rafmacalaba/datause-encoder-data huggingface dataset:
- 16 -

of the investor for the mutual fund portfolios that provide a hedge against the unanticipated changes in the (S-K-N) nonspecified state variables and the N exchange rates. As we saw above, the return on the foreign bond B (j) is perfectly correlated with the changes in the exchange rate e(j). Accordingly, if the return on a foreign bond (in other words, the changes in an exchange rate) is correlated with the unanticipated changes in the nonspecified state variables then the foreign bond can provide a hedge against unanticipated changes n these state variables which are associated with unanticipated changes in the investment and consumption opportunity sets. Similarly, a foreign bond can provide a hedge against unanticipated changes in the exchange rate that are associated with unanticipated changes in the investment and consumption opportunity sets. We can translate this into a vector of demands for foreign bonds using the exact composition of the hedge portfolios.

25. Analyzing the different components of the demand of the investor for the mutual funds does not tell us the final amount invested or borrowed by the investor in each of the N currencies unless we know the composition of the S mutual funds as far as the shares of each of the N foreign bonds is concerned. Investors have limx.ed hedging possibilities because the quality of the hedge depends on the correlation between the unanticipated changes in the prices of the mutual funds and the unanticipated changes in the state variables which is not perfect in this model.
34 the informal sector, the proportion is low (1.6 percent). The 2004 minimum wage was hence all but a binding constraint in the Mauritanian labor market. It was increased in January 2005 (after the EPCV 2004 data was collected) to 21,000 MRO. Although this new minimum wage was not applicable at the time of the survey, it is interesting to note that approximately 30 percent of the employees from the formal sector received wages below this threshold. These calculations should be carried out again once new household data becomes available.

**Table 22. Wage Earners - Econometric Estimates of Wage Determinants**

|**Regre**|**ssion of the log**|**of wages on i**<br>**_Ml_**|**ndividual ch**|**aracteristics**|**_Fl_**||
|---|---|---|---|---|---|---|
|||**_ae_**|_Stdd_||**_emae_**|_Stdd_|
||_Coefficient_|_Significance_|<br>_anar_<br>_Error_|_Coefficient_|_Significance_|<br>_anar_<br>_Error_|
|**Education**|||||||
|None (omitted)|||||||
|Primary|0.052|ns|0.036|0.143|*|0.085|
|Secondary|0.244|***|0.034|0.171|**|0.089|
|<br>Higher|0.615|***|0.045|0.508|***|0.116|
|**Age**|||||||
|15 to 24 (omitted)|||||||
|25 to 49|0.287|***|0.035|0.132|***|0.067|
|50 to 64|0.414|***|0.045|0.042|ns|0.111|
|**Region**|||||||
|Nouakchott|0.154|***|0.039|0.348|***|0.076|
|Dakhlet Nouadhibou|0.029|ns|0.069|0.194|ns|0.147|
|Senegal River Valley|0.027|ns|0.033|0.105|ns|0.073|
|Other (omitted)|||||||
|**Rural**|0.053|ns|0.035|-0.098|ns|0.073|
|**Sector**|||||||
|Agriculture (omitted)|||||||
|<br>Livestock|-0.258|***|0.092|-0.515|*|0.301|
|Fishing|0.370|***|0.100|0.242|ns|0.275|
|Mining|0.439|***|0.105|0.125|ns|0.341|
|<br>Industry|0.211|*|0.130|0.625|*|0.361|
|Construction|0.145|ns|0.099|0.536|*|0.300|
|Transport & Communication|0.023|ns|0.092|0.333|ns|0.301|
|Trade|0.187|**|0.086|0.232|ns|0.213|
|Services|-0.018|ns|0.088|-0.206|ns|0.208|
|Administration|-0.007|ns|0.091|-0.281|ns|0.216|
|Other|0.044|ns|0.088|-0.127|ns|0.210|
|**Type of employer**<br>Public (omitted)|||||||
|Private|0.233|***|0.043|-0.089|ns|0.117|
|Individual household|-0.072|ns|0.046|-0.685|***|0.111|
|Other|0.046|ns|0.062|-0.609|***|0.172|
|**Informal**|-0.174|***|0.037|0.081|**|0.081|
|**Constant**|2.747|***|0.102|0.241|***|0.241|
||Observations||2,274|Observations||564|
||R-Squared||0.306|R-Squared||0.504|

