rafmacalaba/data-use-mentions-extended huggingface dataset:
to about 10% of total sales in 2007. However, as companies accumulate more capital, the incremental amount of new investment relative to existing fixed capital has declined (i.e., the investment to capital ratio (IK) has gone down from about 0.2 to 0.12). This is to be expected as companies mature.

The retained earnings data are only available for 3 years in this period. The right panel of Figure 2 shows the proportions of investment and working capital financed with retained

> 14 Service questionnaire does not include book value of capital and hence makes it impossible to calculate investment to capital ratio. In addition there are no service firms in the 2003 survey.

> 15 Specifically we exclude any investment to capital ratio if it is bigger than 2. This means a firm has purchased 200% of the current value of machinery in one year, which is highly unlikely. We also exclude operating income to sales ratio if it is below -0.99 or above 0.99. That means if the firm’s costs are less than 1% of total sales revenues or more than 99% of sales revenues, as we deem these data unreliable.

17
_εt_ are purely shocks which are idiosyncratic to each variable. However, the former scenario is unwanted as long as we do not assume an overwhelming domination of factors over the idiosyncratic disturbances. The latter scenario does not hold as typically the expected covariance matrix of the disturbances is not the identity, _E_ ( _εtε_<sup>_′_</sup> _t_<sup>) = Ω =</sup><sup>_IN_.</sup> Notice that the Google mobility information is composed of six indicators, which configures _N_ = 6, and those became available from February 15, 2020, making the time dimension roughly _T_ = 60. Notably, with short _N_ and _T_ it becomes difficult to assume that the strong factor assumption holds, and we would need to consider a more consistent approach besides the standard Principal Component Analysis (PCA).

# **4 Estimation procedure**

This section provides a detailed explanation of the empirical procedure for estimating the leading factor, the “Google Mobility Index”, and extending back the resultant index by using air quality-related information with the overall objective of nowcasting the effects of COVID-19 on the industrial production growth rates. Because _T_ and _N_ dimensions are small for consistency of Principal Component Analysis (PCA) or the standard Kalman Filter methods, the econometric procedure relies on the two-step approach introduced by Doz et al. (2012) or typically known as the “quasi-maximum likelihood approach ”, where asymptotic properties perform significantly better for small _T, N_ when compared to standard methods. In addition, the construction of one single Google leading indicator requires that _r_ = 1.

## **4.1 The two-step approach for estimating the leading factor**

The first stage proceeds to obtain consistent estimates of the parameters Ωand Λ for estimating the unobservable factor, _ft_ , using Maximum Likelihood approaches. Specifically, the first stage uses Principal Component Analysis (PCA), while the second stage involves the Kalman 5
period.<sup>1</sup> In the 2018 academic year, of the 272,261 students enrolled in public and private universities in Tunisia, 63 percent were women.

Many Tunisian women who finish their tertiary education join the labor market. As noted, the labor force participation rate for this group of women was 58 percent in 2014. Tertiary-educated men joined in comparable numbers, albeit at a higher rate of 65 percent.

However, even for those entering the labor market there is a substantial gender gap, which is the focus of this study. Once in the labor market, both groups do not have equal job opportunities. For example, in 2014 the unemployment rate for university-educated women reached 39 percent, whereas it was 22 percent for their male counterparts. The discrepancy is even starker when looking at female unemployment in rural areas and by age in 2014: 32 percent of active women between the ages of 25 and 34 are unemployed versus 15 percent of men in that age group. In rural areas, 21 percent of women are unemployed, compared with 10 percent of men. According to data from the National Institute of Statistics (Institut National de la Statistique), women accounted for around 25 percent of employment in 2013, down from 26.5 percent in 2005 (Mouelhi and Goaied 2017). Therefore, although unemployment among the young and university-educated is stubbornly high—and remains higher than that for other groups such as older workers and those with lower education—women remain more disadvantaged than men (figure 3).

# **Figure 3 Unemployment rate by gender, age, and education: Tunisia, 2014**

> 1 World Bank, World Development Indicators (WDI), 2019, https://databank.worldbank.org/source/worlddevelopment-indicators.

