rafmacalaba/datause-encoder-data huggingface dataset:
Table I.12. Robustness Analysis for the Hours Worked Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|-0.229|-0.055|-0.262|-0.292|-0.310|-0.291|-0.229|-0.055|-0.262|-0.292|-0.310|-0.291|
||(0.212)|(0.208)|(0.219)|(0.204)|(0.192)|(0.207)|(0.212)|(0.208)|(0.219)|(0.204)|(0.192)|(0.207)|
|Obs. left|143|143|143|132|140|130|143|143|143|132|140|130|
|Obs. right|301|305|302|579|431|579|301|305|302|579|431|579|
|Mean values (Non-RAMV)|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|-0.049|-0.302|0.017|-0.244|-0.345|-0.227|0.153|0.081|0.138|-0.169|-0.263|-0.198|
||(0.308)|(0.326)|(0.322)|(0.255)|(0.294)|(0.253)|(0.343)|(0.391)|(0.388)|(0.282)|(0.291)|(0.291)|
|Obs. left|152|156|149|153|155|152|152|156|149|153|155|152|
|Obs. right|319|343|310|691|592|691|319|343|310|691|592|691|
|Mean values (Non-RAMV)|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|
|_Panel C: Polynomial of order two_|||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|0.424|0.663|0.543|0.034|0.145|0.176|0.014|0.136|0.138|-0.368|-0.579|-0.334|
||(0.525)|(0.623)|(0.502)|(0.343)|(0.393)|(0.385)|(0.374)|(0.521)|(0.388)|(0.348)|(0.430)|(0.396)|
|Obs. left|155|143|155|155|143|155|155|143|149|155|143|155|
|Obs. right|340|301|331|680|636|607|340|301|310|680|636|607|
|Mean values (Non-RAMV)|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|3.937|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
# **Table 1. The Impact of Trade, Foreign R&D, Education and Governance on log TFP in LAC**

|**Variables**|**(i)**|**(ii)**|
|---|---|---|
|**ln****_NRD_**|**0.248**<br>**_(7.40)***_**|**0.264**<br>**_(6.39)***_**|
|**ln****_NRD_*****_DLAC_**|**-0.221**<br>**_(-4.46)***_**|**-0.243**<br>**_(-6.06)***_**|
|**ln****_NRD_*****_DR_**||**0.081**<br>**_(2.11)**_**|
|**ln****_NRD_*****_DR_*****_DLAC_**||**0.028**<br>**_(3.87)***_**|
|**_E_**|**4.592**<br>**_(2.76)***_**|**3.988**<br>**_(2.51)**_**|
|**_E_*****_DLAC_**|**12.301**<br>**_(3.50)***_**|**11.641**<br>**_(3.85)***_**|
|**_G_**|**0.656**<br>**_(3.11)***_**|**0.658**<br>**_(3.21)***_**|
|**_G_*****_DLAC_**|**2.579**<br>**_(1.93)* _**|**4.478**<br>**_(3.50)***_**|
|**Adjusted R**<sup>**2**</sup>|**0.24**|**0.25**|
|**Observations**|**6948**|**6948**|

Note: Figures in parentheses are t-statistics. Significance levels of 1%, 5% and 10% are indicated by ***, ** and * respectively. Regression results on country, year and industry dummies, and the constant, are not reported. _NRD_ is the trade-related North-foreign R&D. _DR_ = 1 for R&D-intensive industries and _DR_ = 0 for low R&D-intensity industries. _DLAC_ =1 for Latin American and Caribbean countries and _DLAC_ =0 for non-LAC countries. _E_ is the secondary school completion ratio for the population aged 25+. _G_ is the average of the six governance indicators described in Section 3.