Source: EPCV 2004.OLS regression. ns: not significant, ***: significant at the 1 percent level, **: significant at the 5 percent level, *: significant at the 10 percent level.
# Immigrants versus Natives? Displacement and Job Creation<sup>∗</sup>

Ça ̆glar Özden Mathis Wagner World Bank Boston College Keywords: immigration, substitution, scale effect, displacement, output expansion JEL Classification: J23, J61, O15

> ∗The theory in this paper draws on material from Wagner’s Ph.D. dissertation at the University of Chicago on "The Heterogeneous Labor Market Effects of Immigration" (Wagner, 2010). We thank Amir Omar and the Institute of Labor Market Information Analysis for their support, and the Department of Statistics of Malaysia for making the data available. Ximena Del Carpio lead the technical advisory project that forms the basis for this paper. Federico Mantovanelli and Mauro Testaverde provided research assistance. We thank Michel Beine, Simone Bertoli, George Borjas, Michael Clemens, Patricia Cortes, Gordon Hanson, Hubert Jayet, David McKenzie and seminar participants at Boston College, Boston University, Georgetown University, George Washington University, the World Bank, TEMPO Conference at Nottingham University, and the 6th International Conference on Migration and Development for comments and suggestions. Contact information: cozden@worldbank.org, mathis.wagner@bc.edu, Boston College, Department of Economics, 140 Commonwealth Avenue, Chestnut Hill MA 02467, USA.
# Do Shocks within and across Perpetuate Disparities Informal Firms? Evidence from the COVID-19 Pandemic in South Asia<sup>∗</sup>

Arti Grover<sup>†</sup> & Mariana Pereira-López<sup>‡</sup>

## **JEL classification** : D22, L20, L25, O17

: **Keywords** COVID-19, Crisis, Firms, Informality, Recovery, Digital

> ∗Acknowledgments: The authors are grateful to the South Asia Regional Trade Facilitation Program (SARTFP) for funding support and Denis Medvedev for his strategic guidance during the inception of the project. The authors thank several members of the World Bank teams for their efforts in data collection, including Amila Indeewari Dahanayake, Besart Avdiu, Hosna Ferdous Sumi, Mauricio Alejandro Pinzon Latorre, Mohammad Sulaiman Akbari Ojashwi Samser JB Rana, Peter Mousley, Rafay Khan, Ruchita Manghnani and Subika Farazi. The team is grateful to Franklin Maduko, Sarah Hebous, and Reyes Aterido for their careful work in data harmonization. The team thanks Maurizio Bussolo and Siddarth Sharma for their helpful feedback.

