rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9414

# **Abstract**

This study highlights how COVID-19 has affected small and medium enterprises, drawing on newly released World Bank Enterprise Surveys in 13 countries. The study shows that firms of all sizes are severely affected in multiple dimensions; however, firm size matters for the intensity of the different channels of transmission and firms’ responses. Small and medium enterprise sales shrink by more and their cash drains faster than large firms in the same sector and country. Among them, faster growing firms experience the demand shock somewhat less severely, but they are more exposed to international trade disruption, supply, and finance shocks. Yet, a range of firm responses to the downturn seem to be out of reach. Fewer small and medium-size enterprises, for example, start remote work, leaving their workers exposed to health risks. To make it through the pandemic, the majority of smaller firms do not turn to banks for loans; they need grants. Although development finance is not enough to fill the financing gap, development - finance institutions are relevant—in investment mobiliza tion, demonstration, and know-how—as economies move toward recovery and rebuilding. Delivering these requires rapid efforts to build partnerships and gather information in places where development finance has been limited in the past.

This paper is a product of the Economics and Private Sector Development Vice Presidency, International Finance Corporation. 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 iadian@ifc.org, ddoumbia@ifc.org, ngregory@ifc.org, aragoussis@ifc.org, areddy1@ifc.org, and jtimmis@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
<!-- Start of picture text --> Figure 2<br>Evolution of occupational structure, EU-15<br>1995 2000 2005 2010 2013<br>Occupation by task intensity<br>Routine (ISCO 88) Routine (ISCO 08)<br>Non-Routine Manual (ISCO 88) Non-Routine Manual (ISCO 08)<br>Non-Routine Cognitive (ISCO 88) Non-Routine Cognitive (ISCO 08)<br><!-- End of picture text --> Note: this figure shows the evolution of the occupational structure of wage employment of the aggregated EU‐15 countries (excluding Finland, Germany and Sweden) from 1995 to 2013. Occupations are classified according to their task intensity – See Appendix 2 for a detailed description of the classification. The red line indicates the share of wage employment in occupations intensive in routine tasks; the blue line indicates the share of wage employment in occupations intensive in non‐routine, manual tasks and the green line indicates the share of wage employment in occupations intensive in non‐routine, cognitive tasks. The source is the annual EU Labor Force Survey. Because of a change in the classification of occupations used by the survey (From ISCO 88 to ISCO 08) there is a break in the data after 2011.

Our decomposition method follows Bourguignon and Ferreira (2005). We first estimate an occupational choice model linking individual characteristics to occupations, and a set of Mincer equations for each type of occupation and worker, for the initial and final year of a period running from the early 1990s to 2013. We then carry out a set of counterfactual simulations, whereby we alternatively switch parameters and variables’ values in the final year with those of the initial year to assess the importance of each factor. Each counterfactual creates a simulated distribution of wages, which is “in between” the initial and the final distribution. In this way, the full change between the initial and final distribution, in terms of inequality or other indicators, can be decomposed in various parts.

More specifically, the decomposition technique assumes that the earnings distribution changes because of the shifts in the structure of occupations and because of the changes in the returns paid to specific occupations and workers. In addition, the structure of occupation changes because of variations in the characteristics of individual workers (education, age, gender amongst other) and the “utility” returns paid to them. Thus, to isolate the inequality impact of the polarization of the occupations, we proceed in stages.

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In contrast to this agnosticism, following an exhaustive review of the evidence on trade liberalization and poverty, Winters et al. (2004, p. 108) conclude that trade liberalization might be the easiest poverty-alleviating reform to accomplish, and the most powerful direct mechanism to alleviate poverty in a country. If so, knowing more about the links between trade policy and inequality is important since, from a political-economy perspective, knowledge about the links between openness and inequality will inform about the feasibility of policies that increase openness and are likely to reduce poverty.

We bring new evidence on this issue using two data sets covering a larger sample of developing countries than most previous studies. We introduce fixed-effects (FE) so that identification of the effects of globalization is confined to variations in that country’s variables. We also broaden as much as possible the range of control variables to address endogeneity concerns (i.e. controlled for having another omitted variable jointly determining inequality and the righthand-side variables). In this set up, we find rather consistently that trade liberalization is associated with increases in inequality. Second, unlike most previous studies, we find that endowments matter along the lines suggested by the standard HO theory arguments reviewed in section 2. We find consistently that trade liberalization increases inequality in countries that are relatively well-endowed with capital and with highly skilled workers while it decreases inequality in countries relatively well-endowed in primary educated (unskilled) workers. On the other hand, as suggested by Wood (1994, 2002), we find that trade liberalization increases inequality in countries relatively well-endowed with workers lacking basic education.

