rafmacalaba/datause-encoder-data huggingface dataset:
government’s underlying production of public goods. By buying relatively more from the pool of small firms, the government also buys from firms that are fundamentally less productive and thus charge higher prices. Under a constant government expenditure, this phenomenon inevitably translates into a higher price index paid by the government and a lower provision of public goods. When accounting for this reduction in the amount of real final goods provided by the government, the overall effect on GDP shrinks further from 1% to 0.05%.

We also conduct an alternative policy experiment that consists in promoting small firms’ participation by reducing the average size of contracts. This reform aims to mimic the European Commission’s strategy to increase the presence of small firms in public procurement in Europe.<sup>6</sup> Our main finding is that this alternative policy counterfactual would reach a higher number of small firms than in our preferred counterfactual but would actually generate a fall in aggregate GDP of 0.85%. The reason for this decline in GDP is that the reduction in incentives of relatively big firms to accumulate capital – the _long-run general equilibrium effect_ – is particularly high due to the fall in the average size of procurement contracts. Our results hence imply that the particular way in which the promotion of small firms is implemented is crucial to understand its aggregate effects.

# **1.1 Related literature**

There is practically no literature that analyzes how the microeconomic aspects of public procurement can affect the macroeconomy. One recent exception is Cox, Muller, Pasten, Schoenle and Weber (2021), who document several new facts using micro-level data on public procurement contracts awarded by the U.S federal government, and investigate how accounting for these facts— in particular that government spending is concentrated in sectors where prices are more sticky— can affect the short run fiscal transmission mechanism in a New Keynesian model. Our interest differs in that we focus on quantifying the long-run macroeconomic effects of different procurement allocation systems through their impact on firm dynamics.

Governments may harm the long-run economic performance of countries through the implementation of policies that distort the allocation of resources across firms. Some examples are credit subsidies to state-owned-enterprises (Song, Storesletten and Zilibotti, 2011), size-dependent policies (Guner, Ventura and Xu, 2008; Garc ́ıa-Santana and Pijoan-Mas, 2014), labor market regulations (Garicano, Lelarge and Reenen, 2016), tariffs (Berthou, Chung, Manova and Sandoz, 2019), or capital markets regulation (Bau and Matray, 2023). However, one of the most important roles that governments play in modern economies, i.e., their role as buyers of goods and services from private sector firms, has been overlooked. We focus on this by analyzing specific size-dependent procurement policies aimed at helping small firms.

Our focus on firm-level financial frictions as a channel through which public procurement can

> 6See Trybus (2014) for details.

4
**Table 10. Adjustment of log-linear models. Youths from 18-35 years of age of ENCEL 2017. Comparison by sex.**

|||**Consta**<br>|**nt fluidity**<br>|||**Non-co**<br>|**nstant fluid**<br>|**ity**|
|---|---|---|---|---|---|---|---|---|
|**Type of model**|**g.l.**|**G**<sup>**2**</sup>|**X**<sup>**2**</sup>|**BIC**|**g.l.**|**G**<sup>**2**</sup>|**X**<sup>**2**</sup>|**BIC**|
|1. Independence|50|285.96|309.47|72.13|||||
|2. Homogeneous diagonal|49|80.38|81.36|-129.17|48|77.49|77.76|-127.79|
|3. Diverse diagonal|47|72.34|71.55|-128.66|44|68.86|67.81|-119.31|
|4. Symmetry (2p) with homogeneous diagonal|48|76.65|77.65|-128.63|46|72.23|73.25|-124.49|
|5. Symmetry (3p) with homogeneous diagonal|47|76.64|77.67|-124.36|44|71.43|71.87|-116.74|
|6. Symmetry (4p) with diverse diagonal|47|70.92|69.99|-125.81|42|64.8|64.98|-114.82|
|7. Symmetry (5p) with diverse diagonal|45|70.86|69.94|-121.59|40|63.89|63.46|-107.17|
|8. Corners of (2p) with homogeneous diagonal|48|76.99|78.43|-128.29|46|71.62|73.49|-125.11|
|9. Corners of (3p) with homogeneous diagonal|47|74.87|77.1|-126.13|44|66.83|70.3|-121.34|
|10. Corners of (4p) with diverse diagonal|46|67.19|65.94|-129.54|42|61.21|60.57|-118.41|
|11. Corners of (5p) with diverse diagonal|45|63.95|62.92|-128.5|40|54.61|55.3|-116.46|