> †World Bank email: agrover1@ifc.org

> ‡World Bank email: mpereiralopez@ifc.org
<!-- Start of picture text --> variable trade costs, with episodes of tariff liberalization across 18 countries found to be associated<br>with extensive margin growth. Their findings are corroborated for China and Mexico by Feenstra<br>and Kee (2007), while Debaere and Mostashari (2005) find further evidence that changes in tariff<br>rates and preferences can impact the extensive margin. In the context of examining the role of<br>export variety in explaining cross-country productivity differences, Feenstra and Kee (2006) show<br>that tariffs, international distance, and resource endowments can serve as instruments for export<br>variety—and thus by implication, these factors impact exports at the extensive margin.<br>Our key contribution in this paper is to expand the range of factors believed to influence export<br>growth at the extensive margin—or export diversification—to include a range of policy variables<br>related to the costs of trading and domestic market entry. We also compare the sensitivity of diver-<br>sification to changes in these parameters, giving an indication of where the highest policy payoffs<br>might lie. We motivate the selection of this variable set using a version of the Melitz (2003) het-<br>erogeneous firms model of trade, which allows for potential asymmetries in country size, market<br>entry costs, and trade costs. It provides insights into the effects of both reciprocal and unilateral<br>changes in each of these variables. Numerical simulations suggest that for given sector character-<br>istics, reductions in export-specific trade costs (fixed and variable), symmetric trade costs (such as<br>international transport), and domestic market entry costs, as well as increases in import-specific<br>trade costs, are positively associated with export diversification. They also provide evidence of the<br>relative magnitudes of these effects: we expect transport costs to have the largest impact, followed<br>by export costs and destination country tariffs, then source country tariffs, and finally domestic<br>market entry costs.<br>To test these predictions, we construct new measures of export diversification for 118 devel-<br>oping countries using 8-digit mirror (import) data from the European Union, which distinguish<br>among 10,753 distinct products. The World Bank’s Doing Business database provides previously<br>unexploited information on the costs of exporting, as well as data on the costs of domestic market<br>entry. For applied tariffs that take full account of preferential rates, we use the ITC-CEPII Market<br>Access Map (MAcMap) database.<br>3<br><!-- End of picture text -->
Hence, total misallocation for the population of establishments can be decomposed into the sum of two simple terms. The first, _M_<sup> ̃</sup> , is within-group misallocation summed across groups, weighting them by their share _sg_ . The second term, _M∆_ , is a measure of between-group misallocation. It is actually the between-group analogue of the covariance term used in equation (3). This makes strong intuitive sense: total misallocation is the sum of the misallocation of each group where each group is weighted by its factor share to which we add the misallocation across groups.

For instance, it is possible to imagine situations where more productive establishments employ more workers within each district, but that districts that host more productive establishments on average have access to substantially fewer workers than less productive districts. This would be a case of a low level of misallocation within districts but a high level of misallocation across districts. While most of our analysis is concerned with misallocation within districts, we pay some attention to the between component below. We also note that within- and between-district misallocations probably have very different root causes and most likely call for different policies.

# **_3.3 Within and between misallocation: Female- and male-owned establishments_**

Our first decomposition regards the differences between female- and male-owned businesses in the unorganized sector. We use the fact that the nss reports the gender of business owners in the unorganized sector. As women suffer extensive discrimination in India, both as workers and as employers, it is important to assess, how much this discrimination affects misallocation in India.

The results are in table 1. Let us focus our discussion on a reported specific year, 2010. Although the exact numbers differ, the results for the other years follow very similar patterns. The misallocation index for output is -0.687. This is very close to the overall district mean for 2010 of -0.70. By the decomposition proposed in equation (14), -0.687 is the sum of within-group weighted misallocation of -0.605 and between group misallocation of -0.082. Hence, there is more output misallocation between groups rather 11
we would make risk-sharing effects small, without altering the size of the wage effect.16'<sup>1 7</sup> The U.S. states provide a natural experiment for our model of financial integration, since they constitute a set of integrated economies within the OECD.'<sup>8</sup> We can therefore use existing state-level data to help us quantify the importance of this risk-sharing effect. Figure 3 plots the correlation of a state Solow residual with the OECD Solow residual against the correlation of the same state gross state product (GSP) growth with the OECD GDP growth rate, during the 1970-1986 period for which we have data The principal fact which emerges from this picture is that GSP correlations exceed Solow residual correlations in all but 3 states. We interpret this finding as an indication that the risk-sharing effect is small relative to the wage effect.

> **18** In this case, either a storage technology or a capital stock that lasts more than one period should be included in the model in order to allow agents to smooth the effects of productivity shocks over time. If not, making shocks transitory and/or anticipated would only lead to fluctuations in the interest rate. 17 However, empirical tests for unit roots in various measures of productivity disturbances indicate that these productivity measures are highly persistent or, equivalently, that Solow residuals are uncorrelated over time. See, for instance, Razin (1993) or Glick and Rogoff (1995).

