rafmacalaba/datause-encoder-data huggingface dataset:
I explore the export-R & D complementarity and export-import complementarity and decompose gains from export entry from these two channels. Access to foreign markets to sell its products allows firms to undertake activities in foreign sourcing and R & D that improve its productivity. The estimates suggest that about 15 percent of the differences in productivity growth is accounted for by imports of intermediate inputs, while investment in R & D explains about 85 percent of this difference in productivity growth. Thus, despite world R & D being concentrated in seven developed countries, I document that it is an important driver of productivity improvements even in developing countries like India.

The earlier empirical papers which did not find evidence of productivity gains for exporters did not account for firm variation in prices while estimating productivity. Some preliminary evidence on India also indicates that revenue productivity (where revenue is deflated using industry deflators to proxy for output) of exporters is more or less unchanged after firms begin exporting. This suggests a need for a greater understanding of pricing behavior and markups of exporters after they begin selling in international markets.

# **References**

- **Ackerberg, Daniel, Kevin Caves, and Garth Frazer** , “Structural identification of production functions,” MPRA Paper 38349, University Library of Munich, Germany December 2006.

- **Aghion, Philippe and Peter Howitt** , “A Model of Growth Through Creative Destruction,” _Econometrica_ , 1992, _60_ (2), pp. 323–351.

- **Arnold, Jens Matthias and Katrin Hussinger** , “Export Behavior and Firm Productivity in German Manufacturing: A Firm-Level Analysis,” _Review of World Economics (Weltwirtschaftliches Archiv)_ , July 2005, _141_ (2), 219–243.

- **Aw, Bee Yan, Mark J. Roberts, and Daniel Yi Xu** , “R&D Investment, Exporting, and Productivity Dynamics,” _American Economic Review_ , June 2011, _101_ (4), 1312–44.

- **Bernard, Andrew B. and J. Bradford Jensen** , “Exceptional exporter performance: cause, effect, or both?,” _Journal of International Economics_ , 1999, _47_ (1), 1 – 25.

- **Biesebroeck, Johannes Van** , “Exporting raises productivity in sub-Saharan African manufacturing firms,” _Journal of International Economics_ , 2005, _67_ (2), 373 – 391.

- **Blundell, Richard and Monica Costa Dias** , “Evaluation Methods for Non-Experimental Data,” _Fiscal Studies_ , 2000, _21_ (4), 427–468.

- **Boler, Esther Ann, Andreas Moxnes, and Karen Helene Ulltveit-Moe** , “R&D, International Sourcing, and the Joint Impact on Firm Performance,” _The American Economic Review_ , 2015, _105_ (12), 3704–3739.

- **Bustos, Paula** , “Trade Liberalization, Exports, and Technology Upgrading: Evidence on the Impact of MERCOSUR on Argentinian Firms,” _American Economic Review_ , February 2011, _101_ (1), 304–40.

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across the board, but the shift was not substantial. However, the share of high value goods in manufactured exports was highest in Senegal; the share of high-skill and technology-intensive manufactures increased from 17.1 percent during 1995-2005 to 22.1 percent of total merchandise exports compared to WAEMU averages of 8.3 and 8.4 percent, respectively.

**Table 6: Structure of Merchandize Exports 1995-2012**

||Sene|gal|WAE|MU|SS|A|
|---|---|---|---|---|---|---|
|% of merchandize export|1995-<br>2005|2006-<br>2012|1995-<br>2005|2006-<br>2012|1995-<br>2005|2006-<br>2012|
|Primary Commodities and Precious Stones|68.7|66.2|81.7|80.2|79.0|77.4|
|Manufactured goods|31.3|33.8|18.3|18.8|21.0|21.3|
|Labor Intensive and resource intensive<br>manufacture|3.6|8.1|4.8|5.1|8.7|7.0|
|Low Skill and Technology Intensive<br>manufacture|2.2|4.2|2.0|2.8|3.3|4.2|
|Medium Skill and Technology Intensive<br>manufacture|3.5|4.3|3.1|2.6|3.5|3.7|
|High Skill and Technology Intensive<br>manufacture|22.1|17.2|8.4|8.3|5.5|6.4|