Note: g.1. = degree of freedom; G<sup>2</sup> = Deviance; X<sup>2</sup> = Pearson’s chi square; BIC = Bayesian Index Criteria. Source: Own elaboration from ENCEL 2017 (n=3,084).

Another relevant question is if, when the youths migrated from their localities of origin, they experienced different patterns and magnitudes of association between occupational origins and destinations. Table 11 shows the results of the models with and without constant fluidity, considering youths by migratory status. The non-constant fluidity models, which assume differences in relative mobility between both groups, are the ones with clearly a better fit, by presenting differences between 13 and 17 points of BIC regarding the constant fluidity models. Among the array of theoretical models, in the non-constant fluidity version, models 2, 4, 8 and 9, in that order, present the best fits.

37
Table 3. State-level overall Gini and growth at different percentiles of state income distribution (dependent variable = per capita income growth at a given percentile of state income distribution; 1960-2010; five 10-year periods)

||dlny5|dlny10|dlny25|dlny50|dlny75|dlny90|dlny95|dlny99|
|---|---|---|---|---|---|---|---|---|
|Gini|-0.245***|-0.242***|-0.127***|-0.0280|0.0319*|0.0525***|0.0645***|0.0728***|
||(-3.17)|(-3.51)|(-2.97)|(-1.09)|(1.69)|(3.05)|(3.68)|(3.42)|
|edu_ms_age2139|0.0626**|0.0847***|0.0935***|0.0934***|0.0849***|0.0830***|0.0741***|0.0792***|
||(2.34)|(3.52)|(4.70)|(5.48)|(5.40)|(5.57)|(4.89)|(3.81)|
|edushort1518|-0.0372***|-0.0286***|-0.0128**|-0.00963*|-0.0107**|-0.010***|-0.0081**|-0.00426|
||(-3.46)|(-3.40)|(-1.99)|(-1.91)|(-2.56)|(-2.64)|(-2.12)|(-0.81)|
|age015|-0.127**|-0.187***|-0.172***|-0.178***|-0.158***|-0.159***|-0.146***|-0.152***|
||(-2.05)|(-3.36)|(-3.54)|(-4.13)|(-4.25)|(-5.01)|(-4.82)|(-3.98)|
|age65|-0.0640|-0.106**|-0.107***|-0.121***|-0.111***|-0.111***|-0.098***|-0.0680*|
||(-1.00)|(-2.01)|(-2.62)|(-3.47)|(-3.63)|(-3.98)|(-3.05)|(-1.73)|
|olf_female|-0.0957***|-0.0794***|-0.0515***|-0.0446***|-0.035***|-0.028***|-0.027***|-0.0195|
||(-3.69)|(-4.15)|(-3.51)|(-3.62)|(-3.31)|(-2.62)|(-2.65)|(-1.29)|
|Mid-West|0.000151|0.000903|0.00118|0.000610|-0.000247|-0.000793|-0.00165|-0.00216|
||(0.06)|(0.42)|(0.72)|(0.44)|(-0.19)|(-0.67)|(-1.45)|(-1.51)|
|South|0.000778|0.000539|0.000406|0.000119|0.0000001|-0.000039|-0.000814|-0.000795|
||(0.24)|(0.21)|(0.21)|(0.07)|(0.00)|(-0.03)|(-0.55)|(-0.47)|
|West|0.00285|0.00118|-0.000520|-0.000872|-0.000452|-0.000147|-0.000996|-0.000899|
||(0.90)|(0.45)|(-0.26)|(-0.53)|(-0.30)|(-0.10)|(-0.69)|(-0.53)|
|lnyxx|-0.0677***|-0.0725***|-0.0631***|-0.0567***|-0.048***|-0.042***|-0.035***|-0.037***|
||(-9.85)|(-10.20)|(-9.64)|(-9.91)|(-9.44)|(-9.63)|(-7.95)|(-6.59)|
|constant|0.783***|0.852***|0.728***|0.657***|0.559***|0.511***|0.432***|0.464***|
||(8.13)|(8.73)|(8.60)|(9.18)|(9.18)|(9.82)|(8.54)|(7.32)|
|N|245|245|245|245|245|245|245|245|
|adj. R-sq|0.731|0.776|0.795|0.770|0.742|0.758|0.812|0.883|

