rafmacalaba/datause-encoder-data huggingface dataset:
to import capital equipment goods from abroad. Using effectively applied most favorite nation (MFN) tariffs data and input-output matrix, we construct tariff measures on variable inputs and on capital goods separately. We first present evidence that our tariff measures are free of reverse causality concerns. We extend the previous works in the literature and show that input tariff changes are uncorrelated with initial firm and industry characteristics relevant for our analysis during the trade reform under the ‘Eighth Five-Year Plan’. We then exploit this exogenous variation in input tariffs across industries to identify the effect of the availability of foreign variable intermediate goods on firms’ decision to import capital goods taking into account changes in specific tariffs on capital goods.

The empirical findings confirm the theoretical predictions. Firms producing in industries with larger input tariff cuts have a higher probability of importing capital goods. Our results imply that the average input tariff reductions during the 1989-1997 period, 27 percentage points, is estimated to produce a 4.6 percent increase in the probability of importing capital goods for the average firm importing intermediate goods. These results take into account the direct effect of capital goods tariff changes. We then investigate if the impact of input-trade liberalization is heterogeneous across firms. Only those firms in the middle range of the productivity distribution import capital goods after input tariff reductions. Firms in the middle range of the initial productivity distribution increase their probability of sourcing capital goods by almost 10 percent. As predicted by the model, our findings suggest that the least productive firms do not benefit from input tariff cuts to upgrade foreign technology. Input tariff changes do not affect either the most productive firms that might have already adopted the foreign technology before input tariff cuts.

These results are robust to specifications which control for industry and firm observable characteristics that could be related to tariff changes and might change over time. We also take into account other possible explanations related to the incentives of Indian firms to adopt foreign technology. We show that our results remain robust when we explicitly control for other reforms that took place in India, foreign demand shocks (export-channel) and changes in firms’ financial health that also affect firms’ decision 4
# **II. The Challenge of Economic Development: Historical Antecedents and 20**<sup>**th**</sup> **Century Divergence**

Before the industrial revolution, there was little growth in the world economy and the income gap between countries was extremely small. For example, even in 1820, the betweencountry income differences represented less than 15 percent of income equality across people in the world, whereas the between-country share rose to well over half of global inequality by 1950<sup>3</sup> , and the richest country‘s per capita income was only less than four times higher than the poorest (Maddison 2011). The industrial revolution led to the Great Divergence: world growth was driven by a few Western industrialized countries before WWII, and similarly after WWII-with the exception of Japan, which joined the group of advanced industrialized nations.

Maddison (1982) divides the last 1500 years into four economic epochs: agrarianism, advancing agrarianism, merchant capitalism, and capitalism. The capitalism period started at the time of the industrial revolution. The new technology created the potential for new techniques of production that required organizational structures based on capitalist economic relations. It also represented the start of a period of unprecedented growth for the world economy (Table 1).

**Table 1: Growth rates in four economic epochs, according to Maddison (1982)**

||Population|GDP per head|GDP|
|---|---|---|---|
|Agrarianism,500-1500|0.1|0.0|0.1|
|Advancing agrarianism,<br>1500-1700|0.2|0.2|0.4|
|Merchant capitalism, 1700-<br>1820|0.4|0.2|0.6|
|Capitalism,1820-1980|0.9|1.6|2.5|
|Source: Maddison (1982)||||

Prior to the industrial revolution, the global economic landscape was dramatically different. Economies were largely based on agriculture and scientific progress was largely divorced from technological innovation in production (Lin 1995). Agricultural productivity was also similar across nations, and as a result, the largest poles of economic production were in fact the largest population centers. China and India together contributed about half of world GDP during the 17<sup>th</sup> and 18<sup>th</sup> centuries (Figure 1).

