rafmacalaba/data-use-mentions-extended huggingface dataset:
outsourcing destinations, as well as structural factors such as a lower intense relationship between trade and output itself (Timmer et al. 2016; Constantinescu et al. 2015).

Figure 5: Increased services import (input) intensity in India after reforms took place (1990-2017) <!-- Start of picture text --> 1990 1995 2000 2005 2010 2015<br>year<br>Automobile Industry Electronic Industry<br>Textiles and Apparel Chemicals and Pharmaceuticals<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: Prowess; Own calculations. Note: Service import input intensity is measured as share of imported services on total service inputs

# 3. Data and Empirical Strategy at the Firm Level

To systematically investigate the link between the use of service inputs in the production and a firms’ involvement in GVCs, we use detailed firm-level information from the Prowess database collected by the Centre for the Monitoring of the Indian Economy (CMIE). Prowess is a panel of large and medium Indian firms and has information primarily from income statements and annual reports of publicly listed companies in India. We use data from 1990 to 2017. This data set has been used in several papers (see Goldberg et al., 2010a, 2010b) and covers approximately around 70 percent of economic activity in India’s formal industrial sector. Besides financial information for each firm, the data contains information on trade activities (exports and imports of goods and services), ownership, revenue, expenditure on various inputs including the different service inputs. The wage bill is measured in relative to the size of a firm. Access to finance is proxied by financial leverage measured as the ratio of external liabilities to equity. Productivity is estimated using Ackerberg, Caves and Frazer (2015). Table 1 reports average values for some key variables.

Table 1: Summary Statistics

|Variable|Average|Unit|
|---|--
sources of migrations to Switzerland. This last specification is thus chosen as the basis for further interpretation.

Northern and Southern migrants show distinctly different responses to migration factors, and it is particularly true for financial incentives. In column 6, relative incomes are not significant in the migration decision by Northern migrants while they have the expected sign and are highly significant for Southern migrants. Since the geographical division is strongly correlated with education levels (and/or FDI movements), the results suggest that skilled immigration from Northern countries occurs independently of income differences. There may be two reasons for this; first, highly educated migrants would not be well represented by average income per capita and, second, when generated by FDI, migrants’ decision to move is unlikely to be strongly linked to pure monetary benefits. Northern immigrants are also less sensitive to the presence of a network from their own country (i.e., the elasticity is about half as large as for Southern immigrants). This is consistent with evidence at the regional level presented in OFS (2000) where, the maximum value for the distribution of the concentration indexes across cantons in 1995, is much lower for German, or British migrants (1.79, 2.62) than for Italian, Turkish and Spanish migrants (5.33, 4.49, 3.31). Again, in case of link with foreign direct investment, the location decision is unlikely to be made by individual migrants. Finally, the unemployment rate which proxies the quota policy based on labor market needs, has much more impact on immigration from the South than from the North. It is consistent with the fact that Southern immigrants tend to be concentrated in more cyclically sensitive industries and thus, their decision as well as the quota would be strongly affected by the state of the economy.

> 15  We also tried to estimate an equation for the immigration rate but as it is clear from the results that constraining the source-country effect to unity biases the results.

20
<br>6000 80000<br>60000<br>4000<br>40000<br>2000<br>20000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PR<br>10000 20000<br>7500 15000<br>5000 10000<br>2500 5000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>RS SP<br>30000 60000<br>20000 40000<br>10000 20000<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data % difference from SICONFI data<br>Frequency<br>Frequency Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage difference of payment data from the budget execution dataset in relation to data on paid expenses from SICONFI, as described in Section 3.

