rafmacalaba/datause-encoder-data huggingface dataset:
insurance. Data to construct this variable come from Barth, Caprio and Levine (2004). The data refer to the year 1999 or 2000.

We also consider the financial structure of the country, i.e., whether the country’s financial system is more market-based or bank-based. STRUCTURE is a measure of the size of stock markets relative to that of banks. To measure the size of the domestic stock market, we use the market capitalization ratio, which equals the value of domestic equities listed on domestic exchanges divided by GDP. To measure the size of banks, we use the private bank credit ratio, which equals the value of total bank credit to the private sector divided by GDP. STRUCTURE equals the logarithm of the market capitalization ratio divided by the private bank credit ratio. We use the average of this variable over the years 1995-99 as proxy for financial system structure. A higher score indicates a more market-based system, and a lower score indicates a more bank-based system. This variable has previously been used as a measure of financial system structure by Levine (2001), among others. Because he calculates this variable over the period 1980-95, the Levine dataset has data on only 31 of the countries with explicit deposit insurance in our dataset. We therefore update the financial structure variable for the countries in our sample using data on stock market capitalization and credit to the private sector for the years 1995-99, increasing the number of countries with explicit deposit insurance for which data on financial structure are available from 31 to 52. Data on stock market capitalization and private bank credit ratios come from the World Development Indicators database of the World Bank.

Next, we control for the contracting environment of the country. Since moral hazard problems associated with deposit insurance tend to be exacerbated in countries 15
Table 1.1: Consumption Path: Internatio nal Comparison

||Number<br>|Population<br>'|P|er CapitaConsump|tion|
|---|---|---|---|---|---|
|Countries/Regions|of<br>|(000,<br>||||
||Countries|1997)||||
||||Growth"<br>(%)|Standard<br>Deviation<sup>2</sup>'<br>(xIW)|Certainty-<br>Equivalent<br>Growth<sup>'</sup><br>(%)|
|Barbados||265|_1.51_|7.46|0.40|
|Belize||217|0.48|9.92|-149|
|Dommica||74|2.18|5.69|1.54|
|Dominican Republic||7,968|2.91|683|1.98|
|Grenada||96|349|5.31|2.92|
|Guyana||749|2.77|17.13|-3.10|
|<br>Haiti||7,492|-0.07|559|-0.69|
|Jamaica||2,554|107|8.61|-0.41|
|St.KittsandNevis||41|5.75|8.15|4.42|
|St Lucia||150|3.69|11.33|1.12|
|St.VincentandtheGrenadines||112<br>|3.43<br>|8.52<br>|1.98<br>|
|Trinidad andTobago<br>||1,278<br>|2.18<br>|8.17<br>|0.85<br>|
|Caribbean*|12|20,995|2.45|8.56|0.79|
|Argentina||35,672|1.48|5.75|0.82|
|Chile<br>||14,622<br>|3.01<br>|10.10<br>|0.97<br>|
|Colombia||40,042|2.45|2.44|2.33|
|ElSalvador||_5,911_|1.08|5.46|0.48|
|Honduras||5,939|068|3.55|0.43|
|Mexico||93,909|1.75|3.60|1.49|
|Nicaragua||4,680|-0.67|7.71|-186|
|Paraguay||5,085|2.81|6.00|2.09|
|Uruguay||3,265|1.39|5.73|0.74|
|Venezuela||22,777|0.44|4.65|0.00|
|LatinAmerica*|10|231,904|1.44|5.50|0.75|
|Sub-SabaranAfrica*|37|498,877|0.90|7.98|-0.58|
|Middle-East&NorthAfrica*|7|207,460|1.60|7.08|0.37|
|Europeand CentralAsia(NonOECD)*|6|291,422|1.49|4.93|0.89|
|EastAsiaandPaclflc*|8|1,633,758|2.90|3.92|2.55|
|France||58,208|2.45|138|2.41|
|Japan||126,091|4.05|2.53|3.93|
|UnitedKingdom||59,009|2.00|1.65|1.94|
|<br>UnitedStates||271,542|2.00|1.49|1.95|
|OECD|21|742,911|2.50|2.17|2.39|
|Memo(Weightedaverageusing1997population):||||||
|Caribbean|||1.55|7.47|0.43|
|LatinAmerica|||1.71|4.84|1.24|
|Sub-SaharanAfnca<br>,|||0.13|8.28|-1.24|
|Middle-East&NorthAfrica|||1.62|6.75|0.71|
|EuropeandCentralAsia<br>||.|0.15<br>|4.26<br>|-0.21<br>|
|EastAsiaandPacific<br>OECD|||4.63<br>2.59|5.26<br>1.94|4.07<br>2.52|