8
population groups over time. To make comparisons across countries in terms of education level, we define low education as primary or less in LICs and LMICs, and secondary or less in UMICs and HICs.

The estimates from the expanded HFPS sample confirm that the initial impacts of the pandemic on employment were more pronounced for women, urban workers, and those with lower levels of education. For instance, there was proportionally greater employment loss among women relative to prepandemic levels, and this gap persists into 2021 (Table 3). Work – – stoppages the main component of net employment changes decreased over time, but the rate of decrease tended to be lower among women, who report a higher incidence of work stoppages in the later part of the pandemic (Table A6.2). For a number of countries, particularly in the LAC region, women experienced larger proportionate declines in employment 15
STEP survey capture data on _computer use at work_ , which we use as a second measure of cognitive skills for this study. This variable provides the frequency and complexity of computer use on the job and was selected to help determine the extent to which these skills are being rewarded in the labor market. Respondents report the frequency with which they use computers at work—never, a couple of times a week, or more than three times per week—followed by a description of the computerrelated tasks required in their job. Four levels of computer use are defined: level 1 includes browser-based tasks (such as use of email and the internet), level 2 includes basic Microsoft Office functions (such as word processing and graphics), level 3 includes basic programming (such as spreadsheets or databases), and level 4 includes advanced programming tasks (such

> 8 See Pierre et al. (2014), p. 83, for a detailed description of the assessment and levels of proficiency.

8
buildups and other spatial information has been kindly provided by UNOCHA.

**Population and Establishment Census.** Access to the Establishment Census has been negotiated by the author during several visits to the West Bank. The two Census datasets contain data on population, employment, and establishment. The data used from the Establishment Census include jobs in non-farming private establishments, disaggregated by economic sector (ISIC4) and village. The analysis is restricted to establishments in operation at the moment of the data collection, with national or foreign ownership. Activities not classified as establishments or jobs in the public sector are not included in the job count.<sup>22</sup>

**Mobility restrictions.** The data on the mobility restrictions, the Separation Wall, and the location of settlements and military areas come from the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA). Data are available between 2003 and 2017 at a yearly frequency with gaps. UNOCHA also kindly provided geo-referenced data on the road network. Each road segment is classified as regional, primary, secondary, tertiary, residential, or track. UNOCHA also collects data on the road regime, and it classifies roads as either restricted for Israeli cars or unrestricted. The Applied Research Institute Jerusalem (ARIJ) provided information on the location of mobility restrictions at a yearly frequency since 1995.

> 21PCBS Population Housing and Establishment Census 2007.

> 22The data exclude clusters (industry grouping * village) where the number of establishments was one or two. Moreover, between the two time periods, the ISIC classification changed. Some sectors have been merged, others have been disjointed. Economic sectors are reclassified based on the ISIC Revision 4.

13
Policy Research Working Paper 8037

# **Abstract**

Like many emerging economies, South Africa has identified exports as an engine for more inclusive, job-intensive growth. However, employment growth did not follow the substantial export growth that South Africa experienced in the 2000s. This paper uses a newly developed World Bank database—the Labor Content of Exports—to show that the composition of South Africa’s export growth helps to understand the weak relationship between export and employment growth. Minerals exports, which propelled export as well as wage growth, are not job intensive and as a result supported far less job growth. Minerals have also increasingly become an enclave sector with few backward linkages to the domestic economy. In contrast, manufacturing exports support jobs and wages primarily in input-providing sectors, where indirect manufacturing employment is nearly 4.5 times greater than direct manufacturing employment. The paper also documents a shift in the labor content of global value chain–intensive manufacturing sectors away from direct manufacturing to indirect services. Such a shift has been biased toward skilled labor. As a results of these trends, labor in services sectors has been the main beneficiary of South Africa’s export growth, absorbing more than half of the growth in wage income from exports over the 2000s, primarily by supplying inputs to other sectors’ exports.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at chollweg@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
investments, when they connect safe areas to the opportunities and amenities that currently exist in risky areas.

