rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5789

# **Abstract**

In a crisis, do employers place the burden of adjustment disproportionately on female employees? Relying on household and labor force data, existing studies of the distributional impact of crises have not been able to address this question. This paper uses Indonesia’s census of manufacturing firms to analyze employer responses and to identify mechanisms by which gender differences in impact may arise, notably differential treatment of men and women within firms as well as gender sorting _across_ firms that varied in their exposure to the crisis.

On average, women experienced higher job losses than their male colleagues _within_ the same firm. However, the aggregate adverse effect of such differential treatment was more than offset by women being disproportionately employed in firms hit relatively less hard by the crisis. The null hypothesis that there were no gender differences in wage adjustment is not rejected. Analyzing how employer characteristics impact labor market adjustment patterns contributes to the understanding of who is vulnerable in volatile times.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group; and the Chief Economist’s Office, Financial and Private Sector Development Network. 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 author may be contacted at mhallward@ 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
here and are available upon request. 39 Because DHS surveys have no information on household income or expenditure, I calculate the Multidimensional Poverty Index (MPI) following United Nations Development Programme guidelines for the international version of the index. Colombia has a local version (Angulo, Díaz, and Pardo 2013), but some of the indicators are not available in DHS. Here I use the household deprivation score, without capping it for households below the poverty threshold.

> 40 The specification also includes survey round fixed-effects.

> 41 The share is similar for the whole sample of children (including those without birthweight report) or when using only observations with the complete set of covariates.

> 42 There are also a few women who migrated within the municipality and these are included in the sample; 0.73% of them moved because of violence.

> 43 There are no statistically significant differences in birthweight, and only a very small one in the share of low birthweight when comparing the two reporting methods.

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# **Table A.8. Predicted Mean (Log) Consumption Based on Imputation**

|||||**Se**|**cond rou**|**nd**|||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Model**<br>**1**|**Model**<br>**2**|**Model**<br>**3**|**Model**<br>**4**|**Model**<br>**5**|**Model**<br>**6**|**Model**<br>**7**|**Model**<br>**8**|**Model**<br>**9**|**True rates**|
|Bldh f 2015 2018/19|10.7|10.8|**10.8**|10.8|10.8|10.9|10.8|N/A|N/A|10.8|
|angaes, rom to|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|||(0.0)|
||12.1|12.1|**12.0**|12.1|12.1|12.1|12.1|12.2|12.1|12.
****|**0.319*****|**0.375*****|
|**Has RCA**||**0.125**|**0.00621**|**0.0287**|
|**Firm div. (products)**|||**-0.615*****|**-0.264****|
|**Firm div. (destinations)**|||**0.385*****|**0.387*****|
|**Firm div. (P-D)**||||**-0.224***|
|**Firm div. (sectors)**||||**-0.439*****|
|**N**|**5042**|**5038**|**2858**|**2858**|
||*** p<0.10**|**** p<0.05**|***** p<0.01**||

_Source:_ Authors‟ calculation based on Lao PDR Customs data The inclusion of the diversification effects in the survival analysis (Table 7) produces only two significant coefficients, both negative. Exporting many products hurts a firm‟s chances of sustaining its export links past the first year. The specification including all factors (5) reduces the level of significance, indicating a possible endogeneity among our diversity proxies, but the coefficient for the number of product-destination pairs remains negative and significant. In effect, this proxy is presumably closest to our notion of lack of focus – equivalent to a firm that is basically “shooting in the dark” and trying out many such combinations with high failure
# **Figure 1: Real Commodity Price Indices**

<!-- Start of picture text --> Index, constant terms, 2010 = 100<br> 160<br>Agriculture<br> 120<br> 80<br>Metals<br> 40<br>Energy<br> ‐<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br>Source : World Bank<br>Note : Prices have been deflated by the Manufacture Unit Value index.<br><!-- End of picture text -->

# **Figure 2: Nominal Commodity Price Indices**

<!-- Start of picture text --> Index, nominal terms, 2010 = 100<br>180<br>Energy<br>140<br>100<br>Metals<br>60 Agriculture<br>20<br>Jan‐00 Jul‐01 Jan‐03 Jul‐04 Jan‐06 Jul‐07 Jan‐09 Jul‐10 Jan‐12 Jul‐13 Jan‐15<br>Source : World Bank<br>Note : Last observation is December 2015.<br><!-- End of picture text --> _14_
taken from the 1994 survey instrument. Later surveys expanded the listed categories to include additional ownership categories that do not overlap with the primary male/female proprietary categories used in this work. There have been recent efforts to better measure and classify home-based work (Chen 2012), and it is possible that some household-based workers previously considered business owners may be reclassified as wage workers in future surveys. Adjustments during our sample period are very small, if present at all, and would apply uniformly to states in a way that would not bias a differences-indifferences strategy.