Note: All right-hand side variables estimated at time=0. Income is household per capita. Gini calculated across individuals ranked by their household per capita income. _t_ statistics in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Time-period dummies are included but not reported.

19
<u>Table F.1: Public and Placebo Certifcation Efects</u>

||(1)<br>Index|(2)<br>Employed|(3)<br>Hours<sup>c</sup>|(4)<br>Earnings<sup>c</sup>|(5)<br>Hourly<br>wage<sup>c</sup>|(6)<br>Written<br>contract|
|---|---|---|---|---|---|---|
|Public|0.120|0.052|0.201|0.338|0.197|0.020|
||(0.027)|(0.012)|(0.052)|(0.074)|(0.040)|(0.010)|
|Placebo|0.027|0.020|0.039|0.069|0.054|0.005|
||(0.043)|(0.027)|(0.075)|(0.184)|(0.129)|(0.021)|
|p: public = placebo|0.041|0.245|0.045|0.147|0.266|0.471|
|Placebo / public ratio|0.222|0.379|0.196|0.204|0.275|0.238|
|# observations|6609|6607|6598|6589|6574|6575|
|# clusters|84|84|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. All outcomes use a 7-day recall period. Outcomes marked with<sup>c</sup> use the inverse hyperbolic sine transformation. The index in the first column shows the inverse covariance-weighted averages of the 5 labor market outcomes, following Anderson (2008). The mean ratio of placebo to public effects is 0.258 for all 5 non-index outcomes. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t know the answer.

62
**Table 1. Data sources**

|**Variable name**|**Description**|**Transformation**|**Source**|
|---|---|---|---|
|GDP per capita|Gross domestic product<br>per capita, constant prices|Log|IMF World Economic<br>Outlook|
|Gross government<br>debt|General government gross<br>debt|Percent of GDP|IMF World Economic<br>Outlook|
|Climate<br>vulnerability index|Propensity or<br>predisposition of human<br>societies to be negatively<br>impacted by climate<br>hazards.|Index|University of Notre<br>Dame|
|Government<br>effectiveness|Perceptions of the quality<br>of public services|Index|Worldwide Governance<br>Indicators, World Bank|
|Broad money|Annual growth in the sum<br>of currency outside banks;<br>demand deposits; savings<br>and foreign currency<br>deposits; bank and<br>traveler’s checks; and<br>other securities.|Annual growth,<br>percent|World Development<br>Indicators|
|Spending cyclicality|Correlation between the<br>cyclical components of<br>real GDP and government<br>expenditure from 1990 to<br>2021 where the cycle is<br>defined using an HP-filter<br>with lambda of 6.25|Correlation|Own calculations|
|Output gap|Real GDP from HP-filter<br>trend with lambda of 6.25|Percent of trend<br>GDP|Own calculations|
|Primary budget<br>balance|Average primary balance<br>from 2000 to 2021|||

_Source:_ World Bank. _Note:_ HP = Hodrick Prescott.

