rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Are Global Value Chains Women Friendly in Developing Countries? Evidence from Firm-Level Data**<sup>**1**</sup>

**Marize Kalliny**<sup>**2**</sup> **Chahir Zaki**<sup>**3**</sup>

## **JEL Classification :** F12, F16, F23, J16

**Keywords:** Global Value Chains, Gender, Empowerment, Firm-level, MENA

1 The authors gratefully acknowledge the financial and analytical support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs (TTLs: Nelly Elmallakh and Nazmul Chaudhury). 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. The authors are grateful to Mhamed Ben Saleh, Enrico Marvasi and the anonymous referees for constructive feedback.

2 PhD Candidate at Université Clermont Auvergne, CNRS, IRD, CERDI, F-63000 Clermont-Ferrand, France. Assistant Lecturer of Economics, Faculty of Economics and Political Science – Cairo University (on leave). Email: marize.kalliny@feps.edu.eg 3 Chaired Professor of Economics, University of Orléans and Labor d’Economie d’Orléans, Economic Research Forum and EMANES. Email: chahir.zaki@univ-orleans.fr
00497|0.00275|0.00245|0.000869|-0.00317|-0.0148**|
||(0.0140)|(0.00817)|(0.00664)|(0.00361)|(0.00938)|(0.00460)|(0.00874)|(0.00634)|
|VIX Index|-0.0519|-0.226***|0.0264|0.000584|-0.0660**|0.00189|-0.00242|-0.215***|
||(0.0473)|(0.0791)|(0.0218)|(0.0352)|(0.0308)|(0.0448)|(0.0295)|(0.0613)|
|US Economic Policy|0.00403***|0.000156|0.000600|-0.000358|0.00327***|-6.35e-05|-8.47e-05|0.000494|
|Uncertainty|(0.000991)|(0.000500)|(0.000444)|(0.000222)|(0.000626)|(0.000283)|(0.000618)|(0.000388)|
|Commodity Terms of|1.688|-0.841|0.172|0.0626|1.411|-0.419|0.163|-0.375|
|Trade|(1.478)|(0.601)|(0.658)|(0.267)|(0.931)|(0.340)|(0.922)|(0.467)|
|Oil Price|0.221***|-0.0955|-0.0290|
Appendix Table A4 compares our study sample to respondents from the nationally representative INEGI survey, both in Mexico and in Tlaxcala. In Panel A we include all farmers, while Panel B restricts the sample to rainfed farmers. Study farmers have lower yields than both the national and the Tlaxcala sample. In terms of agricultural practices, study farmers are less likely to use hybrid seeds relative to the national average (but comparable to the Tlaxcala sample) and are more likely to have used fertilizer and herbicide than either of the INEGI samples. They are also more likely to have used extension services in the past. Panel B shows similar patterns, and both panels suggests that farmers with greater experience and perhaps those interested in improved inputs were more likely to select into the study.

# **3 Experimental Design**

The five experimental arms are a combination of three components: (a) soil analysis and recommendations (individual or averaged), (b) extension services and (c) and in-kind grants (conditional or unconditional) as outlined in the introduction. To summarize:

- **T1** : _Individualized or plot-level_ soil analysis, input recommendations, extension services; inflexible in-kind grant.

- **T2** : _Averaged or cluster-level_ soil analysis, input recommendations, extension services; inflex-

- ible in-kind grant. (i.e. same as **T1** but with averaged instead of individual soil analyses and recommendations).

- **T3** : Averaged soil analysis, input recommendations, extension services; _flexible_ in-kind grant. (i.e. same as **T2** but with a flexible instead of inflexible in-kind grant).

- **T4** : Averaged soil analysis, input recommendations; extension services. (i.e. same as **T3** or **T2** but with _no in-kind grant_ ).

- **C** : No intervention. Control Arm received averaged soil analysis and recommendations the year after the
**The paper is organized as follows.** Section II assesses the adequacy of the minimum wage in Romania and checks whether the statutory minimum wage is high enough so the working poor<sup>11</sup> can afford a basic consumption basket for their families. Section III describes the data and identification of minimum wage workers in the administrative tax database and presents estimates of the share of minimum wage earners among employees and their profiles. Section IV simulates the ex-ante impact of the hypothetical minimum wage increase on the labor market. Lastly, Section V concludes.

