rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6. Concluding remarks**

This study documented for the first-time patterns in export specialization from an activity perspective and showed how it can generate additional insights beyond those based on the product perspective. First, export incomes from production activities decline and engineering, managerial, and services support activities grow as countries develop. Second, countries initially specialize along the extensive margin (shifting activities across industries) but later along the intensive margin (shifting activities across occupational classes). Third, new activity specialization is strongly related to the proximity of this activity to the initial export basket, in particular for specializations along the extensive margin and in routine intensive occupations. Fourth, countries that defy proximity and diversify quicker appear to grow faster in GDP per capita. This is correlation however and no claim for causation is made.

We see at least two promising avenues for further research. One avenue is in the modeling of structural change and the role of international trade. The canonical macro-structural change framework focuses on the sectoral composition of the economy in terms of employment and valueadded. Trade can shape the sectoral composition in various ways (Alessandria, Yi and Johnson 2021). Lower trade barriers facilitate specialization, for example through shifting comparative advantage and promoting economies of scale. Sectoral specialization will consequently affect the sectoral composition of the economy. And given a set of trade barriers, policy changes or technology shocks to the economy may also affect specialization patterns and consequently sector composition. Trade barriers are typically related to products whereas technological change such as automation affects particular activities rather than products or sectors. Modeling the composition of the economy in terms of activities in addition to sectors appears therefore to be a promising way forward as for example in Bárány and Siegel (2018) and Duernecker and Herrendorf (2022).

A second avenue for research is in further developing an integrated product and activity data set. Our data tracks exports at a rather aggregate industry level compared to the detailed product level export data that is available. Suppose that production technologies for a particular detailed
53% after the scheme. To the extent that the two groups of users (new and continuous) are comparable, there is a notable shift in their travel expenditure patterns, which complements the household-level results presented above.

In interpreting the treatment effect, we note that if paid bus ridership was widespread among women prior to the policy, the eliminated fare would directly reduce their expenditures. The Delhi Survey, however, documents the presence of a large segment of new users following the introduction of the policy. For these women, free bus ridership may imply new travel demand or substitution from other modes of transport. Either of these changes are likely to impact time use, which we turn to next.

# **H Impact on Time Use**

Our first set of main results pertains to time use. To reiterate, the CPHS reports daily time spent on work for employed women, household chores, and travel time, without breaking down the latter by motive for travel. Due to various substitution possibilities between travel modes, it is not clear whether total travel time will increase, decrease, or remain unchanged. It is possible that women switch from paid to free buses and there are no changes in travel time. However, if women who were previously budgetconstrained switch from walking to taking free buses, travel time will be reduced. Yet a third possibility is that the price effect helps women who would have otherwise not gone out to take more buses, increasing their travel time. While our ability to shed direct light on mode substitution is limited by the CPHS data, empirical results could help discern the overall impact. Finally, reallocation from travel time may affect time spent on household chores, and the possibility of cheaper commutes may affect labor supply. In what follows, we first focus on employed women and document time use changes that vary by marriage status and skill level. Subsequently, we offer a simple model of intra-household time allocation, corroborating its implications by showing results for men’s time use. Our final
Future work along the lines presented in this paper will need to focus on a number of areas, including (a) improved methodologies and instrument implementation to measure the incidence of local conflict, (b) better empirical proxies at the local level to measure potential factors associated with conflict, and (c) increased attention to empirical research design to begin to address issues of causality.

With its national coverage, the BPS’s PODES initiative provides an unprecedented insight with regards to the incidence of local conflict beyond those areas that have tended to capture the headlines. The PODES relies on village/neighborhood leadership as key respondents, under the assumption that these people are the most informed and able to report conflict at the local level. The approach potentially provides a systematic mapping of conflict by administrative area, something well beyond the ability of household surveys, given sampling requirements. As the paper points out, there may be certain factors that can cause underreporting of conflicts. In future enumerations of the survey, the statistical agents who implement the

> survey ( _mantri statistik_ ) could be encouraged to cross-check the data on conflict with other key respondents, while at the same time gaining a better understanding of their reporting incentives. Additional work might compare these results with more targeted surveys of household respondents in a subset of localities.<sup>22</sup> We would therefore certainly recommend that BPS retain the local conflict module in the 2005 PODES, but that it consider some refinements to the

> 22 At the time of writing, the University of Gadjah Mada in Yogya with the World Bank is implementing a household survey in an estimated six hundred villages that includes some refined conflict questions.

