rafmacalaba/datause-encoder-data huggingface dataset:
**Biais, Bruno and Enrico Perotti** . 2002. “Machiavellian Privatization.” _American Economic Review_ , 92(1): 240-58.

**Birdsall, Nancy and John Nellis** . 2003. “Winners and Losers: Assessing the Distributional Impact of Privatization.” _World Development_ , 31(10): 1617-33.

**Bishop, Matthew, and John Kay** . 1988. _Does Privatisation Work? Lessons from the UK._ London: Centre for Business Strategy, London Business School.

**Blanchard, Olivier, Rudiger Dornbusch, Paul Krugman, Richard Layard, and Lawrence Summers.** 1991. _Reform in Eastern Europe_ . Cambridge, MA: MIT Press.

**Bonin, John P., and Paul Wachtel.** 2003. “Financial Sector Development in Transition Economies: Lessons from the First Decade.” Financial Markets, Institutions and Instruments, 12(1): 1-66.

**Bortolotti, Bernardo, and Paolo Pinotti.** 2003. “The Political Economy of Privatization.” Fondazione Eni Enrico Mattei Working Paper 2003.45.

**Bortolotti, Bernardo and Domenico Siniscalco** . 2004. The challenges of privatization: An international analysis. Oxford and New York: Oxford University Press.

**Boycko, Maxim, Andrei Shleifer, and Robert Vishny.** 1995. _Privatizing Russia_ . Cambridge, MA: MIT Press.

**Brada, Josef C.** 1996. “Privatization Is Transition – Or Is It?” _Journal of Economic Perspectives_ , 10(2): 67-86

**Brown J. David, and John S. Earle.** 2001a. “Privatization, Competition and Reform Strategies: Theory and Evidence from Russian Enterprise Panel Data.” CEPR Discussion Paper Series 2758.

**Brown J. David, and John S. Earle.** 2001b. “Competition Enhancing Policies and Infrastructure: Evidence from Russia.” CEPR Discussion Paper 3022.

**Brown J. David, John S. Earle, and Almos Telegdy.** 2006. “The Productivity Effects of Privatization: Longitudinal Estimates from Hungary, Romania, Russia, and Ukraine.” _Journal of Political Economy_ , 114(1): 61-99.

**Carlin, Wendy, Steven Fries, Mark E. Schaffer, and Paul Seabright.** 2001. “Competition and Enterprise Performance in Transition Economies: Evidence from a Cross-country Survey.” CEPR Discussion Paper 2840.

**Claessens, Stijn, and Simeon Djankov.** 1998. “Politicians and Firms in Seven Central and Eastern European Countries.” The World Bank Policy Research Working Paper 1954.

32
<!-- Start of picture text --> 0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>-0.1<br>-0.2 intra comp intra struc<br>inter comp inter struc<br><!-- End of picture text -->

# _Industrial segregation_

We further disaggregate the results by industries. We observe that, similar to the private/public sector findings, the contribution of the intra-industrial component (gender gap within industries) dominates, although its magnitude is smaller, varying from 52 to 84 percent during 2004 – 2015 (Table 4). Hence, industrial segregation plays a much more important role when we use industries as the metric of analysis implying that within public and private sectors of the economy female-dominated industries tend to remunerate more poorly than male-dominated industries.

Over time, both intra-industrial and inter-industrial components of the gender wage gap contracted, leading to the overall reduction in the gender wage gap (Table 3). This indicates that between 2004 and 2015 women in Georgia benefitted from the changes in their compensation relative to men within industries and from the movement into the industries with higher wages.

Table 4. Decomposition of the gap into intra and inter components by industry.

||total|Intra|Inter|
|---|---|---|---|
|2004|0.6361|0.4730|0.1598|
||(0.0266)|(0.0398)|(0.0308)|
|2005|0.6877|0.4723|0.2214|
||(0.0266)|(0.0377)|(0.0287)|
|2006|0.6629|0.4322|0.2427|
||(0.0270)|(0.0382)|(0.0277)|
|2007|0.6284|0.3496|0.2795|
||(0.0283)|(0.0372)|(0.0276)|

17
# **5 Calibration**

First, I calibrate the model in order to match the U.S. plant size distribution by assuming that the U.S. is free of distortions and bribery. Then, I also calibrate this model to match Türkiye’s plant-size distribution as well as the bribery payments of different size groups as observed in the Enterprise Surveys.

