rafmacalaba/datause-encoder-data huggingface dataset:
Note: This figure reports the mean percent of fixed assets for manufacturing firms financed through internal funds for noninnovative firms; innovative firms (i.e., innovation dummy equals 1); firms that adopt a new product, service or process (i.e., adoption dummy equals 1); firms that undertake invention proxied by whether a firm spend on research and development.

**_Panel B: Interquartile difference in the financing of fixed assets through internal funds, manufacturing_**

<!-- Start of picture text --> p25 p75 Difference<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.11*** -0.10***<br>-0.2 -0.15***<br>Innovation score Adoption Invention<br><!-- End of picture text --> Source: Own elaboration using data from the World Bank – Enterprise Surveys. Note: This figure reports the mean percent of fixed assets financed through internal funds for innovative firms. The blue bar denotes the mean percent of fixed assets financed through internal funds for firms in the bottom 25 percent of the distribution of the innovation score and adoption score. The red bars represent the percentage of fixed assets financed through internal funds for firms in the 75 percent of the distribution of the innovation score and adoption score. The gray bars characterize the interquartile differences for the three groups of firms. *** indicates statistical significance at the 1 percent level of the test of the interquartile difference in means.

Table 3 reports the estimates of the association of firm financial structure and firm innovation. The dependent variable is a firm innovation dummy. Across estimations, the percentage of fixed assets funded by banks is strongly associated with the probability of firm innovation. Comparing estimations including the largest set of controls (Table 3, columns 4, 6), the magnitude of the effect is stronger when we include high-income countries in the sample (Table 3, column 4). Estimates are also economically significant as increasing by one percentage point the percentage of fixed assets funded by banks is associated with an increase in the probability of innovation of 6.5 percent. Estimates for the percentage of fixed assets funded by NBFI and other sources of funding are, in most regression, statistically insignificant. Looking at the other control variables, size, and labor productivity are strongly and positively associated with higher firm innovation. Conversely, domestic ownership is negatively associated with firm innovation, perhaps suggesting that foreign ownership implies a greater access to financial resources, technology, knowledge and spurs improvements in corporate governance that lead to higher innovation activities (Choi, Lee, and Williams 2011).

12
Gregorio Impavido

**–** **<u>Table 8: Evolution of Separate Technical Provisions (1997 2005, MXN mln.)</u>**

|**1/**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|---|---|---|---|---|
|NPR|2,332.4|14,774.8|27,417.3|40,728.7|55,791.0|63,875.1|65,429.0|68,592.6|70,624.7|
|Pending NPR|51.5|37.2|55.3|60.8|43.2|10.6|7.20|10.8|7.5|
|Contingency|46.7|295.0|548.3|814.5|1,115.0|1,277.0|1,308.0|1,371.0|1,412.0|
|Special Mat.|0.3|9.6|32.7|82.1|139.5|226.2|315.5|445.0|574.4|
|Inv. Fluct.|1.8|33.7|156.2|234.1|333.6|628.0|744.9|747.1|817.8|
|Loss Reserves.|7.4|9.7|15.2|33.8|70.6|97.1|133.2|167.5|203.6|
|NPR/Total(%)|96|97|97|97|97|97|96|96|96|

**Notes** : 1/ Real 2005 Pesos . **Source** : CNSF

Technical provisions also display a similar pattern of high growth until 2001 and much lower growth afterwards reported before for premiums in section II.A. Between 1997 and 2001 technical provisions increased from MXN 2,440 million to MXN 57,493 million while reaching only MXN 73,640.41 in December 2005 as shown in Figure 1.

