rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Exports as a Determinant of Industry-Specific Skill Premiums**

Skill premiums are affected by numerous factors, including demand and supply conditions, policies, and various shocks. Our interest in the correlates of skill premiums is motivated by the literature on wages paid by exporters relative to non-exporters. This literature, pioneered by Bernard and Jensen (1995, 1999), documented the better performance of exporting firms in terms of employment, wages, and productivity. This work has been complemented and expanded by numerous researchers (see for instance the review in Bernand, Jensen, Redding, and Schott 2007): the superior performance of exporting firms (as well as importing firms) is now clearly established. In a related paper, Brambilla, Lederman and Porto (2009) develop a model of exports and skills tested with firm data from Argentina. The ongoing explores a reduced-form analysis to generate evidence in support of claim that the level of exports is a key determinant of the skill premium.

Two leading theories explain this potential link between industry exports and skill premiums. One argues that the act of exporting requires activities that are skill-intensive, although the production of the good may require unskilled labor. Exporting firms, and therefore industries with more exports in general, will thus demand higher skills and pay a higher skill premium. The alternative theory argues that exporting is associated with higher profits (because more productive firms self select into exports) and these higher profits are shared with the workers via profit sharing rules.

The theory focusing on the need to engage in skill-intensive activities in order to export a product is based on Brambilla, Lederman, and Porto (2009). For our present purposes, we assume that skilled labor is imperfectly mobile, as in Goldberg and Pavcnik (2005), Ferreira et al. (2008), and Galiani and Porto (2009). Unskilled workers are perfectly mobile across sectors and earn the economy-wide competitive wage, _wu_ . While total labor supply in a given industry may be fixed due to labor specificity, workers can be induced to supply more effort at higher offered wages. In Figure 2, for instance, the relationship between effective skilled labor supply in industry _j_ and skilled wages _ws_ is increasing (the function _Ls_ ( _ws_ )).

12
Table 8: Sources of household income

||Total<br>harvest ($)|Income<br>from<br>process-<br>sing ($)|Income<br>from<br>poultry/<br>livestock<br>($)|Monthly<br>income<br>self-<br>employ.<br>(planter)<br>($)|Monthly<br>income<br>self-<br>employ.<br>(spouse)<br>($)|Monthly<br>income<br>wage<br>employ.<br>(planter)<br>($)|Monthly<br>income<br>wage<br>employ.<br>(spouse)<br>($)|
|---|---|---|---|---|---|---|---|
|Seedling Subsidy|-356.68***|-4.48|2.63|10.01**|-1.89|-0.60|0.03|
||(110.58)|(6.47)|(3.24)|(4.74)|(1.75)|(3.14)|(0.20)|
|Couples' Training|346.05***|3.16|-2.23|-5.04|1.71|0.17|-0.21|
||(132.46)|(7.92)|(3.19)|(5.60)|(1.89)|(3.11)|(0.21)|
|Seedling Subsidy + Couples'<br>Training (beta)|-10.63|-1.32|0.40|4.96|-0.18|-0.43|-0.18|
|H0: Seedling Subsidy + Couples'<br>Training = 0 (p-val)|0.93|0.85|0.91|0.26|0.92|0.87|0.41|
|Observations|1435|1435|1435|1435|1435|1435|1435|
|Mean for control group|1353.01|26.57|6.34|15.70|10.06|11.07|0.41|
|Mean for individual training|840.80|18.36|9.80|26.49|10.33|13.10|0.37|

Robust and clustered standard errors in parentheses. All values winsorized at 99%.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

# **5 Underlying Mechanisms**

Focusing on the aggregate farming impacts in Section 4, we can see that individual training households experienced a 25% fall in total harvest value as they planted new rubber trees. Those in the couples’ training avoided this drop while achieving a 20% higher level of overall investment (looking at total trees planted between 2016 and 2018). Part of how they achieved this was through using more chemical inputs (a 16% increase in plots using phytosanitary and 36% increase in plots using chemical fertilizer – both significant at the 10 percent level) and perhaps a modest increase in total labor (not significant at conventional levels). While we do not know the shape of the production function(s), these sizable investment and productivity outcomes do not seem to be mainly driven by quantities of input use. We now turn to potential explanations around efficiency focusing on three potential channels: skills, management and incentives.

