rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7: Summary of Results—no initial capture of rents in services by Armenians**

# **(results are percentage change from initial equilibrium, unless otherwise indicated)**

|**Scenario definition**|Benchmark|**EU FTA**|EU<br>Discriminatory<br>Services|EU Tariffs|Border Costs|EU Standards|EU FTA<br>Steady-state|**CIS FTA**|**EU-CIS FTA**|
|---|---|---|---|---|---|---|---|---|---|
|Reduction of discriminatory barriers on EU services firms|No|**Yes**|Yes|No|No|No|Yes|**No**|**Yes**|
|Reduction of discriminatory barriers on CIS services firms|No|**No**|No|No|No|No|No|**Yes**|**Yes**|
|Reduction of discriminatory barriers on ROW services firms|No|**No**|No|No|No|No|No|**No**|**No**|
|Reduction of regulatory barriers for all services firms|No|**No**|No|No|No|No|No|**No**|**No**|
|Removal of tariffs on EU sourced goods|No|**Yes**|No|Yes|No|No|Yes|**No**|**Yes**|
|Removal of tariffs on ROW sourced goods|No|**No**|No|No|No|No|No|**No**|**No**|
|Reduction in border costs|No|**Yes**|No|No|Yes|No|Yes|**No**|**Yes**|
|Reduction in standards for EU exports|No|**Yes**|No|No|No|Yes|Yes|**No**|**Yes**|
|Steady-state capital stock|No|**No**|No|No|No|No|Yes|**No**|**No**|
|**Aggregate welfare**||||||||||
|Welfare (EV as % of consumption)||**1.4**|0.4|-0.1|1.0|0.1|1.8|**0.1**|**1.5**|
|Welfare (EV as % of GDP)||**1.0**|0.3|-0.1|0.7|0.1|1.2|**0.1**|**1.1**|
|**Government budget**||||||||||
|Tariff revenue (% of GDP)|1.0|**0.6**|1.0|0.5|1.0|1.0|0.6|**1.0**|**0.6**|
|Tariff revenue||**-40.9**|0.3|-44.2|5.0|0.7|-40.4|**0.1**|**-40.8**|
|**Aggregate trade**||||||||||
|Real exchange rate||**0.6**|0.2|0.5|0.2|-0.3|0.9|**0.1**|**0.6**|
|Aggregate exports||**13.9**|-0.1|2.5|9.3|1.8|15.1|**0.2**|**14.2**|
|**Factor Earnings**||||||||||
|Capital||**2.0**|0.4|0.5|0.8|0.2|1.0|**0.1**|**2.0**|
|Labor||**2.0**|0.5|0.4|0.9|0.1|2.9|**0.1**|**2.0**|
|**Factor adjustments**||||||||||
|<br>Capital||**0.4**|0.1|0.2|0.1|0.2||**0.0**|**0.4**|
|Labor||**0.4**|0.2|0.2|0.1|0.1|0.6|**0.0**|**0.4**|
|**Capital stock and investment**|||||||1.9|||
|Source: Authors' estimates.||||||||||