> 3 See Bourguignon and Morrisson (2002) for a calculation of global inequality since the start of the industrial revolution and a decomposition between within country and between country inequality.  The figures referred to here are from Theil Index and Mean Logarithmic Difference measures.  The between country share is larger for the ―standard deviation of logarithm‖ method; however, the same trend is followed: a lower share of between-country differences that then grows dramatically in the twentieth century. See Table 2 of Bourguignon and Morrisson for more details.

3
**Figure 12. Predicted Trade Estimates with and without Fixed Effects, 2009–11**

<!-- Start of picture text --> 13<br>8 GBRUSADEU<br>DNKB AUE ENLDSW U JKRSPLF P TCHN I FNOLNT ERAA<br>3 G EZNG LUX SVN B OAPHES G PR F MYALVTB RIRL HUNLLR IDN THAGRT CZ RASTAOM U K IENDC TUO RR CHN<br>COL<br>UR Y<br>-2<br>-7<br>-12<br>-10 -5 0 5 10 15<br>log of predicted services exports, NO FE (09-11)<br>log of predicted services expots, FE (09-11)<br><!-- End of picture text --> Source: WDI, Trade In Services database, World Bank (TIS), STRI, and Centre d'Etudes Prospectives et d'Informations Internationales (CEPII). Notes: In the figure, Russia’s bilateral exports with other EU countries are in blue and are labelled according to their 3‐digit ISO‐country code. Also included are other countries with whom Russia trades, shown in red (other middle‐income countries) and yellow (East and South East Asian countries); plus a 45‐degree line is depicted. FE = Fixed Effects, BGR = Bulgaria, AUT = Austria, BEL = Belgium, CZE = Czech Republic, DNK = Denmark, EST = Estonia, FIN = Finland, FRA = France, DEU = Germany, GRC = Greece, HUN = Hungary, IRL = Ireland, ITA = Italy, LVA = Latvia, LTU = Lithuania, LUX = Luxembourg, NLD = Netherlands, POL = Poland, PRT = Portugal, ROM = Romania, SVN = Slovenia, ESP = Spain, SWE = Sweden, GBR = Great Britain, RUS = Russia, GEO = Georgia, UKR = Ukraine, TUR = Turkey, COL = Colombia, NGA = Nigeria, URY = Uruguay, USA = United States of America, CHN = China, JPN = Japan, BRA = Brazil, ZAF = South Africa, IND = India, IDN = Indonesia, CHN = China, KOR = Korea, MYS = Malaysia, JAP, Japan, THA = Thailand, PHL = Philippines.

36
**Edmonds, E., Pavcnik, N. (2003b):** “International Product Market Integration and Household Labor Supply in a Poor Economy: Evidence from Vietnam.” _Dartmouth College Manuscript_ .

- **Edmonds, E., Pavcnik, N. (2003c):** "The Relationship between Product Market Integration and Child Labor Supply:  Evidence from Vietnam," _Dartmouth College Manuscript_ and formerly “Does Globalization Increase Child Labor?  Evidence from Vietnam.” _NBER Working Paper #8760_ .

- **Edmonds, E., Turk, C. (2004):** "Child Labor in Transition."  in P. Glewwe, N. Agrawal and D. Dollar (eds), _Economic Growth, Poverty and Household Welfare: Policy Lessons from Vietnam_ .  Washington DC: World Bank.

- **Emerson and Portela, A. (2002):** ‘Birth Order, Child Labor and School Attendance in Brazil’, manuscript: University of Colorado, Denver.

- **Foster, A. (1999):** “Altruism, Household Coresidence and Women’s Health Investment in Bangladesh,” _Brown University Manuscript_ .

- **Garg, A. and J. Morduch (1998):** “Sibling Rivalry and the Gender Gap: Evidence from Child Health Outcomes in Ghana,” _Journal of Population Economics_ , 11, 471-493.

- **Glewwe, P. and P. Nguyen (2004):** "Economic Mobility in Vietnam in the 1990s."  ."  in P. Glewwe, N. Agrawal and D. Dollar (eds), _Economic Growth, Poverty and Household Welfare: Policy Lessons from Vietnam_ .  Washington DC: World Bank..

