rafmacalaba/datause-encoder-data huggingface dataset:
- 21 -

consistent with the baseline model without sample selection, confirming that the above estimation results are robust.

**<u>Table 7. Heckman sample selection model</u>**

||IV regressi|on|Heckman|selection|First stage||regression|
|---|---|---|---|---|---|---|---|
|Dep. Var.|ln_W_||ln_W_||_d_1|||
||Coef.<br>|Std.Err.|Coef.|Std.Err.|Coef.|S|td.Err.|
|ln_TAXIBE_|||||0.004||(0.023)|
|ln_SUBUBUS_|||||0.041||(0.024) *|
|ln_MINCBD_|||||0.008||(0.101)|
|ln_RENT_|||||-0.021||(0.007) ***|
|D<sup>�</sup>|3.566|(1.360) ***|1.442|<br>(0.485) ***||||
|ln_AGE_|0.336|(0.118) ***|0.233|<br>(0.107) **|-0.342||(0.085) ***|
|_MALE_|0.548|(0.067) ***|0.542|<br>(0.060) ***|0.060||(0.047)|
|_PRIM_|0.069|(0.113)|0.148|<br>(0.097)|0.048||(0.081)|
|_SECO_|0.052|(0.157)|0.155|<br>(0.121)|0.023||(0.086)|
|_TERT_|-0.069|(0.342)|0.471|<br>(0.154) ***|0.253||(0.094) ***|
|_Q1_|-0.481|(0.194) **|-0.524|<br>(0.162) ***|0.189||(0.113) *|
|_Q2_|-0.464|(0.130) ***|-0.418|<br>(0.131) ***|0.064||(0.106)|
|_Q3_|-0.518|(0.105) ***|-0.485|<br>(0.105) ***|0.162||(0.098) *|
|_Q4_|-0.486|(0.103) ***|-0.372|<br>(0.094) ***|0.117||(0.092)|
|ln_HHS_|-0.052|(0.068)|-0.041|<br>(0.067)|-0.038||(0.070)|
|ln_MINCSB_|0.001|(0.025)|-0.010|<br>(0.024)|0.029||(0.031)|
|ln_ROOM_|0.184|(0.066) ***|0.126|<br>(0.067) *|-0.167||(0.066) **|
|_HOME_|-0.039|(0.065)|-0.041|<br>(0.064)|-0.116||(0.073)|
|_POWR_|-0.018|(0.125)|0.229|<br>(0.102) **|-0.124||(0.116)|
|_TAPW_|0.279|(0.106) ***|0.224|<br>(0.098) **|-0.128||(0.097)|
|_TOIL_|0.146|(0.078) *|0.064|<br>(0.070)|0.094||(0.075)|
|Constant|10.216|(0.578)***|10.307|<br>(0.470)***|0.289||(0.434)|
|Obs.|858||2883|||||
|No. of district|8||8|||||
|dummies|||||8|||
|R squared|0.4346|||||||
|F stat.|26.38|||||||
|Wald chi2|||659.15|||||
|Selection test:||||||||
|Wald chi2|||1.750|||||

# **VII. CONCLUSION**

42. In recent years, there has been renewed interest in access to jobs in relation to transport connectivity. In Sub-Saharan Africa, about 14 million working age people are added to the
**Table 1 SME Support Funds** **<u>and Programs in Mexico: Summary of Results, 2001-2006</u>**

|**Institution**|**Concept**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**Total**|
|---|---|---|---|---|---|---|---|---|
|Ministry of<br>E|Budget<br>(Million USD)|94|150|94|91|168|185|782|
|conomy|Firms|70,136|86,142|142,514|286,191|299,731|N/A|884,714|
|Nafinsa|Budget<br>(Million USD)|1,718|3,774|5,939|8,278|10,566|13,137|43,412|
||Firms|90,574|230,442|353,428|429,136|562,722|877,103|2,543,405|
|Bancomext|Budget<br>(Million USD)|5,512|6,993|6,177|5,423|5,392|4,952|34,449|
||Firms|1,628|1,807|1,202|751|533|N/A|5,921|
|Conacyt|Budget<br>(Million USD)|44|64|65|107|304|393|977|
||Firms|150|422|480|583|917|1,146|3,698|
|Ministry of<br>|Budget<br>(Million USD)|19|14|10|10|11|11|75|
|Labor|Firms|60,386|35,376|26,130|33,746|33,770|36,919|226,327|
|**Total**|**Budget**<br>(Million USD)|**7,387**|**10,995**|**12,285**|**13,909**|**16,441**|**18,678**|**79,695**|
||**Firms**|**222,874**|**354,189**|**523,754**|**750,407**|**897,673**|**915,168**|**3,664,065**|

*This table does not includes specific interventions of the institutions nor inter-institutional interventions such as the National Guarantee System.

