three districts are excluded from the analysis. German data exclude districts of Berlin due to data inconsistencies. The structure of Slovak districts was thoroughly changed in 1997 and we use Slovak data as two separate panels. District level data for 49 Polish voivodships are available only till the end of 1998.Afterwards, data are available only by 16 Polish regions and we use Polish data as two separate panels. The Hungarian data at our disposal start in January 1995.

− 19 −
Figure 5. Sensitivity of inflation expectations to global and domestic inflation shocks A. Sensitivity of inflation expectations to global shocks, all countries <!-- Start of picture text --> Percentage points<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> C. Sensitivity of inflation expectations to global shocks, advanced economies <!-- Start of picture text --> Percentage points<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2018<br><!-- End of picture text -->

# E. Sensitivity of inflation expectations to global shocks, EMDEs

B. Sensitivity of inflation expectations to domestic shocks, all countries <!-- Start of picture text --> Percentage points<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> D. Sensitivity of inflation expectations to domestic shocks, advanced economies <!-- Start of picture text --> Percentage points<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2018<br><!-- End of picture text --> F. Sensitivity of inflation expectations to domestic shocks, EMDEs <!-- Start of picture text --> Percentage points Percentage points<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: World Bank.

Note: EMDEs = emerging market and developing economies.

A.-F. Inflation expectations are five-year-ahead expectations of annual inflation. Time-varying sensitivity is estimated by regressing the change in five-year-ahead inflation expectations on global and domestic shocks, as described in Appendix 3. Solid lines denote the median of estimates and dotted lines indicate the median of 68 percent confidence intervals.

A.B. Based on a sample of 24 advanced economies and 23 EMDEs during 2000H1-2018H1.

C.D. Based on a sample of 24 advanced economies during 1995H1-2018H1.

E.F. Based on a sample of 23 EMDEs during 2000H1-2018H1.

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mirroring the fall in revenue and leaving the fiscal balance largely unchanged relative to the pre-disaster forecast.<sup>12</sup>

**Table 3. D Cyclone Evan on Samoa (December 2012): Deviations of macro-fiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Samoa**||**Other**<br>**SIDS**|**Oceania**|
|---|---|---|---|---|
||2013|2014|2013|2014|
|GDP growth|-1.8|-1.6|0.2|1.0|
|<br>Govt Revenue|-10.4|-6.3|2.5|3.3|
|Govt Expenditure|-11.0|-4.9|-3.6|0.5|
|<br>Net Lending|0.6|-1.3|6.2|2.8|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._ <u>Source: WEO database</u>

**Table 3.E Cyclone Pam on Vanuatu (March 2015): Deviations of macro-fiscal outcomes from projections made immediately prior to the disaster (percent of GDP)**

||**Vanuatu**||**Other**<br>**SIDS**|**Oceania**|
|---|---|---|---|---|
||2015/16|2016/17|2015/16|2016/17|
|GDP growth|-2.6|1.2|1.4|1.4|
|Govt Revenue|15.4|15.1|9.9|12.0|
|Govt Expenditure|18.3|11.8|0.5|3.5|
|Net Lending|-2.9|3.2|9.4|8.5|
|Gross Public Debt|13.5|17.8|-9.0|-9.0|

_The figures in the table show the difference between the outcome for each variable and the projection made just before the disaster. Net lending is equivalent to the overall fiscal balance._ <u>Source: WEO database</u>

> 12 The government debt data for Samoa were not available in the 2012 WEO.

12
52 happens to the unemployed poor will greatly depend on what happens to the people with vocational education. Their position, compared to other educational groups, has deteriorated since the transition possibly due to their pre-transition oversupply.” The problem can be solved in two ways: either there would be an increase in demand for their skills or they would have to be retrained. The first solution is unlikely because their skills seem to be in low demand in a market economy. This then puts the onus on retraining. Retraining is expensive. How will countries that already have grave problems controlling social expenditures succeed in financing training is a mystery. One possibility is, of course, proportionate cuts elsewhere. For unless something is done regarding the people with vocational education, a possibility that an "underclass" appear cannot be ruled out.

What will be the outcome of the key tradeoff faced now, that between the elderly and children?

A reduction in pension spending should allow for some increase in other social transfers, in particular for family allowances and large poor families, a segment of the population the most affected by negative aspects of the transition. A shift in transfers, away from the elderly and towards children can be justified on two grounds.

®Children are now the most vulnerable group with poverty incidence about 1'4 times higher than country-average (see Figure 6); the aged are probably the least vulnerable as a group although there are obvious differences among them.” ®@Children, who are future citizens and workers, should be spared as much as possible the worst pain of the current crisis. This is not purely a social but also an economic argument because impairment at an early age translates into lower education attainment and productivity later in life. If the countries in transition aspire to regain growth and become developed economies, they will need an educated and hard working labor force. None of this would happen if children are stunted in their development at a young age.”!

> This point is made by Flanagan (1993). Vodopivec and Orazem (1994, p.14), Chase (1995, p. 15), Bird, Schwarze and Wagner (1992, p. 12), World Bank (1994, p. 92) all find the decline in the position of those with vocational education compared to people with general secondary or university education.