Source: World Bank and UNCTAD

# **3.3. EMPLOYMENT AND POVERTY REDUCTION**

Senegal’s per capita GDP rose by 1.81 percent during 1995-2005 and by 0.38 percent during 20062013, compared to averages of 0.97 and 1.32 for WAEMU and 1.59 and 2.00 for SSA over the same respective periods. A sectoral decomposition shows that the services sector’s contribution to per capita growth in Senegal rose from 61 percent in 1995-2005 to 75 percent during 2006-2013.<sup>9</sup> Meanwhile, the sector’s contribution increased from 49 percent to 61 percent in SSA and fell from 44 percent to 38 percent in WAEMU.

9 There seems to be a disagreement between the country national accounts and the World Bank database in the construction of GDP per capital growth rates. Using data from country sources, the services sector contributed 69 percent and 76 percent to GDP growth during 1995-2005 and 2006-2013, respectively. Average population growth was 2.59 percent and 2.83 percent during the respective periods. Based on these, one would expect services’ contribution to per capita GDP growth rates to be around 66.4 percent and 73.2 percent for the two periods. However, the numbers from WDI are 61 percent for 1995-2013 and 75 percent for 2006-2013, so that population had more than the expected effect on per-capita GDP growth in the earlier period and less than expected effects in the latter period. There is a potential explanation for this. Keeping output constant, a capital intensive production system will yield higher output per worker than a labor intensive technology. Thus, the observed difference is a reflection of technological change that minimizes the population growth effect significantly.

16
34 <u>Figure 10: Levels and inequalities in inpatient admission rates and pap smears compared</u>

# **Inpatient admission last 12 months**

# **Pap smear last 5 yrs, women age 30‐49**

<!-- Start of picture text --> 12% 90%<br>80%<br>10%<br>70%<br>8% 60%<br>50%<br>6%<br>40%<br>4% 30%<br>20%<br>2%<br>10%<br>0% 0%<br>High income countries Low income countries High income countries Low income countries<br>Q1 (poorest) Q2 Q3 Q4 Q5 (richest) Q1 (poorest) Q2 Q3 Q4 Q5 (richest)<br><!-- End of picture text -->

# **Financial protection data**

In this section, we report details of the financial protection part of the 2018 HEFPI data set, listing the indicators included, the reasons for including them, their sources and definitions, how they were computed, how we derived data for different subpopulations, our quality checks, and lastly some illustrations of the use of the data.

## <u>Indicators included</u>

The indicators in the financial protection part of the 2018 HEFPI database (cf. Figure 2) are: (i) the incidence of ‘catastrophic’ health expenditures (health expenditures exceeding a certain percentage, _x_ , of a household’s total consumption or income), and (ii) the incidence of ‘impoverishing’ health expenditures (expenditures without which the household would have been above the poverty line, but because of the expenditures is below the poverty line). Indicator (i) (catastrophic expenditures) is one of the two UHC SDGs (3.8.2). The catastrophic expenditure indicator tells us whether health expenditures cause household consumption or income to fall by more than _x_ percent, while the second tells us whether health expenditures were sufficiently large to push the household
the results indicate that involving the developing world (i.e. bottom 60% per capita GDP countries), that currently contributes about 15% or 4.9 GT to total emissions, in GHG emission policy dialogs will become more important as their total output more than doubles to 11.4 GT in our BAU. This will be equivalent to 35% of current global emissions.