For the U.S., I use a version of the model where population and technology parameter grow at a constant rate. I borrow the following parameters from Guner _et al._ (2008): population and technology parameter growth rates are set to 0.011 and 0.0255, _ν =_ 0.41, _β =_ 0.94, _δ =_ 0.04 and _γ =_ 0.80. Then I assume a composite lognormal-Pareto distribution for the distribution of managerial ability, _F_ ( _z_ ). The composite lognormal-Pareto distribution is characterized by three parameters: a standard deviation of the lognormal distribution, _σ_ , a shape parameter of the Pareto distribution, _s_ , and a threshold value for distributions, _zT_ .<sup>10</sup> These parameter values are found to match the mean size for plants, the fraction of plants with 1– 49 employees, and the proportion of workers in big plants in the US. Tables 4 and 5 display the calibrated parameter values and the model performance.

For the calibration of the Turkish economy, I borrow some of the parameters from the literature which are calculated using National Income and Product Accounts. I choose the share of capital, _αγ =_ 0.34, depreciation, _δ =_ 0.055 and discount rate, _β =_ 0.93 consistent with Torul & Öztunalı (2018), Atesagaoglu _et al._ (2017) and Atiyas & Bakı ̧s (2014). Similar parameter values are used in the Penn World Tables to make cross-country comparisons.

Guner _et al._ (2008) estimate the span of control parameter, _γ =_ 0.802 for the US in which the mean establishment size is 17.09 employees. In addition, Leal (2014) estimates the same parameter for Mexico whose mean establishment size is 5.5 employees and finds _γ =_ 0.76. Since Türkiye’s mean enterprise size is 3.6 employees, I choose _γ =_ 0.7. Given _γ_ , the value of _α_ can be determined as _α =_ 0.34/ _γ =_ 0.486.

Next, I assume a composite lognormal-Pareto distribution for the distribution of managerial abilities in Türkiye such that the top 1% of the distribution would follow a Pareto distribution. The introduction of distortions requires seven additional parameters to estimate: _λ_ , _τ_ , _φ_ , _z_ ˆ, _α_ 0, _α_ 0 and _b_<sup> ̃</sup> . Finally, there are nine parameters (i.e., two for the distribution of ability and six for size-dependent distortions) to estimate: _σ_ , _s_ , _λ_ , _τ φ_ , _z_ ˆ, _α_ 0, _α_ 1 and _b_<sup> ̃</sup> . I discipline these parameters by matching nine moments from the data: the mean plant size, the fraction of plants with 1–49 employees, the fraction of plants with 50–99 employees, the employment share of plants with 50–99 employees, the employment share of plants 10See Scollnik (2007) for more details.

13
_INTAN_ ) and internally purchased intangible capital, both measured at replacement cost. Internally purchased intangible capital is in turn measured as the sum of knowledge capital _K_ _~~i~~ nt know_ and organization capital _K_ _~~i~~ nt org_ . The perpetual-inventory method is applied to a firm’s past research and development expenses (Compustat item _XRD_ ) to measure the replacement cost of its knowledge capital. Similarly, a fraction (0.3) of past selling, general, and administrative ( _SGA_ ) spending is used as an investment in organization capital, which includes human capital, brand, customer relationships, and distribution systems.<sup>14</sup> The estimates of _ICAP_ , _K int_ _~~k~~ now_ , and _K_ _~~i~~ nt org_ have been made publicly available by Peters and Taylor [2017].