9 The sampling frame for the NSSO is comprised of two frames: the list frame and the area frame. The list frame is used only among urban areas, and is comprised of approximately 8,000 larger unorganized manufacturing units on the basis of census data for manufacturing enterprises. These plants meet one of three detailed size criteria (available from the authors). All units in the list frame are surveyed, and these units are excluded from the list of enterprises in the area frame. The area frame is applied to both urban and rural areas. Village lists from the census form the frame for rural areas, and the frame for urban areas is taken from the latest Urban Frame Survey blocks. The ultimate stage units are households/unorganized-manufacturing enterprises in both areas, as available from the most recent economic census.

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higher debt ratio (especially short-term debt ratio) than firms with good governance. Kirch and Terra (2012) analyze firm-level data from five South American countries and conclude that the institutional quality of a country has a significant positive effect on the level of long-term debt in a firm's financial structure. Using the ownership structures of French firms, Ben-Nasr, Boubaker, and Rouatbi (2015) find that firms with multiple large shareholders tend to have debt with shorter maturity. Awartani, Belkhir, Boubaker, and Maghyereh (2016) show that stronger rule of law, better regulatory effectiveness, better legal protection of creditors, and more developed financial intermediaries are associated with greater use of long-term borrowing by Middle East and North African firms. In contrast with these region- and country-specific studies, our study exploits the richness of international data to investigate the role of cross-country variation in institutional features that mediate the role of governance on debt maturity.

<mark>Our paper contributes to the three strands of the literature, by showing that the governancedebt maturity relationship depends on the larger institutional environment and in particular the strength of investor protection. By examining cross-country differences in legal origin, we are able to address some of the endogeneity concerns associated with examining debt maturity and governance. The rest of the paper is organized as follows. The next section describes the data and methodology. Section 3 presents the empirical results, including baseline regressions that show the effect of governance on short-term debt, the role of investor protection on the relationship between governance and debt maturity, instrumental variables regressions that are aimed to address endogeneity concerns and a number of robustness tests for the alternative definitions of the governance variables. Section 4 concludes the paper.</mark>

# **2. Data**

This section provides detailed information about the sample of firms, data sources and how we construct our variables. Our discussion starts with sample selection. Then, we present the construction of the governance and board measures used in our study, as well as the measures of
from others that would also be consistent with this fact and yet point to other explanations that can potentially lead to contradictory policy advice. Therefore, in this paper we try to disentangle the precise way in which asset wealth matters for adoption.

The data, which were collected by the authors in 2002 and 2004, cover all 65 boatowning fishermen of a fishing village where the first fibre boats appeared in 2000. They include a census as well as time series of individual fish sales. These unique data allow us to discard the view that delayed adoption by poor households is due to the smaller scale of their entreprise as each individual operates the same fibre boat. In addition, aversion to income fluctuations is not likely to be important as there is evidence of informal insurance arrangements among boat owners (Platteau, 1985). Since we do find that wealthier entrepreneurs adopt earlier after controlling for individual ability, we conclude that credit constraints faced by relatively poor boatowners are important. To a lesser extent, we find evidence of aversion towards uncertainty about the gains from the new technology.

This paper is related to the work of Platteau (1984) and that carefully document the introduction of mechanized boats in Kerala. They study an example of how globalization drastically changed the rural landscape. In the words of Platteau, _“by revolutionizing the traditional techniques and methods of catching, preserving and distributing fish, the project succeeded beyond expectations in achieving a ‘revolution’ [...] that changed life in many ways.”_ This process exemplifies what Nissanke and Thorbecke (2004) have in mind when they point out that the relationship between globalization and poverty is complex and may even be non-linear as globalization affects poverty through many channels. In contrast, we can assert that the only major change from 2000 to 2004 in the village we study was the introduction of FRPs. This enables us to focus on the effects of a single channel, namely, technology diffusion.