## **5.1 Skills**

A first potential explanation is an increase in skills. In the couples’ training, two members of the household attend the training rather than one. This could result in additional knowledge acquisition and/or the reinforcement of the planter’s knowledge as the couple discusses what they were taught.

1
||||**Country with Lar**|**gest Increase in**<br>**Country with La**|**rgest Decrease in**|
|---|---|---|---|---|---|
|**Product**|**Product Description**||**Import**|**Value**<br>**Impor**|**t Value**|
|940360|Other wooden furniture||China|Belarus||
|||Change in import value||13,438.00|-8,608.00|
|||Import value in 2008||146,248.60|52,662.09|
|||% Change in import value||9.19|-16.35|
|850440|Static converters||China|Kazakhstan||
|||Change in import value||19,528.00|-19,893.00|
|||Import value in 2008||303,136.60|221,749.70|
|||% Change in import value||6.44|-8.97|
|80830|Pears||Belgium|Belarus||
|||Change in import value||10,153.00|0.00|
|||Import value in 2008||141,622.10|2,498.54|
|||% Change in import value||7.17|0.00|
|950300|Tricycles, scooters, pedal cars and similar wheeled toys; dolls' carriages; dolls; other toys; reduced-size ("scale")||China|Belarus||
||models and similar recreational models, working or not; puzzles of all kinds.|Change in import value||29,929.00|-4,023.00|
|||Import value in 2008||934,350.90|55,951.01|
|||% Change in import value||3.20|-7.19|
|330499|Other beauty or make-up preparations and preparations for the care of the skin (other than medicaments), including||France|Belarus||
||sunscreen or sun tan preparations; manicure or pedicure preparations.|Change in import value||9,893.00|-1,838.00|
|||Import value in 2008||224,295.10|16,040.97|
|||% Change in import value||4.41|-11.46|
|852872|Other color monitors and projectors, not incorporating television reception apparatus; reception apparatus for||Hungary|Belarus||
||television, whether or not incorporating radio-broadcast receivers or sound or video recording or reproducing|Change in import value||10,785.00|-19,890.00|
||apparatus.|Import value in 2008||99,757.77|144,956.50|
|||% Change in import value||10.81|-13.72|
|870120|Road tractors for semi-trailers||Netherlands|Belarus||
|||Change in import value||10,945.00|-3,463.00|
|||Import value in 2008||716,175.40|94,106.72|
|||% Change in import value||1.53|-3.68|
|330300|Perfumes and toilet waters.||France|Belarus||
|||Change in import value||18,613.00|-5,530.00|
|||Import value in 2008||378,971.80|50,324.80|
|||% Change in import value||4.91|-10.99|
|640291|Other footwear with outer soles and uppers of rubber or plastics, covering the ankle.||China|Kazakhstan||
|||Change in import value||22,574.00|-986.00|
|||Import value in 2008||437,397.20|6,763.98|
|||% Change in import value||5.16|-14.58|
|730890|Other structures (excluding prefabricated buildings of heading 94.06) and parts of structures (for example, bridges||Germany|Ukraine||
||and bridge-sections, lock-gates, towers, lattice masts, roofs, roofing frame-works, doors and windows and their<br>|Change in import value||9,939.00|-8,397.00|
||frames and thresholds for doors|Import value in 2008||170,398.90|100,947.20|
|||% Change in import value||5.83|-8.32|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 45
Appendix 2. Definitions and sources of variables (continued)