Providing a strong and holistic risk management framework in a country, region, or city makes it possible for all actors to take the risks that are desirable, avoiding excessive risk-taking without constraining growth and development. It also makes it possible to deal with the rare but unavoidable cases where a physical hazard is so violent that it exceeds protection capabilities and causes large losses. In other words, the same DRM policy which reduces welfare losses from a disaster can also provide benefits even before a disaster strikes.

# **6. References**

Abreha, N.H., 2007. An economic analysis of farmers’ risk attitudes and farm households’ responses to rainfall risk in Tigray Northern Ethiopia (PhD Thesis). Wageningen University.

- Aghion, P., Blundell, R., Griffith, R., Howitt, P., Prantl, S., 2004. Entry and Productivity Growth: Evidence from Microlevel Panel Data. Journal of the European Economic Association 2, 265–276. doi:10.1162/154247604323067970

- Aghion, P., Howitt, P., 1992. A Model of Growth Through Creative Destruction. Econometrica 60, 323– 351. doi:10.2307/2951599

- Ahsan, D.A., 2014. Does natural disaster influence people׳s risk preference and trust? An experiment from cyclone prone coast of Bangladesh. International Journal of Disaster Risk Reduction 9, 48– 57. doi:10.1016/j.ijdrr.2014.02.005

- Albala-Bertrand, J.M., 1993. Political economy of large natural disasters: with special reference to

- developing countries. Clarendon Press ; Oxford University Press, Oxford, [England] : New York.

- Alderman, H., Hoddinott, J., Kinsey, B., 2006. Long term consequences of early childhood malnutrition. Oxf. Econ. Pap. 58,
.17|1.03|1.42|1.22|
|SD|20|20.4|16.6|20.4|20.4|19.7|.|.|.|.|
|Shares|100|76.4|23.5||||.|.|.|.|
|**University**|||||||||||
|Mean|51.1|55.2|34|53|55.2|43.9|1.29|1.04|1.62|1.26|
|SD|31.5|31.7|23.5|31.7|31.7|29.9|.|.|.|.|
|Shares|100|80.1|19.9||||.|.|.|.|
|**Total**|||||||||||
|Mean|32|35.9|22.8|33.1|35.9|26.6|1.17|1.04|1.58|1.35|
|SD|23|24.7|14.8|23.4|24.7|18.5|.|.|.|.|
|Shares|100|69.7|30.3||||.|.|.|.|

_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note: Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers
small versus medium and larger firms.

_Six_ , as the COVID-19 pandemic evolved, governments have expanded the support for businesses. Interestingly, the share of supported firms increased the most in countries that suffered the largest severe drop in mobility. Countries in the low and lower middle income group, as well as those with higher pre-pandemic public debt have been more constrained in their ability to offer support to firms.

Our work makes several contributions to the burgeoning literature studying the impact of the pandemic on firms. _First_ , while the existing studies examine the direct disruptive effects of the pandemic primarily in advanced countries (e.g., Bloom et al., 2021; OECD, 2021; Bartik et al., 2020), our work provides extensive evidence for a large sample of firms in developing countries. _Second_ , barring a few exceptions, most evidence so far has been focused on a cross-section of firms (e.g., Borino et al., 2021; Apedo-Amah et al., 2020). Our work provides a detailed account of the recovery patterns across a range of firm outcomes using two rounds of BPS data with nearly 25,000 panel firms, filling a critical knowledge gap on the recovery process. _Three_ , our work provides a timely assessment on the heterogeneity of recovery by firm and country attributes and the gaps in adjustment response among developing countries. This could potentially help inform the type of firm support interventions. _Four_ , jobs dynamics are fundamental in the context of an analysis of the recovery as crises tend to have a long-lasting impact on employment.<sup>4</sup> To the best of our knowledge, our work is the first to provide firm-level evidence on the asymmetry of employment recovery.