> “Elderly living alone, particularly women, have very high poverty rates. *!An illustration of the severity of the problem is provided by a survey conducted in ten largest cities in Russia: almost 50 percent of children report being hungry at least from time to time and one child in eight misses either breakfast or dinner (see Rimashevskaya, 1993, p. 47).
Table B3: Rider audits: rides (cont.)

|**Variable**|**Defnition**|
|---|---|
||Revealed preferences rides only|
|Chose reserved space<br>Reserved space shown frst<br>Share of reserved space rider|= 1 if rider takes up the reserved space during ride<br>= 1 if option to ride reserved space is on the top of the app screen<br>s who are men<br>Rider report from the platform before check-in<br>_Opportunity cost of riding the reserved space (USD)_|
|0 cent opportunity cost|= 1 if there is no diference in pay of for riding the public and<br>the reserved space|
|5 cents opportunity cost|= 1 if diference between pay of for riding the public and the<br>reserved space is $0.05|
|10 cents opportunity cost|= 1 if diference between pay of for riding the public and the<br>reserved space is $0.10|
|20 cents opportunity cost|= 1 if diference between pay of for riding the public and the<br>reserved space is $0.20|
|Positive opportunity cost|= 1 if opportunitycost of ridingthepublic space larger than zero<br>Randomized car assignment rides only|
|Assigned to reserved space|= 1 if rider is assigned the reserved space for a given ride|
|Assigned to public space<br>Switched spaces|= 1 if rider is assigned the public space for a given ride<br>= 1 if rider moved from reserved to public space, of vice-versa,<br>during a ride<br>_Riders experiences of harassment during ride_|
|Any harassment<br>Physical harassment|= 1 if any of the three following variables is 1<br>= 1 if rider reports being touched intentionally by a stranger<br>during the ride in a way that made them feel uncomfortable|
|Staring|= 1 if rider reports being stared by a stranger during the ride in<br>a way that made them feel uncomfortable|
|Verbal harassment|= 1 if rider reports that a stranger made comments during the<br>ride that made them uncomfortable<br>_Above median on self-reported scale_|
|Overall well-being<br>Happy||
|Sad|For each feeling, riders are asked: “On a scale from 1 to 10, where|
|Tense|<br>1 is the worst and 10 is the best, how much do you fell [feeling]?”|
|Relaxed|<br>Constructed variable is = 1 if the self-reported level is above the|
|Frustrated<br>Satisfed<br>Versus before|median|
# **1. Introduction**

The conventional assumption about a child labor stigma (or norm) is that households feel embarrassment, anxiety, guilt, or shame towards their children working (Grootaert and Patrinos, 1999). What about a situation where a different stigma prevailed? A positive stigma could approve of child labor, or at least it could approve of select forms of work for child labor (López-Calva, 2003). Indeed, households might take pride in work in a household business or farm, especially if the child combines this work with schooling. Such a positive stigma for child labor has been identified among indigenous households in Guatemala, such that households value work not only as a tool for income, but as an activity worthwhile in its own right (Heckt, 1999). Guatemala’s National Statistics Institute and the International Labor Organization (2003) conducted a series of interviews with Guatemalan households that elicited several comments emphasizing the role of child labor in indigenous communities: “In general, (indigenous) leaders discussing child labor mixed together comments of an economic character with other comments on an educational character such as learning from one’s father, the relation between learning and working, and being disciplined and respected. This is to say, the work of children is a way of training children to lead a responsible life... Work in household businesses was valued as having a role in education and training, incorporating the concept and practice of living. Values like honor, dignity, development and learning were expressed as connected to the work of children within a family atmosphere.” In this paper, we introduce a simple empirical framework that assumes a positive stigma towards child labor that is common in some developing areas. We then illustrate our positive stigma framework using nationally representative data from Guatemala. In addition to child labor, we examine household preferences towards schooling.

1
The financial position of different systems<sup>in transitional economies is affected by the growing</sup> problems of evasion and arrears. Evasion<sup>is increasing because of the high contribution rates and the</sup> growing role of the private sector, especially<sup>the emergence of small firms in the service sector. Arrears</sup> are caused by the financial difficulties confronting<sup>many large enterprises, especially in the state sector.</sup> Growing evasion and arrears imply that all<sup>four systems are probably suffering fiom large and growing</sup> deficits. In fact, the Polish system runs a large<sup>deficit amounting to over 6% of GDP, which is a major</sup> drain on fiscal resources. The deficit<sup>of the Hungarian system is hidden by opaque accounting and</sup> reporting practices, while the Russian system<sup>suffers from a growing failure to collect contributions.</sup> Table 2 Required and Nominal Contribution Rates, 1992

||SDR|ARR|RCR|NCR|
|---|---|---|---|---|
|Czech Republic|49|49|24|27.2|
|Hungary|59|49|29|30.5|
|Poland|49|74|36|30|
|Russia|46|34|16|32.6|
|USA|31|31|9.6|12.4|
|Switzerland|43|27|11.6|9.6|

SDR: System Dependency Ratio ARR: Average Replacement Rate RCR: Required Contribution Rate (for break even) NCR: Nominal Contribution Rate

# Source: World Bank Staff Estimates

**High Pension Expenditures and Near**<sup>**Universal Coverage.**</sup> The financial burden of public pension systems is exacerbated by their<sup>largely mono-pillar structure and near universal coverage7.These</sup> translate into a high share of covered<sup>wages as a proportion of GDP and may explain why pension</sup> expenditures correspond to a much higher<sup>percentage of GDP in Central and Eastern Europe than is</sup> generally the case in Latin America and<sup>other low income countries. Two of the four countries surveyed</sup> in this paper had in 1992 pension expenditure<sup>to GDP ratios of slightly over 10%, Poland reached almost</sup> 15%, while Russia had a much lower<sup>level at 5%. In contrast, most Latin American countries have</sup> pension expenditures that are well below<sup>the 5% level. In Poland, total pension spending rose from 6.9%</sup>

7 The coverage of pension systems was historically very high in<sup>former socialist economies. But</sup> growing evasion is cau,;ing a decline in coverage.

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