|Variable|Definition|Source|
|---|---|---|
|Quarterlyseries|||
|Output (per<br>capita) growth|Quarterly real GDP is taken from Haver Analytics and OECD. Quarter-on-quarter annualized growth<br>rates of seasonally-adjusted real GDP are computed. If the original data are not seasonally-adjusted,<br>the U.S. Census X-13 program is used to perform seasonal adjustment first. Quarterly growth for<br>aggregated groups is computed with market-weighted annual output as a weight—the same weights<br>are applied to four quarters in given years. In computing per capita growth, population data are used<br>on an annual basis and taken from the World Bank and PWT, and therefore, the same population<br>growth numbers are used in four quarters in given years. As a result, output per capita growth is<br>calculated as the difference between aggregated annualized quarterly GDP growth and annual<br>population growth. When computing growth rates over two quarters with different samples, countries<br>are restricted to the common samples between these two quarters and with GDP and population.<br>Sample includes 106 countries, though the sample size varies by quarter, over 1960:1-2019:3. Data for<br>the 1950s are excluded,due to the limited data availability.|Feenstra, Inklaar, and<br>Timmer (2015); Haver<br>Analytics; OECD; World<br>Bank.|

Note: Country-group aggregates are not computed, if the sample size is limited—specifically, data need to be available at least for 10 countries in all variables except in financial conditions, short-term interest rates, business confidence, policy uncertainty, and policy interest rates (at least 4 countries, because of more limited data availability in the original series). The sample coverage mentioned in the table is the maximum number of countries and the sample size varies by year or quarter. Figures A2.1 to A2.4 display time series data of global aggregate. Output from other data sources is also utilized for comparison purposes: the IMF World Economic Outlook (October 2019) has annual data available since 1980 and the United Nations National Accounts Main Aggregates Database has annual data starting in 1970.

65
**_"Not everything that can be counted counts, and not everything that counts can be counted"_**

**Albert Einstein**

# **1.  Introduction**

Most scholars, policymakers, aid donors, and aid recipients recognize that good governance is a fundamental ingredient of sustained economic development. This growing understanding, which was initially informed by a very limited set of empirical measures of governance, has spurred an intense interest in developing more refined, nuanced, and policy-relevant indicators of governance. In this paper we review progress to date in the area of measuring governance, emphasizing empirical measures that are explicitly designed to be comparable across countries, and in most cases, over time as well. Our goal here is to provide a structure for thinking about the strengths and weaknesses of different types of governance indicators that can inform ongoing efforts to improve existing measures and develop new ones.<sup>1</sup> We begin in Section 2 by reviewing some of the alternative definitions of governance, as a necessary first step towards measurement. Although there are many broad definitions of governance in circulation, the degree of definitional disagreement can easily be overstated. Most definitions appropriately emphasize the importance of a capable state, accountable to its citizens and operating under the rule of law. Broad principles of governance along these lines are naturally not amenable to direct observation and thus to direct measurement: as the first part of the quote from Albert Einstein reminds us, "not everything that counts can be counted". However as we document below there are many different types of data that are informative of the extent to which these principles of governance are observed across countries. An important corollary is that any particular indicator of governance can usefully be interpreted as a noisy, or imperfect _proxy_ for some unobserved broad dimension of governance. This interpretation emphasizes a recurrent theme throughout this review -- that there is

> 1 We do not provide a great deal of detail on each of the many existing indicators of governance.  All of the measures we discuss have been competently described by their producers, several have attracted their own written critiques and discussions, and there are already a number of existing surveys and user guides to the body of existing governance indicators.  See for example Arndt and Oman (2006), Knack (2006), UNDP (2005), and Chapter 5 of World Bank (2006).  Due to space constraints we also do not attempt to review the very important body of work focused on in-depth within-country diagnostic measures of governance that are not designed for cross-country replicability and comparisons.

