rafmacalaba/datause-encoder-data huggingface dataset:
# **INVENTOR DIASPORAS AND THE INTERNATIONALIZATION OF TECHNOLOGY**

_Ernest Miguélez_<sup>_1_</sup> Key words: diaspora networks, international collaborations, inventors, PCT patents, R&D offshoring JEL: C8, J61, O31, O33 1 Ernest Miguélez (corresponding author) is Junior Researcher in the CNRS, affiliated to the GREThA UMR CNRS 5113 research group, Université de Bordeaux. He is also visiting fellow at the AQR-IREA, University of Barcelona, and research affiliate at CReAM, UCL. His e-mail address is <u>ernest.miguelez@u-bordeaux.fr. The research for</u> this article was financed by the Regional Council of Aquitaine (Chaire d’Accueil en Economie de l’Innovation, to Francesco Lissoni) and the Regional Council of Aquitaine (PROXIMO project, to Christophe Carrincazeaux). The author thanks Michel Beine, Gaetan de Rassenfosse, Stuart Graham, William Kerr, Francesco Lissoni, Catalina Martinez, Çaglar Ozgen, Hillel Rapoport, Massimo Riccaboni, Valerio Sterzi, seminar participants at GREThA-Bordeaux University (2013), GATE-LSE Saint Etienne (2014), Temporary Migration Cluster UC Davis (2015), the workshop "The Output of R&D and Innovative Activities: Harnessing the Power of Patent Data” (JRCIPTS Seville, 2013), the 7th MEIDE conference (Santiago de Chile, 2013), the PSDM 2013 conference (Rio de Janeiro, 2013), the 54<sup>th</sup> ERSA conference (Saint Petersburg, Russia, 2014), and the 2<sup>nd</sup> Geography of Innovation Conference (Utrecht, The Netherlands, 2014) for valuable comments; Julio Raffo for helpful discussions on previous versions of this paper; as well as the contribution of three anonymous referees.
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comprehensive, the study examined incentives ranging from tariffs to free trade zones. The study, however, failed to provide any hard evidence on the effe,tiveness of one or more such incentives. It relied on a survey of opinions of company executives selected arbitrarily to measure the impact of incentives on business location decisions. The executives were simply asked whether they would have still located in a particular country if no incentives were offered to them by the country in question but competitor countries maintained their incentive packages at their traditional levels. The responses in general were expectedly negative. The respondents indicated that "in this hypothetical case, the absence of incentives would have affected their decision, even though, in the real instance, the presence of incentive was not a major factor in their decision" (see Guisinger and Associates, 1986, p. 166). In brief, the Guisinger study suffered from a poor choice of questions for the opinion survey and purely arbitrary sampling design 'i,e., stratified random sampling procedures were not followed), and it failed to shed any new light on the effectiveness of tax measures to stimulate foreign direct investment.

Halvorsen (1992) has also analyzed the Thailand Board of Investment's survey data on responses by investors. It is interesting to learn that investors ranked exemption/reduction of import duties on machinery and equipment and of business taxes as the most important incentive followed by reduction of import duties and business taxes on raw materials. The corporate tax holiday was ranked third. Permission to bring in foreign technicians was ranked as fourth.

B. Estimation of Ad Hoc/Edectic Equations This approach usually specifies private investment to be a function of a host of independent variables including tax related variables. Variables selection and model specification is most often based on a 'fishing expedition' for a high coefficient of multiple determination, **_R_**<sup>**_2_**</sup> **_._** Ebrill (1987) uses cross-section data on 31 developing countries for 1980 to examine the effect of cost of capital on investment. The dependent variable was the share of gross domestic investment in gross domestic product for 1980. Other than the cost of capital, independent regressors included: average annual growth rate of exports; share of minerals in exports; average annual growth rate of _GDP;_ current account balance; inflation rate; and per capita _GDP._ He found that the coefficient of the cost of capital was negative and significant for the sample as a whole but when Argentina and Chile-two high-inflation countries-were excluded, the cost was capital had the negative sign but it was statistically insignificant. Thus, Ebrill's results only confirmed a weak relationship between the cost of capital and the level of investment.
# IMPLEMENTING DIRECT CONSUMPTION TAXES IN DEVELOPING COUNTRIES