On a more macro level, the paper is interesting because we explore the
e. primarily skin lesions) are located in areas where >80% wells are found to be As contaminated. Overlaying on our groundwater risk maps, we find that some 1,000 (3% of total patients) to 22,450 (61%) Asaffected patients are located within the extremely high to high-risk zones (Figure 5). One of the limitations of our analysis of multi-hazard based groundwater risk mapping is that the maps represent an aggregated risk of static levels of arsenic, salinity and declining groundwater storage. We employ groundwater arsenic data from the National Hydrochemical Survey that was conducted in 1998-99 with the assumption that As concentration has limited seasonal and temporal variations (Bhattacharya et al., 2011; Dhar et al., 2008) and, at the basin scale, the 11
# **APPENDIX 2** : **INVESTMENT CLIMATE INDICATORS**

# **_Start-up cost index_** :

Data come from the World Bank database “Doing Businesses”. They are based on the work of Djankov, Simeon, Rafael La Porta, Florencio, Lopez-de-Silanes and Andrei Shleifer who measured the start-up costs of a new firm registered legally as a limited liability society and owned by residents in the country. Those costs include the number of procedures one has to undertake to register the business, the time the whole process takes, the minimum capital requirements and, finally, the monetary cost of the registration process. Both the costs of undertaking the process and minimum capital requirements are measured in percentage of GNI per capita.

To construct the start-up cost index we have followed the methodology used in the annual reports of the “Economic Freedom of the World” and the “Human Development Index” among others.<sup>13</sup> . Each component (procedures, time, cost and minimum capital) has been re-scaled to be between 0 and 10. Then an unweighted average has been taken to calculate the overall indicator. There is data for Europe, the US and all ECA countries, with the exceptions of Estonia, Tajikistan and Turkmenistan.<sup>14</sup>

# **_Access to finance_**

The access to finance index is a summary of the following variables: ratio of domestic credit provided by deposit money to GDP (World development Indicators, World bank); interest rate spread and real interest rate (both from the Word Development Indicators, World Bank); ratio of deposit coverage to GDP, which is used by the IMF as a proxy to collateral<sup>15</sup> ; and the World Bank’s measure of creditors’ protection index. The later comes from the World Bank’s Doing Business database. It is an indicator of creditor rights in insolvency, based on the methodology of La Porta and others (1998)
13 comorbidity is likely to increase the probability of severe disease. To incorporate this into this study would require estimates of odds ratios for each risk factor and their interactions as well as an understanding of the correlation between risk factors as these are likely to not be independent. For example, obesity has been linked to hypertension (Aneja, 2004). What the model seeks to do is provide a framework into which more data on risk factors can be added as and when it becomes available.

Fourth, we make the assumption that the impact of improvements in access to handwashing on prevalence of respiratory illness occurs over a timescale relevant to the pandemic. While previous research from Kenya suggests that the impact can be observed 2-9 months after intervention (Hoekstra _et al._ , 2012), if this timescale is longer in Zimbabwe, or if the improvements themselves take a long time to be implemented, we may not see the desired effects. One potential way to maximize impact would be to prioritize districts for improved handwashing according to their current handwashing risk. Intervention impacts could be maximized by targeting districts or communities with highest handwashing risk first. Outputs from ongoing spatially discrete agentbased modeling, informed by human movement data, could also be used to identify those districts most vulnerable to transmission.

Finally, this paper has only focused on underlying risk factors in the population and has not considered risk of transmission due to e.g. population mobility. The outputs of this work will likely therefore be used as part of a larger agent-based modeling exercise to identify those districts with highest probability of severe disease when transmission is also taken into account, in a separate, forthcoming paper.

In conclusion, this study shows the spatial disparity in severe COVID-19 risk across Zimbabwe. Second, it shows that there is a relationship between access to handwashing facilities and respiratory illness, a known risk factor for severe COVID-19. In addition to any direct effects on transmission of SARS-CoV-2, improving access to handwashing may therefore reduce the probability of severe disease among the population which could
discussed above while reviewing the literature on competition (between formal firms) and R&D activity. Thus, the issue needs to be resolved empirically.

A rigorous empirical analysis of the impact of informal competition on R&D activity is very limited or non-existent. Perry et al. (2007) argue that informality can have negative effects on formal firms’ investment and innovation decisions because it reduces their market share and profitability. However, they do not provide any empirical evidence to support this claim. Mendi and Costamagna (2017) use Enterprise Surveys data to estimate the impact of informal competition on the likelihood of innovation among formal firms. However, the study uses formal firms’ perceptions of the informal sector as an obstacle for their operations as a measure of informal competition faced by the formal firms rather than the actual experience with informal competition. Further, the study is restricted to firms in Africa and Latin America.