2
7 the Taylor expansion) were rather muted: 2.3% of the direct effect in Nicaragua, 3.5% in Peru, 7% in Honduras, and 12.5% in Guatemala.<sup>4</sup> The second shortcoming of the first-order approximation based on net food purchases is, according to Deaton (1997), “a more serious deficiency... its neglect of repercussions in the labor market. Changes in the price of the basic staple will affect both supply and demand for labor, and these effects can cause first-order modifications to the results.” (p.187). Estimates that attempt to incorporate simulated wage responses, based on behavioral models, are available for Bangladesh (Ravallion, 1990) and for Indonesia (Ravallion and van de Walle, 1991), but we are not aware of studies of the recent 2007-2008 food price shock (prior to ours) that attempt to incorporate estimates of labor market effects.

Labor market effects, as well as substitution effects, are absent, for example, from the influential study by Ivanic and Martin (2008), which was based on pre-shock household survey data for ten low-income countries containing information on both purchases and sales of a few key staple commodities. Assuming 100 percent pass-through of international to domestic prices, they estimated the first-order effects of these price increases across the expenditure distributions. Even accounting for the existence of net food sellers among the poor, the authors conclude that poverty is likely to have increased in most countries in their sample.

As the authors note, however, they had no access to data on the evolution of the domestic food prices actually faced by consumers in each of the countries they analyzed. Although the assumption of 100 percent pass-through is a sensible one under these circumstances, it is clearly strong. In a recent survey of commodity price impacts, Brambilla and Porto (2009) argue that the pass-through from international food prices is generally well short of 100 percent. In addition, spatial heterogeneity in infrastructure, transport costs, and market structures within countries often causes non-trivial regional differences in prices, even inside a given country. (We will see below that this is definitely true of Brazil in 2008.) Imperfect pass-through and regional price variation suggest the need for detailed data on changes in consumer prices _within_ developing countries during the food price shock period. Reliance on the time series of world prices for a few key commodities also

> 4 Estimates of substitution effects such as those of Friedman and Levinsohn (2002) and Robles and Torero (2010) are also possibly subject to bias, by ignoring substitution along the quality, rather than quantity, margin. See McKelvey (2011) and Gibson and Kim (2011). Implications of quality substitution for the estimation of welfare effects rather than simply quantity or calorie elasticities remains a subject for future work.
variables is more limited than if we used national indicators. The data used in this exercise are primarily from 2002, the most recent year for which most indicators were available. However, educational performance tests indicators and substance use indicators were only available for 2001. The source of the indicators is given in Table 2, and are easily accessible to the public.

Most of the indicators on socioeconomic status and connection to institutions were obtained from the Brazilian Institute of Geography and Statistics ( _<u>Instituto Brasileiro de Geografia e Estatística</u>_ - IBGE) and IPEA ( _Instituto de Pesquisa Econômica Aplicada_ - Institute of Applied Economic Studies). These institutions utilize census data and regular household surveys to calculate various indicators periodically, which are reported on the institutions’ web pages.

A smaller set of indicators are taken from the administrative data of Ministries and other government institutions. The Ministry of Labor and Employment’s data base was used to derive the share of the population in formal sector employment, defined as those with a signed work card ( _carteira de trabalho assinada_ ). The RAIS database is a census of all workers with signed work cards, thus giving an exact count of formal sector workers, as well as basic demographic information that allows identifying the state of origin and age of workers, among other characteristics. To create a ratio, data from the 2000 Census, managed by the IBGE, was used.

The share of 16-17 year olds who vote was obtained from the Superior Electoral Court ( _Tribunal Superior Eleitoral_ ) and IBGE. The former provides the number of people who voted in the 2002 elections, by state and age level. The latter is used to create a ratio.

The indicators for test scores and school attendance were reported on the Ministry of Education’s administrative data bases. The scores on the math and Portuguese language tests are taken from the National System of Assessment in Basic Education ( _Sistema Nacional de Avaliação da Educação Básica -_ SAEB). These tests are given annually, across Brazil, to measure the progress of learning of Brazilian students in the 4<sup>th</sup> , 8<sup>th</sup> and 11<sup>th</sup> grades (last year of education before tertiary).