33
consumers from costlessly shifting demand from one producer to another. As a result, “more efficient (lower cost) plants cannot lure away all demand from their less efficient industry rivals simply with lower prices” (p. 534). This enables lower productivity plants to survive, even in long run equilibrium, despite their lower productivity. Similarly, an implication of the Melitz‐ Ottaviano (2008) model is that high transport costs can reduce the scale of market selection via firm entry and exit.

As outlined by Melitz‐Ottaviano (2008), the more differentiated a product is (in the sense of lower product substitutability), the lower is its industry (average and minimum) productivity and the greater is the within‐industry dispersion of productivity. Any exogenous increase in market size would therefore lead to growth in the average (or expected) plant‐level productivity and alter its dispersion relative to the industry average. The lower the degree of product differentiation, the larger these changes will be because they are the outcome of market expansion that speeds up the reallocation of market shares and factor inputs (both within the industry itself and between the industry and the rest of the economy) by raising producers’ entry and exit rates to and from that industry.

We test these predictions using a 10‐year panel of manufacturing firms from Ethiopia.<sup>1</sup> This panel contains annual production data for the period 2000 to 2010 and covers all manufacturing firms employing 10 or more employees.<sup>2</sup> It is arguably the most comprehensive longitudinal data set on manufacturing firms in Sub‐Saharan Africa at this moment. Industries are classified in the dataset at the 4‐digit ISIC level. Importantly, the Ethiopian data include producers’ physical outputs, q� , along with their respective prices, p� . This allows us to distinguish between revenue‐ based measures of total factor productivity (TFPR) and those based on physical outputs (TFPQ). As is standard in
br>**100%**||**81.2017**<br>**100%**|

Notes: The ’Mean values’ for Dev7 and Vietnam are taken from the whole data set and represent the same values used for Section 2 - Endowment Differences. They are included here to reiterate the categorization of variables into WEALTH and ED WEALTH, depending on their comparative values between Dev7 and Vietnam. Underlined values represent those variables mentioned within the analysis.

35
# **Occupational Dualism and Intergenerational Educational Mobility in the Rural Economy: Evidence from China and India**

**M. Shahe Emran**<sup>1</sup> IPD, Columbia University

**Francisco Ferreira** World Bank

**Yajing Jiang** Charles River Associates

**Yan Sun** World Bank

**Key Words** : Educational Mobility, Rural Economy, Occupational Dualism, Farm-Nonfarm, Complementarity, Coresidency Bias, China, India

**JEL Codes:** O12, J62

1 We are grateful to Forhad Shilpi for help with REDS data and insightful comments throughout this project, and to Yang Huang for help with the CFPS data. We would like to thank the participants in Equal Chances conference 2018, and LACEA conference 2019, and Matthew Lindquist, Guido Neidhofer, Reshad Ahsan, and Hanchen Jiang for helpful comments on an earlier draft and Rakesh Gupta Nichanametla Ramasubbaiah for excellent research assistance. An earlier version of the paper was titled “Intergenerational Educational Mobility in the Rural Economy: Evidence from China and India”. This project was partially funded by World Bank RSB. The standard disclaimers apply. Email for correspondence: shahe.emran@gmail.com.
19 into account and there would have been no pay-outs in those years, leading to a cheaper product and one which is also a better fit to the farmers’ actual yield losses, apart from making them more insurable from the insurance companies’ perspective.

To further compare the relative accuracy of the rainfall index and the hybrid index (rainfall and irrigation) products, a correlation analysis of the aggregated loss data (as obtained from the district damage ratios and supplemented with loss data using feedback from farmers) was carried out. Losses and insurance pay-outs were simulated stochastically, based on historical rainfall, tank storage, and crop loss data. Correlation analyses were carried out between the levels of simulated losses and simulated pay-outs for the two different types of indices. For losses, both the incidence of losses (using a binary indicator) and the extent of losses (using a Gamma distribution) were correlated to the incidence of payouts (using a binary indicator) and the extent of pay-outs (based on the index structure).

**Table 3.  Summary of Correlations for Vavuniya**

||Correlation of incidence of<br>losses and occurrence of pay-<br>outs|Correlation of extent of losses<br>and extent of pay-outs|
|---|---|---|
|Rainfall index|57%|49%|
|Hybrid<br>index<br>(Rainfall<br>+<br>Irrigation)|76%|64%|

**Table 4: Summary of Correlations for Anuradhapura**

||Correlation of incidence of<br>losses and occurrence of pay-<br>outs|Correlation of extent of losses<br>and extent of pay-outs|
|---|---|---|
|Rainfall index|58%|52%|
|Hybrid<br>index<br>(Rainfall<br>+<br>Irrigation)|81%|69%|