# **2. Related Literature**

Our study is linked to the strand of the literature aiming to understand the impact of COVID-19 on the performance and economic decisions of incumbent firms over time. The assessment of the impact of the pandemic on firm performance, particularly SMEs, has been
-|---|---|---|---|---|---|---|---|
|Total container cargo handled|TEU,annual|460,000|158,201|331,191|500,119|420,00<br>0|336,30<br>8|7,900|
|Container dwell time|Days|13|12|7|12|25|42|30|
|Truck turnaround time<br>|Hours|4|6|5|2.5|8|6|24|
|General-cargo vessel pre-berth<br>waitingtime|Hours||48|24|2.9|9.6|36|38.4|
|General-cargo vessel turnaround<br>time|Hours|—|48|60|2.2|48|40.8|45.6|
|Craneproductivity|Containersper hour|—|—|—|18|13|12|—|
|Craneproductivity|Tonnesper hour|23|15|—|16|14|9|8|
|Container-cargo-handlingcharge|$ per TEU|220|180|160|260|168|155|—|
|General-cargo-handlingcharge|$ per tonne|9|9|15|14|10|8|8|
|Bulk dry-handlingcharge|$ per tonne|5|5|5|5|3|—|—|
|Adequate road accesspresent|0=no;1=yes|0|0|0|—|0|0|0|
|Landlord model is used|0=no;1=yes|1|0|0|1|1|1|1|

_Source:_ AICD ports database (www.infrastructureafrica.org/aicd/tools/data); data as of 2006. _Note:_ TEU = 20-foot equivalent unit. — = Not
has helped establish such school clinics, and given the apparent effects that VHI has had on anthropometric measures of health status among younger children, this is a cause for concern.

_Table 7: Double-difference estimates of VHI impact on contact probability among sick_

|Age in<br>1992/93|Treatment<br>effects|z-stat|F-test consumption<br>quintile differences|Prob> F|
|---|---|---|---|---|
|0-4|0.0389|0.95|F(4, 432) = 2.28|0.060|
|5+|0.0473|3.41|||
|All|0.0433|3.38|||

We can get richer utilization information by focusing on the 1997-98 survey. In so doing, we are forced to use the single-difference matching estimator, and are therefore unable to eliminate any biases introduced by selection on time-invariant unobservables as we were able to with the double-difference approach. Table 8 reports results separately for under-fives and individuals over the age of five. For both age groups, VHI appears to stimulate use of commune health centers (CHCs). Among the under-fives, VHI appears also to encourage visits to pharmacists to obtain prescription medicines, and to reduce visits to pharmacists to obtain advice and non-prescribed medicines. Among individuals aged five and over, but not among young children, VHI encourages the use of hospital care—including inpatient care—and to a much lesser extent the use of polyclinics. With the exception of visits to CHCs, impacts do not differ significantly across the consumption distribution; VHI only has a significant impact on the use of CHCs among the poor. These results are broadly similar to those reported by Trivedi (2003), though he did not report separate results for children and adults, or for the two different types of pharmacy visit. He also found VHI increased utilization of CHC and hospital care, and decreased pharmacy
sector. If foreign enterprises are elastic on their Cambodian garment labor demand at higher levels of minimum wages, the impacts of future hikes could lower garment employment. The evidence in this study shows that, at least at the current levels of minimum wages, the elasticity does not seem to be too high.

# References

- **Addison, John T, McKinley L Blackburn, and Chad D Cotti.** 2012. “The effect of minimum wages on labour market outcomes: County-level estimates from the restaurantand-bar sector.” _British Journal of Industrial Relations_ , 50(3): 412–435.

- **Bell, Linda A.** 1997. “The impact of minimum wages in Mexico and Colombia.” _Journal of Labor Economics_ , 15(S3): S102–S135.

- **Boeri, Tito, Pietro Garibaldi, and Marta Ribeiro.** 2011. “The lighthouse effect and beyond.” _Review of income and Wealth_ , 57: S54–S78.
5 preparation, combined with chemical inputs, and (d) a modern production package (MP) involving the use of a tractor and chemical inputs.