46
**Table 4. Regression results. Dependent variable: Height for age z score**

# **<u>(standard errors adjusted for sampling structure)</u>**

||(1)<br>OLS<br>Individual<br>and<br>household|(2)<br>OLS<br>Individual,<br>household<br>and<br>community|(3)<br>OLS.|(4)<br>Instrumental<br>Variable|(5)<br>Individual<br>Random<br>Effect with|
|---|---|---|---|---|---|
||variables|variables|Children 6-9|Regression|instruments|
|||**Co**|**mmunity Varia**|**bles**||
|Partage or feeding posts|0.256|0.286|-0.016|0.543|0.437|
||(2.30)**|(2.68)**|(0.16)|(1.83)*|(1.90)*|
|Urban||<br>0.169|<br>0.060|<br>0.170|<br>0.247|
|||(1.25)|(0.34)|(1.18)|(2.68)***|
|Health worker in the village||<br>-0.052|<br>-0.158|<br>-0.008|<br>0.000|
|||(0.72)|(1.60)|(0.10)|(0.00)|
|Minimum distance to the closest||<br>0.012|<br>0.014|<br>0.017|<br>0.007|
|health center||||||
|||(0.83)|(0.74)|(1.09)|(0.91)|
|Is motorable road near||<br>0.488|<br>0.432|<br>0.482|<br>0.510|
|||(4.46)***|(3.42)***|(3.85)***|(2.94)***|
|Road impassable||-0.005|-0.114|-0.032|-0.051|
|||(0.05)|(0.89)|(0.27)|(0.68)|
|Ratio of children vaccinated||<br>0.650<br>|<br>0.041<br>|<br>0.700<br>|<br>0.222<br>|
|||(3.99)***|(0.14)|(3.52)***|(2.10)**|
|||<br>**Househol**|<br>**d and individua**|<br>**l variables**||
|Log per capita consumption|0.206|0.184|0.154|0.207|0.103|
||(3.95)***|(3.55)***|(1.96)*|(2.52)**|(2.48)**|
|Farm income/total income|<br>-0.322|<br>-0.215|<br>-0.517|<br>-0.175|<br>-0.160|
||(2.11)**|(1.36)|(2.83)***|(1.02)|(1.79)*|
|Household size|<br>-0.008|<br>-0.005|<br>-0.030|<br>-0.008|<br>-0.006|
||(0.86)|(0.64)|(2.96)***|(0.85)|(0.88)|
|Aged 12_23 months|<br>-1.118|<br>-1.189||<br>-1.186|<br>-1.156|
||(11.89)***|(13.06)***||(13.00)***|(19.77)***|
|Aged 2435 months|-1.233|-1.238||-1.236|-1.257|
|_|(7.65)***|(8.12)***||(8.14)***|(12.57)***|
|Aged 36_47 months|<br>-1.468|<br>-1.479||<br>-1.482|<br>-1.510|
||(9.66)***|(9.94)***||(9.89)***|(14.76)***|
|Aged 48or more months|-1.255<br>|-1.257<br>||-1.259<br>|-1.438<br>|
||(8.68)***|(8.76)***||(8.70)***|(13.84)***|
|Aged 8 years|||0.034|||
||||(0.48)|||
|Aged 9 years|||-0.036|||
||||(0.51)|||
|Born in rainy season and aged|-0.238|-0.197||-0.198|-0.419|
|<br>less than 2||||||
||(2.24)**|(1.79)*||(1.78)*|(4.58)***|
|Born in rainy season and aged|<br>-0.008|<br>-0.035||<br>-0.029|<br>-0.178|
|more than 2||||||
||(0.07)|(0.29)||(0.24)|(1.93)*|
|Father dead|<br>-0.289|<br>-0.324|-0.295|<br>-0.316|<br>-0.178|
||(2.36)**|(2.65)**|(2.47)**|(2.49)**|(2.03)**|
|Mother dead|<br>0.142|<br>0.172|<br>-0.038|<br>0.161|<br>0.099|
||(0.60)|(0.74)|(0.26)|(0.68)|(0.81)|
|Male|<br>-0.242|<br>-0.249|<br>-0.118|<br>-0.254|<br>-0.301|
||(3.63)***|(3.59)***|(1.35)|(3.66)***|(4.42)***|
|Father’s height|<br>0.043|<br>0.042|<br>0.031|<br>0.041|<br>0.038|

21
-57 -

The Kuna reaction to the road proposal passed through two phases. The first was an attempt to pre-empt colonisation by establishing a Kuna agricultural project at the point where the road would enter the Comarca. This effort commenced in 1975 and was led by Guillermo Archibald, a young Kuna who had obtained a basic training in agronomy. Gradually, the project accumulated support amongst the Kuna but, by 1980, it became clear that the terrain was quite unsu1itable for conventional agriculture - though this would not necessarily have deterred colonists.