* Unweighted average.

"Refers to the expected instantaneous growth of per capita consumption defined as p in Box 1.1.

2' Refers to the standard deviation (or volatility) of per capita consumption growth defined as cf in Box 1.1.

3 Refers to the certainty-consumption growth defined as , in Box 1.2. We assume that p = 4 which is consistent with available empirical evidence.

Source: S[MA database (1960-97) 6
**Figure 6.2:    Incidence of Education by Level, Bahia (2001)**

<!-- Start of picture text --> 10 0 % N o e d u c a tio n<br>P rim a ry<br>9 0 % L ow e r s e c o n da ry<br>U p p e r s e c o n da ry<br>8 0 % T e rtia ry<br>7 0 %<br>6 0 %<br>5 0 %<br>4 0 %<br>3 0 %<br>2 0 %<br>1 0 %<br>0 %<br>1 st (poo rest) 2n d 3 rd 4th 5th (richest)<br>In co m e Q u in tiles<br>Incidence of Education<br><!-- End of picture text --> <!-- Start of picture text --> Source: Author’s calculations based on PNAD 2001.<br><!-- End of picture text --> Bahia lags in preschool and early childhool development (ECD) programs. The group of children that has suffered the greatest lack of educational attention is the pre-school group. As the legal responsibility for the provision of pre-school has shifted to the municipal level, the number of preschool places in Bahia actually declined in recent years. Daycare and early childhood education represent a future investment in the human capital of the state, and should be a priority over the long term. Studies have shown that children who have attended pre-school perform better academically than those who have not. Programs have been in place to monitor and improve the health outcomes of small children—from early pregnancy through the first years of life. Given the high incidence of poverty and vulnerability among families with children under age 5 (see Section 5), and given the increased economic participation by women, a program of financial transfers linked to early childhood development and education centers (along the lines of _Bolsa Escola_ and PETI, see below) could be influential in reducing poverty. The federal government has recently launched the _Bolsa Alimentação_ , a program of financial tranfers to the mothers of children age 5 and younger, linked to stringent prenatal, postnatal, and developmental checkups by health professionals. This type of program could be linked to and used as the vehicle for strengthening and expanding early childhood education and care.

Policies to improve access of the poor to early childhood development programs, kindergartens, and secondary and higher education linked with improved quality of education should be the core of the government’s poverty-reduction strategy. Furthermore, to reduce disparities in human capital targeting of social policy and public spending is called for to reduce spending on education.

35
is the main transport mode, freight rates are found to be subject to various thick market and network externalities (Brancaccio, Kalouptsidi and Papageorgiou, 2020; Wong, 2018; Asturias, 2020). However, little is known about the behavior of trucking costs in developing countries due to a lack of transactionlevel data. In India, several online portals have been in operation (e. g. trukky, truckguru, blackbuck, trucksuvidha, truckbhada, logisticsjunction, etc.) in recent years. These portals match truck suppliers/operators with customers where the matching is d1 through a bidding process. The transactions covered by the platform can be characterized as spot transactions. The nearly half a million _actual_ transactions that took place through 1 of the platforms form the main data set for our analysis. The data set is exceptional in three ways: it recorded details of each transaction including freight cost, location of origin, and destination along with vehicle and commodity characteristics; its geographical coverage is extensive, reaching nearly all corners of India, and its temporal coverage (January 2017 to February 2021) spans the entire first wave of the COVID-19 pandemic period.