# George R. Zodrow and Charles E. McLure, Jr.

# I. INTRODUCTION

Recent years have seen the development of two important trends in the area of consumption taxation. First, and most obvious, is the ascendancy of the value added tax (VAT) as the preferred form of broadbased tax, in bothl developed and developing countries. As little as twenty-five years ago, the VAT existed only in France, and even there in only a very rudimentary form. Since then, adoption of the VAT has been made a prerequisite for membership in the European Economic Community (EEC), several European countries that are not members of the EEC have adopted the VAT, and the tax has spread throughout the Third World. In all some forty countries now utilize the VAT.<sup>1</sup> Paralleling the rise of the use of the VAT has been increased interest in a different form of consumption tax, a direct tax that can be tailored to the economic circumstances of the taxpayer. Direct consumption taxes have been given such names as an expenditure tax, a personal tax on consumption, a tax on consumed income, a cash flow lifetime income tax, a personal exemption VAT, the Simplified Alternative Tax, and simply "Plan x...12 Academic economists have been interested in taxes of this type primarily because they generally do not alter the terms cn which present consumption can be exchanged for future consumption; under certain highly

- 1/ See Shoup (forthcoming), and Tait (forthcoming).

- 2/ Among standard references are Andrews (1974), Bradford (1980, 1986), Lodin (1978), Institute for Fiscal Studies (1978), Aaron and Calper (1985), Mieszkowski (1977, 1980), and U.S. iiepartment of the Treasury (1977).
Table 13: Management, innovation and productivity

||**All firms**|**50+ employees**|
|---|---|---|
|**_Panel A: Laborproductivity_**|||
|**Management score**|0.459**|0.684**|
||(0.206)|(0.278)|
|**Innovation**|0.242**|0.186|
||(0.0960)|(0.301)|
|**Observations**|3,766|1,089|
|**R‐squared**|0.353|0.802|
|**_Panel B: Totalfactorproductivity_**|||
|**Management score**|0.165|0.871**|
||(0.212)|(0.355)|
|**Innovation**|0.182**|0.00813|
||(0.0908)|(0.190)|
|**Observations**|1,396|459|
|**R‐squared**|0.310|0.800|

Notes: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Survey weights are used in all regressions. All regressions control for establishment characteristics.

# 4.3 Addressing endogeneity

The results discussed so far account for the sector and location of operation, as well as several establishment characteristics that may be related to both management and performance (such as age, number of employees, foreign ownership, and legal form). However, based on these results it is hard to say whether better management practices lead to more efficient use of resources and, thus, higher productivity, or whether better performing establishments have access to greater resources that enable them to recruit better managers. This can be investigated by looking at the sub‐sample of establishments that have externally‐recruited managers (i.e., managers who had experience in the sector before joining the establishment), as opposed to those who were promoted to manage from within the establishment. As column (1) of Table 13 shows, externally‐recruited managers have higher management scores, even after taking into account sector, location and a range of establishment‐level characteristics (this was also shown in Table 5). However, columns (2) and (3) of Table 13 show that having an externally‐recruited manager does not lead to greater labor productivity or total factor productivity. This is the first indication that the improved performance of better managed establishments is not due to a link between performance and the ability to recruit better managers from outside the establishment.

26
We take advantage of the availability of panel data for these households and make a comparison between 1997 (before the program started) and 2000. For the sequential probit regressions we take three dependent dichotomous variables: first, it takes value 1 if the child goes to school and does not work; second, it takes value 1 if the child goes to school and works; and third, if the child does not go to school and works. We omit the case where the child does not go to school and does not work. We also run a multinomial logit equation for three cases of interest compared to a child that goes to school and does not work: the probability that the child goes to school and works, does not go to school and works, and does not go to school and does not work. Finally, we take several poverty and inequality measures among indigenous, bilingual and Spanish-speaking households for both years. For a better examination of the depth and severity of poverty in indigenous, bilingual and Spanish groups, the Foster-Greer-Thorbecke index is used.