In 1981, an impact study brought the situation to the attention of US-AID, which was lending money for the road. After meeting with the Kuna, AID officials asked the Costa Rican institute, CATIE (Centro Agronomico Tropical de Investigacion y Ensenanza) to make a survey. Tl._.. results confirmed the Kunas' experience and led to CATIE suggesting that the area be developed into a forest park and wildlife refuge catering to ecological tourism. The Kuna subsequently asked the Smithsonian Tropical Research Institute (STRI) in Panama City for assistance. This led to funding support from US-AID and the Inter-American Foundation (IAF) for three Kuns to take conservation training at CATIE and make preliminary studies.

Chapin (ibid) suggests several reasons why the Kuna so readily adopted the idea of protecting rather than exploiting the resources of the area. The agricultural experiment had failed. Their own agricultural zone was sufficient. They were not being deprived of land. Finally, there is a tradition within Kuna society of reserving "spirit sanctuaries", even in good agricultural land. These would be used for the collection of medicinal plants - as ethnobotanical reserves.

With support from a labour organisation of Kuna working in the general economy, the Union de Trabajadores Kunas (UTK), the Kuna submitted in 1983 a three-year program to start PEMASKY. Support was gained from the Smithsonian Tropical Research Institute (STRI), US-AID, IAF, CATIE, WWF, the Tropical Science Centre (TSC) in Costa Rica, the Panamanian Agency of Renewable Natural Resources (RENARE) and the US Armed Forces Bases in the Canal Zone. The total cost approximated $1 million, with 57Z provided by the IAF.

This preliminary work included location and demarcation of the boundaries, ecological mapping and inventory and construction of facilities for personnel and visiting scientists. The Kuna have maintained a strong controlling presence in the project. Ten people went to visit the park and reserve system of Costa Rica before deciding upon the general development scheme. The delivery of technical assistance is preferred under a counterpart system.

In 1987, an IAF consultant visited Panama to make a progress report on PEMASKY (Butler 1987). He found an active project team of about 20 persons, administrators, technical staff and guards. Most of the technical team had university degrees, including an architect, 2 topographers, an environmental educator, a forester, a biologist and an agronomist. The team worked well as a unit and was often assisted by other Kuna as volunteers.
|Toth,<br>126(591):317-357.<br>23(2):251-269.<br>25(15):1965-1978.<br>103(2):620-642.<br>Kolesar,<br>Identification<br>33(4):474-484.<br>Sokoloff,<br>Suffurage<br>Michaels, G. (2008). The Effect ofTradeon the Demand for Skill:<br>90(4):683-701.<br>coefficient<br>37(2):187-204.<br>90(4):643-665.|
|---|
# **Vancouver**

|**Initiatives from the city hall**|**Initiatives from the private**<br>**sector**|**University based initiatives**|**Other initiatives**|
|---|---|---|---|
|The City of Vancouver has created a<br>framework for State of the**Environment**<br>**Reporting.**The first step was to produce<br>the 1995 State of the Environment||**Simon Fraser University - Regional**<br>**Vancouver Observatory - RVO**<br>Initiated in 2004, the RVO represents the<br>first local observatory of the global|**BC Progress Board**<br>Even though it is not directly related to<br>urban indicators, it is important to mention<br>that, in 2001, the Premier formed the BC|
|Report. A number of key environmental<br>issues facing the City were identified in<br>this report. By reviewing the past<br>histories of these issues and current City<br>activities and trends, an environmental<br>report card was created to rate the City's<br>progress in protecting the environment.||network of UN observatories to be<br>established in the developed world. The<br>aim of the observatories is to improve the<br>world-wide base of urban knowledge by<br>helping governments, local authorities<br>and organizations of the civil society<br>develop and apply policy-oriented urban<br>indicators, statistics and other urban<br>information.<br>http://www.rvu.ca|Progress Board as an independent Panel of<br>eighteen eminent British Columbians from<br>a variety of backgrounds, and since then the<br>Board has delivered Annual Benchmarking<br>Report The report includes 71 performance<br>indicators in topics such as economic,<br>innovation, education, environment, health<br>and<br>social<br>performance<br>regionally,<br>nationally<br>and<br>internationally.<br>This<br>experience may be used by cities.<br>http://www.bcprogressboard.com/index.php|

In 2005 the Council of the City of Vancouver approved a common adopted a set of indicators and targets, as a preliminary framework for on-going monitoring of environmental, social, and economic sustainability goals for the SEFC community 53
In addition to the effect of demographic change on per capita growth, similar specifications are tested to analyze savings. Based on the GMM method with several co-variates (Table 8, GMM, S3), it is estimated that an increase of 1 percentage point in the share of working-age population is associated with an increase of 0.78 percentage point in domestic savings as a share of the gross national income (GNI).