The
)|(0.001)|(0.003)|(0.002)|(0.001)|(0.002)|(0.000)|(0.000)|(0.000)|(0.000)|
|#Dependents|0.004***|-0.004**|-0.001|0.005***|-0.003|-0.000|0.003|-0.003|0.002***|-0.002***|-0.000|0.003***|
||(0.001)|(0.002)|(0.001)|(0.002)|(0.004)|(0.004)|(0.002)|(0.004)|(0.000)|(0.001)|(0.000)|(0.001)|
|Observations|43484|43484|43484|43484|14444|14444|14444|14444|107947|107947|107947|107947|

_Note:_ Robust standard errors in parentheses. Sample is restricted to males aged 20-59. Marginal effects of Logit/Mlogit model are reported. Data source: Bangladesh HIES 2016; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. * p _>_ 0.1, ** p _>_ 0.05, *** p _>_ 0.01.
the labor market, and role model effect in each country. It is likely that mothers become the natural role model for a daughter, and fathers for sons. Social norms regarding gender roles might also play a big role on who, sons or daughters, obtain more years of schooling. Second, in line with our other findings, we note a decline in father–child and mother–child intergenerational elasticity ( _β_<sup>ˆ</sup> ) across birth cohorts after the 1960s. The mother–child intergenerational elasticity declined more than father–child’s elasticity in Ghana, Guinea, Madagascar, Nigeria, Rwanda, and Uganda over the 50 years. In the Comoros, Malawi, and Tanzania, we document more gains in mobility from fathers to children.

Figure 5: The education of mothers or fathers?

<!-- Start of picture text --> a. Comoros-2004 b. Ghana-2013 c. Guinea-2003<br>1939 1949 1959 1969 1979 1948 1958 1968 1978 1988 1937 1947 1957 1967 1977<br>5-years birth cohorts 5-years birth cohorts 5-years birth cohorts<br>d. Madagascar-2005 e. Malawi-2010 f. Nigeria-2010<br>1940 1950 1960 1970 1980 1946 1956 1966 1976 1986 1946 1956 1966 1976 1986<br>5-years birth cohorts 5-years birth cohorts 5-years birth cohorts<br>g. Rwanda -2000 h. Tanzania-2009 i. Uganda-2005<br>1935 1945 1955 1965 1975 1945 1955 1965 1975 1985 1941 1951 1961 1971 1981<br>5-years birth cohorts 5-years birth cohorts 5-years birth cohorts<br>bmother bfather rmother rfather<br>1.5 1.5 1.5<br>1 1 1<br>.5 .5 .5<br>0 0 0<br>1.5 1.5 1.5<br>1 1 1<br>.5 .5 .5<br>0 0 0<br>1.5 1.5 1.5<br>1
A. Sanghi and D. Johnson are developing plans to offer Chinese language classes in primary and secondary schools (KICD 2014).

## **4.3 Lack of quality Chinese aid data**

Since OECD and other official sources do not track China’s aid, researchers at AidData have created their own database of Chinese aid to Africa.<sup>12</sup> The database uses media reports, government investment websites, and other official sources to calculate the total amount of Chinese development finance to various African countries by project. The data collection occurs in two stages. First, researchers look through Factiva, a Dow-Jones owned media search engine, to identify media reports about aid projects in various countries. The team also uses donor and recipient country government websites to search for projects that they may have missed in the initial Factiva search (Strange _et al_ 2013). After selecting the first group of projects, AidData re-searches using Google and other country search engines such as Baidu to retrieve the date, location, project cost, financial details, and status of the project. AidData then uses other staff members to review project details and information sources to correct mistakes.

### **4.3.1 Drawbacks of existing Chinese aid data: Media based data collection is problematic**

Compiling data from various sources to create something usable for analysis is commendable and brings more transparency to the debate on Chinese aid. Since the database is open source, anyone can suggest improvements and make corrections, allowing the database to improve with time. Relying on media reports, however, is problematic. Newspapers often report inaccurate information on the size and type of financial flows, or cover projects that are later cancelled. 6 of 20 Chinese deals cited in an AidData paper, for example, never actually happened, and China only financed 38 percent of the Merowe hydropower project in South Sudan. The authors, as a result, find that the total of the 20 Chinese deals was US $38 billion
03)|(0.04)|(0.03)|
|N||||172|205|377|

_Notes:_ Standard errors in parentheses; * denotes the male-female difference is significant at 10%. ** at 5%. *** at 1%. Based on the Ghana and Malawi Agricultural Labor Surveys.