Correspondingly, we also adjust the profits in the numerator to account for the use of intangible capital in computing invested capital. Thus, the new ROIC is given by: where _ADJPRit_ = _EBITit_ + _AMit_ + _XRDit_ + 0 _._ 3 _× SGAit_ where _δRD_ is the depreciation rate associated with knowledge capital and is set to 15% following Peters and Taylor [2017] and _δSGA_ is the depreciation rate associated with organization capital and is set to 20% following Falato et al. [2013].

Note that using an adjustment for intangible capital affects ROIC in two ways. First, it increases the denominator by the amount of the adjustment for intangible capital. Second, R&D and a portion of SGA expenditure, which would previously have been expensed are now treated as additions to capital stock. Thus, it is not subtracted from the firm’s conventionally calculated earnings (EBIT) to obtain the adjusted earnings. However, since the stock of intangible capital is now treated as an asset, an additional depreciation expense is now deducted from EBIT. This second adjustment either increases or decreases the numerator of ROIC, depending on the level of

> 14Since Compustat item _XSGA_ is the sum of SG&A and R&D, we follow the procedure in Peters and Taylor [2017] to isolate SGA as XSGA-XRD-RDIP where RDIP is In-Process R&D. We replace missing values of XSGA, XRD, and RDIP with 0.

12
various regions and various points of time, often using very different methodologies. It will also show that the BEEPS dataset, with its cross-sectional and time-series dimension, is particularly appropriate for studying the issues of convergence at the micro-level across the transition countries and over time.

The process of economic transition has long been thought as consisting of two key elements, reallocation and restructuring (Blanchard 1997). The former involves intersector adjustments of production, from low value added sectors to those generating high value added. The term “intersector adjustment” is used here in a broad sense meaning reallocation from the state sector to the private sector as well as by branches of economic activity. “Restructuring” refers to the behavior of surviving firms: in particular, changes in the level and technical composition of labor and capital, in search of efficiency improvements.

The economics literature on the transition process can be characterized by two broad views which differ by the relative importance attached to these two processes. One view emphasizes the inability of the old sector to quickly adapt to the new market environment and suggested that productivity improvements are mainly attained via the development of a new private sector. Thus, reallocation from the old sector to the new one is a focal point of the transition process according to this perspective. According to the other view, the restructuring of the existing firms, implying a collection of measures undertaken by these firms in order to survive and succeed in the market conditions, is an important contribution to improved productivity and economic growth. The second view thus emphasizes a particular aspect of convergence, namely convergence of the old firms with the new private businesses in terms of behavior and productivity. This is, however, a difference of emphasis; there is a broad consensus that both sources of productivity growth are important.

A voluminous literature has studied reallocation, growth and productivity at the firm level in the transition countries. While a thorough review of these studies is outside the scope of this paper and can be found elsewhere (see, e.g., Djankov and Murrell 2002; 11
Table 2 below reports regressions of firewood collection on dummies for main cooking fuels including biogas and controls. The omitted category for the main cooking fuel is firewood.

Table 2: OLS Models of Reported Annual Firewood Collection (kilograms) in 2010‐ 11, with Ward Fixed Effects