# **II. Assessing the Gap between the Statutory Minimum Wage and Living Wages in Romania**

**Setting adequate wages is crucial for ensuring a decent living standard for workers and their families, while also maintaining the viability of the firms that employ them.** To be meaningful, minimum wages have to be set at a level that covers the needs of workers and their families **.** However, wage setting should balance both the needs of workers and economic considerations taken into account for the potential impact of the labor market and other economic indicators. If wages are set solely based on workers and families' needs, they may exceed what many businesses can afford, potentially leading to job losses, increased informality and non-compliance (especially in countries with high informality levels), reduced investment, and diminished export competitiveness. On the other hand, focusing only on economic factors like export competitiveness could result in wages that are too low, leading to high levels of working poverty, stagnant wages,  weak aggregate demand and limited ability to protect workers and their families against low pay or poverty (ILO 2016, ILO 2024). Levels should also be adjusted with inflation to maintain real purchasing power. Yearly adjustments allow for predictability and smooth adjustments, but more frequent adjustments may be required in countries with hyperinflation or accelerating inflation in specific periods. Some countries (e.g., France) allow automatic adjustments when inflation accelerates above
than downward mobility in both medium-term periods is not very different from our finding that this finding holds for the long term and mostly for the short term.

> 21 More precisely speaking, we analyze the panel of household heads, where we define heads according to the RLMS survey manual’s recommendations: (1) the oldest working-aged male in the household, (2) if no working-aged males, then the oldest working-age female, (3) if no working-age females, then the youngest retirement-age male, (4) if no retirement-age males, then the youngest retirement-age female, (5) if no retirement-age females, then the oldest child. 22 There is no established equivalence scale for Russia. Different equivalence scales are often applied in studies of poverty using the RLMS-HSE data (e.g., Lokshin _et al._ , 2000; Denisova, 2012).

27
is not the same. Information on self-employed workers is lacking on many indicators. For instance, the labor income of the self-employed is missing across most surveys, and it has not been possible to approximate labor income by using household income or consumption.

Although subsistence agriculture and self-employment are two of the largest sources of employment in low- and lower-middle-income countries and include the most vulnerable groups of workers, survey data on these labor market participants are extremely limited at best and unreliable at worst. Moreover, because self-employment and unpaid work are typically associated with low-quality jobs in developing countries, the proposed JQM can be considered a lower bound of job quality in each country, with an implicit value of zero for the quality of employment in categories other than wage employment. However, future research should expand the analysis to include nonwage workers.

Driven by the availability of data across countries, this analysis therefore focuses on four main dimensions of the quality of jobs and only one category of workers: wage workers. The four dimensions of job quality covered by the harmonization exercise are (1) sufficient income to move a family out of poverty and into the lower-middle class, (2) receiving benefits, (3) the stability of employment, and (4) adequate working conditions. This restriction to focus on a subset of dimensions because of data constraints is shared with the literature, while this paper expands the scope of countries to include all regions. Table 2 provides a summary of the four dimensions, the relevant indicators, and the definition of each indicator for the final set of survey indicators. The table shows there is a single indicator for the income dimension, six indicators for the benefits dimension, four indicators for the stability dimension, and three indicators for the working conditions dimension.

Table 2. Job Quality Dimensions and Indicators

|_Dimension_|_Indicator_|_Definition of quality_|
|---|---|---|
|Income|Out of poverty|Total wages exceed the international extreme poverty line<br>
populated and in the east of the country. Only 9.2 percent of the country’s 2017 population resided in the 69 districts for which there are zero respondents; and the districts overwhelmingly came from the governorates of Hadramaut and Al Mahrah, where only 39 and 33 percent of districts in each respective governorate had at least one respondent. Importantly, these governorates are the most food secure in the country over the time period under analysis and are not the primary focus of the analysis.

> 18See Appendix 5.

> 19Appendix 6 compares the 2017 WFP survey to population estimates in the 2014 HBS. The comparison demonstrates that every single indicator of food security collected that is replicable in the 2014 HBS dramatically declined, consistent with the reports of wide-spread food insecurity (e.g., IPC 2017, FEWS NET 2018, etc.); home ownership declined, the prevalence of renting increased, and the size of households all increased, which is consistent with the widespread issue of internal displacement in the country (e.g., TFPM 2018); and access to services dramatically declined, where essentially no households had access to an electricity network and only 25 percent had access to a water network, which is consistent with the reporting of humanitarian agencies (e.g., OCHA 2017).

8
# **4.3 Model predictions for import protection during other recessions**

In light of the evidence from the last section that IRR estimates changed for the Great Recession relative to 1988:Q1-2008:Q3, one last question we investigate is the ability of the model to predict new TTB import protection during _earlier_ cyclical downturns. Here we consider the question in the context of the United States. Our approach is to estimate the US model with data from 1988:Q12000:Q4 and to then use the estimated IRRs to predict out-of-sample TTBs for 2001:Q1-2007:Q4, given the realizations of aggregate variables during that period.<sup>34</sup> We continue to implement a basic model that also allows for trading partner-specific channels of aggregate fluctuations to bilateral real exchange rates and foreign real GDP growth to affect the formation of new TTBs. Nevertheless, the exercise can also be viewed as examining whether identification of the model’s parameters for a period that includes only one major US recession - i.e., the 1990-1991 downturn – can be used to predict trade policy activity alongside the subsequent domestic recession of 2001.