**Table 8 Growth of the working-age share of the population can increase savings**

||Fist-Diffe|rence||Panel Fix|ed-effects||Generalized|Method of|Moments|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of WAP|0.551**|0.698***|0.725***|0.578**|0.852***|0.753***|0.897**|0.628*|0.780***|
||(0.227)|(0.264)|(0.244)|(0.250)|(0.229)|(0.272)|(0.364)|(0.332)|(0.296)|
|Initial GDP-pc|||-0.0191|-2.063||-1.374||-0.949|2.023|
||||(0.888)|(2.806)||(3.383)||(1.151)|(1.613)|
|Schooling (years)|||-0.180|||-0.614|||-1.099|
||||(0.277)|||(0.464)|||(0.826)|
|British colony|||-0.00680||||||-0.766|
||||(0.981)||||||(0.886)|
|French colony|||-0.0343||||||0.265|
||||(0.900)||||||(1.333)|
|Non-colony|||-0.903||||||-2.142|
||||(1.325)||||||(2.103)|
|Landlocked|||0.0143||||||0.0399|
||||(0.0356)||||||(0.0530)|
|Latitude|0.551**|0.698***|0.725***|0.578**|0.852***|0.753***|0.897**|0.628*|0.780***|
||(0.227)|(0.264)|(0.244)|(0.250)|(0.229)|(0.272)|(0.364)|(0.332)|(0.296)|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

_Source:_ Authors’ estimations. _Note:_ First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World _Development_ Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

# **6. Implications of demographic change for poverty and shared prosperity**

The macroeconomic dividends previously described can affect poverty reduction by boosting economic growth (Dollar et. al. 2002, 2015). However, there are also direct channels through which demographic change affects households in the bottom of the income distribution. Particularly, due to the positive association between fertility rate and income level, it is likely that 18
27

# a. Peru

The hyperinflation in Peru resulted from<sup>the over-expansionary domestic</sup> policies of Alan Garcia (annual data for Peru<sup>is presented in table 5.c). In</sup> August 1985, his administration launched a<sup>so called stabilization program</sup> aimed at reducing inflation which was mainly<sup>based on income policies, in the</sup> form of price and wage controls, and a fixed<sup>exchange rate. This was</sup> accompanied by expansionary monetary<sup>and fiscal policies. While monthly</sup> inflation initially fell from 10 to 3<sup>percent, the success was short lived,</sup> as could have been easily predicted. The<sup>government succeeded in preventing</sup> a full blown increase in inflation by<sup>keeping public sectors prices and the</sup> official exchange rate artificially low,<sup>and by financing the expansion in</sup> economic activity through losses in international<sup>reserves.</sup> In the end, however, the government ran out of reserves,<sup>and this triggered the beginning</sup> of a long hyperinflation.

One unique and intriguing feature<sup>of this hyperinflation was that, by</sup> and large inflation did not accelerate<sup>in an explosive manner, except at the</sup> very end. This stands in contrast with<sup>the CH, where once inflation reached</sup> hyperinflation levels it very quickly<sup>exploded (see figure 3.c). There was</sup> an extreme increase in inflation in<sup>September 1988 (when inflation exceeded</sup> 100 percent), but, to a large extent, this was equivalent<sup>to a once and for</sup> all increase in the price level in non-inflationary<sup>economies.</sup> Inflation then remained at the 40 percent per month<sup>step for around 7 months, and then</sup> fell to the 30 percent per month step for<sup>around a year. This ability to</sup> maintain relatively stable inflation at rates<sup>as high as 30 or 40 percent per</sup> month is unique to Peru, since the available<sup>evidence from high inflation</sup> economies indicates that inflations<sup>in excess of 20 percent per month are</sup> unstable and lead to hyperinflation<sup>(that was the case in Argentina and</sup> f-;'~~~~~~~~~~~~~~~~~~~~~~~~~
Table 5: Propensity score matching results