||Model_1|Model_2|Model_3|
|---|---|---|---|
|VARIABLES|firewd_coll|firewd_coll|firewd_coll|
|Main cooking fuel‐ Bio‐gas|‐1,165***<br>(108.7)|‐1,161***<br>(108.0)|‐1,062***<br>(119.7)|
|Main cooking fuel‐ dung|‐763.3***|‐756.2***|‐449.2***|
||(131.3)|(132.3)|(154.8)|
|Main cooking fuel‐ leaves/straw|‐471.6***|‐470.5***|270.6|
||(168.6)|(168.3)|(234.3)|
|Main cooking fuel‐ LPG|‐1,125***|‐1,111***|‐856.0***|
||(108.1)|(107.9)|(159.5)|
|Main cooking fuel‐ Kerosene|‐982.0***|‐989.0***|986.2***|
||(134.0)|(132.9)|(183.6)|
|Main cooking fuel‐ other|‐818.3***|‐850.3***|‐216.5|
||(188.3)|(185.0)|(458.0)|
|Household size|151.6***|155.9***|241.3***|
||(28.39)|(28.37)|(38.86)|
|Hh size squared|‐2.916|‐2.788|‐5.344*|
||(2.300)|(2.260)|(2.872)|
|Annual per capita household consumption|0.00306***<br>(0.00112)|0.00307***<br>(0.00113)|0.0108***<br>(0.00319)|
|Per capita consumption squared|‐5.85e‐<br>09**|‐5.84e‐<br>09**|‐2.66e‐08*|
||(2.62e‐09)|(2.64e‐09)|(1.60e‐08)|
|Household head less than 5 yrs education|49.49|46.67|36.96|
||(55.38)|(55.58)|(68.30)|
|Household head completed 5‐7 yrs education|46.39|43.23|39.15|
||(63.19)|(63.10)|(79.66)|
|Household head completed 8‐10 yrs|68.65|70.92|93.69|
|education||||
||(70.00)|(69.95)|(104.0)|
|Household head completed 11+ yrs education|8.237|7.897|‐53.69|
||(52.40)|(52.29)|(96.86)|
|Total area of land owned (ha)|76.51*|76.40*|109.7*|
||(44.76)|(44.83)|(61.77)|
|Land owned squared|‐5.112**|‐4.755**|‐11.48***|
||(2.275)|(2.281)|(3.507)|
|Household with one cow/buffalo|89.28|80.98|‐9.687|

8
**Land fragmentation in Orissa by operational holding size**

**Table 9:**

|**Size of operational**<br>**holding (ha)**|**196**|**1-62**|**198**|**1-82**|
|---|---|---|---|---|
||**No. of parcels per**<br>**holding**|**Average area per**<br>**parcel (ha)**|**No. of parcels per**<br>**holding**|**Average area per**<br>**parcel (ha)**|
|0.00|||_1.56_||
|0.002-0.20|2.19|0.03|1.28|_0.05_|
|0.21-0.40|3.52|0.08|3.98|0.07|
|0.41-1.00|4.60|0.14|4.53|_0.15_|
|1.01-2.02|6.08|0.23|6.24|0.24|
|2.03-3.03|8.32|0.28|6.66|0.37|
|3.04-4.04|9.92|0.34|7.17|0.48|
|4.05-5.05|10.64|0.42|7.58|0.59|
|5.06-6.07|9.91|0.53|9.10|0.60|
|6.08-8.09|12.59|0.53|9.19|0.72|
|8.10-10.12|11.08|0.80|9.23|0.96|
|10.13-12.14|15.46|0.65|15.24|0.70|
|12.15-20.24|10.58|1.53|8.50|1.67|
|20.25 and above|8.30|2.78|6.51|8.04|
|All sizes|6.39|0.31|5.02|0.29|

Source: Compiled from Thangaraj (1995).

# _4.2.2 Legal provisions and progress of land consolidation in Orissa_

Consolidation of land holdings is designed to reverse fragmentation through a scheme of redistribution of lands in compact rectangular blocks<sup>33</sup> . The Orissa Consolidation of Holdings and Prevention of Fragmentation of Land (OCH & PFL) Act, 1972 aims to provide a compact parcel of agricultural land to the cultivator in lieu of his scattered plots. In this scheme, the fragmented patches of landowners are brought under one, two or three _chaks_ and the right, title and interest of a landowner is decided in the preparation of ROR and village map. Simultaneously, a separate category of land is reserved for communal and developmental purposes of the village. The Act also aims to set aside land for drainage canals in irrigated villages while in unirrigated villages, farmers are encouraged to utilise groundwater resources.