Taking advantage of this data set, we provide evidence on freight rates (per ton-km) for trucking in India during the pre-pandemic, nationwide lockdown, and post-lockdown periods, and on the determinants of trucking cost in terms of route characteristics. The evidence indicates that the freight rate per ton-km (mt-km) is relatively cheap in India (about 5 cents) and post-lockdown freight rates were about 32 percent higher compared with the immediately preceding pre-pandemic level. The rise in the freight rate during the post-lockdown period was also higher for long-distance trips. Evidence suggests several advantages for destinations with larger market sizes: the cost of incoming freight is relatively lower and consequently, their trading networks have much longer geographic reach and costs are lower if the origin is also a large city.

To see how the freight rate shock as observed during the post-lockdown period would transmit along the trade network, we estimate the elasticity of trade/shipment flow with respect to freight rate as well as numbers#:~:text=Trucks%20carried%2057.7%20percent%20of%20the%20value%20of,carried%2084.5%20percent%20of%20the%20value%20%28Ta 3
effect of non‐labor income such as husband’s income. We fully recognize the presence of endogeneity in this relationship and thus present these as only descriptive correlations.

Table 7: Determinants of female labor force participation

||(<br>|1)<br>|(2|)|
|---|---|---|---|---|
|Dependent variable=>|Spouse of<br>emp<br>|male head is<br>loyed|Female m<br>empl<br>|ember is<br>oyed|
||β|s.e.|β|s.e.|
|Monthly wage of male head|‐0.026<sup>*</sup>|(‐1.93)|‐0.040<sup>***</sup>|(‐3.16)|
|Province (Base: Provinces other than Northern, Eastern|, Western)||||
|Northern Province|‐1.607<sup>***</sup>|(‐4.43)|‐1.274<sup>***</sup>|(‐4.12)|
|Eastern Province|‐1.605<sup>***</sup>|(‐4.52)|‐1.408<sup>***</sup>|(‐4.60)|
|Western Province|‐0.038|(‐0.16)|0.062|(0.32)|
|Province X Monthly wage of male head|||||
|Northern Province X Monthly wage of male head|0.144<sup>***</sup>|(3.38)|0.112<sup>***</sup>|(3.08)|
|Eastern Province X Monthly wage of male head|0.130<sup>***</sup>|(3.35)|0.115<sup>***</sup>|(3.41)|
|Western Province X Monthly wage of male head|‐0.012|(‐0.48)|‐0.014|(‐0.69)|
|Age|0.149<sup>***</sup>|(10.95)|0.168<sup>***</sup>|(16.58)|
|Age, squared|‐0.002<sup>***</sup>|(‐11.30)|‐0.002<sup>***</sup>|(‐16.07)|
|Urban|‐0.115<sup>**</sup>|(‐2.11)|‐0.042|(‐0.92)|
|Household size|‐0.026<sup>*</sup>|(‐1.77)|‐0.014|(‐1.21)|
|Years of education|0.043<sup>***</sup>|(6.96)|0.046<sup>***</sup>|(8.56)|
|Share of children <5 years in household|‐0.736<sup>***</sup>|(‐4.39)|‐0.758<sup>***</sup>|(‐5.41)|
|Ethnicity (Base: Sinhala)|||||
|Sri Lankan Tamil|0.131|(1.61)|0.043|(0.62)|
|Moor|‐0.465<sup>***</sup>|(‐5.75)|‐0.542<sup>***</sup>|(‐7.73)|
|Others|0.784<sup>***</sup>|(9.46)|0.628<sup>***</sup>|(9.78)|
|Relationship to head (Base: Spouse)|||||
|Child|||0.285|(1.53)|
|Parent|||‐0.294|(‐0.53)|
|Other relative|||‐0.183|(‐0.58)|
|Relationship to head X Monthly wage of male head|||||
|Child X Monthly wage of male head|||‐0.012|(‐0.58)|
|Parent X Monthly wage of male head|||0.022|(0.35)|
|Other relative X Monthly wage of male head|||0.021|(0.59)|
|Constant|‐3.289<sup>***</sup>|(‐10.68)|‐3.617<sup>***</sup>|(‐15.94)|
|N|6,317||9,731||