The paper is organized as follows. Section 2 discusses the main channels linking openness and trade liberalization to 4
Section J provides concluding remarks.

# **2 Background**

In India, limited infrastructure and transport services restrict mobility for both men and women. Still, women frequently experience extra socio-cultural and economic factors that negatively affect their commute patterns (Srinivasan and Rogers, 2005; Tripathi et al., 2017; Alam et al., 2021). Given the sizeable gender wage gap (Duraisamy and Duraisamy, 2016; Deshpande et al., 2018) and the additional barriers in accessing the financial system compared to men (Khera, 2018), paid access to transportation is plausibly harder for Indian women. Besides, the low rate of female usage of public transport might raise a perception problem since more female presence in public transportation makes women feel safer (Sajjad et al., 2017). In a survey of 3,800 students at Delhi University, Borker (2021) found that women are willing to travel 27 minutes more per day or 40 % more than their daily travel time if they can use a perceived safer transport route. These factors put women at a disadvantage regarding access to transport services and infrastructure (Astrop et al., 1996; Dominguez Gonzalez et al., 2020), potentially affecting their participation in labor markets (Patacchini and Zenou, 2005; ILO, 2017; Sajjad et al., 2017; Martinez et al., 2020).

In light of these challenges, the Delhi government introduced a scheme offering free bus rides to all women in the city from November 2019 onward (Kejriwal, 2019). The scheme makes bus travel free for women in all Delhi Transport Corporation (DTC) and Cluster buses. On each ride, bus operators provide a pink ticket to each woman. Afterward, Delhi’s government compensates the bus operators with |10, the equivalent of $0.14 (all currency conversions use the 11/2019 exchange rate), per pink ticket ride (The Economic Times, 2019; Durai, 2021). The program showed an early response: just 20 days after the scheme’s launch, female daily ridership in DTC and cluster buses increased from 33% to 44%.<sup>5</sup>

Spurred by the good reception of the initiative in Delhi, on April 1 and May 7, 2021, Punjab and Tamil Nadu implemented free bus ride schemes for women in their states, allowing free travel in government-owned public buses.<sup>6</sup> From July 2021 to March 2022,

> 5https://shorturl.at/cAFMT, last accessed on May 13, 2024.

> 6In Punjab, these include PEPSU Road Transport Corporation (PRTC), PUNBUS, Punjab Roadways Buses, and City Bus Services, but did not include AC buses, Volvo Buses, and HVAC Buses (Express, 2021) In Tamil Nadu, the free ride scheme includes tickets for the Tamil Nadu State Transport Corporation (TNSTC) ordinary city buses.

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_PolJsh FLcal_ **_CrHA, 17_**

**_-_** effect (recall that IB+IA HCA+A-CF). Most of the change in profitabilitv is driven by the drop in the inflation bias and the increase in amortization allowances. Taking into account the indirect impac of wages we find that the full impact on Table 7 summarizes the direct and indirect impact of changes in the variables on enterprise profitability. The cumulative impact of 1990-91 changes in all the variables is a drop of 18% in HCA profitability, which is about what actually happened."<sup>7</sup> The changes in the inflation bias and amortization allowances alone would have been -15% and -18%. In other words, we can explain most or all of the 1991 collapse in profitability with these two factors alone, and with the inflation bias as the single most important factor. Conversely, the large inflation bias in 1990 was the key reason for that year's low unit labor costs and high CCA (as well as HCA) profits.

Table 7: Direct and Indirect Impact of Changes In Various Factors on Enterprise Sector profitability In 1990-91 All quantities are measured as a percentage of sales at producer prices.

<!-- Start of picture text --> ,WB -8 to -10 I<br>1-9.7 l<br>AIA -2to 0 -' l -13.1 l<br>AA -3.5 ' -12.6 * 1.43 = 18<br>ADIV -0.1<br>ATIP +0.8 I<br><!-- End of picture text --> Identifying the impact on the government budget is somewhat more involved. Calculating the direct impact on profit tax revenue of the decline in the inflation bias and the increase in amortization allowances is simple (a loss in revenues of about 34% of GDP). However, wages are taxed, and so an upward movement in wages will uoth cause profit tax revenue to fall (the indirect impact coi profit tax revenue) and wage tax revenue to rise.