**Figure 1: Evolution of Total Technical Provisions (Real 2005 MXN mln.)**

<!-- Start of picture text --> 80,000<br>70,000<br>60,000<br>50,000<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text -->

**Source** : CNSF

A major change was introduced in 2002 in the regulation with respect to the valuation of technical provisions of life, accident and illness, and P&C lines of businesses by amending Article 47 of the Insurance Law (LGISMS). In essence, the new rules state that the valuation of technical provisions has to be conducted on the basis of actuarial standards requiring an actuarial valuation of future risks.<sup>21</sup> In the case of annuities, it was

> 21 Regarding the unearned premium reserve for all lines of business (excepting annuities) the old methodology for the calculation and valuation of the technical reserves was based on premiums.  Insurance companies that followed this regulation scheme could face actuarial insufficiency of technical reserves, because reserves relayed only on the adequacy of premiums.  Consequently, if the risk premium was insufficient to pay claims, the technical reserve would also be insufficient.  In addition, in high competitive

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The survey assignments were carefully piloted. A qualitative debriefing with the field supervisors took place at the end of each day during the pilot, in order to solicit their feedback on a range of issues.<sup>15</sup> In addition, a subset of households was selected for qualitative interviews with respondents, in order to see whether wording and structure of the questionnaire could be further improved. During this qualitative interview, respondents were asked open-ended questions to solicit how they thought about the questions, why they chose the responses they did, and how they thought about concepts such as work, household production, and their primary activities.

The field work was conducted from September 2007 to August 2008 in villages and urban areas from 7 districts across Tanzania: one district in the regions of Dodoma, Pwani, Dar es Salaam, Manyara, and Shinyanga region and two districts in the Kagera region. Households were randomly drawn from the listing of villages (urban clusters) and randomly allocated to one of the four survey assignments. The total sample is 1,344 households, with 336 households assigned to each of the four survey assignments. Although the sample of 1,344 is not designed to be nationally representative of Tanzania, the districts were selected to capture variations between urban and rural areas as well as along other socio-economic dimensions to inform survey design related to labor statistics and consumption expenditure for low-income settings.

> 15 The feed back focused on nine areas: 1. General impressions of the respondent’s comprehension; 2. Question phrasing; 3. Question sequencing; 4. Completeness of lists of question responses; 5. Clarity of interviewer instructions; 6. Completeness of interviewer manual to resolve field problems encountered; 7. Questions that should be restructured for greater clarity and respondent comprehension; 8. Conceptual or cultural difficulties in translating questions to local language; 9. Areas of emphasis for training enumerators. One of the most important parts of the questionnaire to pilot was the selection of proxy and self-reporting respondents. After a day of training, interviewers spent significant time practicing with examples. They appeared to have no trouble in the field selecting proxies or selfreported respondents following the protocols designed for the experiment.

17
occupational changes, this section aims at clarifying the drivers of occupational change and thus link these drivers to the inequality of earnings.

The first partial occupational shift is depicted in Figure 9 alongside the observed full shift. In Germany, the simulation and the observed data look very similar. This means that changes in structural parameters account for the bulk of the occupational change. In the case of Poland and Spain, the decline in routine intensive occupations is closely approximated, but the growth in non‐routine, manual task intensive jobs is overestimated. Interestingly, the shift in parameters plays a negligible role in accounting for the observed growth of jobs intensive in non‐routine cognitive tasks. In these two countries, the impact of the parameters‐related polarization of jobs on earnings must be taken with a grain of salt, given that the polarization simulated with just the shift in parameters (that reported in column (1) of table 1) does not correspond so closely with the observed polarization.

– <u>Figure 9 Simulation of occupational change, holding constant all variables except occupation structural parameters</u> <!-- Start of picture text --> Germany Poland Spain<br>19.8%<br>15.6% 14.7%<br>12.0% 11.4%<br>6.9% 5.8%<br>1.6% 2.5% 1.6%<br>‐1.2%<br>‐4.2%<br>‐8.5% ‐8.3%<br>‐10.5%<br>‐13.0%<br>Actual Simulation ‐18.5%<br>‐27.6%<br>Non Routine Routine Non Routine Non Routine Routine Non Routine Non Routine Routine Non Routine<br>Man Cog Man Cog Man Cog<br><!-- End of picture text --> Note: this figure compares the observed change in occupational structure (dark colors) with the one in the counterfactual scenario (light colors) where the occupation structural parameters in 2013 are the same as in the 1990s.