_t_ statistics in parentheses<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. Note: Estimation sample includes working age female spouses of head in (1) and working age females in (2). Coefficients are from a probit regression. The monthly wage of male head is in logged form. Dataset: Labor Force Survey 2015.

The dependent variable in specification (1) is whether the spouse of a male head of household is employed. Specification (2) includes all working‐age women in the household. We see that the coefficient on the (logged) monthly wage of the male head is negative and statistically significant, consistent with a negative income effect as predicted by economic theory. The interaction of the province on the monthly wage of the male head suggests that women’s employment is much more responsive to the wage of the 23
4 the bulk of agricultural GDP in most African countries. Gender disaggregated data on labor input in livestock activities are also not yet systematically available. Food processing, which is typically the exclusive domain of women, is further excluded, consistent with the time use surveys reviewed by FAO (2011) and the aforementioned claims regarding female labor share in agriculture. The study further probes into the underlying processes and factors that affect, at the household-level, women’s time allocation to crop production. The robustness of the findings is assessed by controlling for possible gender and knowledge bias in reporting, which may occur when responses on labor input come from proxies (i.e. other household members) as opposed to self-reporting.<sup>6</sup> By way of headline number, the population-weighted female share of labor in crop production across the six African countries examined here is 40 percent. This is substantially less than the widely quoted figures of 60 to 80 percent. Consistent with FAO (2011), wide variation is recorded across (and within) countries, with the country-level shares ranging from 56 percent in Uganda to only 24 percent in Niger. For Nigeria as a whole, the share is 37 percent; it is only 32 percent in the North, and 51 percent in the South.<sup>7</sup> The empirical finding that women do not disproportionately contribute to crop production proves robust to possible gender and knowledge biases in reporting. The primary factor underlying differences in female labor input across households is the gender composition of the household. There is little systematic difference across countries in female labor provision across crops or agricultural activities. These findings attenuate the premise on which recent calls for boosting agricultural output by increasing female agricultural (whether land or labor) productivity are based. However, they do in and of themselves neither invalidate nor validate the conclusion of the argument as such. Other premises, such as substantial

> 6 See Bardasi et al. (2011) for an analysis of the effects of proxy versus self-reporting on employment.

> 7 The female share of agricultural labor for Nigeria overall may seem low at 37 percent given that the shares for the North and South are 32 percent and 51 percent respectively. However, although the population is about evenly split between North and South, a much larger share of the Nigerian population engaged in agriculture resides in the North.
**Table 5 Output effects** <u>(percent change in output)</u>

||Good<br>Liberaliz|s<br>ation|Servic<br>Liberaliz|es<br>ation|Goods + Se<br>Liberaliza|rvices<br>tion|Goods + Ser<br>India M|vices +<br>FN|
|---|---|---|---|---|---|---|---|---|
||US|India|US|India|US|India|US|India|
|Agriculture (%)|0.10|-0.06|-0.05|0.05|0.05|-0.01|0.15|-1.99|
|Processed agriculture (%)|0.05|-0.31|-0.02|0.07|0.04|-0.24|0.03|-8.89|
|Coal, oil, gas and other mining (%)|-0.05|-0.16|-0.04|0.13|-0.09|-0.04|-0.03|-1.91|
|Textiles and apparel (%)|-0.92|7.05|-0.18|0.68|-1.10|7.83|-1.28|19.07|
|Other light manufacturing (%)|-0.13|0.74|-0.07|0.47|-0.20|1.20|0.03|7.89|
|Paper and mineral products (%)|0.02|-0.49|-0.06|0.07|-0.04|-0.42|0.04|-1.12|
|Chemical, rubber and plastic products (%)|0.05|-0.47|-0.18|0.4|-0.12|-0.07|-0.05|4.31|
|Iron, steel and metal products (%)|0.29|-1.17|-0.21|0.53|0.08|-0.65|0.08|-1.54|
|Petroleum and coke products (%)|0.09|-0.21|0.01|0.08|0.09|-0.13|-0.13|6.51|
|Transport equipment (%)|0.00|-1.55|-0.14|0.47|-0.13|-1.09|-0.14|-0.44|
|Electronics and machinery (%)|-0.07|-1.01|-0.24|0.72|-0.3|-0.3|0.12|2.13|
|Other manufacturing (%)|0.81|-0.88|-0.23|0.65|0.58|-0.25|-0.16|8.71|
|Services(%)|0.00|0.02|0.03|-0.13|0.03|-0.11|0.00|0.06|