# **5. Data Description**

The data used here are the PROGRESA/OPORTUNIDADES survey instruments that ask the question whether the person speaks an indigenous language. They also include information on earnings, household structure, school, work and social transfers. Examination of this data set gives valuable policy information on whether or not government programs are useful for decreasing inequality, particularly by pulling up the lower tail of the distribution, where indigenous people largely reside. PROGRESA/ OPORTUNIDADES in Mexico is one of the most comprehensive demand-side financing (or conditional cash transfer) programs in the world. The program provides cash stipends to poor families in exchange for ensuring their children’s school attendance. Parker and Skoufias (2001) indicate that PROGRESA has led to significant improvements in educational indicators and outcomes. In fact, the program has led to higher school attendance rates and lower school dropout and repetition rates. There is also evidence of reduced child labor. PROGRESA has nearly eliminated the school enrollment gap between rich and poor. Finally, PROGRESA is very cost-effective. PROGRESA, therefore, has the effect of increasing the income of poor households, which are largely indigenous in Mexico, especially in rural areas and the poor southern states. The 17
**Table 2c.  Sector Value Added in Uganda  (Percentage unless otherwise indicated)**

||<br>La|<br>bor|||<br>G|<br>DP|
|---|---|---|---|---|---|---|
||Skilled<br>labor|Unskilled<br>labor|Capital|Natural<br>Resources|USD in<br>billion|% of total|
|**Business Services**|||||||
|Air Transport|8.9|38.8|52.3|0.0|0.0|0.1|
|Communication|20.2|29.5|50.3|0.0|0.1|1.1|
|Insurance|0.0|17.5|82.5|0.0|0.0|0.1|
|Business Services nec|36.9|53.7|9.4|0.0|0.5|4.7|
|Financial Services nec|35.4|51.5|13.1|0.0|0.2|1.5|
|Transport nec|7.4|32.4|60.2|0.0|0.2|2.0|
|Water Transport|3.2|13.8|83.0|0.0|0.0|0.0|
|**Dixit-Stigliz Goods**|||||||
|Chemicals Mineral and Metal Products|3.3|17.8|78.9|0.0|0.3|2.8|
|Energy and Minerals|2.7|15.4|43.2|38.7|0.8|7.7|
|Food Products|9.5|47.0|43.5|0.0|0.5|4.9|
|Petroleum and Coal Products|6.5|30.1|63.3|0.0|0.0|0.0|
|Other Manufacturing|5.0|26.9|68.1|0.0|0.2|1.9|
|Textile and Apparel|6.2|41.0|52.8|0.0|0.1|0.6|
|Wood and Paper Products|7.6|45.1|47.3|0.0|0.0|0.3|
|**CRTS**|||||||
|Agriculture and Forestry|0.6|74.1|22.6|2.7|2.6|24.4|
|Other Services|24.8|22.6|52.7|0.0|3.5|32.7|
|Trade|6.7|29.2|64.0|0.0|1.3|12.6|
|Utilities|4.2|7.9|87.9|0.0|0.3|2.8|

**Source:** Authors’ calculations based on the GTAP 8.1 data set.

42
**Figure 23. Combined scenarios: Poverty and inequality in 2020 (%-age point deviations from** **_base_ )**

<!-- Start of picture text --> 3<br>2<br>1<br>0<br>-1<br>-2 allgrw<br>allgrw-tr<br>-3<br>-4<br>-5<br>-6<br>-7<br>Headcount poverty (P0) Poverty gap (P1) Gini<br><!-- End of picture text -->

# **5 Final remarks**

In this study, we have explored the impact on the evolution of the Moldovan economy until 2020 from a set of external and domestic shocks and policy changes, defined in light of the change toward an export-oriented strategy that is envisaged by the government. First, we studied the potential effects of increased export demand, coupled with faster productivity growth within agriculture or manufacturing. The payoffs are more positive when these shocks are directed to manufacturing as this sector is more export-oriented than agriculture, for which a considerable share of production is part of subsistence farming.

The role of improvements in road maintenance, trade facilitation and the regulatory environment is simulated by increasing the productivity of transports and communications. The results suggest that this may offer an important avenue for faster growth with benefits that are widely diffused, reaching all household categories in a relatively equitable manner compared to foreign trade-induced growth impulses.

As expected, adverse shocks in form of higher prices for energy imports and a decline in remittances from household members working abroad result in slower growth. Although their impact on GDP is of the same order of magnitude, their effects differ notably in other respects. While the decrease in remittances would hurt growth more than rising energy prices, the resulting poverty increase is higher for the increase in fuel prices. In addition, agriculture seems particularly vulnerable to rising energy prices through its direct and indirect use of energy in form of fertilizers. The households 35
# **Transition**

Where an effective insurance agency and/or broking sector has already been built up, strong pressures can be placed on governments to slow or even ban Bancassurance activities. Examples of this can be found in Canada and Morocco<sup>9</sup> (table 2.5), where banks are limited in the range of products they may sell directly. Another example is in Kuwait, where Bancassurance is effectively banned.