**Source:** Bancomext (2006a and 2006b); Conacyt (2003, 2005, 2006 and 2007); Nacional Financiera (2002, 2003, 2004, 2005, 2006a and 2006b); Secretaría de Economía (2006c); and Secretaría del Trabajo y Previsión Social (2006).

# _Nacional Financiera_ (Nafinsa)

Nafinsa is Mexico’s largest development bank. Traditionally focused on large industrial projects with direct subsidies, in the 1990s the bank started to shift increasingly towards smaller firms and second-floor operations, and in 2001 it underwent a deep restructuring to set SME support as its top priority. Nafinsa provides credit and guarantees, training and technical assistance, and Internet-based information services to SME firms.

## **Table 2 Nafinsa: Main results 2001-2006**

|**Year**|**Credit and**<br>**guarantees**|**Business Development**<br>**Program (training and**<br>**technical assistance)**<br>**Firms supporte**|**Electronic**<br>**Information**<br>**Services**<br>**d**|**Total**|**SME**<br>**Private sect**<br>**(million**|**Total**<br>**or credit**<br>**USD)**|
|---|---|---|---|---|---|---|
|2001|N/A|N/A|N/A|90,574|1,718|2,684|
|2002|54,130|83,920|92,392|230,442|3,774|5,066|
|2003|90,566|158,377|104,485|353,428|5,939|7,615|
|2004|139,599|211,191|78,346|429,136|8,278|10,309|
|2005|397,179|99,366|66,177|562,722|10,566|12,886|
|2006|643,062|166,780|67,261|877,103|13,137|15,640|
|Total|1,324,536|719,634|408,661|2,543,405|43,413|54,199|

**Source:** Nacional Financiera (2002, 2003, 2004, 2005, 2006a and 2006b)

4
**Table 2:  Correlates of village access to community radio**

<u>(</u> _Commune fixed effects, robust p-values in parentheses_ <u>)</u>

||Number of Community<br>Radios|Share of Community<br>Radios|
|---|---|---|
|Village population (1,000s)|-.014<br>(0.78)|.002<br>(0.77)|
|Distance of village to nearest urban center|-0.00609<br>(0.14)|-0.000312<br>(0.65)|
|Distance of village to nearest bus or train|-0.00410|-0.000156|
|stop|(0.30)|(0.75)|
|Number of functional private schools|-0.227<br>(0.28)|-0.0133<br>(0.72)|
|Fraction of surveyed households with|-0.383|-0.0825|
|income < 30,000 CFA Francs|(0.23)|(0.20)|
|Fraction of surveyed households with a|0.346|0.109|
|mobile phone|(0.47)|(0.33)|
|Most common language in village and|0.301|0.0249|
|commune is the same|(0.24)|(0.48)|
|Probability that main language in any two|-0.558|0.0608|
|households in the village is the same|(0.27)|(0.35)|
|Mean number of houses in village with brick|0.298|0.159|
|walls|(0.46)|(0.11)|
|Mean number of houses in village with more|-0.355|-0.0584|
|than 5 rooms|(0.52)|(0.54)|
|Fraction of surveyed households with a|1.118|-0.274|
|<br>television|(0.41)|(0.17)|
|Observations|200|200|
|R-squared|0.78|0.69|

Note: The columns report OLS estimates of the association of measurable village characteristics with the number or share of community radio stations to which villages have access, controlling for commune fixed effects.