20
# The Budget

# The current account of the budget is given by:

(A1.10) OFIb + P&Lc + iF*FXbc-l+ TI + TDo + TDp + Tfb = = Sub + Tbo + Tbp + iR*CRb-l+ ic Bbd-1+ ic0 Bbp-l+ iF-E-FGb-1+ Cb + Sb Distributed profits and losses, interest receipts from FX deposits at Central Bank and interest payments on advances of the Central Bank do not appear in the original fiscal data. However, their inclusion does not alter total expenditure or revenue by construction. From total factor income obtained as hard data from fiscal accounts we subtract profits and losses from the Central Bank (P&Lc) and interest receip. on FX deposits (iF.FXbc-l) to obtain other factor income from the budget. This operation leaves total fiscal revenue unaffected. On the expenditure side current transfers to the private sector (Tbp) adjust to maintain the total level of expenditure.

The other variables of the budget's current account with the ex_eaption of subsidies (Sub), public consumption (Cb), transfers from abroad (Tfb)and stocks of domestic debt (Bbp,Bbd) are also obtained from original fiscal data. Subsidies and public consumption are taken from the national accounts to ensure consistency.<sup>5</sup> Transfers from abroad are obtained from the balance of payments by multiplying the dollar value with the average 5 Although consumption data from both sources were identical, subsidies could not be identified from the fVscal data.

24
611) life-cycle theory with optimal zero assets at the end of one’s life is manifestly wrong as people routinely leave large inheritances. There were, Piketty intimates, also some political undertones which made these theories particularly attractive: constant factor shares led to the shelving of the issue of distribution, human capital put on the same footing of “capitalists” workers and property-owners; lifecycle theory implied that we need not worry about inherited wealth. And when one thinks of these theories together, rather than separately, it does seem that they all had a very optimistic halo which may be perhaps at odds with what the Zeitgeist is today. It does not necessarily makes them wrong though, but Piketty is, I think, right to underline that, from Ricardo and Corn Laws, all successful economic theories tended to reflect the prevalent issues and the spirit of the times. Indeed this may be an advantage for the acceptability of Piketty’s theory today, but nobody can tell whether this would remain so in the entire century and beyond.

**7. Concentration of incomes vs. inequality or fiscal data vs. household surveys.** Piketty has revolutionized the field of income distribution by the use of fiscal sources and by his focus on top income shares. It is thus striking, although not altogether surprising, to read a book which in a significant part deals with inter-personal income distribution, but contains not a single reference to household surveys or to the Gini coefficient. In effect the latter is dismissed as an “aseptic” measure of inequality because of its lack of intuitive meaning (what does a Gini of 0.45 mean to an average person?). It conveys, Piketty argues, very little information about income distribution. Piketty thinks that it is perhaps that very “aseptic” feature that contributed to Gini’s popularity with statistical offices and politicians. On the contrary, income shares are intuitive and meaningful. Piketty’s preference is to split the distribution into four parts: bottom 50 percent, the next 40 percent, top decile, and as a part of it, top 1 percent.  It is indeed an appealing way to look at the distribution even if the reader may at times get tired or confused by a plethora of numbers and shares, which at some point, not unlike the reviled Gini, begin to lose their intuitive appeal.

Piketty’s use of fiscal data can also be questioned as the sole (or even the best) approach to the analysis of income distribution. Its advantages are already mentioned: long-term series (century or more in developed countries), ability to focus on top incomes and to capture them much better than household surveys.<sup>18</sup> And also to focus on what Piketty correctly calls “concentration” of incomes rather

> 18 Partly because household surveys are always samples and rich individuals are few in numbers (although if we had data on the entire population their inclusion may have a non-trivial impact on inequality statistics), and partly because the rich refuse to participate in surveys more often than the non-rich (see e.g.  Deaton 2005).