_Source_ : Authors’ simulation results.

25
- 19 -

For (plot-specific) individual attributes _Z_ , the basic characteristics of land plots are included, such as land area (ha) and land slope. The land area may be important to think of firms’ crop choice, because large-scale farmers are likely to be less constrained to choose to grow export crops, which must of necessity involve certain risks. Small farmers may not have much flexibility to choose other than subsistence crops. The latter is defined by a dummy variable, which is set to one if a plot is slightly sloped or steep. The land title status is also included as a dummy variable. Only 10 percent of the households reported that they own land by the title deeds.

Following the literature (e.g., Moreno and Sunding, 2005; Edmeades et al., 2008; Klasen et al., 2013), the basic household characteristics, such as gender and school attainment of household head, household size, and household wage income, are also included in _Z_ . In addition, access to other infrastructure services than transport is also different among households. Mobile access is included, given the recent evidence showing the ICT effects on farmers (Kiiza and Pederson, 2012; Zanello 2012).

Access to electricity is also taken into account, which remains a significant challenge in Africa. In Tanzania, only 17 percent of households are electrified. In rural areas, the electrification rate is estimated at less than 1 percent. The literature argues various positive impacts on non-agricultural aspects, such as job creation and nonfarm income growth (e.g., Khandker et al. 2009; Dinkelman 2011). In theory, the farmers’ crop choice can also be affected, particularly if they rely on electricity for irrigation water pumping.<sup>15</sup> In our data, power access is defined by the dummy variable, which is set to one if a household relies on grid, solar or diesel power for lighting, and zero otherwise.

Finally, last-mile transport accessibility is measured by the distance to the nearest road. The data come from the LSMS. Most of the households live within 5 km distance from the

> 15 Irrigation remains to be developed in Tanzania. In the LSMS data, only 1.7 percent of plots have some irrigation systems. And the vast majority use gravity systems or rely on manual labor for pumping water. Only 0.2 percent of the surveyed plots have modern irrigation systems with motor pumps.
tered.<sup>1</sup> Our final data set includes 8,748 firm observations. However, management practice information for manufacturing firms was collected only if the firm had over 20 employees, which reduced our sample size to about 4,000 firms.<sup>2</sup> The overall or general management score is calculated using eleven questions from the management practice module. These questions map to eight questions from the United States Management and Organizational Practices Survey (U.S. Census Bureau, 2015). They cover four categories of practices: target-setting, monitoring, incentives, and operations. Firms’ responses to questions on management practices are aggregated into an overall management score following Bloom et al. (2019) in two steps. First, the responses to each of the management practice questions are normalized on a 0-1 scale: the response associated with the most structure and prevalence is normalized to one, and the one associated with the least is normalized to zero. Second, the overall management score is calculated as the unweighted average of the individual question scores. We use these scores to compute descriptive statistics. For regression analysis, we take the z-score of the overall management score for ease of interpretation. Summary statistics for management practices and other firm characteristics are shown in Appendix Table A2.

