rafmacalaba/datause-encoder-data huggingface dataset:
similar share in manufacturing (24 percent) and a smaller share in retail (18 percent). The BDM sample is smaller in size on average than the TIOLI sample. Only 39 percent of firms in the BDM sample have any paid employees, and only 4 percent more than 5 employees, compared to 44 percent with employees and 17 percent with more than 5 employees in the TIOLI sample. Monthly sales average J$86K (median 30K) in the BDM sample and J$214K (median 40K) in the TIOLI sample, while monthly profits average 13K (median 3.8K) and 89K (median 8k), respectively.<sup>8</sup> It would therefore take firms in the BDM (TIOLI) sample around 5 (3) months of profits to pay for the full cost of the course. Only 14-15% in both samples indicate they would not be able to finance an investment of J$20,000 (the cost of the course). It is likely that a large share of entrepreneurs have access to collateral since 53-54% of them report owning a vehicle for the business (one of the components of the wealth index, not reported in the table).

Participants score high on a personal initiative index (average of 6.3 over 7 for both samples) and in willingness to take risk (7.5 over 10 in the BDM sample, and 8 over 10 in the TIOLI sample). However, when they compare themselves to other entrepreneurs in terms of proactiveness, they report a value of 6.6 over 10 (6.9 over 10 for the TIOLI sample). There is room to improve business practices in both samples, with the mean firm doing 49 percent (BDM sample) and 55 percent (TIOLI sample) of the business practices we measure. Around 80 percent of participants in the BDM sample expect that the training will increase their sales by more than 10 percent, while the other 20 percent expect an increase of up to 10 percent. In the TIOLI sample, 89 percent of participants expect an increase of at least 10 percent in sales from the training. The mean (non-incentivized) willingness to pay for business training is J$13K (median 5K) in the BDM sample and J$17K (median 20K) in the TIOLI sample. This implies that the median firm in the BDM sample reports being willing to pay up to one fourth of the cost of the course, while in the TIOLI sample, the median firm reports being willing to pay the full cost.

# **3 Eliciting the Demand for Business Training**

## **3.1 Demonstration Sessions**

Business training is a commodity with several aspects that makes it difficult to sell. It shares the property of experience goods (Nelson, 1970), that it can be difficult for consumers to assess quality without purchasing and consuming it. It is not something that most firm owners purchase often, making it hard to learn about quality by repeated experiences. Moreover, since it is a service, if firm owners are dissatisfied with their purchase, 8The exchange rate at the time of the study was around 130 Jamaican dollars per one US dollar.

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which is able to be estimated empirically.

This teacher supply framework has various drawbacks when applied to empirical work. One obvious limitation is that earnings data are rarely available across any appreciable time span in the life cycle. This poses problems for meaningful econometric analysis. This means that little can be said about life cycle earnings without some assumption concerning earnings growth. In addition, even though panel data can provide a few observations per individual at different points of time, the existing panel data sets in Indonesia (i.e. three waves of “Indonesia Family Life Survey”) do not have large enough samples for teachers, particularly of teachers with college education. In this paper, we use labor force survey data for a few consecutive years and control for yearspecific fixed-effects in earnings estimation for teachers and non-teachers.

The second limitation of the model is that the non-pecuniary rewards of teaching, or the individual’s “propensity to teach,” cannot be directly measured. Females with children may have higher propensity to become a teacher given that teaching is usually a more flexible job, and has less wage penalty when temporary leave happens (Flyer and Sherwin 1997). Dolton (1990) used the probability of having the first job as a teacher as a proxy for propensity to teaching when estimating occupation change between teacher and non-teacher at later stages of one’s career. However, this requires panel or historical data that is not easily available for the intended analysis. Even under this approach, equal non-pecuniary rewards to jobs were assumed for prior entry into any job any individual has. In this paper we include variables such as dummy for female, marital status, 13
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|_t_=2014|T2014|498|0.175|0.380|0<br>1|
|---|---|---|---|---|---|
|_t_=2015|T2015|498|0.237|0.426|0<br>1|
|_t_=2016|T2016|498|0.229|0.421|0<br>1|

The censored regression is performed where the dependent variable is the number of journeys per month. Our dependent variable is censored to (i) once, (ii) 2 to 4 times, (iii) 5 to 8 times, and (iv) more than 8 times, because of our simplified survey design.<sup>6</sup> The results show that the mobility of people is crucially dependent on proximity to the nearest feeder road in good condition ( **Table 11** ). It is significantly reduced when people live more than 1 hour from an all-weather road. The coefficients of _FEED1-2hr_ and _FEED2-3hr_ are estimated to be significantly negative. On the other hand, the proximity to RSDP roads does not seem to affect people’s demand for transportation. To examine the interaction effect between RSDP and feeder roads, a dummy variable representing those who have a good feeder road in less than 1 hour is created. As expected, this variable has a positive and significant coefficient of 0.563. However, there is no significant interaction effect on the transport demand between the main and feeder road accessibility.

The estimated demand equation also indicates what constraints on mobility people face in rural Ethiopia. One of them is efficiency of transport services: The coefficient of travel time, _TIME_ , is significantly negative, meaning transport speed is important to meet people’s demand for transportation. The waiting time variable ( _WAIT_ ) also has a negative and weakly significant coefficient. That is, transportation demand would be reduced when waiting time is longer. The finding is in line with the positive coefficient of bajaj, which is a three-wheeler taxi now widely used in Ethiopia. Frequency of transport services really matters. This is why people seem to prefer to use bajajs, because they do not have to wait for a taxi or bus to be fully laden with passengers before departing. On the other hand, transport fares have a negative coefficient, as expected, however, there is no statistical significance.

> 6 In our sample, no one responded that she traveled more than 8 times. Thus, we have only 3 discrete choices that are actually observed.
aiming to fortify their market position and ensure long-term viability in the agri-food sector.

Figure 8: Exiters vs. Continuing Firms <!-- Start of picture text --> 25<br>20<br>20<br>15 15<br>10 10<br>5<br>5<br>0<br>2015 2016 2017 2018 2019 2020 2021 2022 0<br>2015 2016 2017 2018 2019 2020 2021 2022<br>Continuing firms Exiting firms<br>75th/25th Percentile 75th/25th Percentile Continuing firms Exiting, extinct firms, no parent<br>Median Median Median Median<br>Average Average Average Average<br>weighted firm degree (t-1)<br>weighted firm degree (t-1)<br><!-- End of picture text -->

**Note:** The left panel displays the 25th, 50th, and 75th percentiles, as well as the average number of supplier and customer links for exiting and continuing firms. The sample comprises actively covered agribusiness firms in a given year, meaning firms that appear as source companies in at least one record. Exiting firms are entities which are not observed in any subsequent year during our sample period. We observe a total of 253 exits in our sample of actively covered agribusinesses. The right panel shows the average and median for a very restrictive definition of exiters. Exiters are identified as actively covered agribusinesses that exit our sample in a given year, which are marked as extinct by the data provider as of March 2024, and whose parent also exits the data (all subsidiaries, if any, of the ultimate parent of the exiting firm and, if applicable, the ultimate parent itself exit the data). We observe a total of 28 extinct exiters without surviving ultimate parent. Given the small number of exiters per year for the restrictive exit definition, we only show averages and medians in the right panel.

# **5 Discussion**

This section delves deeper into our findings, linking them to existing literature and outlining potential avenues for future research. A significant contribution of this study is the demonstration that the of the cor- probability firms exiting agri-food industry inversely relates with the robustness of their networks. Firms with diverse customer and supplier links are notably less likely to exit the industry, highlighting the critical role of strong network connections in enhancing firm resilience and longevity.

However, not all linkages exert the same influence. Recent research on the US agribusiness sector reveals that while horizontal diversification strengthens resilience, vertical diversification may weaken it (Stevens and Teal, 2024). This distinction underscores the 28
# **LIST OF ABDRNVTATION1 AND MACRNYM8**

|AENFE:<br>|Adult Education and Non-formal Education<br>|
|---|---|
|DIU:<br>DRDPR:<br>DRU:<br>|District Implementation Unit (Mahila Samakhya)<br>Department of Rural Development and Panchayati Raj<br>District Resource Unit (Mahila Samakhya)<br>|
|DRDA:<br>DWCRA:<br>DWDA:<br>HRD:<br>|District Rural Development Agency<br>Development of Women and Children in Rural Areas<br>District Women's Development Agency<br>Human Resource Development<br>|
|ICDS:<br>|Integrated Child Development Services<br>|
|IDARA:<br>|Information Development and Resource Agency<br>|
|IDS:<br>|Institute of Development Studies<br>|
|IRDP:<br>|Integrated Rural Development Program<br>|
|NAEP:<br>|National Adult Education Program<br>|
|NCSW:<br>|National Commission for Self-Employed Women and Women<br>in the Informal Sector<br>|
|NGO:<br>|Non-government Agency<br>|
|PD:<br>|Project Director<br>|
|PO:<br>|Project Officer<br>|
|SITARA:<br>|State Information, Training and Resource Agency (Mahila<br>Samakhya)<br>|
|TRYSEM:<br>UNICEF:<br>UNFPA:|Training of Rural Youth for Self-Employment<br>United Nations Children's Fund<br>United Nations Population Fund|
In addition to these two dimensions, the distributional welfare consequences of global warming may depend on many other factors which are important but beyond the scope of this study. They include the covariance between the realized incidence of additional extreme heat events in the locality of household _i_ ���� and household _i’s_ endowment of physical capital – especially forms of capital that can help households adapt to increased heat stress (e.g. electricity and air conditioning). Other possible covariates of interest may include the age and health status of individuals in poorer and richer households; the covariance between expected temperature stress and changes in prices of particular necessity goods; the covariance between expected temperature stress and other climate stressors that may affect non‐labor outcomes, including agricultural productivity; the covariance between expected temperature stress and institutional settings (in settings where work hours are more flexible, the impacts of temperature‐related productivity shocks may be reduced relative to settings with rigid wage contracts).

Given the data available, we focus on geographic and occupational exposure bias. Exploration of the other dimensions listed above are left for future research.

The specific empirical questions we take to the DHS data may be summarized as follows:

- 1) _Geographic Exposure Bias_ :

What is the cross‐sectional relationship between household wealth and extreme heat exposure within countries?

- _2) Occupational Exposure Bias:_

What is the cross‐section relationship between household wealth and occupational exposure to heat stress within countries?

# IV. Data

## _Household‐level Wealth and Occupation Data and Cluster‐level Geographic Identifiers (DHS)_

The DHS is a household survey that provides nationally representative and standardized data on health, population, and a limited number of socioeconomic variables in developing countries, administered by ICF International and hosted by the United States Agency for International Development (USAID). One of these socioeconomic variables is a household‐level “wealth index”, which is computed based on each household’s ownership of a standard set of assets, housing construction materials, and quality of water access and sanitation facilities.<sup>19</sup> DHS also includes a limited set of household characteristics including size of household (number of occupants), whether or not the household is located in an urban or rural setting, which we use as a vector of household specific controls.

> 19 DHS wealth scores are especially valuable in this context because many of the poorest countries lack reliable government‐collected income data.

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<!-- Start of picture text --> All Retail<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Population percentage Population percentage<br>Jun, 2020 (Gini = .971) Oct, 2020 (Gini = .983) Feb, 2021 (Gini = .973) Jun, 2020 (Gini = .926) Oct, 2020 (Gini = .946) Feb, 2021 (Gini = .938)<br>Cumulative sum of online sales Cumulative sum of online sales<br><!-- End of picture text --> Figure 14: Lorenz curve for online sales by wave <!-- Start of picture text --> All Retail<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Population percentage Population percentage<br>Jun, 2020 (Gini = .879) Oct, 2020 (Gini = .891) Feb, 2021 (Gini = .879) Jun, 2020 (Gini = .827) Oct, 2020 (Gini = .883) Feb, 2021 (Gini = .848)<br>Cumulative sum of in-person sales Cumulative sum of in-person sales<br><!-- End of picture text --> Figure 15: Lorenz curve for offline sales by wave Another way of measuring inequality is showing the ratio between the median firm and the 90th percentile firm. Figure 16 shows this measure for the sales per worker by channel - offline vs online sales. As mentioned above, inequality is larger in the online sales market. For every dollar that the median firm sells online, the 90th percentile firm sells 24.4. This differs vastly from offline channels where one dollar for the median firm corresponds to 10.5 in the 90th percentile. This is 2.3 times lower than in online markets. Overall, this brings a new perspective and challenge for competition policy.

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some distance education often spent much less time learning than if they would have during in-person instruction, and were exposed to pedagogies and curricula that had been hurriedly adapted to remote learning. Moreover, teachers often did not receive adequate training in remote instruction and digital skills (UNESCO, UNICEF, World Bank and OECD, 2021). Finally, as discussed below, data from actual measurement of learning losses support the assumption that mitigation was not effective: newly collected data on learning levels emerging from some low- and middle-income countries shows major learning losses across a range of contexts (UNESCO, UNICEF and World Bank, 2021).

For purposes of illustration, we focus our narrative below on estimates of losses underlying the intermediate scenario in Azevedo et al. (2022). The results in terms of global and regional effects on the LAYS do not vary dramatically across the scenarios. Our preference for the intermediate scenario in the narrative builds on our understanding of the evidence to date, which suggests that: the mitigation strategies put in place have largely been ineffective (as discussed above); and many countries with educational systems that reported partial closures (on average for those last two years) had a large fraction of the system fully closed. The parameter choices under the intermediate scenario reflect that evidence. As more and better data becomes available, we will be able to periodically improve these estimates.

Globally, the average baseline LAYS pre-COVID were 7.8 (for low- and middle-income countries, it was 6.8). This means that children around the world only achieve 7.8 years of quality education on average (compared to 11.9 non-quality adjusted years of schooling). Our simulation results suggest that the average Learning - Adjusted Years of School (LAYS) may fall due to COVID-19 school closures. In the intermediate scenario, school closures due to COVID-19 could bring the average learning that students achieve during their lifetime from 7.8 to 6.7 Learning Adjusted Years – a reduction of 1.1 years, as shown in Figure 2.<sup>14</sup> _Figure 2: Results of simulation: Effect on Learning Adjusted Years of Schooling (LAYS)_

> 14 For reference, the 2021 simulations suggested that LAYS would go down from 7.8 to 6.9 under the pessimistic scenario – a reduction of 0.9 years (Azevedo et al. 2021). For this exercise, we interpret 7.8 as the expected Learning Adjusted Years of Schooling (LAYS) for the current generation of students by the time they enter the labor market at age 25. The simulated LAYS, 6.9 in the intermediate scenario, can be seen as revised expected values considering the model’s parameters in the absence of effective remediation.

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Figure 6: Impact of NAFTA on productivity by integration status for all firms <!-- Start of picture text --> 1994 1996 1998 2000 2002<br>year<br>Exporter and Importer Just Exporting<br>Just Importing<br>Source: INEGI, BANXICO and Authors’ Calculations<br>.3<br>.2<br>.1<br>Treatment Effect<br>0<br>−.1<br><!-- End of picture text --> specification highlight important elements of heterogeneity related to the integration status of the firm. Hence splitting integrated firms in different groups taking into account their integration status (i.e. exporter, importer, or fully integrated) allows us to capture heterogenous treatment effects that would otherwise be ignored if we were lumping together all integrated firms regardless of their integration status as some previous studies did (Pavcnik 2002, L ́opez-C ́ordova 2003).

Regarding the import-competing channel, as expected a priori, the coefficient on the log of import tariffs (ψ in equation 1) is negative. Everything else constant, a firm facing a tariff reduction equal to, say, 10 percent tends to increase its productivity by 1 percent. Under NAFTA, Mexican tariffs were reduced from an average of 16 percent to 5 percent, or 11 percentage points, representing a reduction of almost 69 percent on average tariff. According to our results, this policy decision, fostered competition and increased firm-level productivity by 6.8 percent (69*0.099).

Another concern that we try to control for is the issue of exit from the sample. Every year about 5-10 percent of plants exit the sample because of attrition and not taking this into account may generate a bias. In order to control for this we follow Amiti and Konings (2007) and re-estimate our equation by adding a dummy that is equal to one when the plant is going to exit in t + 1 and zero otherwise. The results are shown in column (5) of Table 1 and confirm that future exiting firms tend indeed to have a significatively lower productivity than other plants, however our results are substantially unchanged.

As it was mentioned before, an important methodological difference between the present 19
# **Intellectual Property Rights, Human Capital and the Incidence of R&D Expenditures**<sup>∗</sup>

Claudio Bravo-Ortega Department of Economics University of Chile Daniel Lederman Development Research Group The World Bank

**Abstract**

Numerous studies predict that developing countries with low human capital may not benefit from the strengthening of intellectual property rights. The authors extend an influential theoretical framework to highlight the role of intellectual property rights in the process of innovation and structural change. The resulting theory is consistent with a stylized fact that appears in the data, namely that countries with poor intellectual-property protection may accumulate human capital without a corresponding increase in research and development investment as a share of national income. The model predicts that without minimum intellectual-property protection, additional education may result in more imitation rather than innovation. The preponderance of the econometric evidence presented in this paper suggests that interactions between human capital and intellectual property rights determine global patterns of research and development effort, and intellectual property rights tend to raise the effect of education on the incidence of research and development.

Keywords: Research and Development, Intellectual Property Rights, Human Capital, Education JEL Classification: O11, O31, O34

> ∗ We thank Javier Cravino and Valentina Paredes for stellar research assistance. We had helpful discussions with Francisco Rodriguez about non-linear functional forms in econometrics. We thank Bill Maloney, Edwin Goni, Juan Carlos Muñoz and Jose Miguel Benavente for helpful discussions about R&D models and data. We are particularly grateful to Keith Maskus for his detailed comments on an earlier version of this paper. We gratefully acknowledge financial support from Fondecyt Grant 1061137, from the World Bank's Latin America and the Caribbean Regional Studies Program, and from the World Bank executed Multi-Donor Trust Fund on Trade, Intellectual Property and Innovation. The usual disclaimer applies and all remaining errors are ours.
# <u>Figure 3.1</u>

RENEGOTIATIONS (GATT ARTICLE XXVIII), EMERGENCY ACTIONS (GATT ARTICLE XIX), AND VOLUNTARY EXPORT RESTRAINT AGREEMENTS (VERs), 1948–2004 <!-- Start of picture text --> 30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>1948-1949 1950-1954 1955-1959 1960-1964 1965-1969 1970-1974 1975-1979 1980-1984 1985-1989 1990-1994 1995-1999 2000-2004<br>Time Interval<br>Renegotiations (Article XXVIII) Emergency Actions (Article XIX) VERs in Place<br>24.4<br>21.0<br>13.8<br>11.6<br>10.0<br>Average Number of Initiations / Year 7.8 7.8<br>7.2 7.0 7.0<br>6.6<br>5.0 5.0<br>4.6<br>2.5 3.0 3.6 3.4 2.4 3.2 2.8 3.8 3.0<br>1.8<br>0.8<br>0.0 0.0 0.0<br><!-- End of picture text --> <u>Sources: Amelia Porges, Petros C. Mavroidis, Friedl Weiss, Analytical Index: Guide to GATT Law and Practice, GATT, Geneva, Switzerland, 1994 (for actions under Articles XIX and XXVIII before</u> 1995); The WTO Committee on Safeguards, Annual Reports (for safeguards after 1995); The <u>International Trade Environment, GATT—Report by the Director General 1989-1990, GATT,</u> Geneva, Switzerland, 1990, p. 21.

<u>Notes: VERs for 1970–1989 do not include bilateral quantitative restrictions under the Multi-</u> Fiber Agreement; data were not available for VERs before 1970 and during 1990–1994.

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