15
20 even more significant, ecosystem processes, rather than just stocks of physical resources, need to be considered as resources and capital which should be conserved - as well as used more effectively through new technology. Differnces in the area of rate limitadons on the physical flow of matter and energy through the economy (out of, then back into the ecosystem at large) arm also important. These need to be integrated into a common discipline. This would lead to a much more explicitly managed relationship between man and nature, sometimes still involving "trade-offs," but with better accounting of the true values of functioning natural systems to economies, this perception will decrease. The approach is sdll anthopocentric at its core; aU this concern for nature is based on the fact that hurting nature is beginning to hurt economic man. Thus, the instrumental economic paradigm prevails, only it is enlarged to encompass some basic ecological principles in an attempt to maintain ecosystem/life support system stability for the support of sustainable development.

39. This approach has been called the _"Global Efficiency"_ path.<sup>29</sup> It expands economic analysis with systems analysis methods. The model of the closed economic system is replaced with the "biophysical economics" model of a thermodynamicaly open economy embedded within the ecosystem: biophysical resources (energy, materials, and ecological processing cycles) flow from the ecosystem into the economy and degraded (non-useful) energy and other by-products (poilution) flow back out to the ecosystem (see Figure 3).30 Energy efficiency in particular and resource conservation (or efficiency improvement) in general,<sup>3</sup> 1 pollution prevention (rather than clean-up) technologies, restoration ecology, ecosystem and social health monitoring, and the "polluter pays principle"<sup>32</sup> are management strategies that will probably be implemented on a large scale. Correcting incentive and punishment systems in order to haress market forces for efficient environmental management is a major theme. _In essence, ecology is being economized._ Much of the work is focussed on "getting the prices (of all resources) right." 40. The mislabeling of various societal messes as "environmental problems" is in many cases what helps to perpetuate them, because it enables professionals to conceive of them as "externalities" to be solved, cleaned up, or managed by different people from those who were responsible for creating the messes, rather than as evidence of a faulty system of logic by which :S Sachs, Wolfgang, 1988. "The Gospel of Global Efficiency: On Wordwatch and odier repow on th stat of te world.' _IFDA Dossier 68:_ 33-39 (November-Decembr).

30 Daly, Herman E., 1989. Q.ci1.

31 IUCN, 1980. _World Conservation Strategy,_ Ineraonal Union for the Consemvaon of Naue and Naual Resources, Gland, Swizerland.

32 OECD, 1975. _The Polluter Pays Principle,_ Paris, 117 pp. See also, Kapp, K. William, 1950,1971. _The Social Costs of Private Enterprise._ Schocken Books, New Yock&
dollar amounts across the distribution increase quickly as we move toward richer households, showing that the regressivity of these subsidies is steep and consistent across countries. Diesel is important only in Morocco and in Tunisia and only for the top quintile.

Figure 1: Distribution of Energy Subsidies, in US$ at PPP/capita/year <!-- Start of picture text --> Liquefied Petroleum Gas Electricity<br>400 1,000<br>350 900<br>300 800<br>250 700<br>200 600<br>150 500<br>100 400<br>300<br>50<br>200<br>0<br>Libya Iran, Islamic Djibouti Tunisia Morocco 100<br>Rep. 0<br>Iran, Islamic Rep. Libya Tunisia Morocco<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Gasoline Diesel<br>1,200 140<br>1,000 120<br>100<br>800<br>80<br>600<br>60<br>400<br>40<br>200 20<br>0 0<br>Iran, Islamic Rep. Libya Tunisia Morocco Tunisia Iran, Islamic Rep. Libya<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Energy subsidies, in US$ at PPP/capita/year Energy subsidies, in US$ at PPP/capita/year<br>PPP/capita/year PPP/capita/year<br>Energy subsidies, in US$ at Energy subsidies, in US$ at<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

The variety and amounts of food subsidies are much smaller than energy subsidies (Figure 2). They are below $40-PPP for flour and oil and below $20-PPP for sugar. The only significant subsidies are for bread in Libya and the Islamic Republic of Iran where the amounts can reach $900-PPP and $200-PPP, respectively, for the richest quintile, and the pattern is regressive. In general, larger subsidies accrue to richer quintiles with monotonic increases across quintiles. This pattern holds for sugar, bread, and oil for all countries and for flour in Libya and Djibouti, but not for Morocco and the Islamic Republic of Iran, where for flour subsidies are larger for poorer quintiles. Therefore, exceptions to the pro-richness of subsidies may exist also for food products.