**Grootaert, K. and H. Patrinos (1999):** _The Policy Analysis of Child Labor: A Comparative Study_ .  St. Martin's Press: New York.

**Krugman, P (1991):** _Geography and Trade_ , Cambridge: MIT Press.

- **Leamer, E. and J. Levinsohn (1995):** International Trade Theory: The Evidence in Grossman G. and K. Rogoff (eds.) _Handbook of International Economics_ : Elsevier

- **Levison, D., R. Anker, S. Ashraf, and S. Barge (1998)** .  “Is Child Labor Really Necessary in India’s Carpet Industry,” in R. Anker, S. Barge, S. Rajagopal, and M.P. Joseph (eds.). _Economics of Child Labor in Hazardous Industries in India_ .  New Delhi: Hindustan Publishers.

- **Morduch, Jonathan (2000):** “Sibling Rivalry in Africa,” _American Economic Association Papers and Proceedings_ , May 2000.

- **Parish, W. and R. Willis (1993):** “Daughters, Education, and Family Budgets: Taiwan Experiences” _Journal of Human Resources_ , 28(4), 862-898.

- **Poverty and Human Resources Division of the World Bank (2000):** "Vietnam Living Standards Survey, 1997-98: Basic Information."  Manuscript.

- **Thomas, D. (1994):** “Like Father, Like Son; Like Mother, Like Daughter: Parental Resources and Child Height,” _Journal of Human Resources_ , 29(4), 950-988.

45
|**Table 4: Effects on Additional Outcomes(Survey Ev**|(1)<br>**idence 10**|(2)<br>**Month**<br>**Compa**<br>**(Flex**<br>**Standa**|(3)<br>**s After D**<br>**ring Flexi**<br>**→Flex & S**<br>**rd Contra**|(4)<br>**isbursem**<br>**ble Contra**<br>**td→Flex) t**<br>**ct(Std→St**|(5)<br>**ent)**<br>**ct**<br>**o**<br>**d)**|
|---|---|---|---|---|---|
||Treat<br>effect||Std<br>Contract|<br>Flex<br>Contract|<br>Std<br>Contract|
|_Dependent variable_|(SE)|p-value|mean|N|N|
|**Panel A: Additional business and financing outcomes**||||||
|[1]<br>Has any informal loan|0.01<br>(0.02)|0.71|0.23|1,074|632|
|[2]<br>Has any formal loan from institution other than FMSD|0.01<br>(0.02)|0.68|0.29|1,074|632|
|[3]<br>Number of business improvement activites (out of 12)|0.15<br>(0.11)|0.15|1.40|1,074|632|
|[4]<br>Hours worked per day|-0.02<br>(0.22)|0.94|6.53|1,074|632|
|**Panel B: Loan-related stress outcomes**||||||
|[5] Loan-related stress index (average of rows [6]-[9])|-0.01<br>(0.01)|0.33|0.35|1,073|632|
|[6]<br>Thinks about loan repayments at least once per week|-0.04<br>(0.02)|0.06|0.27|1,071|631|
|[7]<br>Anxiety rises in the days prior to loan payment deadlines|-0.05<br>(0.02)|0.06|0.59|1,070|631|
|[8]<br>Had problems with loan payments in last year|0.01<br>(0.03)|0.63|0.50|1,073|632|
|[9]<br>Not confident that loan will be repaid|0.02<br>(0.01)|0.08|0.04|1,069|630|
|**Panel C: General stress outcomes**||||||
|[10] General stress index (average of rows [11]-[17])|-0.01<br>(0.01)|0.17|0.14|1,073|632|
|At least once per week felt:||||||
|[11]<br>Nervous or stressed|-0.06<br>(0.02)|0.01|0.26|1,071|632|
|[12]<br>Upset about unexpected events|0.00<br>(0.02)|0.81|0.11|1,073|632|
|[13]<br>Unable to control the important things in life|0.00<br>(0.01)|0.83|0.05|1,072|632|
|[14]<br>Not confident about the ability to handle personal<br>problems|-0.01<br>(0.01)|0.57|0.05|1,072|632|
|[15]<br>Stressed by job|0.00<br>(0.02)|0.94|0.15|1,073|632|
|[16]<br>Job prevented from giving time to partner/family|0.00<br>(0.02)|0.87|0.10|1,073|632|
|[17]<br>Too tired after work to enjoy things at home|-0.02<br>(0.02)|0.30|0.23|1,073|632|