Health indicators – neo-natal mortality, infant mortality, adolescent morbidity, and AIDS – were presented in the Ministry of Health data bases. Data on the hospital 6
CTT account for CTT operations; 21.25% to an account in the University President's office; 21.25% to an account for the inventor's laboratory or department; and 42.5% to the inventor personally. At any point in time, on an accrual accounting basis, the balance in the revolving fund account managed by the university CTT should always be "zero." As for funding of patents, CTTs in the United States expect the companies receiving the innovations for commercial application to pay for the patent application costs, as a "cost of doing business" or a "cost of goods sold," similar to labor costs or material costs. In the Top 25 Research Universities in the United States, there is much variance in the success of the institutions in receiving reimbursement of patent expenses, ranging from 16% of expenses funded by industry at the California Institute of Technology to 90% of expenses funded by industry at the University of Pennsylvania.

For patents funded "at risk" (without a company to reimburse the costs), there are a variety of sources for securing the funds. In a recent survey of the Top 25 Research Universities in the United States, 23 of the 25 institutions responded to the survey. One of the questions asked was: "What is the source of funding for patent applications prosecuted at risk (without a licensee to reimburse/fund expenses)?" Responses (23): 8 - Patent expenses funded from royalty income accumulated over time in the CTT.

14- Patent expenses funded from university accounts; university funds were (i) allocated to the TLO in an annual budget for TLO management; **<u>OR</u>** (ii) controlled by the Vice President for Research or other university official; **<u>OR</u>** (iii) provided by departments of faculties (colleges) for inventions arising from the applicable department or faculties (college); **<u>OR</u>** (iv) combinations of the above sources.

1 - Patent expenses funded by an external organization.

No National Governmental funds are available to CTTs in the United States to pay for patent applications.

# **<u>START-UP COMPANIES</u>**

There are approximately 200 university CTTs in the United States. Accordingly, there are an equal number of ways that universities manage start-up companies. Every university has a different set of policies, procedures and attitude towards start-up companies. Some universities are very supportive of start-up companies and participate in their formation. Other universities may not participate in start-up companies in any manner. Additionally, other countries manage their start-ups differently. For instance in the United Kingdom, there is an organization called UNICO which represents "UNIversity COmpanies"....these are external companies formed for the sole purpose of creating start-up companies based upon a university's technology. Typically, a UNICO is owned - in whole or in part - by the associated university. For details, see: <u>http://www.unico.org.uk/</u> 90
All prices are expressed in dollars and take into account an agricultural version of purchasing power parity. When we run a panel regression of this food price index, controlling for the usual country-level characteristics (like in Regression 3) we find no statistically significant effect of private credit on prices. While we do not want to give too much emphasis to this result, it nevertheless suggests that a reduction in prices is not the main channel by which financial sector development reduces undernourishment. This implies that finance is important more directly. We will turn to next to the importance of access to financial services for reducing undernourishment.

_F. The role of access to financial services for the impact of finance on undernourishment_ So far, we have used a very aggregate measure of financial sector development, private sector to GDP, and associated increases in that measure with greater access to financial services. But access to financial services may be unequal across households and it may not be the poor or undernourished that benefit from greater financial sector development. Unfortunately, there are little data on access to financial services by individual households or small firms across a large set of countries and covering any consistent time span (see Honohan, 2005 and 2006 for what data are available and data deficiencies). What we do have are measures of the number of access points to the formal financial system, specifically the number of branches and ATMs for the year 2003-2004. These distribution data can be useful proxies for access. Burgess and Pande (2005), for example, show the importance of the banking system distribution in case of India. We use these data, scaled by the size of the country in square kilometers, in our cross-section regressions to distill the joint impact of financial sector development and access to financial services on undernourishment, productivity, productivity enhancing inputs and prices. We control in these regressions for the degree of country openness, the size of government, and the degree of inflation, as well as food production per capita and food net exports in the price regression. The results are reported in Table 9.

We find that there is a beneficial effect of the reach of the financial system on undernourishment (Regression 1). The effect of financial reach is actually so strong as to make the coefficient for private credit no longer statistically significant. The effects of 25