The village survey includes data on village distance from urban centers and to a train or bus stop; smaller and more remote villages could both be likely to have fewer accessible radio signals and lower investments in health. The most recent census in Benin, from 2002, has information on village population for nearly all of the villages in the sample. Poorer villages might have unobserved characteristics that affect both radio access and be targeted by the government for bed-nets distribution. Though there is no census or statistically representative data of incomes and economic 14
|]<br> _your age_|_as HIV_|(9)|0.034**<br>(0.013)|[0.035]<br>0.137***<br>(0.030)<br>-0.031|(0.037)<br>[0.654]|4.986|0.034|Yes|0.337|ance at the 1,||not live with|an secondary||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_at a person_|_mmunity h_|(8)|0.024**<br>(0.011)|[0.070]<br>0.116***<br>(0.016)||4.986|0.034|Yes|0.337|enote signifc||e, single, does|ned higher th||
|_Prob. th_|_in the co_|(7)|0.027**<br>(0.011)|[0.055]||4.986|0.021|Yes|0.337|, ** and * d|ns.|oken at hom|other obtai||
|_r is HIV_||(6)|-0.002<br>(0.005)|[0.928]<br>0.142***<br>(0.027)<br>-0.047|(0.037)<br>[0.531]|3.452|0.031|Yes|0.055|clusters). ***|000 replicatio|on, english sp|education, m||
|_hat partne_||(5)|-0.005<br>(0.005)|[0.528]<br>0.110***<br>(0.019)||3.452|0.031|Yes|0.055|ntre level (80|based on 10,|ars of educati|an secondary||
|_Prob._<br>_t_|_positive_|(4)|-0.006<br>(0.005)|[0.410]||4.066|0.018|Yes|0.058|reening ce|e brackets,|school, ye|higher th||
|_currently_||(3)|0.000<br>(0.003)|[0.947]<br>0.073**<br>(0.028)<br>0.025|(0.034)<br>[0.654]|4.986|0.016|Yes|0.019|ring at the sc|lues in squar|tly attending|ther obtained||
|_of being _|_itive_|(2)|0.001<br>(0.002)|[0.721]<br>0.088***<br>(0.017)||4.986|0.015|Yes|0.019|ed for cluste|djusted p-va|, age, curren|e owner, fa|language.|
|_Dep. Var:_<br>_Prob._|_HIV pos_|(1)|Treated<br>0.000<br>(0.002)|[0.852]<br>Y_t−_1<br>Treated*Y_t−_1||Observations<br>4.986|R-squared<br>0.006|Controls(_a_)<br>Yes|Mean Dep. Var. (Control)<br>0.019|Notes. Standard errors in parentheses adjust|5 and 10 percent level, respectively. FWER a|(a) Controls in each regression include: female|the family, household size, wealth index, hom|education, muslim, speaks Yoruba as a native|

xxi
amount of non-farm rural activities<sup>(Table2),but it isthebest wecan dowith theGTAP model</sup> which does not distinguish activities<sup>by locationwithina country.9</sup>

# **What results can we anticipate?**

In a recent paper, Carter and Estrin<sup>(2001)developamodelwithtwosectors,agricultureand</sup> non-agriculture, that assumes capital<sup>is sector-specificand laborisintersectorally mobile.They</sup> assume the trade reform from China's<sup>WTOaccessionwilllower therelativepriceof non-farm</sup> goods (larger tariff cuts for non-farm<sup>thanfarm products).Ifthe economystarted from</sup> equilibrium in the labor market,<sup>thatprice changewouldlowerlabor'swageintermsof farm</sup> goods and would cause labor to<sup>migratefromnon-farm to farmactivities. If,however,the</sup> economy starts from a situation in<sup>whichfarm workersfaceimpedimentstomigratingto non-</sup> farm employment, the wage in the<sup>non-farmsectorwould have exceededthatinagriculture.In</sup> that case, a fall in the relative<sup>priceofnon-farm products wouldlowerthenon-farmwage</sup> without changing the low farm wage (if<sup>bothareagainmeasured in termsof agriculturalgoods).</sup> If the government were to simultaneously<sup>removethebarriertoout-migrationoffarmlabor,</sup> another welfare gain would accrue<sup>to theoveralleconomy. Farmworkerswouldbenefitfrom</sup> trade reform alone in so far as they<sup>consumenon-farmproducts whosepriceis lowerafterthat</sup> policy change. They would benefit<sup>evenmoreif therestrainton their migration wereto be lifted.</sup> Non-farm workers, however, would<sup>sufferwagecutsintermsof farmproductsfromeachof</sup> those two policy changes, cuts that<sup>may offsetthefallinthe priceof non-farmproducts they</sup> consume. Carter and Estrin therefore<sup>concludethatChina'srural-urbanincomeinequality(at</sup> least for laborers) would be reduced<sup>from furthertradeliberalization andmore soif thecurrent</sup> policies that inhibit off-farm migration<sup>were relaxed.</sup> Neat though the Carter/Estrin analysis<sup>is,itignoresthedifferencesbetween skilled,</sup> unskilled non-farm and unskilled<sup>farmworkers.Also,itsassumptionthattheincentiveto</sup> produce non-farm relative to farm<sup>productswillfallfollowingWTOaccessionneedstobe</sup> tested, since there will be some positive<sup>and somenegativechangestothepriceof bothsetsof</sup> highly tradable products. Furthermore,<sup>thedemandfordifficult-to-tradeproductswill rise</sup> following the income gains from trade<sup>reform, and their cost of productionwillchange. Doesthe</sup>