The large increase over time in the share of farmers producing only or mainly for sale in Malawi calls for further investigation. Table 4 examines the proximate factors that may explain this increase, i.e. changes in the crops/animals that household members were working during the reference week vs. an increase in the share of each crop/animal intended for sale, by showing start- and end-of-season descriptive statistics for the subsample of household members engaged in farming at both points in time.

The results show that the main driver of changes over time are the products that these household members were working on (Table 4, upper panel): there is much less time spent in the last seven days on the cultivation of crops at the end of the season, especially of maize, which is largely intended for own use (79 percent at the start of the season vs 7 percent at the end of the season). Conversely, there is _more_ livestock activity at the end of the agricultural season; this includes rearing of goats, which are mainly

> 28 Mechanically, the estimates for Ghana will show fewer male-female differences than the estimates for Malawi. This is because in Ghana, one respondent in the household was asked about the intended use of output of all the crops produced by the household, whereas in Malawi each individual working in agriculture could report on the intended use of output of each product s/he produces (thus allowing for intra-household variation). Moreover, the product-level estimates in Ghana do not consider livestock products, only crops.

> 29 There is also a degree of correspondence when the analysis is done at the individual level. For all
Policy Research Working Paper 9247

# **Abstract**

This paper proposes a leading indicator, the “Google Mobility Index,” for nowcasting monthly industrial production growth rates in selected economies in Latin America and the Caribbean. The index is constructed using the Google COVID-19 Community Mobility Report database via a Kalman filter. The Google database is publicly available starting from February 15, 2020. The paper uses a backcasting methodology to increase the historical number of observations and then augments a lag of one week in the mobility data with other high-frequency data (air quality) over January 1, 2019 to April 30, 2020. Finally, mixed data sampling regression is implemented for nowcasting industrial production growth rates. The Google Mobility Index is a good predictor of industrial production. The results suggest a significant decline in output of between 5 and 7 percent for March and April, respectively, while indicating a trough in output in mid-April.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jsampibravo@worldbank.org and cjooste@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
or valuations as part of the mortgage process. However, none of these sources are feasible for automated data collection in EMDEs as the various institutions holding these price data do not yet have a standardized way of collecting, publishing, or even digitizing them. In markets characterized by lax regulation or enforcement, transacting parties may under-declare property prices to avoid negative ramifications with respect to paying additional registration costs or taxes.

We opted to collect property price data for EMDEs through a web scraping approach of real estate websites. While not yielding transaction prices, obtaining listing prices of formal properties is a viable alternative to gathering price data in EMDEs, where data are otherwise non-existent. At least in the context of developed markets, strong evidence exists that listing prices are correlated and a good leading indicator for transaction prices (e.g., Ardila et al. 2021; Anenberg & Laufer 2017; Lyons 2019). For each economy, we scrape property prices and additional data points for all available listings, capturing, to the extent possible, location aspects. Scraping unique property transactions has several advantages: first, they allow us to provide actual property price data of an entire housing market in EMDEs, facilitating analysis beyond aggregated or indexed data. Particularly in EMDEs, we expect significant differences in prices between the biggest business city and rural areas, and more considerable skewness in data even within cities. Second, by web scraping property prices of the entire formal housing market, we can analyze sub-markets in greater detail. Third, collecting house price data from the entire formal, online housing market allows us to also capture the quantity of housing available at different price segments and can, therefore, provide an overview on the 11
. Ritchie (Eds.), _Modeling Plant and Soil Systems_ , pp. 5–29. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America.

- Sarsons, H. (2015). Rainfall and conflict: A cautionary tale. _Journal of Development Economics 115_ , 62–72.

- Shah, M. and B. M. Steinberg (2017). Drought of opportunities: Contemporaneous and long-term impacts of rainfall shocks on human capital. _Journal of Political Economy 125_ (2), 527–61.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2014). National Survey on Household Living Conditions and Agriculture 2011. Public Use Dataset. Ref: NER ~~2~~ 011 ~~E~~ CVMA ~~v~~ 01 M. Downloaded from `https://microdata.worldbank.org/index. php/catalog/2050` on 6 September 2019.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2016). National Survey on Household Living Conditions and Agriculture (ecvma) 2014. Public Use Dataset. Ref: NER 2014 ~~E~~ CVMA ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2676` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2011). Tanzania National Panel Survey 20082009, Wave 1. Public Use Dataset. Ref: TZA ~~2~~ 008 ~~N~~ PS-R1 ~~v~~ 03 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/76` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2012). Tanzania National Panel Survey 20102011, Wave 2. Public Use Dataset. Ref: TZA ~~2~