|Dependent variable:|Full tim|e school=1|
|---|---|---|
|Director’s enthusiasm level|-0.12|(0.30)|
|Director’s experience|-0.04|(0.03)|
|Avg. household size|0.41|(0.29)|
|Avg. nr. of rooms in homes|0.63|(0.47)|
|Avg. household equipment p.c.|-0.82|(0.88)|
|Perc. of mothers with at least secondary education|3.23|(4.77)|
|Perc. of mothers with tertiary education|-9.75|(6.96)|
|Total teacher experience|0.05|(0.03)|
|School in economic quintile 4|-0.02|(0.57)|
|School in economic quintile 5|-0.30|(0.73)|
|School equipment p.c.|0.03|(0.28)|
|Auxiliary buildings p.c.|-0.26|(0.35)|
|Class size|0.01|(0.03)|
|School has morning shift|-2.13|(0.71)**|
|School has afternoon shift|-2.25|(0.72)**|
|School has 2 shifts|-1.29|(0.49)**|
|Constant|-4.28|(2.43)|
|Observations|141||
|Pseudo R2|0.27||
|ChiSq|37.97||
|Number of full time schools|28||
|Number of comparison schools|113||

Notes: The dependent variable is a categorical variable that takes value 1 if the school became a full time school between 1996 and 2002. The explanatory variables are measured at baseline in 1996. The sample only includes schools that were identi...ed in 1996 as belonging to socioeconomic context quintiles 3, 4 and 5, excluding the most advantages quintiles 1 and 2. This is because none of the schools from quintiles 1 and 2 were transformed into full time schools, and hence the model has no statistical support for those quintiles. The omitted category is quintile 3. Household equipment, school equipment, and school auxiliary buildings are calculated using principal components estimation. (cf. section 3.4) The model is probit at the school level. Variables concerning mothers/homes/households were collected for students who took the 6th grade exam, and were aggregated at the school level. Standard errors are in parentheses. * signi...cant at 10 percent; ** signi...cant at 5 percent; *** signi...cant at 1 percent.

15
that has aimed to measure job quality. To construct our measure, we follow the approach of Del Carpio et al. (2017), and complement it using tetrachoric correlation matrices to account for the dichotomous nature of the indicators that compose the PCA. Furthermore, this represents the first job quality study using a PCA in Bolivia and adds to previous literature that uses different methodologies (Ya ̃nez (2018), Brummund et al. (2018), Hovhannisyan et al. (2022 _a_ )). As such, we measure job quality from the supply side of the labor market. Second, this paper uses a rich household dataset to assess the quality of jobs across time and in a country characterized by stringent labor market regulations and a high segmentation between formal and informal sectors. Our data allows us to assess the quality of jobs for non-wage workers in a developing country context, contributing to a literature that has, in many cases, been restricted to data availability only for the salaried cases (Hovhannisyan et al. 2022 _a_ ). Finally, our time period allows us to assess the consequences of the pandemic on job quality, contributing to the literature on the economic impacts of the COVID-19 shock.

Assessing job quality will provide policy makers with valuable insights into the state of labor markets, complementing traditional variables such as participation or unemployment rates that may mask labor market performance in developing countries with significant informal sectors. Firstly, it aids in identifying workers who experience poor job quality, taking into account not only sociodemographic characteristics but also analyzing the economic sector and employment structure. Secondly, recognizing that job quality holds welfare implications for workers and their families (Clark 2005 _b_ ), the study identifies specific dimensions of labor conditions that require improvement to enhance overall job quality, considering common factors such as social benefits, wages, and contracts, as well as additional factors like overqualification and child labor. Thirdly, it presents an easily replicable and evaluative approach for assessing labor conditions across different contexts. Finally, evaluating how different economic shocks, such as the COVID-19 pandemic, affect labor conditions is crucial, particularly for informal sector workers and those engaged in face-to-face (F2F) interactions.