Figure 10 illustrates the second partial occupational change, the one accounted for by changes in individual characteristics. As expected, in the case of Germany, the simulated occupational change is very small. In the case of Poland and Spain, this second simulation, by underestimating the growth in non‐ routine, manual task intensive skills, offsets the overestimation of the first (parameters‐related) simulation. At the same time, this second simulation matches quite well the increase in the share of non‐ routine, cognitive task intensive jobs.

24
Banerjee and Iyer’s (2005) study of historic land tenure institutions and agricultural performance is of interest to our analysis for three reasons. First, the British decision on which land tenure system to adopt depended more on the preferences of individual administrators rather than a systematic evaluation of region specific characteristics.<sup>14</sup> Thus the choice of institutional arrangement is largely exogenous to regional attributes. Second, landlords were allowed to extract as much as they wanted from their tenants, thus making their behavior predatory, leading to high inequality and low general investment in their districts. Further, as most wealthy landlords were not cultivators themselves, this reduced pressure on the state to deliver services important to farmers as well as general public goods. The consequences of this system are observed in terms of lower education and health infrastructure and outcomes. Third, rural institutions have considerable bearing on urban and industrial development (Rao and Woolcock 2001). Rural class structures and social networks do not disappear once people move to cities. In fact, rural or origin based identities are even stronger as people sort themselves into homogenous groups within heterogeneous settings.

We link Banerjee and Iyer’s (2005) land revenue classification with the 1991 district boundaries using geographic information system (GIS) based matching. For the analysis, we use the land revenue system of the district where the city is located and assign that value to the city. We then identify the cities whose districts had landlord based/ _zamindari_ systems and recode these as 1 and code other cities with a 0. Thus, we end up with a binary measure of landlord control. To examine if there is any association

> 14 Banerjee and Iyer note that these decisions were often based on not much more than instincts of administrators.

23
- Department of Census and Statistics. 2009–10. “Household Income Expenditure Survey (HIES).” Ministry of Finance and Planning, Colombo.

- Engel, E., Fischer, R., & Galetovic, A. (2009). Public-Private Partnerships: When and How (Working Paper 257). _Santiago, Chile: Center of Applied Economics, University of Chile._

- Estache, A., Fay, M., (2010). Current Debates on Infrastructure Policy. In M. Spence & D. Leipzoger (Eds.), _Globalization and Growth Implications for a Post-Crisis World_ (pp. 151-193). Washington, DC: World Bank.

Estache, A., & Yepes, T. (2004). Assessing Africa’s Investment Needs. Washington, DC: World Bank.

- Fay, M., & Yepes, T. (2003). Investing in Infrastructure: What is needed from 2000-2010 (Policy Research Working Paper 3102). Washington, DC: World Bank.

- Foster, V., & Briceño-Garmendia, C.M. (2009). _Africa’s Infrastructure: A Time for Transformation._ Washington, DC: World Bank.

- Galiani, S., Gertler, P.J., & Schargrodsky, E. (2005). Water for Life: The Impact of the Privatization of Water Services on Child Mortality. _Journal of Political Economy, 113,_ 83-120.

- Ganelli, G., & Tervala, J. (2010). Public Infrastructures, Public Consumption, and Welfare in a New-OpenEconomy-Macro Model. _Journal of Macroeconomics, 32_ (3), 827-837.

- Grimsey, D., & Lewis, M. K. (2007). _Public Private Partnerships: The Worldwide Revolution in Infrastructure Provision and Project Finance._ Cheltenham, United Kingdom: Edward Elgar Publishing Limited.