22
_Table 2: The impact of precipitation on GDP growth by income level_

||(1)<br>High<br>Income|(2)<br>Developing|(3)<br>Low<br>Income|(4)<br>Lower Middle<br>Income|(5)<br>Upper<br>Middle<br>Income|
|---|---|---|---|---|---|
|Rainfall|-0.204|1.595***|2.201**|1.792***|1.614*|
||(0.546)|(0.356)|(0.827)|(0.440)|(0.686)|
|Rainfall<sup>�</sup>|-0.113|-0.253***|-0.791**|-0.257**|-0.265+|
||(0.125)|(0.072)|(0.281)|(0.085)|(0.150)|
|Temperature|0.246*|0.579+|-0.072|2.898+|0.428|
||(0.115)|(0.318)|(0.538)|(1.502)|(0.349)|
|Temperature<sup>�</sup>|-0.012**|-0.016*|-0.009|-0.068*|-0.012|
||(0.004)|(0.008)|(0.011)|(0.030)|(0.010)|
|Observations|201,466|667,981|110,324|165,151|392,506|
|R-squared|0.443|0.344|0.392|0.403|0.303|

Clustered standard errors at the administrative 1 level in parentheses 13
Figure 7: Pre-Exit Behavior of Establishments (Simulation) <!-- Start of picture text --> 3000<br>2500<br>2000 >11<br>Sales<br>1500 9-11<br>1000<br>6-9<br>500 3-6<br>0-3<br>0<br>1 2 3 4 5 6 7 8 9 10 11<br>Age<br><!-- End of picture text -->

# 4 Empirical Analysis of Industry Evolution

In this section, I estimate the model separately for five 3-digit industries in the Chilean Manufacturing sector. I analyze whether the dispersion observed in establishment behavior in the aggregate manufacturing sector also holds at the 3-digit industries. All the values of sales and wages are given in thousands of 1986 real Chilean peso. The nominal values are deflated by the aggregate GDP deflator from World Bank Development Indicators database.

## 4.1 Industry Comparison

Five industries that will be analyzed, their 3-digit SIC codes, and the total numbers of establishments observed were given in Table 1. Doing the same estimation exercise five times aims to capture the flexibility of the model in explaining different industry structures.

In the Appendix, I show the data analysis on the shape of the size distributions, on turnover, and on growth rates across industries. The size distribution of each industry is estimated nonparametrically (using Kernel regressions) (see Figure 10). The shapes of these distributions change very little over time. Food and Paper industries differ from the other three industries in several ways with the most outstanding difference being on the shape of the size distribution. The average sales and the variation of sales are larger and the coefficient of skewness of the log size distributions is higher in these two industries (see Figure 11). The ranking of the industries is preserved for the variation in size when I analyze the industry data with respect 21
interest appears to have come much later. But possibly there are long lags. It is likely that the English-language literature of the late 20th century is more representative of the Englishspeaking world as a whole than that in 1800, which was far more focused on Britain, Western Europe and America.

There are other factors to be aware of in using the _Viewer_ . It does not allow stratifications of the corpus beyond language, so we can only make limited conditional searches beyond language. (One can search books published in America, which are identified as “American English” in the _Viewer_ window for language; the same holds for “British English.”) One cannot identify how a given word count is distributed across books. And the new tool has little or no ability to directly measure linkage between ideas, such as when one set of ideas influences (positively or negatively) another. That requires more careful reading. And there are subtleties of usage and context that could probably never be detected by a machine.

While recognizing these limitations, the _Viewer_ allows us to systematically quantify the evolution of public awareness of ideas, as revealed by the literature. The new tool allows us to trace the flow of ideas, and their relative influence. I will focus mainly on the English language, which has been the main language of the social sciences in the 20th century. However, given the prominence of the French literature on inequality (in particular), I will also make some use of the French language _Google Books_ .