The impact of informal competition on other aspects of formal firms’ performance has been discussed in the literature. In an early attempt, Tokman (1978) finds that in the city of Santiago, informal foodstuffs commercial establishments can successfully compete with formal sector counterparts (modern supermarkets). Gonzales and Lamanna (2007) analyze firm-level survey data on formal manufacturing firms collected by the World Bank’s Enterprise Surveys for 14 countries in Latin America. Their findings suggest that about 40 percent of the firms in the region face significant informal competition, with sizeable variation across industries and firm sizes. Using Enterprise Surveys data for Nicaragua, Pisani (2015) explores the firm characteristics that determine the likelihood of formal firms to face informal competition. To reiterate, none of these studies assesses the impact of informal competition on formal firms’ R&D effort.

La Porta and Shleifer (2008) use Enterprise Surveys data to examine the expected effects of informality on the formal sector firms. However, the authors do not investigate the impact on 4
pursued fiscal consolidation more forcefully if there had been more awareness about the risk of default (Cole and Kehoe, 1996). The Theory index would then have picked up if Mexico was getting close to the default zone and would have coded it as a value of 3.

Figure 12 below illustrates an output for a hypothetical country given a set of calibrated parameters. In this chart, the y-axis expresses the future debt-to-GDP ratio, while the x-axis the current debt-to-GDP ratio. The red line is a 45-degree line. Any value above the red line suggests that government’s optimal policy (black line) is to increase the debt-to-GDP ratio. On the contrary, any value below the red line implies an optimal policy of reducing future debt-to-GDP ratios. In this scenario, the lower threshold is about 70 percent of GDP and it divides the safe zone from the crisis zone. The upper threshold is about 90 percent of GDP and it divides the crisis zone from the default zone. Above this level, debt drops to a value of zero given that the country’s optimal decision is to default even if investors were willing to refinance the stock of debt. Hence, investors will not lend to this country and the future debt level drops to a value of zero.

Finally, once we compute the time varying probability of a panic, we can express the thresholds also as a varying function of time. In Figure 13, we illustrate this for South Africa and Argentina in the sample using a simple 1 period maturity. The upper and risky thresholds change over the sample period. Argentina breaches the threshold by 2018, while South Africa is reaching the upper threshold as it accumulates more debt. Interestingly, the

> 10Due to computational restrictions, we have created a mapping between the theoretical model’s probability of panic and the subjective probability. The probability of panic is 0.4 times the subjective probability.

> 11We obtained maturity rates on non-concessional borrowing for 42 countries from 5 regions using Bloomberg. To increase the sample size to
by 14 percentage points. The other countries where the effect of being male significantly reduces the wealth gap are Egypt, India, Morocco, Mozambique and Pakistan. Typically though, the wealth gap is not mitigated by being male.

The results from Table 8 are derived from a pooled sample of urban and rural households, and include a dummy variable equal to one for urban residence. As discussed in which to take this average, are included. In those cases, the average is set to zero.

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We have defined country-level governance as the exercise of authority through formal and informal traditions and institutions for the common good, thus encompassing: (1) the process of selecting, monitoring, and replacing governments; (2) the capacity to formulate and implement sound policies and deliver public services, and (3) the respect of citizens and the state for the institutions that govern economic and social interactions among them. The three dimensions in this definition of governance are then further unbundled to comprise two measurable concepts per dimension, for a total of six components: (1) _voice and external accountability_ (i.e., the government’s preparedness to be externally accountable through citizen feedback and democratic institutions, and a competitive press); and also (2) _political stability and lack of violence, crime, and terrorism_ . Then (3) _government effectiveness_ (including quality of policymaking, bureaucracy, and public service delivery); (4) _lack of regulatory burden_ ; and, finally, (5) _rule of law_ (protection of property rights, judiciary independence, and so on); and (6) _control of corruption_ . These components are subject to empirical measurement at the country level, for which a worldwide dataset has been constructed.<sup>8</sup>

# **3.2 Defining Urban Governance**

Despite the fewer numbers of citations that can be found in the literature search which give access to specific empirical data sets that are comparable, there is no lack of citations on the definition and use of the term “urban governance”. In simple terms, UNESCO defines urban governance as the processes that steer and take into account the various links between stakeholders, local authorities and citizens. It involves bottom-up and top-down strategies to favor active participation of communities concerned, negotiation among actors, transparent decision-making mechanisms, and innovation in strategies of urban management policies.<sup>9</sup> More comprehensively, one can find definitions of urban governance, as well as descriptions