Ignoring for the moment the popiwek, we can estimate the effect on tax revenues of a change in unit labor costs as follows. Let t, = tax rate on profits t2 = (combined) payroll tax rates R = total revenue from taxation of wages and profits _w_ = profits w = wage 17. This is not **a** coincidence, since our behavioral assumptior of constant (small) retained profit was roughly what was actually **observed.**
allowances which are common for agricultural workers and miners. We restrict our sample to workers to those who worked at least 30 hours per week during the past month. Our results are robust to using different wage measures: monthly, hourly, and with or without non-monetary compensation.

The LFS records the state people live in (we include Putrajaya in Selangor throughout) and their employment status (employed, unemployed or out of labor force). We aggregate the educational classification into five main categories: those with at most primary education (which includes those without any formal education), lower secondary, upper secondary (which also includes post-secondary, STPM), certificate / diploma (vocational training), and university degree and above. In addition, the LFS asks about an individual’s age, gender, marital status, and the month in which the survey took place. Our main unit of analysis is a sector (out of 23 main sectors), in a region (one of the 15 states) in a particular year, for which we calculate total employment for various education and age categories of both natives and immigrants.

# 3.2 Background

There are two types of formally registered immigrants to Malaysia: expatriates and foreign workers.<sup>18</sup> Expatriates are skilled managerial, professional, and technical workers who are able to obtain long-term visas and enjoy special privileges, such as the ability to bring their spouse. These make up only 2 percent of the total immigrant stock. The remaining 98 percent of immigrant workers only receive temporary work permits, which are valid

> 18The material in this section draws on Del Carpio et al. (2013) who provide an extensive discussion of the Malaysian immigration system.

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firms in a certain distance can also be interacted with the treatment status of the firm, to allow for spillover effects that vary by treatment status.

The sampling design also allows to estimate ANCOVA specifications, that is, to condition all impact estimates on pre-intervention outcomes. This further increases the power. ANCOVA is of greatest value when the autocorrelation of outcome measures across time is low (McKenzie 2012). Equation (1) transformed into an ANCOVA specification can be written as follows: where Yik,PRE is the pre-intervention (or baseline) level of the outcome. ANCOVA is more efficient than either the simple post estimator shown in Equation (1) or the difference-in-difference estimator, which would be another possible specification with pre- and post-intervention data (Frison and Pocock 1992; McKenzie 2012).

Depending on the nature of the outcome variable in each case and its distribution, we also use in some cases nonlinear models and where useful transform outcome variables with a simple log function or the inverse hyperbolic sine (ihs) function (Burbidge, Magee, and Robb 1988).

# **6. Results**

# _6.1 Use of grants_

Before we analyze the impacts of the intervention, we focus on the use of the two types of grants according to the entrepreneurs’ business plans they had submitted to MEBF.

**Figure 3. Planned investments and purchases (without BDSs)**

<!-- Start of picture text --> 99%<br>100%<br>90%<br>77%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30% 21%<br>20%<br>10% 2%<br>0%<br>Equipment Construction Inputs, incl. Land, especially<br>Livestock farmland<br><!-- End of picture text --> _Note_ : Equipment refers to the purchase of machines, production materials, and so on; Inputs refer to the purchase of livestock used in production (for example, donkey), livestock for breeding, and other production inputs; construction work refers to the construction of a warehouse, wall, hangar, cowshed, piggery, pond, and so on; and land refers to the acquisition of farmland, developed land, and so on. _Source:_ Business Plans.

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Mbabazi, J., Morrisey, O. and Milner, C. (2002) "Inequality, trade policy and growth: An explanation for the africa dummy", mimeo, Nottingham.

Ravallion, M. (1997), Can high-inequality developing countries escape absolute poverty?, _Economics Letters,_ **56** 51-57.

Ravallion, M. (1998), Does aggregation hide the harmful effects of inequality on growth?, _Economics Letters,_ **61** 73-77.

Thorbecke, E. and Charumilind, C. (2002), Economic inequality and its socioeconomic impact, _World Development,_ **30** (9) **,** 1477-1495.

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# **Table 5:  Robustness Tests of Table 4 Results: Alternative Specifications**

||Depen<br>|dent Variable<br>|
|---|---|---|
||(1)|(2)|
||Ln height|Years of schooling|
|**PANEL 1: OLS Cluster FE without IVs for initial schooli**|**ng/health**||
|Mother died|-0.010*|-1.019***|
||(0.006)|(0.389)|
|Father died|0.000|-0.386|
||(0.004)|(0.300)|
|Number of treatment observations mother died|52|52|
|Number of treatment observations father died|98|98|
|Number of observations|718|718|
|**PANEL 2: Cluster FE without initial schooling/health**|||
|Mother died|-0.013**|-0.866**|
||(0.006)|(0.408)|
|Father died|-0.001|-0.450|
||(0.005)|(0.315)|
|Number of treatment observations mother died|52|52|
|Number of treatment observations father died|98|98|
|Number of observations|718|718|
|**PANEL 3: Propensity Score Weighted Regression**|||
|Mother died|-0.017**|-1.142**|
||(0.009)|(0.468)|
|Number of treatment observations|46|46|
|Number of observations|383|383|
|Father died|-0.008|-0.431|
||(0.006)|(0.396)|
|Number of treatment observations|86|86|
|Number of observations|370|370|