# **Historical perspectives on the two Poverty Enlightenments**

What might account for the surge in attention to poverty in the last half of the 20th century in Figure 1? I can suggest two factors. The first is the sharp rise in concern over poverty in affluent societies that built up from the 1960s, notably in the US. In the wake of the civil rights movement (starting around 1955), the rediscovery of poverty in the midst of affluence in mid-20th century America was stimulated by important social commentaries, including John Kenneth Galbraith‟s (1958) _The Affluent Society_ and Michael Harrington‟s (1962) _The Other America_ (both best sellers at the time). These helped initiate a political response in the US, including new social programs targeting poor families, under the Johnson administration‟s _War on Poverty_ (Sundquist, 1968). References to both Galbraith‟s _The Affluent Society_ and 7
|**Product**<br>**Product Description**|**Increase in Import Value**<br>**(US$, thousands)**|**Country with**<br>**in Imp**|**Largest Increase**<br>**ort Value**<br>**Country with Largest Decrease in**<br>**Import Value**|
|---|---|---|---|
|851762<br>Machines for the reception, conversion and transmission or regeneration of voice, images or other data,|4,660|China|Russian Federation|
|including switching and routing apparatus||Change in import value|3,418<br>-543|
|||Import value in 2008|114,461<br>8,054|
|||% Change in import value|2.99<br>-6.74|
|940360<br>Other wooden furniture|3,454|China|Russian Federation|
|||Change in import value|3,584<br>-3,624|
|||Import value in 2008|18,574<br>49,285|
|||% Change in import value|19.30<br>-7.35|
|850440<br>Static converters|3,352|China|Russian Federation|
|||Change in import value|1,579<br>-1,236|
|||Import value in 2008|29,845<br>12,263|
|||% Change in import value|5.29<br>-10.08|
|401110<br>New pneumatic tyres, of rubber of a kind used on motor cars (including station wagons and racing cars)|3,217|China|Russian Federation|
|||Change in import value|3,802<br>-4,137|
|||Import value in 2008|26,356<br>65,852|
|||% Change in import value|14.43<br>-6.28|
|870410<br>Dumpers designed for off-highway use|3,152|Japan|Belarus|
|||Change in import value|1,211<br>-1,297|
|||Import value in 2008|13,845<br>11,455|
|||% Change in import value|8.75<br>-11.32|
|80810<br>Apples|3,128|China|Kyrgyz Republic|
|||Change in import value|3,069<br>-2,065|
|||Import value in 2008|22,575<br>13,553|
|||% Change in import value|13.59<br>-15.24|
|880230<br>Aeroplanes and other aircraft, of an unladen weight exceeding 2,000 kg but not exceeding 15,000kg|2,878|Brazil|Russian Federation|
|||Change in import value|2,580<br>-1,492|
|||Import value in 2008|61,146<br>21,733|
|||% Change in import value|4.22<br>-6.87|
|854449<br>Other insulated wire, cable, and other insulated electric conductors, whether or not fitted with connectors;|2,779|Italy|Russian Federation|
|optical fibre cables, made up of individually sheathed fibres||Change in import value|3,200<br>-3,638|
|||Import value in 2008|41,250<br>80,282|
|||% Change in import value|7.76<br>-4.53|
|640291<br>Footwear covering the ankle with outer soles and uppers of rubber or plastics|2,632|China|Russian Federation|
|||Change in import value|2,898<br>-277|
|||Import value in 2008|53,270<br>2,006|
|||% Change in import value|5.44<br>-13.81|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 57
# **References:**

Bennear, L., and S. Olmstead. 2008. The impacts of the ‘right to know’: Information disclosure and the violation of drinking water standards. _Journal of Environmental Economics and Management_ .

Bennear, L.S. 2008, What Do We Really Know: The Effect Of Reporting Thresholds On Inference Using Environmental Right-To-Know Data, _Regulation and Governance_ , 2(3): 293-315.

- Blacconiere, W.G., and W.D. Northcut. 1997. Environmental information and market reactions to environmental legislation. _Journal of Accounting, Auditing and Finance_ , 12, 2, 149-178.

.

- Blackman, A., S. Afsah and D. Ratunanda.  2004.  How do public disclosure pollution control programs work? Evidence from Indonesia. _Human Ecology Review_ 11(3): 235-246.

- Bougherara, D., G. Grolleau and N. Mzoughi.  2005.  Can information-based policies do more harm than good? INRA-ENESAD UMR CESAER Working Paper No. 2005/5.