- Guasch, J. L. (2004). _Granting and Renegotiating Infrastructure Concessions: Doing it Right._ Washington DC: World Bank.

- Gupta, M. R., & Barman, T. R. (2010). Health, Infrastructure, Environment and Endogenous Growth. _Journal of Macroeconomics, 32_ (2), 657-673.

- Hallegatte, S., Shah, A., Lempert, R., Brown, C., & Gill, S. (2006). Investment Decision Making Under Deep Uncertainty: Application to Climate Change (Policy Research Working Paper 6193). Washington, DC: World Bank.

- Institute for Population Sciences. 2007–08. “District Level Household Survey (DLHS-3).” Ministry of Health and Family and Welfare, Delhi.

- Jalan, J., & Ravallion, M. (2003). Does Piped Water Reduce Diarrhea for Children in Rural India? _Journal of Econometrics, 112,_ 153-173.

- Karydas, D. M., & Gifun, J. F. (2006). A Method for the Efficient Prioritization of Infrastructure Renewal Projects. _Reliability Engineering & System Safety, 91_ (1), 84-99.

- Lee, L.-, Rosenzweig, M.R., & Pitt, M. M. (1997). The Effects of Improved Nutrition, Sanitation, and Water Quality on Child Health in High-Mortality Populations. _Journals of Econometrics, 77_ (1), 209-235.

36
Fund and UNDP. The census data set contains individual-level information such as age, sex, education and occupation as well as household-level information such as housing characteristics and asset holdings. It also contains the employment status of each individual. We used a 33 percent sample of the census, which contains records for every third household organized by an administrative unit. The sample selection was made by GSO. The sample includes 5,553,811 households and 25,447,457 individuals.

Third, we use a compilation of geographic variables. These include elevation, precipitation, soil quality, sunshine duration and access to cities. Some of the variables are based on remotely sensed data, while others are mean values from community-level data. The geographic variables can be merged into the census and the survey by the administrative codes.

Finally, we use the 2000 Social Accounting Matrix (SAM) for Vietnam as a core building block of the CGE model, representing 97 production activities and commodities, 13 factors of production (labor and capital), 5 household types, and 94 international trading partners. The aggregated version of SAM includes aggregate wage incomes for eight labor segments defined by male/female, skilled/unskilled and urban/rural. It also includes the non-wage household incomes for urban and rural areas.

Let us now briefly discuss the measurement of poverty. In the standard analysis of socioeconomic survey data such as the VLSS, poor people are defined as those living in households whose per capita consumption is below the poverty line. Consumption has several advantages over other income measures and proxies. First, it is a money metric measure and easy to interpret. Second, it does not vary in the short run, unlike income. Despite these advantages of consumption, however, we use the per capita income measure for the household. This is because we need to aggregate the information in the VLSS data set in a way that is consistent with the SAM and to allow the individuals in the microsimulation to switch their employment status. We shall come back to the details of this point in the next section.

To calculate the income measures, we first identified the employment status of all the individuals in the potential labor force. We regarded individuals aged between 15 and 8
21 mechanisms and uniform wage setting. Labor is also frequently highly immobile across countries, and it is very difficult or expensive for employers to be perfectly informed about the acquired skills of potential employees.

At the same time, there is empirical evidence indicating that rates of return to education are not able to reflect cross-country quality differences in education. For example, data on rates of return collected by Psacharopoulos (1994) show that rates of return to education tend to decline with higher per capita income. For example, the returns to any level of education are highest in Africa and lowest in the industrial countries. As such, if rates of return did accurately reflect quality differences in education, then the data would imply that the quality of education is generally higher in the developing countries as compared to the developed countries, which is very unlikely<sup>34</sup> . Consequently, qualitative differences in education are probably not well captured by the available data on country-specific rates of return to education.