This paper is organized as follows: Section 2 reviews the relevant literature in this research domain. Section 3 provides a concise overview of the labor regulation landscape, while Section 4 outlines the methodology employed for constructing the index. In Section 5 we present the diverse findings of the constructed Job Quality Index (JQI), and finally, Section 6 concludes the study with key insights and conclusions.

3
LESS DEVELOPED COUNTRIES

# 1. Estimates and Projections

|vear|putatiun|0".<br>ratio|Pertid|Growth<br>rt<br> rate|<br>cm|cm|TFR|SO<br>**Holm**|00<br> Finale|<br>R|X1000|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1965|<br>3665423|70.6|1985-89|2.10|30.9|9.7|3.89|60.8|63.2|78|109|
|1990|4070968|67.1|1990-94|1.97|28.9|9.0|3.58|62.2|64.9|70|96|
|1995|492598|65.8|1995-99|1.81|26.6|8.3|3.31|63.6|66.6|62|84|
|2000<br>2005|<br>4919089<br> <br>5350443|63.6<br>59.,|2000-04<br>2005-09|1.68<br>1.56|24.7<br>23.2|7.8<br>7.5|3.11<br>2.93|65.0<br>66.1|68.5<br>69.7|54<br>49|73<br>66|
|2010|<br>57U424|56.4|2010-14|1.46|21.9|7.3|2.77|67.2|71.0|43|58|
|2015|<br>6220396|54.2|2014-19|1.37|20.9|7.1|2.65|68.4|f2.4|38|50|
|2020|<br>6661296|53.7|2020-24|1.27|19.8|7.0|2.55|69.6|73.9|32|42|
|2025|<br>7099278|53.4|2025-49|0.91|16.6|7.5|2.27|72.7|77.6|20|25|
|2050|<br>8920513|54.5|2050-74|e!.47|13.8|9.1|2.08|76.4|81.9|8|11|
|2075<br>2100|10042148<br>10632609|60.8<br>67.7|2075-99<br>2100-24|0.23<br>0.11|12.6<br>12.1|10.4<br>11.0|2.06<br>2.06|78.9<br>80.9|84.9<br>87.3|5<br>3|6<br>4|
|2125<br>2150|10922198<br>11065164|71.0<br>72.7|2125-49|0.05|11.9|11.3|2.05|81.7|88.3|2|3|

# 2. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 754.<br>70-74<br>65-69<br>60-64<br>Dr s ! ~~~55.59 E<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>1 5-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br>3. Distributioni of population (percent): 1990 and 2025<br>hCCC22)<br>LD. C' 7 7<br><!-- End of picture text -->

# 3. Distributioni of population (percent): 1990 and 2025

3
Table 5: OLS estimates of the effect of ICT use on wages: Manufacturing

||(1)|(2)|(3)|
|---|---|---|---|
|**Dependent variable:** ∆**ln(Wage of whi**|**te-collar wo**|**rkers)**||
|∆Share of labor that uses computers|0.0011***|0.0010***|0.0011***|
||(0.0002)|(0.0002)|(0.0002)|
|∆Share of labor that uses Internet|0.0011***|0.0011***|0.0011***|
||(0.0002)|(0.0002)|(0.0002)|
|∆ln(stock of computer equipment/worker)|0.0137***|0.0038|0.0034|
||(0.0045)|(0.0049)|(0.0049)|
|**Dependent variable:** ∆**ln(Wage of blue**|**-collar wor**|**kers)**||
|∆Share of labor that uses computer|0.0007***|0.0007***|0.0007***|
||(0.0001)|(0.0001)|(0.0001)|
|∆Share of labor that uses Internet|0.0008***|0.0008***|0.0007***|
||(0.0002)|(0.0002)|(0.0002)|
|∆ln(stock of computer equipment/worker)|0.0299***|0.0173***|0.0169***|
||(0.0037)|(0.0041)|(0.0040)|
|**Controls**||||
|∆ln(capital/worker)|No|Yes|Yes|
|Age dummies|No|No|Yes|
|Size dummies|No|No|Yes|
|Observations|11,438|11,438|11,438|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

29