<!-- Start of picture text --> Bottom 60 population weighted GDP scenario Bottom 60 population weighted poverty projection<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>Forest cover weighted % tree cover loss, Population weighted PM25 concentrations,<br>projection for bottom 60 projection for bottom 60<br>Historical Historical<br>Pessimistic Pessimistic<br>BAU BAU<br>Opportunistic Opportunistic<br>2000 2005 2010 2015 2020 2025 2030 2000 2005 2010 2015 2020 2025 2030<br>Year Year<br>Total carbon emission output,<br>projection for bottom 60<br>Historical<br>Pessimistic<br>BAU<br>Opportunistic<br>2000 2005 2010 2015 2020 2025 2030<br>Year<br>10000 35<br>30<br>8000<br>25<br>Poverty rate<br>GDP per capita 6000 20<br>4000 15<br>40<br>0.6<br>38<br>0.5 36<br>34<br>% Tree cover loss 0.4 PM25 concentration<br>32<br>0.3 30<br>12<br>10<br>8<br>6<br>CO2 Billion Tons<br>4<br>2<br><!-- End of picture text --> Figure 7: Projection for environmental outcomes in the developing world. Table 6 contains aggregate statistics of the BAU line and highlights the distributional changes across income.

To shed final light on the challenges that await global leadership at the fronts of global warming and reducing carbon emission outputs, we have broken down total carbon emissions in per capita GDP quintiles in fig. 11. Emissions in the bottom 20% are not significantly contributing to world aggregates. Starting at the second quintile, we see considerable emission outputs all growing at alarming rates. Even the emissions of the current top 20% 29
hampered by the challenges related to nation-building. Most FSU countries, with the notable exception of the three Baltic States, are still behind the CEECs in implementing reforms.

As a result of this profound political and structural change, annual FDI flows in the region, of which a majority originated from EU, began to rise quickly from a low level. However, drastic economic and political reforms promoted in the CEECs at the beginning of the transition caused almost all members to suffer an economic recession to various degrees. Consequently, FDI inflows in the same period were adversely affected. Starting 1994, regional FDI picked up largely due to a successful transition in many of the CEECs. The almost doubled annual FDI inflows during this period from $12.3 billion in 1995 to $23.5 billion in 2000 were mainly induced by large-scale of privatization in the more advanced CEECs. For example, the purchase of a majority share in Poland’s Telekomunikacja Polska by France Telecom for $4 billion in 2000 was one of the largest privatization and FDI transactions in the region. The European Bank for Restructuring and Development (EBRD) compiles annually indicators depicting the progress in structural reforms related to the transition to market economy. EBRD (2007) shows the progress in both large-scale and small-scale privatization in our sample countries between 1990 and 2004. In Figure 3, we show the extent of privatization using an index, with a value of 4.0 denoting the level of reform comparable to a fully functioning market economy and a 1.0 being a completely unreformed economy. According to this Figure, we can see that many countries in the Central and Eastern Europe had completed the large-scale privatization by mid-1990s, while in a few countries the process was still 6
Column (4) of Table 5 shows that sole proprietorships have worse management practices, which is in line with the results of Lemos and Scur (2012, 2018) who find that family‐owned firms have significantly lower management scores. Column (5) shows that establishments that offer formal training to their workers, unsurprisingly, have better management practices in place. This echoes the findings of Bloom and Van Reenen (2010) who show that firms that use human capital more intensively (as proxied by more educated workers) are better managed. Interestingly, the significance of the relationship between sole proprietorship and management practices disappears once other characteristics are accounted for, but the relationship between formal training and management is more robust to the inclusion of other controls (column (7)). Finally, column (6) suggests that establishments who recruited their manager from outside the establishment (as opposed to those who promoted internally) have better management practices—a relationship that also holds after controlling for other establishment characteristics (column (7)).

# 4.2 Management practices and productivity

Across countries in both the developing and developed world, studies have shown that there are positive and significant returns to improved management practices. Furthermore, research by Bloom et al. (2013) has shown particularly strong positive impacts in some developing‐world contexts. Our results based on seven countries in Latin America confirm that there is indeed a significant relationship between improved management practices and productivity.