> **18** This approach has been taken by Atkeson and Bayoumi (1993). Obsffeld (1993) and Lewis (1995) have examined the implications of financial market integration among countries, using various proxies to control for financial integration.

21
terms, the total per quintal value of the three subsidies increased gradually over the period (Fig 23).

Figure **23:Three states 1981-90: weighted average of non-traded input subsidies to cane growing in 1981 prices** <!-- Start of picture text -->
*3.5<br>2 .' o  . . ..  ...<br>205<br>~198 ~<i  9 82,  I1983,,,  194  . 9198  986.-:1987  `1988  I98§g99<br>~  INDIAN  FISC<br><!-- End of picture text -->

Source and notes: Annex 23. IS=Canal irrigation subsidy; ES=electricity subsidy; CS=credit subsidy.

Figure 24: **Weighted averages of three states 1981-90. Shares of non-traded input subsidies in cane reference prices** <!-- Start of picture text --> e a. = i s. :CS<br>PRN; ,.9f,1N4DI.0 l\aN<br>Souce and note:,--nex 23 IS=aa Irrgto susiy ES=\ elcrct usd. CS cedt ubid.I<br><!-- End of picture text --> On average however the total value of the subsidies was quite low in relation to cane prices about 2. _5_ percent on average, varying from about half of one percent in 1981 to a maximum of 5 1
Figure 2.1 <!-- Start of picture text --> ASEAN Structure of Output GDOP) -1999<br><!-- End of picture text --> <!-- Start of picture text --> 70 -<br>0--_. _ ____ '__ _.__.' __v-_<br>60<br>b40<br>-- ~~~~~~~~~~~~~~~~~~~~OService<br>20 --<br>10<br>Cambodia Indonesia Leo PDR Malaysia My4imar Philppines Singaore Thailnd Vistmrn<br>NOTE: Data for Brunei Darussalam not available<br>Source: The World Bank, World Development Indicators 2001<br><!-- End of picture text --> In the aggregate, the largest<sup>contributionto output for ASEANis fromservices</sup> (47 percent of GDP), although<sup>industryisa closesecondat 39percent with mostof this</sup> being from manufacturing.<sup>(SeeFigure 2.2 belowand TableS.4intheStatistical</sup> Appendix.) In the European<sup>MonetaryUnion (EMU),'42the contributionto output from</sup> services (71 percent of GOP)<sup>farexceeds that of industry(27percent)andagriculture(2</sup> percent). Again, this is a<sup>reflectionof the widedifferencein the overall development</sup> levels of East Asia and Europe.

37
In their study of power generation and the US SO2 program, Considine and Larson (2006) considered the use of the atmosphere for the disposal of emissions as a factor of production, priced by tradable emission permits, and derived related input demand schedules in a cost-function framework. The authors applied a similar approach in their paper on the European Union’s program for greenhouse gases (Considine and Larson 2009).

Several studies estimated the abatement cost function by separating cost of abatement from the cost of production. Using data from the U.S. Census Bureau, Hartman, Wheeler, and Singh (1994) estimated air pollution abatement costs by industry sectors. Assuming that the abatement cost function was separable from the firm’s production cost function, they estimated abatement costs as a quadratic function of emissions abatement. Hamaide and Boland (2000) defined abatement costs as a second-order polynomial function of abatement alone. While estimating the cost of abating agricultural nitrogen pollution in wetlands, Bystrom (1998) estimated linear, quadratic, and log-log specifications of a cost function.

For the projects that generate CERs only, total project costs are synonymous with abatement costs. However, in some cases, project investments increase power generations as well as mitigate greenhouse gas emissions. In this paper, our initial focus will be on a separable cost function; consequently we calculate a net cost of abatement by subtracting out net revenues from the expected sales of electricity, a “by-product” of abatement. Later, we repeat the analysis using total project costs, but include expected power increases as a control.