37
we find no statically significant difference in the pre-settlement population density and urbanization between municipalities with varying shares of refugees in 1928, conditional on geographic features. Moreover, Figure A.3 shows that, conditional on geographic characteristics and province fixed effects, there is no statistically significant correlation between pre-settlement population growth, i.e., between 1907/1913 and 1920, and the distribution of refugees across communities.<sup>31</sup> While we cannot explore this question any further at the community level due to lack of data, we can leverage richer data at the province-level. Table A.12 shows that provinces that hosted more refugees in 1928 were less developed in 1920, conditional on region fixed effects and on the covariates displayed in Table A.11. The share of refugees in 1928 is correlated with a lower share of employment in the nonagricultural sector and in manufacturing and with a lower literacy rate in 1920. We also examine pre-settlement trends in the literacy of the 1920 population. Figure A.4 shows that the increase in literacy rates across cohorts born between 1861 and 1905 follows a very similar pattern between high- and low-refugee provinces. Reassuringly, a statistical test of parallel pre-trends cannot be rejected, which suggests that places of settlement were not growing faster prior to refugees’ arrival.

> 31Greece conducted a population census in 1907 in all of its territories at that time, which comprised Central Greece, Peloponnese, Thessaly, Ionian Islands, and Cyclades Islands. After the annexation of Macedonia, Epirus, Crete, and Aegean Islands in 1913, an additional population census, similar to the one in 1907, was conducted in these regions. Population data for 1907/1913 is not available for Thrace as it joined Greece only after WWI.

40
Table B2: Effect of Extended Provider Training and Patient Information on Malaria Testing – at the Clinic: Patient Survey No Additional Controls

||(1)<br>Any<br>Malaria<br>Test|(2)<br>RDT Only|(3)<br>Microscopy<br>Only|(4)<br>Multiple<br>Tests|
|---|---|---|---|---|
|Extended Training|-0.0428|0.0525|-0.0359|-0.0667<sup>_∗∗_</sup>|
||(0.0550)|(0.0565)|(0.0688)|(0.0250)|
|Patient Information|-0.0699<sup>_∗∗∗_</sup>|-0.0470<sup>_∗∗_</sup>|-0.0184|0.00677|
||(0.0187)|(0.0202)|(0.0251)|(0.00868)|
|Mean (Basic Training, No Pat. Info.)|0.58|0.21|0.25|0.07|
|N|1973|1973|1973|1973|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata and survey date fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

Table B3: Effect of Extended Provider Training and Patient Information on the Alloca– tion of Malaria Treatment No Additional Controls

||(1)<br>Full Sample|(2)<br>Has Valid Ho|(3)<br>me-Based RDT|
|---|---|---|---|
|
the number of high-skilled workers in the city, reflecting the number who use motorized transport, and decreases with the number of kilometers of lanes of highway.

# **_Parameters_**

Where possible, estimates of parameters for the model use data sources from Dhaka. Household consumption shares are estimated from the 2010 Bangladesh Household Income and Expenditure Survey (BBS 2010). Input-Output matrices are constructed for each sector for the Asian Development Bank’s 2006 calculations (ADB 2016), aggregated into our sectors. Other parameters are derived using sources from the economics literature, with details provided in the appendix.

# **2.3  Calibration**

Given technology, preferences, geography (the connectivity structure) and endowments (labor of each type and land in each place) the model computes the equilibrium, determining land use, location of residence and employment, and consumption and production. The model is calibrated by finding productivity and amenity values, for each productive sector and household type (by skill and housing type) in each place, such that the model exactly fits the base data for 2011. Maps documenting the employment in each union, the population in each union, as well as the distribution of rents, wages, and the amenity and productivity parameters, are provided in the appendix.