10
- Dinkelman, T., Kumchulesi, G., and Mariotti, M. (2024). Labor migration, capital accumulation, and the structure of rural labor markets. _Review of Economics and Statistics_ , pages 1–46.

- Dinkelman, T. and Mariotti, M. (2016). The long-run effects of labor migration on human capital formation in communities of origin. _American Economic Journal: Applied Economics_ , 8(4):1–35.

- Docquier, F., Lodigiani, E., Rapoport, H., and Schiff, M. (2016). Emigration and democracy. _Journal of Development Economics_ , 120:209–223.

- Docquier, F. and Rapoport, H. (2012). Globalization, brain drain, and development. _Journal of Economic Literature_ , 50(3):681–730.

- Dustmann, C., Sch ̈onberg, U., and Stuhler, J. (2017). Labor supply shocks, native wages, and the adjustment of local employment. _The Quarterly Journal of Economics_ , 132(1):435– 483.

- Dustmann, C., Vasiljeva, K., and Piil Damm, A. (2019). Refugee migration and electoral outcomes. _The Review of Economic Studies_ , 86(5):2035–2091.

- Egorov, G. and Sonin, K. (2023). Why did Putin invade Ukraine? A theory of degenerate autocracy. Technical report, National Bureau of Economic Research, Cambridge, MA.

- Endoh, T. (2010). _Exporting Japan: Politics of emigration to Latin America_ . University of Illinois Press.

- Foged, M. and Peri, G. (2016). Immigrants’ effect on native workers: New analysis on longitudinal data. _American Economic Journal: Applied Economics_ , 8(2):1–34.

- Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X. (2010). MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. _Remote Sensing of Environment_ , 114(1):168–182.

45
identified in this case.' In this study, the aggregate Cobb-Douglas production function is represented in the natural logarithmic form, with all the variables, except chronological time, expressed in terms of their natural logarithms. Thus, the parameters may be interpreted as production elasticities. The elasticity of educational capital is, however, allowed to differ across geographic regions. Five regions are distinguished: Sub-Saharan Africa, East Asia, Latin America, Middle Ea, +-North Africa, and South Asia. - Three different measures of edu'cational capital are used primary education, secondary education, and primary and secondary education together. In addition, the trend rates of growth of total factor productivity, or equivalently the trend rates of growth of output net of the growth of inputs, are also allowed to differ across regions as well as between pre- and post-1980. These trends reflect not only the influence of technical progress, but also other regional or worldwide factors, for example, the macroclimatic change in Sub-Saharan Africa, the slowdown of world trade and the drying-up of credit and aid flows to the developing world in the 1980s, depletion of natural resources, the rising public concern for the environment in the newly industrialized economies, and the decline in the average length of the workweek, etc.

The aggregate Cobb-Douglas production function is estimated, after the natural logarithmic transformation, in the first-differenced form, by ordinary least-squares. The rationale for using the firstdifferenced form lies in our assumption concerning the stochastic disturbance term in the production function. We a.sume that the stochastic disturhance te- -i represents mostly technical change, which, while unpredictable, has persistent effects on current as well as future outputs once it has occurred. This may be contrasted to the case in which the stochastic disturbance term represents mostly weather, the 'The non-identification of input-specific augmentation factors is well-known for the Cobb-Douglas case. With other production functions, such as the transcendental logarithmic production function introduced by Christensen, Jorgenson and Lau (1973), the augmentation factors can in fact be separately identified and estimated.

7
description of the specialization and diversity in Indian manufacturing districts and the trends over time. Section 4 estimates the role of these district traits in manufacturing production functions and the employment growth of districts over the 1989-2010 period. The final section concludes and provides some thoughts about future work on these important topics.