To examine the relationship between management practices and productivity, we estimate the model described in equation (1) on the establishments surveyed; the results of this regression using labor productivity as the outcome variable are presented in table 6 below. Column (1) shows that there is a positive and significant link between an establishment’s _average_ management score and its sales per worker. It also illustrates which other establishment characteristics are, _ceteris paribus_ , associated with higher labor productivity; namely size (bigger establishments have higher labor productivity), foreign ownership, exporting status, and less concentrated ownership structures (relative to sole proprietorships, all other legal forms have higher average labor productivity) _._ Table 6 also presents the results of the relationship between each of the individual practices and labor productivity. Column (2) of table 6 shows that there is a positive and significant relationship between the component of management practices that describes how establishments respond to problems and labor productivity. On the other hand, the survey data reveal no significant relationship between the number of performance metrics monitored (column (3)), nor the difficulty of targets (column (4)) and labor 16
on developing countries (and Japan) and use countries that have sufficient data on revenues, capital and a measure of labor input (the minimum requirements for the analysis).<sup>7</sup> We use only firms in the manufacturing sector and, given our focus on within-industry moments, use only country-year-industry cells with at least 10 firms. The data cover the period 2002-2010. There are seven countries in our Orbis sample: Argentina (ARG), Brazil (BRA), Japan (JPN), Malaysia (MYS), Taiwan, China (TWN), Thailand (THA) and Turkey (TUR).

We supplement this sample with data on four additional countries. First, we use data on US publicly traded firms from Compustat. Second, we use data on Chinese manufacturing firms from the Annual Surveys of Industrial Production conducted by the China National Bureau of Statistics. Next, we use data in Colombia (at the establishment level) from the Colombian Annual Manufacturers Survey and lastly, data in Mexico (also at the establishment level) from the Mexican Annual Industrial Survey.<sup>8</sup> Throughout the paper, we report results first for the seven Orbis countries (in alphabetical order) and then for the four additional countries (again in alphabetical order).

Our use of multiple data sources has the advantage of yielding a wider set of countries on which we can perform our analysis – nine developing and two developed. A disadvantage is that direct cross-country comparisons become more difficult, given differences in coverage, sample sizes, the set of firms included, etc.<sup>9</sup> However, we will show that despite these difference, our study leads to a number of robust cross-country patterns with respect to the sources of observed misallocation.

**Investment Moments.** The key input into our estimation procedure is a set of carefully chosen moments from data on firm-level capital and revenues, both of which are directly available in our data. Since we are interested in capital allocation across firms within an industry, we extract the firm-level idiosyncratic components of each data series by regressing the measured values on industry-by-year fixed effects and working with the residuals. Industries are defined at the four-digit SIC code level.

We report a number of moments for each of the countries in Table 1, grouped into four categories. The first group describes the stochastic process of firm-level productivity.<sup>10</sup> In our theoretical framework, which uses a standard Cobb-Douglas production function and constant

> 7The measure of labor varies depending on reporting within each country.  For Malaysia and Taiwan, China,  we have measures of the wage bill. For the remaining countries, we have number of employees. 8For a more detailed description of these data sets, see, e.g., David and Venkateswaran (2017) and the references therein.

> 9It turns out that these issues crop up even using a single cross-country database. For example, as previously noted, the coverage differs widely even within the Orbis database alone.

> 10As is well known, in standard models of misallocation/firm dynamics, measures of productivity can also be interpreted as idiosyncratic demand factors.

4
markets, such as a local credit crunch or a local housing market decline caused by tradable job losses.

Since this is a local multiplier, this does not capture inter-county linkages and the effects on federal government. For example, this does not capture induced declines in inter-county tourism from laid-off tradable workers. Neither does it capture job losses due to inter-county input-output linkages in production and services. Finally, the local multiplier ignores the effect on the federal government’s tax revenue, which could affect federal employment. Therefore, the estimates are likely to underestimate the true cascading impacts of job gains or losses.

The rest of the paper is organized as follows: section 2 reviews the literature; section 3 discusses the identification strategy; section 4 reports data; section 5 presents the baseline results; section 6 provides the estimates during the Great Recession and the recovery; section 7 concludes.