- Bui, L. _Public Disclosure of Private Information as a Tool for Regulating Emissions: Firm-Level Responses by Petroleum Refineries to the Toxics Release Inventory_ , 2005. Working paper 05-13, US Census Bureau

- Dasgupta, S., B. Laplante and N. Mamingi.  2001.  Pollution and capital markets in developing countries. _Journal of Environmental Economics and Management_ 42: 310-335.

- Dasgupta, S., J.H. Hong, B. Laplante and N. Namingi.  2004.  Disclosure of environmental violations and the stock market in the Republic of Korea.

- Dasgupta, S., H. Wang, D. Wheeler, 2006, “Disclosure Strategies for Pollution Control,” in T. Tietenberg and H. Folmer (eds.), _International Yearbook of Environmental and Resource Economics_ , Vol. X ( _Edward Elgar_ : Cheltenham, U.K), 2006.

- Delmas, M., Montes-Sancho, M., and J. Shimshack.  2007. _Information Disclosure Policies: Evidence from the Electricity Industry_ . Working paper, Department of economics, Tufts University.

- Ferraro, P.J. and T. Uchida.  2005. _Investor Reactions to Information Disclosure: Can Providing Public Information About Firms’ Pollution Improve Environmental_

30
# Table 4. Logistic regressions of interest in migration by gender

||Random<br>Forest<br>Predictors -<br>Women|Random<br>Forest<br>Predictors-<br>Men|LSS<br>Predictors -<br>Women|LSS<br>Predictors -<br>Men|All<br>Predictors -<br>Women|All<br>Predictors -<br>Men|
|---|---|---|---|---|---|---|
|**Determinants validated by both methods**|||||||
|Age|-0.036***|-0.033***|-0.033***|-0.043***|-0.028***|-0.044***|
||(0.006)|(0.006)|(0.006)|(0.007)|(0.007)|(0.007)|
|Village Male Labor Force Participation|-1.529***|-3.190***|-1.543***|-2.350***|-1.304***|-2.691***|
||(0.458)|(0.501)|(0.407)|(0.445)|(0.480)|(0.534)|
|Knowledge of documents required to work abroad|0.051***|0.053***|0.018|0.019|0.015|0.017|
||(0.012)|(0.012)|(0.013)|(0.014)|(0.013)|(0.014)|
|Lack of knowledge about Indonesian migrants' expected salary in 6 main|||||||
|destination economies (Malaysia; Saudi Arabia; Taiwan, China; Singapore;|||||||
|Hong Kong SAR, China; Brunei Darussalam)|-0.756***|-0.578***|-0.710***|-0.470***|-0.568***|-0.455***|
||(0.103)|(0.106)|(0.083)|(0.110)|(0.108)|(0.114)|
|Highest female education in the household (Years of schooling)|-0.077***|-0.015|-0.050***||-0.079***|-0.011|
||(0.028)|(0.010)|(0.012)||(0.029)|(0.011)|
|Length of migration of latest returning migrant in the household|0.000***||0.000***|0.000***|0.000***|0.000***|
||(0.000)||(0.000)|(0.000)|(0.000)|(0.000)|
|Monthly expected wages abroad relative to village mean income|-0.017**|-0.009**||-0.015***|-0.018***|-0.016***|
||(0.007)|(0.004)||(0.005)|(0.007)|(0.005)|
|Lack of knowledge about Indonesian migrants' type of work in 6 main|||||||
|destination economies (Malaysia; Saudi Arabia; Taiwan, China; Singapore;|||||||
|Hong Kong SAR, China; Brunei Darussalam)|-0.169|-0.701***||-0.408**|-0.320*|-0.407**|
||(0.164)|(0.160)||(0.170)|(0.172)|(0.175)|
|Respondent total work income (logged)|0.028**|-0.009||-0.009|0.021*|-0.017*|
||(0.011)|(0.009)||(0.006)|(0.012)|(0.010)|
|Have PM's friends/acquaintances working: Taiwan, China; Hong Kong SAR,|||||||
|China; Singapore|0.786***||0.511***||0.511***||
||(0.082)||(0.085)||(0.089)||
|Type of work friend/acquaintances are doing abroad: domestic worker|0.304***||0.098||0.082||

22