Notes: Columns 1 and 2 show results for regressions with Flexible Contracts (pooled Std-Flex and Flex-Flex) as the treatment group and Standard Contracts as the control group. Outcomes in rows [3] and [4] are winsorized at the top and bottom 1 percent. P-values based on regressions that control for treatment assignment probability; for additional details, see Section 4.
# **<u>Data Appendix</u>**

||**Table A1.**<br>|**Number of**<br>|<br>**Firms by Countries a**<br>|**nd Survey Years**<br>|<br>|
|---|---|---|---|---|---|
||**Country Name (survey**<br>**year)**|**Number**<br>**of Firms**|**Percent of Global**<br>**Sample**|**Percent of**<br>**Firms with**<br>**New Product**|**Predicted Percent**<br>**of Firms with**<br>**New Product****|
|1|Albania (2000,2004)|137|0.53|58.39|58.36|
|2|Argentina (2006)|642|2.49|75.08|75.10|
|3|Armenia (2002,2005)|290|1.12|50.69|50.60|
|4|Azerbaijan(2002,2005)|257|1.00|56.42|56.28|
|5|Belarus (2002, 2005)|97|0.38|75.26|75.26|
|6|<br>Benin(2004)|142|0.55|34.51|34.30|
|7|Bolivia (2006)|362|1.40|75.97|75.89|
|8|Bosnia and<br>Herzegovina* (2002,<br>2005)|137|0.53|59.85|59.52|
|9|Brazil(2003)|1,628|6.31|67.57|67.44|
|10|Bulgaria (2002,2005)|106|0.41|61.32|61.19|
|11|Cambodia (2003)|55|0.21|63.64|63.50|
|12|Chile (2004)|684|2.65|44.88|44.77|
|13|China (2003)|1,324|5.13|26.28|26.29|
|14|Colombia (2006)|634|2.46|68.77|68.80|
|15|CostaRica (2005)|339|1.31|52.51|52.49|
|16|Croatia (2002,2005)|106|0.41|52.83|52.73|
|17|Czech Republic (2002,<br>2005)|149|0.58|41.61|41.47|
|18|Ecuador(2003)|123|0.48|63.41|63.21|
|19|Egypt, Arab Rep.<br>(2004)|965|3.74|14.30|14.05|
|20|ElSalvador(2003)|462|1.79|62.12|62.02|
|21|Estonia (2002,2005)|67|0.26|53.73|53.67|
|22|Georgia (2002,2005)|83|0.32|35.00|34.99|
|23|Germany*(2005)|220|0.85|45.78|45.82|
|24|Greece*(2005)|94|0.36|41.49|41.47|
|25|Guatemala (2003)|435|1.69|54.02|53.92|
|26|Guyana (2004)|149|0.58|46.31|46.40|
|27|Honduras (2003)|442|1.71|46.83|46.74|
|28|Hungary (2002,2005)|405|1.57|33.83|33.92|
|29|Indonesia (2003)|595|2.31|35.29|35.14|
|30|Ireland*(2005)|172|0.67|51.16|51.19|
|31|<br>Kazakhstan (2002,<br>2005)|402|1.56|35.57|35.59|
|32|Korea,Rep.*(2005)|213|0.83|46.01|45.93|
|33|Kyrgyz Republic (2002,<br>2003,2005)|205|0.79|51.22|51.24|
|34|Latvia (2002,2005)|59|0.23|44.07|43.96|
|35|Lithuania (2002, 2004,|245|0.95|||
||2005)|||45.71|45.61|
|36|Macedonia,FYR(2002,|99|0.38|46.46|46.40|

21
average growth rate of loans to households was almost 30 percentage points higher than the average growth of the loans to enterprises.