Tables 10 and 11 provide an indication of the progress of land consolidation operations in Orissa since 1974 when the OCH & PFL Act was operationalised. These data suggest that land consolidation has so far been completed on only 17 percent of the total operated area in Orissa. Some 1 1 percent of this total was consolidated over the period 1995-98 at a cost of Rs. 626 million (Table 11), or around Rs. 6,000 per hectare. However, these official data must be interpreted with caution owing to wide variations in actual implementation across the state. Efforts to consolidate land holdings in western Orissa, in particular, appear to have unsuccessful (see section 4.2.4). While the process may be have been 'completed', in the sense that consolidation officers have processed the necessary paperwork and filed reports under the OCH & PFL Act, little change may actually have taken place on the ground as farmers frequently refuse to exchange plots or alter existing plot boundaries.

- 33 While some regard land consolidation to be a potentially important component of India's land reforms (e.g. Oldenburg 1990), others argue that land consolidation does not constitute land reform on the grounds that it usually attempts scrupulously to ensure that land distribution remains unchanged.

43
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(Gijc (linkages;-) (demostration;, (Xijc
Table A. 1. Poverty indexes by selected characteristics, pre- and post-crisis

|||**Pre-crisis**|||**Post-crisis**||
|---|---|---|---|---|---|---|
||P0|**P,**|**P2**|Po|**P,**|P2|
|Total|0.522847|0.191716|0.095749|0.6284|0.238315|0.120332|
|Farmers|0.526387|0.174568|0.079025|0.665777|0.254793|0.133904|
|Hiredinagriculture|0.588324|0.246257|0.13198|0.723632|0.282903|0.143689|
|Hiredinnon-agricult.|0.493054|0.164581|0.078056|0.575333|0.210156|0.103547|
|<br>Selfemployed|0.462196|0.176227|0.082084|0.431166|0.160987|0.074991|
|<br>Pensioners|0.499351|0.177157|0.086791|0.605333|0.231347|0.118672|
|Other|0.499081|0.187508|0.098231|0.642988|0.246628|0.119706|
|Higher education|0.289928|0.079073|0.0311|0.368637|0.126109|0.061988|
|Secondary|0.559445|0.211481|0.107283|0.669449|0.25696|0.12968|
|Primary|0.518922|0.182945|0.091048|0.631121|0.231638|0.118941|
|Illiterate|0.486518|0.140285|0.055575|0.58919|0.249041|0.126143|
|Non-elderhead|0.528788|0.1962|0.098193|0.633982|0.24089|0.120985|
|Elderhead|0.48889|0.166091|0.081781|0.59622|0.223471|0.116568|
|Femalehead|0.480933|0.166072|0.075944|0.596015|0.227663|0.11651|
|Malehead|0.537837|0.200888|0.102833|0.639772|0.242055|0.121674|
|Largecities|0.327451|0.090731|0.038317|0.400783|0.133715|0.061152|
|Other towns|0.571854|0.202273|0.094009|0.717616|0.289296|0.15098|
|Rural|0.564993|0.217059|0.112029|0.674409|0.256993|0.130454|
|Nochildren|0.385642|0.130426|0.061688|0.535338|0.185077|0.087313|
|1child|0.517413|0.17487|0.084054|0.619528|0.225628|0.110518|
|2children|0.647163|0.253991|0.134463|0.693972|0.283936|0.151436|
|3children|0.686777|0.260835|0.126317|0.785225|0.328016|0.174997|
|4ormore children|0.655872|0.322879|0.177487|0.781755|0.322656|0.168949|
|Noelders|0.523868|0.192007|0.095469|0.621771|0.233785|0.116326|
|Ielder|0.518136|0.186924|0.091521|0.643127|0.245064|0.125311|
|2or moreelders|0.521405|0.200244|0.110454|0.669307|0.274621|0.154934|
|Non-owner|0.492111|0.152909|0.069892|0.526927|0.199439|0.101052|
|Owner|0.527585|0.197699|0.099736|0.642506|0.243719|0.123012|
|Single|0.304259|0.115108|0.061636|0.543903|0.203302|0.113117|
|Married|0.553631|0.203897|0.102324|0.644748|0.245239|0.123466|
|Separate|0.303592|0.097596|0.040203|0.543437|0.259715|0.152728|
|Widowed|0.464395|0.171588|0.086224|0.591335|0.220285|0.10996|
|Divorced|0.332566|0.105817|0.043824|0.508872|0.173704|0.084588|

_Note*_ P0 **is** the headcount ratio, PI the poverty gap, and P2 the square poverty gap. Poverty lIme used was z=89, as calculated in Signoret and Murrugarra (2001).