# **3. Projection to 2035: Scenario A**

The model is calibrated to 2011, but the policy experiment – the dike and associated transport improvements – will not be completed until the early 2030s. The base-line for the experiment has therefore to be constructed, and this is done by using the Dhaka Structure Plan 2016-2035 (RAJUK 2016) in combination with our model, to project out to 2035. The Dhaka Structure Plan 2016-2035 (RAJUK 2016) provides the following as ‘data’. Total population of the Greater Dhaka area reaches 24.6 million in 2035, so the average annual population growth rate between 2010 and 2035 is 2.3% percent. Of the population in 2035, 40% are high-skilled
evidence of an effect of market power on the dependent variable, and our previous results regarding the effect of preferential liberalization and TTBs still hold.<sup>46</sup> One potential and likely contributing explanation behind the weakness of our results linking any influence of import market power to changes in external tariffs is due to our poor measures of import market power, which may simply be ill-suited to this particular application. Given the necessity for our exercise to utilize the tariff data from the early 1990s, in order to match the Estevadeordal et al. (2008) approach, the analysis had to be conducted at the relatively aggregated ISIC 4-digit level. As we have noted, measurement error for the elasticities is a generally recognized problem, and we have attempted to address it through a variety of standard techniques. Nevertheless, in this particular setting, it may simply be that the measurement error may have been compounded given that the elasticity estimates were constructed from a slightly later time period and given the need to concord elasticity estimates from the 6-digit HS to 4-digit ISIC level. Future work utilizing different, and perhaps more disaggregated data and potentially improved measures of elasticities may be a promising avenue to pursue, given the richness of the policy variation in the underlying setting.<sup>47</sup> Nevertheless, a separate and important potential explanation that we also cannot rule out, and which is supported by the broader patterns in the underlying data, is that Argentina and Brazil did not exploit their market power after formation of the customs union because, institutionally, _de facto_ there was no customs union. Given that each country pursued its TTB policies independently, easy access to this “escape clause” may have resulted in both Argentina and Brazil being unable to exploit any of their joint market power by raising their (joint) levels of import protection toward PTA outsiders in concert. Put differently, market power may be more likely to matter if Argentina and Brazil had been committed to actually changing the common external MFN tariff jointly or
and tariffs, and the consequent aggregation bias produces tariff elasticity that are smaller in magnitude (Redding and Weinstein, 2019).<sup>16</sup> Using the simple count of provisions to approximate the RTAs’ depth implicitly gives the same importance to any type of provision. One may want to assign relatively higher value of depth to those RTAs including rare provisions. So, as a robustness check, in Table 4 we show results by using weighted count to approximate the depth of RTAs (the weight is equal to one minus each provision’s frequency in the matrix of the RTAs mapped by the World Bank Deep Trade Agreements database). The resulting index weights relatively more RTAs containing rare provisions. Results, reported in Table 4, support the robustness of our baseline results. Interestingly, the estimations coefficients in Table 4 point to a stronger impact of deep RTAs on exports when approximated by a weighted sum. This suggests that the inclusion of rare provisions in RTAs is a good signal of the extent of trade costs reductions associated to deep RTAs between member countries.

The effect of RTA depth is robust to the inclusion of dummies for the presence of a RTA between country _i_ and _j_ at time _t_ . See results reported in Table A6. To take into account the presence of non-mapped active RTAs (i.e. those RTAs not included in the World Bank database), in Table A6 we take the list of RTAs from CEPII and assign a value of depth respectively equal to one (see columns 1-2), or equal to the closest (in time) country-pair’s RTA depth to nonmapped active RTAs (see columns 3-4). As expected, the presence of an empty (active) RTA – i.e. a RTA with zero depth – has null effect on the export of firms (see coefficient on RTA _adjusted_ in table A6 columns 1-3). The presence of a RTA has positive effects on export only if the agreement has some depth (i.e. positive number
There are four parameters calibrated by simulation: the government’s discount factor _β_<sup>_F_</sup> , the parameters associated with the exogenous default cost _d_ 0 and _d_ 1, and the pricing kernel parameter _κH_ . I choose these four parameters to match four targets in the data: (i) an average public debt of 44 _._ 4% of GDP, (ii) an average level of spreads of 267 basis points, (iii) an increase in the spread during high risk premium periods of 296 basis points, which is the average increase in the sovereign spread observed in Mexico during the three high risk premium episodes identified in the data, and (iv) a historical probability of default equal to 2%, which is a rough estimate based on the 2 default episodes observed in Mexico in the last 100 years according to Catao and Mano (2017). The government’s discount factor mainly determine the average debt level, the values of the default cost mainly determine the behavior of spreads, and the pricing kernel parameter is mainly disciplined by the probability of default.