The composition of the credit growth to households according to the type of the credit exposure illustrates an asymmetrical growth of the different credit products. The annual growth of exposure from credit cards and overdrafts to households was especially high and in some periods it reached almost 200 percent (Chart 13). The growth of these credit products contributed by more than half to the total credit growth of loans to households. As a result, at the end of the first quarter of 2008, the exposures from credit cards and overdrafts reached their peak (39.5 percent of the total banks’ credit portfolio to households).

**Chart 13. Annual credit growth of certain types of credit exposures to households (2005–March, 2008)**

<!-- Start of picture text --> 250.00%<br>199.10%<br>200.00%<br>182.40%<br>150.00%<br>128.34%<br>100.00% 97.24%<br>58.69%<br>50.00% 39.60%<br>48.74%<br>0.00%<br>Credit cards and overdrafts Other exposures<br><!-- End of picture text --> Source: NBRM In addition, this credit growth was followed by a deterioration of the overall credit quality of loans to households, which was particularly emphasized at the end of 2006 and throughout 2007. The analysis of the so-called old credit portfolio, that is, exclusion of the impact of the new loans granted to households during the period of September 2006 to September 2007, confirms the deterioration of the credit quality. For the duration of this period, the share of exposures to households classified in risk categories ―C‖, ―D,‖ and ―E‖ (categories with a higher credit risk) rose from 4.9 percent to 8.5 percent. In contrast, the deterioration of the credit quality of the loans to enterprises was less evident.<sup>24</sup> Once more, the exposures from credit cards and overdrafts recorded the highest decline of their credit quality. If analyzed through the same indicator, the share of ―C‖, ―D,‖ and ―E‖ exposures in the total amount of exposures from credit cards and overdrafts increased by more than 109 percent, annually. Their increase factored the annual growth of the total "C", "D," and "E" exposures to households by almost 40 percent.

There are several reasons for the decline of the credit quality of exposures from credit cards and overdrafts. First of all, they were relatively new products on the domestic financial

> 24 The share of ―C‖, ―D,‖ and ―E‖ exposures rose from 12.2 percent to 12.9 percent.

17
<!-- Start of picture text -->
|i= | Inflation maximizing seignorage SO [= [= [= (S(T [SN = ( [ [—|<br>Money demandis assumed to be modelled according a Cagan money demand, assuming unitary elasticity of real money balances to income<br>y=Semi-elasticity of inflation 0.3<br>zs 2005 2006 2007 2008 2003 2010 2011 Steady state growth rates<br>| 2 |<br>| a |Base money(eop.) 7498.53 41395,040.08 45,036, 986.47 48,036, 771.63 53182, 036.57 57, FO5 578.25 62,009, 344.66<br>| 10 | Nominal @OP 481,913,760.59 536,258,722 46 588 155,326.04 638,903,475.65 654,328,352. 16 75456 1336.71 820,019 532.67<br>[it| Real GOF 449259.8191 INGE. 5159 1579973992 15834207 TAteB ated 182932, 2192 191973.8299 ee<br>Real GOP growth rate 48% 5.0% 5.0: 5.0% 5.0% 5.0% 5.0% 5.0% ae<br>CPI Indes (in percent] 4206883.53 454404.53 sTE47 431049.13 BOS235.24 Se6024.10 B44. 4 i<br>Be Sealing macra O07 ce<br>[18|Base moneu in percent of GOP 6.1 6.05% 6.0tr B.0tK 6.0tr Bote B.0tK a<br>Projected seignorage OFM 0.63% 0.6 OBI O.6t 0.6 Ob ee<br>Inflation maximizing seignorage 28 a3 38 a3 a3 38 38<br>This block caluclates seignorage assuming Seigniorage (GDP %) Steady state seignorage oS<br>varying inflation rate For each period. meted ie<br>The masimum seignorage is reported inrow 22. o.000 20% Oe OAK Oe O4n 0.0% OAK i<br>Inflation maximizing seigniorage reported in raw 24. 0.050 22K Oi ars U7 Ors O4% ars ce<br>lid | 4 | > |b) Seigniorage O50 Be 44h AZ_npe486 |4| ——446= ee4am 436 eerie48HEeIr+<br><!-- End of picture text -->
**Table A2.CR.A: Intergenerational Persistence in Schooling in Rural China -** **<u>(18 35 Years Age Cohorts)</u>**