# **2. Literature review**

This paper is most related to Moretti (2010, 2012). Moretti (2010) uses three years of data (1980, 1990 and 2000) and a Bartik-typed instrument to estimate the _long-term_ employment multiplier across U.S. cities. Although using a similar approach to Moretti (2010, 2012), this paper’s focus is different. While Moretti’s focus in on the long-run “geography of jobs” (i.e. how jobs are allocated across the country), we are interested in estimating the dynamic short-run multiplying effect of job creations or job losses within a small area, in this case, a county. This is a topic of great interest, particularly since the Great Recession. Having this short-run focus allows us to estimate time-varying multipliers, such as those during the Great Recession and the Recovery. Finally, we could also estimate the dynamics of employment multipliers.

5
**<mark>Milgrom, P., & Roberts, J.</mark>** <mark>(1986). Price and advertising signals of product quality.</mark> _<mark>The Journal of Political Economy</mark>_ <mark>, 796-821.</mark>

**<mark>Mizala, A., & Urquiola, M.</mark>** <mark>(2013). School markets: The impact of information approximating schools’ effectiveness.</mark> _<mark>Journal of Development Economics</mark>_ <mark>, 103, 313-335.</mark>

**Muralidharan, K., & Sundararaman, V.** (2011).Teacher performance pay: Experimental evidence from India. _Journal of Political Economy_ , 119(1), 39-77. **PEIP (Private Educational Instituions in Pakistan)** (2000). Pakistan Bureau of Statistics, Government of Pakistan.

**Rockoff, J. E., & Turner, L. J.** (2008). Short run impacts of accountability on school quality (No. w14564). National Bureau of Economic Research. **<mark>Rogosa, D. (2002).</mark>** <mark>Irrelevance of Reliability Coefficients to Accountability Systems: Statistical Disconnect in Kane-Staiger “Volatility in School Test Scores”.</mark> _<mark>Retrieved from:</mark>_ <u>http://www-stat.stanford.edu/~rag/api/kscresst.pdf</u> **<mark>Shapiro, C.</mark>** <mark>(1983). Premiums for high quality products as returns to reputations.</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>,</mark> _<mark>98</mark>_ <mark>(4), 659-679.</mark>

**Wolinsky, A.** (1983). Prices as signals of product quality. _The Review of Economic Studies_ , _50_ (4), 647-658.

**World Bank** (2004). World Development Report: Making Services Work for Poor People.

**World Bank** (2014). World Bank Data Platform. Retrieved on: April 17, 2014.

33
Finally, to construct the skill structure of the USA, the information on the skill content by occupation as defined in **X** and the labor force structure ΘUSA is combined (Acemoglu and Autor 2010). The skill structure will be a vector with five elements, one per skill aggregate: We apply the above methodology to data from thirty countries, ranging from low-income countries to higher middle-income ones, for which nationally representative household surveys were available in the required form. Based on the household data, we characterize the occupation structure by estimating the shares of all occupations **_i_** in country **_j_** included θi,j **:** Therefore, the labor market structure for country j will be defined by: To assess the skill structure for country **j** , we employ the skill intensities for each occupation as presented in the O*NET for the United States. This is an important assumption.<sup>7</sup> No country-specific applications of the O*NET equivalence table exist so that we are applying the Unites States skill equivalence to all countries in our sample. By applying the O*NET to

> 7 Some of the limitations of O*NET were documented by Haendel (2007).

**8**
|south|Southern region (1/0)|
|---|---|
|centre|Central region (1/0)|
|wsemarid|Warm semi-arid region (1/0)|
|wsubhumid|Warm sub-humid region (1/0)|
|csemarid|Cold sub-humid region (1/0)|
|MPvst|Member of Parliament visit village in past months (1/0)|
|hlivedvg|Head has always lived in village (1/0)|
|devrainmedian|Deviation of rainfall from median amount|
|vglandchief|Village chiefs receive money to access and use land (1/0)|
|shockfprol|Shock resulted in food production loss in past 2 years (1/0)|
|shockfsl|Shock resulted in food stocks loss in past 3 years (1/0)|
|shockfpurl|Shock resulted in food purchases loss in past 3 years (1/0)|

37