I use total public debt data from IMF datasets. While this data includes domestically held and long-term debt, in the model all domestic bonds mature in one period and are held by foreigners.<sup>30</sup> Since the model is not rich enough to consider many debt instruments, there is a trade-off when choosing the debt instrument that disciplines the model.<sup>31</sup> While there is no perfect way to solve this trade-off, I choose to target total public debt because it is the most general instrument that approximates the impatient behavior of the government. In the appendix, I present an alternative calibration in which the government’s discount factor is calibrated to match debt service.

# **5.2 Key Statistics: Model vs Data**

Now, I report long-run moments in the data and in the model simulations.<sup>32</
Companies and individuals looking to hire on oDesk fill out a job description, including the skills required, the expected contract duration, and some preferred worker characteristics. After oDesk’s founding, most of the jobs posted were hourly positions for technology-related or programming tasks (e.g., web development), but postings for administrative assistance, data entry, graphic design, and smaller categories have become more prevalent as the platform has grown. After a company posts a position opening, workers apply for the job and bid an hourly rate. Firms can interview workers via oDesk, followed by an ultimate employment relationship being formed.

We study the role of the Indian diaspora in facilitating oDesk contracts to India. Our data begin at oDesk’s founding in 2005 and run through August of 2010. The data were obtained directly from oDesk with the stipulation that they be used for research purposes and not reveal information about individual companies or workers. oDesk does not collect a person’s ethnicity or country of birth, so we use the names of company contacts to probabilistically assign ethnicities. This matching approach exploits the fact that individuals with surnames like Chatterjee or Patel are significantly more likely to be ethnically Indian than individuals with surnames like Wang, Martinez, or Johnson. Our matching procedure exploits two databases originally developed for marketing purposes, common naming conventions, and hand-collected frequent names from multiple sources like population censuses and baby registries. The process assigns individuals a likelihood of being Indian or one of eight other ethnic groups.<sup>7</sup> Several features of this work should be noted. First, some records cannot be matched to an ethnicity, either due to incomplete records for listed ethnicities (e.g., very obscure names) or to uncovered ethnic groups (e.g., African ethnicities). Second, this approach can describe ethnic origins, but it cannot ascertain immigration status. For example, a U.S.-based company contact with the surname Singh is assigned to be of
27

## **Number of Phones (per 1,000 persons)**

Telephone mainlines are telephone lines connecting a customer's equipment to the public switched telephone network. Data are presented per 1,000 people for the entire country. Mobile phones refers to users of portable telephones subscribing to an automatic public mobile telephone service using cellular technology that provides access to the public switched telephone network, per 1,000 people. Telephones per 1,000 population is the sum of telephone mainlines and mobile phones and provides a better indicator of connectivity than either in isolation. Data for the number telephone mainlines and mobile phones (per 1,000 persons) were obtained from the World Bank World Development Indicators.

## **Number of Television Sets (per 1,000 persons)**

Television sets refer to those in use, per 1,000 people. The data series was obtained from the World Bank World Development Indicators.

## **Number of Radios**

Radios refer to radio receivers in use for broadcasts to the general public, per 1,000 people. The data series was obtained from the World Bank World Development Indicators.

## **Number of Newspapers**

Daily newspapers refer to those published at least four times a week, per 1,000 people. The data series was obtained from the World Bank World Development Indicators.