# **3.2 Descriptive Analysis**

In this paper we focus on six outcomes reported in the WBES COVID-19 surveys, including: (i) change in sales (%), (ii) closure (temporary and permanent), (iii) number of employees furloughed or laid off (as a share of total pre-COVID-19 initial level), (iv) partial or full adjustment or conversion in product or service mix (“pivoting”), (v) an increase in online work arrangements, and (vi) probability to fall into arrears.<sup>3</sup> In our descriptive analysis,

> 1We dropped observations for the Turkish Cypriot Community (TCC) and Armenia due to lack of data on lockdown stringency, and for Mongolia due to concerns about the quality of data on management practices.

> 2Manufacturing firms with more than 20 employees had 35 times larger median pre-COVID-19 revenue and experienced 1.4 times higher labor productivity relative to those with fewer than 20 employees. A larger share of these firms are exporters and have a website. These firms also witnessed lower decline in sales post-COVID-19, and were more likely to remain open. See Appendix Table A1.

> 3Outcomes (ii), (iv), (v), and (vi) are binary responses.

6
Urdal & Hoelscher: Urban Youth Bulges and Social Disorder Gleditsch, Kristian S., 2002. ‘Expanded Trade and GDP Data’, _Journal of Conflict Resolution_ 46: 712724.

Gleditsch, Nils Petter; Petter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Håvard Strand, 2002. ‘Armed Conflict 1946–2001: A New Dataset’, _Journal of Peace Research_ 39(5): 615–637.

Goldstone, Jack A., 1991. _Revolution and Rebellion in the Early Modern World_ . Berkely, CA: University of California Press.

Goldstone, Jack A., 2001. ‘Demography, Environment, and Security’, in Paul F. Diehl & Nils Petter Gleditsch, eds, _Environmental Conflict_ (84–108). Boulder, CO: Westview.

Goldstone, Jack A., 2002. ‘Population and Security: How Demographic Change Can Lead to Violent Conflict’, _Journal of International Affairs_ 56(1): 3-21.

Hegre, Håvard; Tanja Ellingsen, Scott Gates & Nils Petter Gleditsch, 2001. ‘Toward a Democratic Civil Peace? Democracy, Political Change, and Civil War, 1816–1992’, _American Political Science Review_ 95(1): 33–48.

Hoelscher, Kristian, 2008. ‘Demography, Governance and Urban Insecurity: An Analysis of the Risk of Conflict in Cities’. Unpublished MSc. Dissertation. London School of Economics: London.

Homer-Dixon, Thomas F., 1999. _Environment, Scarcity, and Violence_ . Princeton, NJ: Princeton University Press.

Hudson, Valerie M. & Andrea M. den Boer, 2004. _Bare Branches: The Security Implications of Asia's Surplus Male Population_ . Cambridge, MA: MIT Press.

Huntington, Samuel P., 1996. _The Clash of Civilizations and the Remaking of World Order_ . New York: Simon & Schuster.

ILO, 1998. ‘Employability in the Global Economy: How Training Matters’ in _World Employment Report 1998-99_ (179-183). Geneva: The International Labor Organization.

ILO, 2004. _Human Resources Development and Training_ . Report IV(2B), International Labour Conference, 92nd Session. Geneva: The International Labor Organization.

ILO, 2008. _Global Employment Trends for Youth, 2008_ . Geneva: The International Labor Organization.

Kahl, Colin H., 1998. ‘Population Growth, Environmental Degradation, and State-Sponsored Violence: The Case of Kenya, 1991–93’, _International Security_ 23:80–119.

Lee, Everett S., 1966. ‘A Theory of Migration’, _Demography_ 3(1): 47–57.

Lia, Brynjar, 2005. _Globalisation and the Future of Terrorism: Patterns and Predictions_ . London & New York: Routledge.

Lundborg, Per, 1991. ‘An Interpretation of the Effects of Age on Migration: Nordic Migrants’ Choice of Settlement in Sweden’, _Southern Economic Journal_ 58(2): 392–405.

20