> 8 Of this, 28 percent is explained<sup>by the registrationdummy variable,and 6.5 percent by differencesin living costs.</sup> 9 For a more-detailed breakdown<sup>showing bothfarmandnon-farmruralhouseholds,seeHertel,ZhaiandWang</sup> (2002).

10
# **Table 5A**

# **Working Age Adults**

||Loc|20<br>al|05<br>Migr|ant|Lo|20<br>cal|10<br>Mig|rant|
|---|---|---|---|---|---|---|---|---|
||Mean|s.d.|Mean|s.d.|Mean|s.d.|Mean|s.d.|
|Age|40.605|11.606|34.041|9.150|41.062|11.852|34.839|10.019|
|Years of Education|11.572|2.769|8.760|3.141|11.995|2.964|10.244|3.298|
|Post Secondary Education|0.235|0.424|0.052|0.222|0.300|0.458|0.159|0.366|
|Married|0.763|0.425|0.799|0.401|0.759|0.428|0.752|0.432|
|Female|0.510|0.500|0.479|0.500|0.514|0.500|0.504|0.500|
|Rural Hukou|0.024|0.154|0.821|0.384|0.041|0.198|0.756|0.430|
|Contract|0.419|0.493|0.120|0.325|0.448|0.497|0.302|0.459|
|Working|0.621|0.485|0.897|0.304|0.641|0.480|0.879|0.326|
|Self-Employed|0.157|0.364|0.727|0.446|0.161|0.368|0.522|0.500|
|Employer-Based Pension|0.634|0.482|0.085|0.279|0.675|0.468|0.146|0.353|
|Employer-Based Medical Insurance|0.509|0.500|0.066|0.248|0.640|0.480|0.128|0.334|
|Unemployment Insurance|0.213|0.409|0.019|0.135|0.272|0.445|0.053|0.224|
|Residence-Based Pension|n.a.|n.a.|n.a.|n.a.|0.059|0.235|0.027|0.163|
|Residence-Based Medical Insurance|n.a.|n.a.|n.a.|n.a.|0.123|0.328|0.043|0.203|
|NRCMS|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|0.049|0.215|
|Government|0.052|0.222|0.004|0.064|0.041|0.199|0.003|0.059|
|Public Institute|0.117|0.322|0.041|0.198|0.127|0.333|0.039|0.194|
|SOE|0.165|0.371|0.028|0.166|0.160|0.366|0.042|0.200|
|Collective|0.029|0.167|0.020|0.139|0.026|0.159|0.019|0.138|
|Private|0.094|0.292|0.086|0.280|0.126|0.332|0.215|0.411|
|Foreign-Invested|0.041|0.198|0.027|0.162|0.047|0.212|0.046|0.209|
|Others|0.047|0.211|0.039|0.195|0.026|0.158|0.056|0.231|
|Observations|5310||4385||7045||5249||

* n.a. : not applicable. **Working Age refers to age 18-60. Source: 2005 and 2010 China Urban Labor Survey (CULS).