# **4.4 Indicators of Economic Regime and Institutional Quality**

In this paper, we employ the Sachs and Warner (1995) index of trade openness to serve as a broad indicator of the type of economic regime that prevails within a country. Similarly, to proxy for the quality of institutions, we use the Knack and Keefer (1995) index of the degree of property rights protection.
institutions are less developed. This is also an especially relevant question for micro, small and medium enterprises (MSMEs), since small firms have been shown to be the most vital job creators in developing countries,<sup>4</sup> as well as the most likely to be credit constrained.<sup>5</sup> In this paper, we use comprehensive firm-level data across a large set of developing countries to analyze the impact of access to finance on job growth and the heterogeneity in this relationship across firm size. In particular, we study the differential impact of access to finance on MSMEs’ ability to create jobs relative to that of larger firms.

Establishing a causal effect from access to finance to employment growth is complicated, since the variables measuring a firm’s access to finance may also reflect its demand for labor. Moreover, there are potentially many factors that could drive both firm employment growth and access to finance. We alleviate these identification concerns using a number of empirical strategies. First, we consider an exogenous shock to the supply of credit in the form of the introduction of a credit bureau.<sup>6</sup> We use a difference-in-difference approach in estimating the impact of the introduction of credit bureaus on employment growth, by comparing countries which underwent reforms and countries without reform, and years pre- and post-reform. Second, we use propensity score matching (PSM) to more closely match the treatment and control group of countries and re-estimate the difference-in-difference specification. Finally, following Rajan and Zingales (1998), we identify credit supply effects using industry measures of external finance dependence interacted with the credit bureau reform variable. If increased access to

> 4 Ayyagari, Demirguc-Kunt, and Maksimovic (2011) show SMEs to be important job creators. Haltiwanger, Jarmin and Miranda (2013) also find a strong role for SMEs in job creation in the US.

> 5 See Love and Mylenko (2003), Beck et al. (2005), Galindo and
measures of relative prices. Nevertheless, to rule out a potential omitted variable bias, we run regressions that explicitly control for trade policy in the form of tariffs and find that the coefficient of policy uncertainty is not affected (see Appendix Table A2).

**Table 4.** **_Association between import volume growth and economic policy uncertainty growth: estimations using yearly data_**

|Dependent variables:|Growth in good<br>import v|s and services<br>olume|Growth in g<br>volu|oods import<br>me|
|---|---|---|---|---|
||(1a)|(1b)|(2a)|(2b)|
|Growth in the Economic Policy Uncertainty (EPU) index|‐0.0290**|‐0.0377**|‐0.0378**|‐0.0462***|
||(0.0135)|(0.0135)|(0.0164)|(0.0158)|
|Growth in Real GDP|1.464***|1.457***|1.441***|1.435***|
||(0.212)|(0.212)|(0.285)|(0.284)|
|Growth in Real effective exchange rate|0.160|0.154|0.169*|0.163*|
||(0.0989)|(0.0976)|(0.0829)|(0.0812)|
|Constant|0.0719***|0.0703***|0.0755***|0.0737***|
||(0.0162)|(0.0152)|(0.0183)|(0.0168
is an exception. This study recognizes that banks have a choice to lend cross-border or to do so locally in host countries via their foreign affiliates. Using data for German banks, the authors test whether these two forms of financing are complements or substitutes by running regressions of one type of foreign funds visà-vis the other, while controlling for other determinants of international banking activities. They conclude that in the case of German banks both types of claims are complementary. The problem with their approach is that complementarity could be a result of endogeneity, where both types of foreign claims are driven by omitted factors. Also, the authors do not investigate why different countries get different shares of local claims relative to what they receive in cross-border flows.

3
%) Estimated difference<br>C4.3. SOSD: Social Sustainability Index C4.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.97*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>Spearman 0.93*** 0.9*** 0.58*<br>A<br>Kendall tau-b 0.83*** 0.83*** 0.56**<br>Spearman 0.93*** 0.98*** 0.93***<br>M0<br>Kendall tau-b 0.83*** 0.94*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference 0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C5. Change in the indicators included for each dimension, one indicator removed at