**Table 2.5 Canadian Bancassurance Constraints**

|Activity|Authorized Types of<br>Insurance|Other Types of Insurance|
|---|---|---|
|Sales|Can be sold in bank<br>branches.|Can be sold through<br>insurance subsidiaries of<br>banks, but not bank branches.|
|Advice/Referrals|Can provide advice on an<br>authorized type of insurance<br>or a service in respect to an<br>authorized type of insurance.|Can provide advice that is<br>general in nature and not<br>related to a specific risk,<br>insurance policy or service,<br>or insurance company agent<br>or broker. Cannot refer<br>customers to particular<br>insurance companies, agents,<br>or brokers.|
|Promotion|Can promote policies of<br>authorized types of insurance<br>as well as companies, agents,<br>and brokers that deal only in<br>authorized types of<br>insurance.|Can promote these types of<br>insurance to all customers or<br>credit charge card holders<br>who receive regularly mailed<br>statements of account or to<br>the general public, outside of<br>bank branches. Cannot<br>―target market‖ customers,<br>that is, cannot segment the<br>customer base and promote<br>specific types of insurance to<br>elected customers. The same<br>restrictions apply to bank<br>promotions of companies,<br>agents, and brokers that sell<br>these types of insurance.|

_Source:_ Starky 2006.

In Korea, Bancassurance was phased in, against the strenuous objections of more traditional insurance intermediaries (box 2.2). In the United States, banks were allowed to sell insurance products only relatively recently. They still face strong 43
## _Robustness (2): Non-Linear Estimation_

A separate concern lies with mis-specification due to non-linearity of the data generating process. Both the CSI and Months Hungry have a mass point at 0. Furthermore, Months Hungry is a discrete count variable, taking on integer values from 0 to 12. Hence there is a concern that using a linear regression does not properly reflect the underlying data-generating process. As a robustness check we estimate our principle specification across fragmentation measures using two alternative Maximum Likelihood Estimators (MLE). Table 7 col (3) estimates a Poisson MLE, and Table 7 col (4) estimates a negative binomial MLE. Because we use a non-linear estimator, to compare the average marginal effects we multiply the coefficients by the sample average of the outcome variable. The results are consistent with the results reported in Table 4 in sign, magnitude and significance.

# **Mechanism**

What drives this relationship between land fragmentation and reduced food insecurity? If we allow that land fragmentation decreases yields and profits as the literature suggests, the effect on food security must be through risk mitigation. Building on Blarel et al. (1992) we argue that land fragmentation allows households to better manage the downside risk of shocks such as drought. With incomplete access to credit and markets, households with multiple parcels are endowed with an inherently more diverse portfolio. This diversity is reflected in the difference in parcel level characteristics, which is correlated with decreased food insecurity. Households can take advantage of this diversified portfolio by tailoring the crop grown to the parcel characteristics. Households with more land fragmentation also grow a greater diversity of crops, which is correlated 19
Table 3b: Employment share in unorganized sector by services industry

|NIC|IndustryDescription|2001|2006|
|---|---|---|---|
|55|Hotels and restaurants|0.75|0.71|
|60|Transportation and railway|0.94|0.92|
|61/63|Freight and cargo|0.59|0.49|
|64|Communications|0.92|0.95|
|70|Real estate|0.78|0.75|
|71|Renting of equipment|0.91|0.88|
|72|Computer hardware|0.25|0.12|
|73/74|Business services and research|0.72|0.76|
|80|Education and training|0.32|0.28|
|85|Health|0.76|0.63|
|90|Sanitation|0.99|0.93|
|91|Organizations|0.89|0.90|
|92|Media & recreation|0.57|0.53|
|93|Personal service activities|0.98|0.98|
||Total|0.76|0.73|
||Traditional|0.95|0.94|
||Modern|0.60|0.54|

Notes: See Table 1a.
National Institute of Statistics of Cameroon in partnership with the World Bank. These are nationwide cross-sectional and representative surveys that collect a comprehensive set of information about household characteristics and the characteristics of their economic activities. The surveys identify thousands of active household members aged 15 and above operating in the informal sector defined as “activities and production units that do not have written formal accounts and/or are not registered with the tax authorities.” We present the most salient features of the Cameroon informal sector using both these surveys.