46
that is, _cantons_ with 10% higher income receive 5% more Venezuelan migrants than otherwise similar cantons. The estimated coefficients for the distance to the gateways are typically negative, as expected, but the magnitudes are small and imprecisely estimated.

In conclusion, the estimates show that Venezuelan migrants are highly geographically mobile and that their location choices are fundamentally driven by the economy of the receiving region. It is interesting to note that our findings differ from those reported in Beine et al. (2019), which analyzes the determinants of the location choices of Syrian refugees across provinces in Turkey using a similar estimation approach. Their analysis shows a large and highly significant effect of distance and local policy interventions (such as boycotts and availability of economic aid), but they do not find that economic activity at destination (measured by night-lights intensity) plays a role in shaping the migrants’ location decisions.

# **6 Labor Market**

We now turn to our main goal in this paper, the estimation of the effects of Venezuelan migration on Ecuador’s regional labor markets. Our analysis relies on the comparison of the changes in local labor market conditions in areas that received large inflows of Venezuelans (relative to population), versus areas that did not, around the main period of arrival of the migrants. Importantly, we also examine whether the effects of immigration differ across different groups of workers, defined by education, age and gender.

## **6.1 Summary Statistics**

We focus on the working-age population (age 15 and higher) and construct a variety of canton-year labor market outcomes: participation rate, employment-to-population ratio, the rate of adequate employment (relative to the working-age population), the rate of informality (defined as employment over working-age population), the log of earnings and the log of hourly wages.

Table 3 presents summary statistics. We use one quarterly survey per year for the period 2013-2019. The resulting number of canton-year observations is 1,413. The (simple) mean working-age population across years and _cantons_ is almost 58,000 individuals, corresponding to a 68% participation rate. Mean employment across _cantons_ and years is slightly over 36,000 individuals, entailing an employment-to-population ratio of 66%. In addition, for the average canton-year cell, 21% of the working-age population have 13
clearly useful in studying poverty transitions, recent studies point to several unsettled issues. For example, with respect to the former approach based on retrospective data, recent research indicates that accurate recall data can be quite difficult to collect (see, for example, Beegle et al., 2012). With respect to asset indices, there are concerns that these are not easily interpreted for the purpose of drawing policy conclusions.

In this paper we pursue an alternative estimation strategy to measure poverty mobility in Senegal. We construct synthetic panel data using two rounds of cross sections in 2005/06 and 2011 based on the methods proposed by Dang, Lanjouw, Luoto, and McKenzie (2014) and Dang and Lanjouw (2013). The key idea behind these methods is that we can exploit the relationship between a household’s time-varying poverty status and time-invariant individual and household characteristics (with appropriate adjustments for the error terms), in order to predict, for each household, a past consumption level that can be compared to directly observed current consumption levels. These comparisons then permit an examination of transitions in poverty status over time at the household level. Under reasonably standard assumptions, the timeinvariant household characteristics in the two cross sections can function as the panel connectors that are employed to construct synthetic panel households. Dang et al. (2014) and Dang and Lanjouw (2013) report on a number of validation studies and illustrate that estimates based on these constructed synthetic panel data closely match those based on true panel data for a number of countries, such as Bosnia-Herzegovina, Indonesia, Lao People’s Democratic Republic, Peru, United States, and Vietnam, spanning different regions and widely varying income levels.<sup>2</sup>

> 2 One major concern with panel data is that these data may not provide representative estimates of the population over time due to issues including attrition bias or measurement errors. These countries were selected for validation because of their panel data are generally considered to be of high quality. Another validation study of the Dang et al. (2014)’s method using data from Latin American countries is by Cruces et al. (2014).

4