31
<!-- Start of picture text --> Increase in International Prices<br>ACim 7,<br>+. PY,P<br>Pass-through<br>uo + .<br>F<br>5<br>A}<br>a<br>_<br>—<br>A’ Production ofX¢<br>3 Price-wage<br>S elasticity<br>a<br>= A” Wages, Employment<br>e a A Welfare via ~ aAin Welfare AC in Welfare via the<br>Ss NO A in teal income / QO via A TS labor market<br>Consumption Effect Income Effect —————— Second-order income effect<br><!-- End of picture text -->
̃ ̃ _<u>qUS,MEX,z,t</u>_ equivalent of the double difference trade flows as _qzt_<sup>_DD_</sup> _≡_ ln � _q_ ̃ _US,RoW,z,t_<sup>_/_</sup><sup>_<u>q</u>_ ̃</sup> _q_ ̃<sup>_EU_</sup> _EU,RoW,z,t_<sup>_<u>,MEX,z,t</u>_</sup> �, where _q_ ̃ _i,c,z,t_ is the summation of monthly flows from (9) to the annual frequency. The trade-flow and trade-consumption elasticities are then estimated following equation (4) derived in Section 2.1, providing an estimate at each horizon _h_ .

Columns 1 and 2 of Table 4 report the results of estimating (4) with _h_ = 1, i.e. the annual elasticities. Panel A shows the results of the average elasticities of all goods. There is a sizable difference of around 56% in the two elasticities. The estimate drops from 4.2 when using import flows to 2.7 when using consumed imports. Panel B suggests that this difference is driven almost entirely by the phased-out goods. For phased-out goods, the annual elasticity drops from 7.4 to 2.9. This is consistent with the strong anticipatory responses of phased-out goods found in Section 2.4. In contrast, non-phased-out goods yield similar estimates when using imports flows or consumption of imports. It is also noticeable that the estimated annual elasticity for the phased-out goods becomes almost equal to the one for the non-phased-out goods when considering the consumed imports.

Columns 3 to 8 estimate the two elasticities at longer horizons, with _h_ = 3 _,_ 5 _,_ 7. Throughout the different specifications of _h_ , the difference between the trade-flow and trade-consumption elasticity is now much smaller. In the case of the average over all goods (Panel A), the difference diminishes to only 8% after 7 years. The diminished difference between the two elasticities is also the case of the phased-out goods, although the trade-consumption elasticity remains 20% smaller. This permanent difference can be explained by the fact that as tariffs are fully phased-out the _liberalization bumps_ dissipate, but the preceding _anticipatory slumps_ remain in the denominator of the growth rates. Consistent with this explanation, the difference between the two elasticities disappears entirely in the case of non-phased-out goods.

18
Policy ReseaRch WoRking PaPeR 4586

# **Abstract**

This paper examines the determinants of remittance behavior for Vietnam using data from the 2004 Vietnam Migration Survey on internal migrants. It considers how, among other things, the vulnerability of a migrant’s life at the destination, their link to relatives back home, and the time spent at the destination affect remittances. The paper finds that migrants act as risk-averse economic agents and send remittances back to the household of origin as part of an insurance exercise in the face of economic uncertainty. Remittances are also found to be driven by a migrant’s labor market earnings level. The paper highlights the important role of remittances in providing an effective means of risk-coping and mutual support within the family.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to analyze the impact of migration on poverty and economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at yniimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
29 so that _re_ ( _s, p_ ) flatten out with experience. If _<u>re</u>_<sup>_<r_ ̄</sup><sup>_e_, then the returns to experience</sup> also embody a college premium.