Employing a multinomial treatment effect model, the findings reveal that intensification increases land and labor productivity, especially when agro-chemicals and mechanized land preparation are combined. Returns to labor double to triple, inducing specialization and greater market orientation as well as greater food security, while productively releasing agricultural labor for other activities. Labor in agriculture becomes more waged. The gender balance remains the same. Child labor input does not decrease.

The paper proceeds as follows. Section 2 lays out the materials and metrics used, i.e., the sampling procedure and the data collected, the production packages applied in the study zone, and the outcome variables studied. Section 3 presents the econometric strategy. Section 4 discusses the findings. Section 5 concludes.

# **2 Materials and metrics**

- 2.1 Information base and indicators

The empirical application uses cross-sectional baseline data from rice growing households collected under a World Bank pilot study on smallholder inclusion in the rice sector in Côte d’Ivoire. Based on the prevalence of smallholder rice production in the regions, the regions’ agroecological diversity, and the presence of a medium-sized mill that could develop into an engine for inclusive rice value chain development through rice contract farming, three regions were purposively selected: Poro, Tchologo and Tonkpi. Within a 25 km buffer zone of the rice mill in
in which individuals and firms interact in markets is conditioned by the nature of non-market institutions – formal and informal rules and norms of behavior, and the agencies that enforce them. Among the most important roles of these non-market institutions are the definition and enforcement of property rights and contracts. People will not invest if property rights are not well defined and enforced, or if they believe that the contracts they write will not be honored. The state must also provide a whole set of other inputs apart from social order and fair contract enforcement. These include various types of public services and regulations. Lying behind well-functioning markets are legal systems, judges, policemen, and, ultimately, social groups and politicians.

But institutions, like policies, are not designed by a benevolent dictator. They evolve over time in response to the actions of individuals and groups who seek to protect their own interests. It follows that institutions – again just like policies – need not be optimal from a social viewpoint. It is perfectly possible that the institutions that are best-suited to the short-term interests of a particular group in a particular generation are not those most conducive to broad-based economic growth and development. If that particular group

> 11 Mazumder’s estimate is considerably higher than previous estimates (of around 0.4). See, e.g. Solon (1992). The difference arises mainly from the author’s use of a long-term social security earnings history of fathers and children, which allows him to reduce the variance of the transitory component of incomes inherent in previous measures of father’s economic status, usually drawn from the Panel Study of Income Dynamics (PSID) or from the National Longitudinal Surveys (NLS).
that Bogotá had already started a district road-building plan, the program was also justified as a mechanism to counteract future congestion.

PYP has been enforced through the imposition of fines and vehicle immobilization. Since the moderate phase of the program, the annual number of fines has decreased by, on average, 40 percent, and hence this infraction has moved from second to fifth place on the list of the most frequent traffic fines imposed by the Metropolitan Transit Police (see Secretaría de Movilidad 2010). These facts have been used by the authorities to argue drivers’ progressive voluntary acceptance of and compliance with PYP.<sup>9</sup>

# II. SELECTION OF CAR USE AND POLLUTION INDICATOR

Traffic counts may be considered a direct measure of vehicle use for evaluating driving restriction programs such as PYP. However, as such data is only available for the drastic phase of PYP and then only for certain parts of the city and days of the year, it is not possible to construct a continuous time series pre- and postimplementation based on traffic counts.<sup>10</sup> Thus, instead of traffic counts, CO concentration is used as a proxy for car use. CO has several advantages compared with other possible proxies (Gallego, Montero, and Salas 2013). First, CO is mainly emitted by traffic (85–98 percent of total CO

> 9. The decline in fines may have other potential explanations as well, e.g., weak monitoring or drivers gradually learning how to circumvent the policy. Yet several years into PYP, weak monitoring appears unlikely since the police had reasonably improved their control procedures. Paying bribes to police officers is also unlikely since a driver may have to pay several bribes in a single trip, making it very costly.

> 10. See Gallego, Montero, and Salas (2013) for a brief discussion on the use of traffic counts to evaluate car use. Certainly, vehicle speed is also an alternative measure to analyze congestion. However, there