# **4.2. Measuring Innovation and Technology Adaptation**

## **USPTO Patent Counts**

One of the most basic measures of the level of domestic innovative activity taking place within an economy that bears commercial value is the annual number of U.S. patents filed by residents of the country. According to the U.S. Patent and Trademark Office (USPTO) website, a U.S. patent for an invention is the grant of a property right to the inventor(s), issued by the USPTO. The right conferred by the patent grant is, in the language of the statute and of the grant itself, "the right to exclude others from making, using, offering for sale, or selling" the invention in the United States or "importing" the invention into the United States. Note that while there are several patenting offices in the world, we have chosen to only use U.S. patent data. This is because different patenting agencies have different criteria as to what constitutes a novel and original innovation. By using only U.S. patent data, we are adhering to a consistent set of minimum standards for an innovation.

The data set, which we obtained from the USPTO website, provides information on the number of U.S. patents distributed by the country of origin, which is determined by the residence of the inventor listed first on the patent application. The patent data set

> 34 Psacharopoulos (1985) attributes the observed inversely relationship between the rate of returns to education and per capita income to the relative scarcity of human-to-physical capital within each group of countries.
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In view of the close working relationship between WHO and the World Bank, joint discussions might be helpful on ways to mobilize external financing for SM.

The percentage attribution for maternal health components within the WHO budge! follows the criteria applied to this review. However, other major program categories would qualify for partial attribution, as in the examples of general health services development, primary health care and malaria control. The attributed data in this estimate are limited to the more specific maternal health activities.

# Estimated Financial Flows to Developing Countries S millions

||1986|1987|1988|1989|
|---|---|---|---|---|
|WHO budget<br>|271.6<br>|271.6|318.4|318.4|
|Extra-budgetary sources|260.1|260.1|228.5|228.5|
|Total|531.7|_531.7_|546.9|546.9|
|Of which maternal health<br>estimated to include:|||||
|50% of regular MCH budget<br>|2.2|2.2|2.5|2.5|
|100% regular HRP budget<br>|.3|.3|.4|.4|
|_50%_extra-budgetary MCH budget<br>100% extra-budgetary HRP<br>|12.5|12.5|8.5|8.5|
|budget|17.1|17.1|20.5|20.5|
|Total maternal health|32.1|32.1|31.9|31.9|

Source: WHO Program Budgets for 1988-89 and 1990-91. Note: The biennial budget has been divided by two to yield annual estimates.

World Health Organization Geneva, Switzerland
the average assets holding represented by the constant term. We stress, therefore, that the estimated lack of an effect on asset accumulation cannot be attributed to low-powered tests, but to very small differences between treated and control communities.

Table 5 shows that the program had no significant impacts on household expenditures, at least in the hypothesized direction. Indeed, the second column of Table 5 indicates that households in LEAP communities suffered a reduction in miscellaneous expenditures. We think it is best to chalk that result up to random error. The main point of Table 5 is illustrated by the standardized mean effect presented in column 6: LEAP produced no increase in expenditures in the villages where it operated.

# **_4.3 Social capital outcomes_**

We turn now to another downstream effect that programmers were interested in achieving: greater social capital among and civic engagement by the poor participants in LEAP. We bring three pieces of evidence to bear on this question: the villagers’ actions in the laboratory behavioral activities (our measure of pro-social norms), the networks survey described above and self-reports of pro-social action in villagers’ responses to a retrospective survey.

The results clearly indicate that the program had no effect on subjects’ pro-social behavior in the laboratory nor did it have any overall effect on network linkages among villagers beyond the creation of the SHGs themselves. There was some mixed evidence from the retrospective survey that the program caused greater participation in some community groups (other than the obvious case of the SHG itself) especially in rice seed banks and no evidence from the retrospective survey that members of LEAP communities engaged in more community voluntary action.

We first present the effects of the program on behavioral measures of pro-social norms from the lab. We then turn to measures of the effect of LEAP on density of social linkages using our crosschecked measures of social networks. We close with a discussion of the results from the self-reported behavior in the retrospective survey.

17