The estimated IRRs for the model for 1988:Q1-2000:Q4 are qualitatively similar to the full sample of IRR estimates for the period of 1988:Q1-2008:Q3 in Table 3. They continue to align with theoretical expectations although the IRR for the real exchange rate is smaller in magnitude and it is not precisely estimated in the limited sample.<sup>35</sup> Figure 5 illustrates the predicted amount of new TTBs over 2001:Q1-2007:Q4 using the US model estimates based on the 1988:Q1 – 2000:Q4 data and the realized macroeconomic data for this later period. We illustrate predictions in comparison to the actual US TTBs taking place during that period.

As Figure 5 illustrates, the model does a reasonable job at predicting the quantity of protection over several quarters. First, the model predicts a general increase in TTBs for the period of 2001:
SEDLAC (2022) Note: World Bank Analytical Classifications using data for 2017. Results were obtained using 38 synthetic panels built with available surveys within +/- 2 years circa 2017 and 100 bootstrap repetitions.

# Robustness check: Increasing the time span

When estimating the International Poverty Lines, Jolliffe et al. (2020) select for each country one survey that was conducted in 2017 or the closest year. The proposed analysis in this paper followed the same principle by choosing a two-year interval around 2017 (i.e., 2015 and 2019) for LAC countries to capture their income and poverty dynamics.<sup>14</sup> However, expanding the time span - including more rounds- might increase the statistical support by embracing changes that are not necessarily assigned to a specific period. To check the robustness of both thresholds, new two-year synthetic panel rounds were constructed for the 2010–2015 interval. Figure 3 shows that when using more than 90 synthetic panels (2010-2019), variations in bounds estimates are not significant: the median lower-middle class line

> 14 Lopez-Calva & Ortiz-Juarez (2014) exploit the longitudinal data for 3 or 4-year intervals: Chile (2001-2006), Mexico (20022005), and Peru (2002-2006).

13
br>n=544|18.6 **|9.5|21.5<br>n=216|9.1|
|**Either of the two programs above †**|28.3 ***|31.4 ***<br>n=722|30.0 ***|14.7 *|20.5 *<br>n=514|14.5 *|
|**IDT Program †**<br>**(block grants )**|17.7 ***|14.5 *<br>n=978|18.2 ***|33.4 ***|39.2 ***<br>n=352|35.9 ***|
|legend: p < 0.1 *, p < 0.05 ** , p < 0.01 ***<br>‡ Pooled data from 1997 IFLS2 and 2000 IFL<br>|S3;† Data fr|om 2000 IFLS3|data only||||

Focusing on the sample of households of rural Java, we find that households exposed to the rainfall shock but residing in communities with the infrastructure or programs mentioned above have on average a significantly higher level of per capita expenditure than households in the comparison group. Households in communities with the INPRES credit program had an average of 25% higher total per capita expenditure and 38% higher food per capita expenditure than comparison households, suggesting that the program furnished an important coping mechanism to households affected by the shocks. The Padat Karya (public works) and PDMDKE (public works / credit) safety net programs also appear to have helped households cope with the impacts of the shocks. The difference in average total per capita expenditure between 15
Results lend support to our hypothesis. We find that the competing mediators through which market concentration is expected to impact financial stability are at play simultaneously. However, their magnitude and net effect varies with the level of concentration. We find evidence that at lower levels of concentration _profitability_ dominates: increasing concentration increases financial stability. Conversely, at higher levels of concentration, the _cost of credit_ , _diversification_ and the _ease of monitoring_ dominate, and increasing concentration leads to financial instability. For intermediate levels of concentration, which we estimate in the range of 55-66 percent in our sample, the competing mediators cancel each other out, and concentration has no significant impact on banking system stability, suggesting that an intermediate level of market concentration may be optimal for welfare. Several robustness checks, including alternative proxies for the mediators, an alternative measure of concentration, a different subsample, and different econometric models confirm our findings.

This paper is structured as follows. Section 2 provides a description of the data and the econometric methodology. Section 3 discusses our main results, including a battery of sensitivity tests. Section 4 concludes, offering some policy implications.

# **2. Data and methodology**

# **_2.1. Data_**

Our data set is composed of annual observations from 68 countries over the period 19972015. Both the sample composition and time period are driven by data availability. We take information concerning financial stability from an updated version of the Reinhart and Rogoff 7
>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2018<br><!-- End of picture text --> F. Sensitivity of inflation expectations to domestic shocks, EMDEs <!-- Start of picture text --> Percentage points Percentage points<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: World Bank.

Note: EMDEs = emerging market and developing economies.

A.-F. Inflation expectations are five-year-ahead expectations of annual inflation. Time-varying sensitivity is estimated by regressing the change in five-year-ahead inflation expectations on global and domestic shocks, as described in Appendix 3. Solid lines denote the median of estimates and dotted lines indicate the median of 68 percent confidence intervals.

A.B. Based on a sample of 24 advanced economies and 23 EMDEs during 2000H1-2018H1.

C.D. