rafmacalaba/datause-encoder-data huggingface dataset:
values of the Herfindahl-Hirschman index imply greater concentration of sales/output among firms in the industry and therefore lower competition.

Next, we control for supply-side factors. These include the proportion of women in total population and the fertility rate. The data source for these variables is WDI, World Bank. Fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. High fertility rates have a direct effect on women’s involvement in the labor market via less time available for working (see for example, Bloom et al. 2009; Morrison et al. 2007; World Bank 2011). Regarding the proportion of women in total population, a higher proportion implies a larger supply of women workers and therefore proportionately more women in the workforce.

Labor laws that discriminate against women vs. men can have significant impact on women’s participation (relative to men’s) in the formal labor market. For instance, using firmlevel survey data for a cross-section of countries, Amin and Islam (2016) find that the share of women in total workers among registered private firms is significantly higher in countries that have implemented laws that prohibits discrimination against women in hiring practices. Islam et al. (2019) reach a similar result for an overall measure of gender-based disparity in the laws. Following this body of work, we control for an overall measure of disparity in the laws as they apply to women vs. men in the area of starting a job. We control two additional variables for disparity in the laws that we believe may alter the strength of the relationship between exports and female workers. These variables are a dummy variable equal to 1 if women can work in the same industries as men and 0 otherwise, and a dummy variable equal to 1 if women can work the same night hours as men and 0 otherwise. Data for all the controls for legal gender disparity are from Women, Business and Law (WBL), World Bank.

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shipments, medium/large freight forwarders tend to consolidate similar products of different clients, while small freight forwarders tend to consolidate different products and different clients.

**Figure 4. Frequency of Shipment Consolidation by Freight Forwarders**

<!-- Start of picture text --> 80%<br>70% 67%<br>60%<br>50% 45%<br>43%<br>40%<br>30% 28% 27%<br>20% 17%<br>14% 13%<br>11% 11%<br>8% 8%<br>10%<br>3% 3%<br>0%<br>0%<br>Always Frequently Sometimes Rarely Never<br>Small Medium/Large Total<br>Share of fright forwarders<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

Interestingly the benefits from shipment consolidation are similar across freight forwarder size: close to 70% of freight forwarders of any size in Serbia consider the additional earnings from organizing a consolidated shipment (relative to a non-consolidated shipment) to be in the range of 1% to 20% in Table 4. Given the comparable magnitude of the benefits, the lower frequency of consolidation by small freight forwarders might be due to the following reasons. First, small freight forwarders may be more vulnerable to the risks associated with consolidated shipments, namely the delays and uncertainty regarding the time a shipment will spend before clearing customs. Second, consolidation may imply additional logistics or organizational processes that are more difficult to undertake by small freight forwarders. Third, small freight forwarders may not have enough business to consolidate frequently.

**Table 4.  Additional Earnings on Consolidated Shipments for Freight Forwarders**

||Number of<br>freight<br>forwarders|1 to 5 %<br>Share o<br>cons|5 to 10%<br>f firms indicati<br>olidating relat|10 to 20%<br>ng range for ad<br>ive to not conso|20 to 50%<br>ditional earnin<br>lidating a ship|More than<br>50%<br>gs from<br>ment|
|---|---|---|---|---|---|---|
|Small|107|22%|21%|22%|22%|14%|
|Medium/Large|11|27%|18%|27%|9%|18%|
|Total|118|22%|20%|22%|21%|14%|

Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders.

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Thus, it appears that a greater emphasis on supply-side policies may be more effective in stimulating more radical frontier innovation when local capabilities are in place, a greater emphasis on demand-side policies may be more effective in spurring firms to introduce incremental environmental innovations (both frontier and catch-up). The available evidence does not contradict a judicious combination of supply-side and demand-side policies, appropriate to the local context.

**Figure 10. Benefits of environmental innovations, 2006-08**

( _As a percentage of innovative firms)_ <!-- Start of picture text --> Reduced energy use per unit of output (producer benefits)<br>%<br>60<br>Reduced energy use by end-user (end-user benefits)<br>50<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Source: OECD, based on Eurostat (CIS-2008) and national data sources, June 2011.<br><!-- End of picture text -->

# **3.3 Developing absorptive capacity**

At the level of the firm, the ability to ̳learn‘ is seen as essential for innovation. Firm learning or ̳absorptive capacity‘ refers to a firm‘s ability to understand, adapt and use technologies. According to Cohen and Levinthal (1989), a firm‘s learning capacity is enhanced by its R&D activities, so that R&D has important roles in both frontier and catch-up innovation.

Griffith, Redding, and Van Reenen (2004) find empirical support in a panel of industries across 12 OECD countries for these ―two faces of R&D‖, this notion that R&D, in addition to stimulating the generation of new frontier technologies, also improves the firm‘s ability to globally search for and adapt existing technologies, and allows firms to more easily understand and assimilate the discoveries of others. Interestingly, they find that countries further from the technological frontier have higher rates of return to R&D than those at the frontier, due to the enhanced learning that R&D facilitates.<sup>41</sup> If returns to R&D rise for countries further from the frontier, then why don‘t low-income developing countries that are further from the frontier than the OECD countries in the study invest even more in R&D? One explanation of why returns to R&D don‘t keep rising in developing countries, and in fact fall significantly below those of the frontier countries, is that the business environment worsens typically as countries get further from the frontier, so that the general accumulation of all assets (including intangible knowledge assets) worsens. Faced with worse incentives for productive entrepreneurship, even the best ideas yield lower

> 41 While the US, typically at the technological frontier, had a rate of return to R&D of 57% with R&D‘s contribution to TFP growth largely due to innovation, Finland had a rate of return of 105% with less than half the estimated effect due to innovation and enhanced learning (absorptive capacity) quantitatively more important.

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FIGURE 3 Correlations between inflation and nominal effective exchange rate changes

# A. Advanced economies: Correlation rate

<!-- Start of picture text --> Correlation rate<br>0.8<br>Median Interquartile range<br>0.4<br>0<br>-0.4<br>-0.8<br>C. Average correlation rate<br>Correlation rate Interquartile range<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>Advanced economies EMDEs<br>1997 2013<br>Q+3 Q+2<br>1995 1999 2001 2003 2005 2007 2009 2011 2015 2017 2018<br>Q Q+1 Q+2 Q Q+1 Q+3<br><!-- End of picture text -->

# B. EMDEs: Correlation rate

<!-- Start of picture text --> Correlation rate<br>0.8 Median Interquartile range<br>0.4<br>0<br>-0.4<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2018<br><!-- End of picture text --> D. EMDEs: Average correlation rate, by sub-groups <!-- Start of picture text --> Correlation rate<br>0.8 Interquartile range Median<br>0.4<br>0.0<br>-0.4<br>Trade Exch. Inflation Energy Current<br>Openness regime target account<br>Low High Float Peg Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; GDP = gross domestic product; IMF = International Monetary Fund; IT = inflation targeting.

A.B.D. Correlation over a three-year rolling window between inflation and nominal effective exchange rate depreciations after one quarter. The sample includes 51 economies. The median and interquartile range are for three-year window correlation during 1995-2018.

C. Q, Q+1, Q+2, and Q+3 represent the correlation between inflation and nominal effective exchange rate depreciations over the same quarter and after one, two, and three quarters, respectively.

D. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

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**7**

Our I variables - the base set of variables that we always include - in the regressions are LSEC, the log of **the** initial (1960) secondary school enrollment rate, LYO, the log of initial real GDP per capita, and REVC, the number of revolutions and coups.5 **This** is a different set of I variables from the set used in LR. We choose this new set of I variables %ecause they correspond to the "Barro-style" regressions that have become the standard cross-country growth regression. **Thus,** choosing these I variables facilitates comparisons with other ***tudies.** We began by using the complete set of Barro (1991) control variables but dropped the log of the initial p%rimary schnol enrollment rate, the numbot of assassinations, and the 1960 average deviation from unity of **the** purchasing power parity index for investment goods since the inclusion of these variables did not importantly alter our findings.' Thus, equation (1) becomes

> _GYP - C + PLYO 2_<sup>_LSEC+_</sup> _3_<sup>_RvC_+</sup><sup>_Pr_</sup> PIZ + _u._ (2)

First, for each variable of interest, X, we run a base regression with only the basic set of variables included, i.e., we do not include any Z variables. This base regression determines whether the variable of interest is significantly correlated with long-run growth after controlling for a base set of variables designed to control for initial conditions and the degree of political stability.

Second, we run separate regressions including each variable - one at a time - from the pool of potential Z variables in regression equation f'). Then we run separate regressions including every combination of two variables from the pool of potential Z variables; finally, we run separate regressions including every combination of three variables from the pool of potential Z variables. Out of all of these regressions, we
# **The Rural-Urban Divide and Intergenerational Educational Mobility in a Developing Country: Theory and Evidence from Indonesia**

**Md Nazmul Ahsan**<sup>1</sup> Saint Louis University

**M. Shahe Emran** IPD, Columbia University

**Forhad Shilpi** World Bank

**Keywords:** Intergenerational Educational Mobility, Rural-Urban Divide, complementarity, Convexity, Returns to Education, School Quality, Peer and Role Model Effects, Coresidency, Sample Truncation, Ability Heterogeneity

**JEL Codes:** J62, O12

> 1We would like to thank Hanchen Jiang for helpful discussions. Emails for correspondence: ahsanm@slu.edu, shahe.emran.econ@gmail.com, fshilpi@worldbank.org. Standard disclaimers apply.
# **Table 19. Second Stage Regressions of Time Use: Females**

||**% of time**<br>**inside the**<br>**far**|**working**<br>**household**<br>**m**|**% of time**<br>**agricultur**<br>**in other h**<br>**fa**|**working in**<br>**al activities**<br>**ousehold**<br>**rm**|**% of time**<br>**non- agr**<br>**activities**<br>**househ**|**working in**<br>**icultural**<br>**in other**<br>**old farm**|**% of leisu**<br>**other a**|**re time and**<br>**ctivities**|**% of tim**<br>**domestic c**<br>**taking care**<br>**and other**<br>**the h**|**e spent in**<br>**hores and**<br>**of children**<br>**members of**<br>**ouse**|
|---|---|---|---|---|---|---|---|---|---|---|
||**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|
|Violent Shock|-0.0040|-0.0216|0.0006|-0.0002|0.0079*|0.0184|-0.0068|-0.0120|0.0023|0.0154|
||(0.0065)|(0.0216)|(0.0025)|(0.0092)|(0.0042)|(0.0137)|(0.0088)|(0.0296)|(0.0085)|(0.0306)|
|Observations|3,076|3,076|3,076|3,076|3,076|3,076|3,076|3,076|3,076|3,076|
|R-squared|0.145|0.145|0.0264|0.0264|0.0265|0.0265|0.0991|0.0991|0.101|0.101|
|Hansen Test||0.582||0.156||0.780||0.244||0.281|
|Estimated effect of a standard deviation increase in<br>probability of violent shock (in standard deviations of<br>dependent variable)||-0.044||-0.001||0.054||-0.020||0.021|

Cluster by community Regional Fixed Effects *** p<0.01, ** p<0.05, * p<0.1 Source: CLS-WILL: first wave – household questionnaire.

# **Table 20. Second Stage Regressions of Time Use: Males**

||**% of tim**<br>**inside the**<br>**fa**|**e working**<br>**household**<br>**rm**|**% of time**<br>**agricultur**<br>**in other h**<br>**fa**|**working in**<br>**al activities**<br>**ousehold**<br>**rm**|**% of time**<br>**non- agr**<br>**activitie**<br>**househ**|**working in**<br>**icultural**<br>**s in other**<br>**old farm**|**% of leisu**<br>**other ac**|**re time and**<br>**tivities**|**% of tim**<br>**domestic**<br>**taking car**<br>**and other**<br>**the**|**e spent in**<br>**chores and**<br>**e of children**<br>**members of**<br>**house**|
|---|---|---|---|---|---|---|---|---|---|---|
||**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|**OLS**|**2SLS**|
|Violent Shock|0.0053|-0.1628**|-0.0015|0.0081|0.0005|0.0726**|-0.0089|0.0353|0.0046|0.0469**|
||(0.0175)|(0.0703)|(0.0116)|(0.0438)|(0.0081)|(0.0344)|(0.0104)|(0.0312)|(0.0063)|(0.0232)|
|Observations|2,981|2,981|2,981|2,981|2,981|2,981|2,981|2,981|2,981|2,981|
|R-squared|0.0945|0.0945|0.250|0.250|0.0634|0.0634|0.152|0.152|0.105|0.105|
|Hansen Test||0.928||0.632||0.128||0.662||0.0306|
|Estimated effect of a standard deviation increase in<br>probability of violent shock (in standard deviations of||-0.176||0.008||0.134||0.065||0.113|
|dependent variable)|||||||||||

Cluster by community Regional Fixed Effects *** p<0.01, ** p<0.05, * p<0.1 Source: CLS-WILL: first wave – household questionnaire.

49
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Several airports in East Africa—including Kenya, Tanzania, Rwanda, and Uganda—are equipped with air traffic surveillance systems of some sort. Kenya is an exception in the region, with over 90 percent radar coverage. Ethiopia, although it is an important hub, had no radar coverage as of 2008. Rwanda’s radar is primarily for military use, and Uganda and Tanzania have systems covering the approaches to their main airports in Entebbe and Dar es Salaam.

The air fleet in East Africa underwent a dramatic modernization in 2007. Across Africa, there has been substantial investment in new aircraft, leading to an overall renewal in the aircraft fleet. This trend is dramatically different from that observed in West Africa. The share of aircraft rated ―western recent‖ and ―western somewhat recent‖ in use in East Africa increased by 30 percent between 2001 and 2007 (figure 2.14).

The air cargo of Africa, the Middle East, and Latin America constitutes around 11 percent of global air cargo traffic. The bulk of Africa’s air cargo traffic is handled by three countries: South Africa (20 percent), Kenya (15 percent), and Egypt (14 percent). Several other East African countries handle smaller volumes of Africa’s air cargo traffic, namely Sudan (13 percent), Ethiopia (3 percent), and Uganda (3 percent). Europe is the primary destination for African air cargo, accounting for two-thirds of African air cargo. Air cargo traffic from Africa to Europe is expected to grow at a rate of more than 5 percent between 2005 and 2025. Primary exports are counter-seasonal flowers and perishables, with relatively little return cargo. Kenya is a large exporter of flowers to the Netherlands, Nairobi is one of the three main airports in Africa that handles air cargo traffic. In addition to exporting cut flowers, it handles strong domestic demand for imports shipped via air. The airport has become one of Africa’s largest air cargo gateways (box 2.3).

**Figure 2.14. Age distribution of airline fleet in East Africa**

<!-- Start of picture text --> 2001<br>2 2007<br>15<br>26<br>28<br>15<br>50<br>43<br>20<br>Western recent<br>Western recent Western somewhat recent Western somewhat recent<br>Western old<br>Western old Western age unkown Western age unkown<br><!-- End of picture text --> Source: Derived from Bofinger 2009.

Two East African airlines—Kenya Airways and Ethiopian Airlines—in addition to South African Airways provide high quality air cargo services in collaboration with other major international operators. The small volume of air cargo originating from Africa has often placed constraints on air cargo handling 33
2013). This comprehensive dataset was shared by the Socioeconomic High-resolution RuralUrban Geographic Platform for India (SHRUG), which aggregated the firm-level data to broader geographic units by matching Economic Census data with the 2011 Population Census of India including demographic data at the town and village level. This aggregation facilitates integration at the village level, resulting in a “SHRID” level dataset. A SHRID describes a geographical unit that can be mapped consistently across multiple rounds of the Indian economic censuses. In the majority of cases, a SHRID is a village or town. This dataset includes the number of private firms and their employees, which is used to calculate the average size of private firms in about 505,000 spatial units as of 2013 (Asher et al. 2021).

**Temperature.** The temperature data consist of the average daily maximum temperature in the South Asia region (Copernicus Climate Change Service 2019). The data are then aggregated to the annual level to compute the 5 year annual average maximum temperature. The 5-year annual average maximum temperature is calculated for the period between 2014 and 2018, the latest available flood data and approximate DHS survey year used in the calculation of the RWI. The temperature data are then matched to RWI grids. The merged RWI and temperature dataset contains about 606,000 spatial units covering Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. For firms, the 5-year period ranges between 2009 and 2013. The temperature data are then matched to the SHRID-level firm data.

**Flood.** The flood data we use were compiled by the Dartmouth Flood Observatory. The dataset is a comprehensive collection of all flood events that occurred in the world between 2000 and 2018 (Tellman et al. 2021). Floods are identified through a combination of news reports, government data, instrumental observations, and remote sensing technologies, including satellite imagery. Floods of Severity Level 1 and higher are included in the analysis, where a Level 1 flood represents "significant damage to structure or agriculture, fatalities, and/or 5-15 year interval since the last similar event". Based on these data, RWI grids that have been flooded between 2000 and 2018 are identified. The flood data are also used to count how many times the grids have been flooded. A similar process is repeated to identify firms’ experience with flooding between 2000 and 2013 using SHRID-level firm data.

**Urbanicity** . Urbanicity is defined based on travel distances to cities. Places within 10 minutes from cities with more than 10,000 people are coded as urban and the rest are rural. Based on this

6
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