# **2.1 The Conceptual Model**

As a starting point for the derivation of our applied model, consider the expected project value function, where the value of the investment, V0, is determined by the discounted value of two streams of profit from a project initiated in year 0 and expected to last _n_ years<sup>7</sup> :

> 7 See Timilsina and Lefevre (1999) for a related discussion.

6
ulation in 2001). The 2011 Population Census also describes the three primary crops grown in each village; we consolidate these into an indicator for whether one out of the three is something other than a cereal (rice, wheat, etc) or pulse (lentils, chickpeas). The Population Censuses also provide the basis for linking all other datasets together at the village level.

Figure 1 provides a visual representation of the timing of the major datasets used in this project, along with year-by-year counts of the number of villages receiving PMGSY roads for the years of this study. Road construction is negligible before baseline data collection in 2001, then slowly ramps up to a peak of over 11,000 roads constructed annually in 2008 before slowing down slightly.

The analysis sample is restricted to the 11,474 villages that (i) did not have a paved road in 2001; (ii) we were able to match across all primary datasets; and (iii) had populations within the optimal bandwidth from a treatment threshold. Column 1 of Table 1 reports the average characteristics of the villages in the sample; they are very similar to the average unconnected village in India.<sup>9</sup>

# **V Empirical Strategy**

The impacts of infrastructure investments are challenging for economists to measure for several reasons. First, the high cost and large potential returns of such investments mean that few policymakers are willing to allow random allocation. Political favoritism, economic potential and pro-poor targeting would lead infrastructure to be correlated with other government programs and economic growth, biasing naive estimates in an unknown direction. Second, because roads are costly, road construction programs rarely generate large treatment samples. Sample surveys not directly connected with road construction programs are thus unlikely to have a sufficient number of treated and control groups; in contrast, analysis at

> 9Table A2 shows village-level summary statistics for _all_ villages in the 2001 Population Census, separated into those with and without roads. Villages without paved roads (which comprise nearly half of of all villages) are less populated (731 vs 1,708), have fewer public goods (e.g. 26% electrified vs 55%), have less irrigated agricultural land and are farther from the nearest urban center than villages with paved roads. The extent to which differences like these are endogenous or causal is the central question of this paper.

11
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for boys. By looking at each season separately, we observe significant differences in the share of children involved in farm labor activities regardless of the respondent type. The overall rate of labor force participation is 30 and 23 percentage points higher in the harvesting season compared to the main and short rainy seasons. Regardless of the gender of the child, corresponding self/proxy reporting differences in child labor show seasonal variation: 6.6 percentage points (15%) in the main rainy season, 2.7 (5.3%) in the short rainy season, and 4 percentage points (5.4%) in the harvesting season.

Moreover, we observe that labor participation for boys is typically more frequent and regular across seasons than that for girls. The coefficient of inter-variability of the self-reported measures of child labor, which gives a sense of how close the experimental values are to each other across seasons, is twofold for girls (34%) relative to boys (17%).<sup>13</sup> As a result, the gender gap in farm work participation varies across seasons as it reaches 20, 16, and 10 percentage points in the main rainy, short rainy, and harvesting seasons, respectively. While these numbers may reflect a pattern of gender stratification of intrahousehold time allocation with the salience of farm work for boys and more irregular work schedules for girls across the agricultural calendar, we consistently observe higher self/proxy discrepancies in child labor reporting for girls than that for boys across all seasons, reaching 9.2 percentage points (28%) in the main rainy season, 4.4 percentage points (11%) in the short rainy season, and 8 percentage points (12%) in the harvesting season. These results are statistically significant at 5% for all but the short rainy season.

The second panel reports child labor statistics at the (unconditional) intensive margin. Results are mostly consistent with the patterns emerging from the extensive margin of work: boys work at the farm several more hours than girls regardless of respondent type. For the pooled data, this difference reaches 10 monthly hours, or 50% higher monthly hours than the mean for girls.

17