We require values of each of these parameters for a representative developing economy and a representative developed economy. We obtain the population growth rates _gb_ and the mortality functions _δ_ ( _·_ ) from United Nations (2019), corresponding to the “More developed regions” and “Less developed regions”. We use the medium variant forecasts over the 2020-2050 period, assuming that _δ_ ( _a_ ) = 1 for _a ≥_ 99.<sup>48</sup> The return parameters are the average estimates for each group, weighted by population (see col. (1) of rows 9-10 in Table 2 and col. (1) of rows 14-15 in Table 1). For the developed economy, we use _<u>rs</u>_<sup>= 6.7%,</sup><sup>_r_ ̄</sup><sup>_s_= 13.0%,</sup><sup>_<u>r</u>_</sup> _~~e~~_<sup>= 4.4% and</sup><sup>_r_ ̄</sup><sup>_e_= 4.2%.For</sup> the developing economy, we use _<u>r</u>_ _~~s~~_<sup>= 6.8%, ̄</sup><sup>_rs_= 13.8%,</sup><sup>_<u>r</u>_</sup> _e_<sup>= 2.0% and ̄</sup><sup>_re_= 2.8%.</sup> We set the schooling transition probabilities _πs_ ( _·_ ) to match the schooling distribution in developed and developing economies (I2D2).<sup>49</sup> We assume agents may only accumulate up to 25 years of schooling as some countries only record schooling up to 25. Finally, we set the employment probabilities _πe_ ( _·_ ) using participation, youth unemployment and adult unemployment rate averages reported by World Bank (2018a).

See Table 5 for a summary of the values of the parameters we used. See Web Appx. Section A.6 for details of the parameterization of the accounting framework.

# **6.3. Main Quantitative Findings**

A typical developing economy has 24% of the GDP per capita of a typical developed economy (World Bank, 2018a). In the simulations, we find a value of 48 _._ 5%. The gap between 48 _._ 5 and 100 is 68% of the gap between 24 and 100. Thus, in our accounting model, differences in education, experience and the returns thereof, plus demographic and labor market differences, account for two thirds of the income gap.<sup>50</sup> Demographic and labor market differences are small, however, with the exception of labor market participation. Assume instead that demographic and labor market factors are the same as in the developed economy, allowing only differences in the stock of schooling and the returns to human capital. Then, we find that relative _pcGDP_ is 53 _._ 5 percent, accounting

> 48See Web Appx. Fig. 8(a) for the mortality distributions.

> 49See Web Appx. Fig. 8(b) for the distribution of schooling we use, based on the I2D2 data (1990-2016).

> 50We compare GDP per capita in levels. An alternative approach would be to compare GDP per capita in log levels. In that case, the gap is of 51%. In any case, the main point of our inquiry in this section is to compare the relative contribution of education and experience, so the absolute metric is not important.
Table 1b: Output and wage growth in India's manufacturing sector

||1989|1994|2000|2005|2010|Share<br>1989|Share<br>2010|Growth<br>1989→2010|Growth<br>2000→2010|
|---|---|---|---|---|---|---|---|---|---|
|A. Total sales (Billions of 2005 Rup|ees)|||||||||
|Total|2,674|2,769|5,206|10,896|24,198|||805%|365%|
|||4%|88%|109%|122%|||||
|Informal tradables|23|127|472|722|2,245|1%|9%|9556%|376%|
|||448%|270%|53%|211%|||||
|Informal non-tradables|52|220|697|1,094|2,645|2%|11%|5009%|279%|
|||325%|217%|57%|142%|||||
|Formal tradables|1,043|1,055|1,949|3,871|7,771|39%|32%|645%|299%|
|||1%|85%|99%|101%|||||
|Formal non-tradables|1,556|1,366<br>-12%|2,088<br>53%|5,210<br>150%|11,537<br>121%|58%|48%|641%|453%|
|B. Average wage (2005 Rupees)||||||||||
|Total|140|632|1,340|348|1,739|||1139%|30%|
|||350%|112%|-74%|400%|||||
|Informal tradables|121|589|1,412|2,125|5,429|||4382%|285%|
|||386%|140%|51%|156%|||||
|Informal non-tradables|81|405|1,011|1,703|6,829|||8357%|575%|
|||402%|150%|68%|301%|||||
|Formal tradables|8,854|25,757|31,343|55,199|138,310|||1462%|341%|
|||191%|22%|76%|151%|||||
|Formal non-tradables|6,734|19,833|23,970|37,896|112,859|||1576%|371%|
|||195%|21%|58%|198%|||||

Notes: Indian descriptive statistics taken from Annual Survey of Industries and National Sample Statistics. Tradables definition comes from Kothari (2014) based upon abovemedian values of exports plus imports to gross production at the three-digit industry level. Output is measured as annual gross sales. Average wage is measured as total annual wages divided by the number of employees in the firm. Output and average wages across all years are converted into 2005 rupees.