_Notes_ : Regressions in Panel 1-2 are specified as those of table 4, but with different specifications as documented in the panel’s heading. Following Hirano et al. (2003), Panel 3 estimates average treatment effects through a weighted regression with unity weights for treatment observations and _s_ /(1 _-s_ ) weights for controls, where _s_ is the estimated propensity score. Samples were trimmed following the criteria set out in Crump et al. (2006). Depending on the specification, observations with propensity scores below 0.4-0.8 and above 0.92-0.96 were dropped. Standard errors in parentheses. * significant at 10%, ** significant at 5%, *** significant at 1 %.

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unemployed heads and dependents was clearly not sustainable. In early 2002 concerns were emerging about the projected budgetary cost of _Jefes_ . And there were serious signs (based on largely anecdotal evidence) that the program was being heavily captured by people not in most need. Administrative data provided by the Ministry of Labor based on registration records indicated that over half of _Jefes_ participants were in fact women, and probably not “heads of households.” In practice, the administrators did not check whether an applicant was really a head of household. There were also anecdotal claims that municipalities and provinces were signing up their employees to cope with the liquidity crisis, as well as claims that local civil servants were sending their wives (not in the workforce) to sign up for _Jefes._ Possibly the program’s benefits were spilling over heavily to people who were not much affected by the crisis, or had the personal resources to cope adequately. At the heart of this concern is the fact that verification of “unemployment” is problematic in Argentina, where over half of employment is in the informal sector. All that the administrators could reasonably verify with confidence was whether an applicants had a formal sector job, and so was registered as such.

Prompted by these concerns, a counterpart work requirement was introduced in early 2002, with the aim of helping to assure that the transfers reached those in greatest need.<sup>3</sup> The work requirement was not as demanding as for the _Trabajar_ program. Participants were required to do 20 hours of basic community work, training activities, school attendance or employment in a private company with a wage subsidy for six months. The municipalities (together with local NGOs) were in charge of organizing the counterpart activities. Provincial offices of the Ministry of Labor, together with municipal and provincial councils were responsible for monitoring the work activities under _Jefes_ .

> 3 As a condition for financing the program, the World Bank insisted that the vast majority (90% was the target) of _Jefes_ participants had to be doing the counterpart work.

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This stability suggests that our trends and results are not reflecting some systematic transition from a second sector. Moreover, the relative wage points for manufacturing are at the low end of the scale compared to other major activity types (comparable to agriculture/mining and substantially less than services and transportation). These earnings distributions, while not separating out owner earnings from wage workers, suggest that most of the induced entry from outside manufacturing that we are observing is likely to have come from unpaid domestic work or from shifts away from agriculture/mining. An appreciation of this margin is again helpful for evaluating potential channels behind these entry responses. See also the related analysis in Ghani, Kanbur, and O'Connell (2013).

# _Potential Channel: Reporting Bias_

Iyer et al. (2012) find that reported crimes against women increased after state-level implementations of political reservations. They credit this increase not to higher underlying crime rates, but instead greater confidence among women to report crimes. One possible channel for our findings is that a similar growth in confidence or security led women to be more likely to report their household-based establishments after the reservations were implemented.

We doubt that this type of phenomena is behind our work for two reasons. First, conversations with experts on Indian data and the NSSO surveying procedures have not raised flags about this concern. As discussed in Section 3, the structure of the NSSO sampling frames does not build upon features like tax payments or business registrations, instead being derived from the Economic Census and household enumeration. This NSSO design limits the potential for any structural response being captured by our data due to these reforms. As these firms are legally permitted to be unorganized, there is no incentive to evade surveys. We have also identified a field in the NSSO that allows the enumerator to indicate whether respondents appear to be non-responsive or evasive. While the variable is only modestly populated, we have not identified any patterns to suggest behavioral changes of female-owned establishments around the reforms.

(especially female, household-based entrepreneurs) is indeed other than their manufacturing activity. In the establishment surveys, businesses are surveyed regardless of the intensity with which the owner is involved.

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