# **2. Indian Manufacturing Data and Index Calculations**

This section begins with a description of the Indian and U.S. manufacturing data that we use in our study. We then outline how we calculate our indices of district-level specialization and diversity and describe some of their important empirical properties.

## _Indian Manufacturing Data_

We employ repeated cross-sectional surveys of manufacturing establishments carried out by the government of India for the fiscal years of 1989, 1994, 2000, 2005, and 2010. In all cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. The organized and unorganized sectors of Indian manufacturing are surveyed separately, as described next. In every period except the last one, our surveys for the two sectors were undertaken contemporaneously. In the last period, we combine the 2009-2010 survey for the organized sector with the 2010-2011 survey for the unorganized sector. We will again refer to this period as 2010 for simplicity.

The organized sector comprises establishments with more than 10 workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. The organized sector accounts for over 80% of India’s manufacturing output, while the unorganized sector accounts for over 80% and 99% of Indian manufacturing employment and establishments, respectively (Ghani et al., 2013a).

The organized manufacturing sector is surveyed by the Central Statistical Organization through the Annual Survey of Industries (ASI). Our data for the unorganized sector come from the National Sample Statistics (NSS). These surveys are used for many published reports on the state of Indian businesses and government agency monitoring of the Indian economy. The typical 4
order to verify import duties. Thus, s<sup>M</sup> increases with t : Second, it is generally easier to detect fraud related to misclassi...cation of products or misdeclaration of country of origin for di¤erentiated products than for homogeneous products. For example, customs o¢ cers can easily tell a German car from a Japanese car, but may have a hard time identifying Chinese steel rods from Brazilian steel rods. Thus, s could be higher for di¤erentiated products than homogeneous products. This could lead to the e¤ect of NTMs on discrepancies to be smaller for di¤erentiated products than for homogeneous products if s<sup>X</sup> increases more than s<sup>M</sup> .

Third, developed countries may have better customs administrations and resources to inspect products, relative to developing countries. Thus, the di¤erences between the detection rates of the importing and exporting countries will be larger when the exporting country is a developing country rather than a developed country. This implies that the e¤ect of NTMs on discrepancies will be larger when the developed countries import from the developing countries.

Finally, the e¤ects of tari¤s on trade discrepancies depends on the e¤ect of tari¤s on the detection rate: Equation (11) shows that, if the detection rate is very sensitive to tari¤s, then an increase in tari¤s could lead to a rise in trade discrepancies. This is because while the importing ...rms may be increasing under-reporting when facing a higher tari¤, they risk being detected and face a much larger penalty. So in equilibrium, the size of under-reporting may actually 11
# **Table 16 Tunisia’s tariff structure, 2005**

|_Sector_|_EU preferential_<br>_tariff (%)_|_MFN tariff_<br>_(%)_|_Imports 2004_<br>_($US $ million)_|
|---|---|---|---|
|Live animals, products|91.8|91.8|40.5|
|Dairy products|94.2|95.3|42.2|
|Coffee, etc, sugar, cut flowers|66.1|70.3|188.5|
|Fruit and veg|94.6|96.8|61.7|
|Cereals|45.1|45.3|342.3|
|Oil seeds and fats|38.0|42.6|309.9|
|Beverages and tobacco|23.9|31.3|70.7|
|Other agricultural products|21.8|28.0|126.8|
|Fish and products|40.0|40.1|28.7|
|Mineral products|5.3|25.6|584.8|
|Metals, products|4.0|20.4|1051.1|
|Chemical etc|2.1|15.5|1371.4|
|Leather products|7.6|32.8|270.1|
|Wood products, pulp, paper|8.2|31.6|426.2|
|Textiles and apparel|9.1|31.1|2464.8|
|Transport equipment|4.1|18.3|924.4|
|Other machinery and equipment|2.7|12.5|1474.5|
|Electrical machinery|5.1|24.0|1448.7|
|Other manufacturing|4.2|23.7|396.8|

_Source_ : WTO (2005).

46