The Cameroon labor market is characterized by a large informal sector. The informal sector employs around 90% of the occupied workforce in Cameroon. In 2010, there were approximately 2.5 million (90.5%) of Informal Production Units (upi) in Cameroon. The regional distribution of households with informal businesses revealed that the highest number of informal businesses were in the Littoral region, including Douala (19.0%), followed by the Centre region, including Yaoundé (17.4%), the FarNorth (14.4%), and the West (12.1%). The regions with the least number of informal businesses are respectively the South (2.1%), the Adamawa (4.3%), and the East (4.3%). The informal sector is largely concentrated in agricultural, and non-tradable services (e.g. retail trade, transportation, hotels or restaurants). Figure 1 shows the distribution of informal and formal firm by sector of activity in Cameroon. As can be seen, most informal businesses are primarily engaged in agricultural (48.5%) followed by services sector (23.1%), while formal firms operate mainly in services (74.2%). The sectoral distribution of informal businesses is heterogeneous across regions in Cameroon. While predominant activities in Douala and Yaoundé are related to services (about 50%), agricultural businesses are dominant in other regions. Manufacturing (26%) and hotels and restaurant (38%) businesses are more predominant in Douala (26%), while food processing (26.5%) is more common in the Far-North.

The Cameroon informal sector is a great contributor to its economy. This sector accounts for more than 30 percent of the gdp. During the first decade of the 2000s, there was an increase in the contribution of the informal sector to Cameroon’s gdp—from 37.3 percent in 2005 to 38.4 percent in 2010 (see Figure 2). Given its large contribution to the gdp, any diagnostic on Cameroon productivity and growth should include policy recommendations for the informal sector. The most important challenges are therefore how to alleviate constraints to business environment and improve productivity in the informal sector and incentivize them to transit into the formal sector. Indeed, despite this substantial contribution to the gdp and the labor market, the Cameroon informal sector consists of micro-units, often with one worker, very low-paid jobs and low labor productivity. The average size of informal units decreased from 1.5 employee in 2005 to 1.3 employee in 2010. This is driven by an increase in the share of 7
cultivation survey to measure trends in input costs at the state level.

**SHRUG Data:** To measure baseline conditions in the districts in the primary analysis, I use data from the 2001 Census compiled in the new Socioeconomic High-resolution Rural-Urban Geographic panel for India (SHRUG) dataset (Asher et al., 2019). I use the night lights data provided in SHRUG in robustness checks as well.

**Pollution Data:** Previous work on the relationship between agricultural fires and pollution has relied on data from air quality monitoring stations (Pullabhotla, 2018). Like with weather data, this has the disadvantage of limiting the analysis of pollution to areas with monitors that have been active over the full time period. To get around this issue, I use satellite re-analysis data from the Modern-Era Retrospective analysis for Research and Applications (MERRA) database provided by NASA (Rienecker et al., 2011). This is a satellite based product used by economists to study air pollution from coal fired power in India (Barrows et al., 2018) that provides data on the monthly average emissions rates for black carbon, organic carbon, and sulfur dioxide (SO2) on a 0.5<sup>_◦_</sup> _×_ 0.625<sup>_◦_</sup> grid. Importantly for my study, MERRA separately identifies the emissions of the above pollutants by source, including biomass burning. I also collect concentrations of black carbon, organic carbon, sulfur dioxide, and sulfate, which allows me to calculate the concentration of PM2 _._ 5 (He et al., 2019).

# **3.2 Empirical framework**

Following the difference-in-differences approach of Shah and Steinberg (2015), I estimate the effect of MNREGA on fire use as the change in fires before and after implementation of MNREGA within a district, controlling for month by year and district fixed effects. In the primary framework, districts are treated when MNREGA becomes statutorily effective in the district. Appendix Figure A1 shows the pattern of MNREGA roll-out.

I assume that the number of fires _Fimy_ in district _i_ in month _m_ of year _y_ follows a Poisson distribution. This is appropriate given both the count nature of the data and the skewness of the distribution of monthly fires. Many district-months do not have any fires as a result of the nature 13