|||**LINEAR CEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.27|0.33|0.062|0.27|0.38|0.11|
||(0.015)***|(0.015)***|(0.021)***|(0.042)***|(0.045)***|(0.061)*|
||[0.040]***|[0.045]***|[0.025]**|[0.096]***|[0.11]***|[0.062]*|
|Quadratic Coefft.||||-0.00025|-0.0047|-0.0045|
|||||(0.0035)|(0.0037)|(0.0051)|
|||||[0.0058]|[0.0075]|[0.0050]|
|Intercept|7.59|6.78|-0.81|7.59|6.73|-0.86|
||(0.11)***|(0.12)***|(0.17)***|(0.12)***|(0.13)***|(0.18)***|
||[0.36]***|[0.42]***|[0.32]**|[0.40]***|[0.48]***|[0.33]**|
|No. Observations|3955|4085|8040|3955|4085|8040|
|||**Estima**<br>|**tes of Mobility**|**from Quadrati**|**c CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|7.59|6.78||**_IGRC_**|0.27|0.33|
|**_ES6 _**|9.21|8.76|||||
|**_ES9_**|10.02|9.75|||||
|**_ES16_**|11.91|12.06|||||

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
traditional apprentices in treatment and control firms. The fact that master craftsmen do not charge a fee is consistent with the program intent in subsidizing access. Employers were requested not to charge fees. In parallel, firms make lower payments to formal apprentices, and these lower payments are mostly driven by a decrease in payments for transport, room and board. The firms internalize that the program subsidy covers such costs, so they strongly reduce their contribution. This behavioral response from firms implies that part of the program subsidy is in fact transmitted to firms. Formal apprentices still receive similar “bonus” payments than traditional apprentices in treatment firms. This shows that firms complement the subsidy offered by the program and attempt to directly motivate apprentices. The decrease in overall payment by firms is consistent with dissatisfaction with labor earnings among apprentices, as reported above.

The table also provides some information on the indirect costs incurred by firms to train apprentices. These costs are key to understand firms’ training decision (Acemoglu and Pischke (1999)). The interpretation of the increase in the net value of work provided by apprentices in firms as marginal profits depends on the magnitude of indirect training costs, as mentioned above. The follow-up survey asked apprentices about the number of hours they spent working independently, under direct supervision of master trainers or watching their master. The lower panel of Table 8 presents the results. In the control group, youth spent on average 2.5, 2.6 and 1.7 hours during their last day of work, working independently, under the supervision of their master, respectively watching him/her. These figures suggest that youth learn mostly by working alongside master craftsmen, inducing limited investments in time spent solely teaching apprentices from master craftsmen. There are also few differences between the various types of apprentices.

# **6 Conclusion**

Evaluations of employment programs usually focus on direct impacts on participants, but these programs can have a range of indirect effects that are rarely taken into account. This paper quantifies the net number of new positions created by a subsidized apprenticeship program in Côte d’Ivoire. We report results from a double-sided randomized control trial specifically designed to measure direct effects among youths and indirect effects in firms.

36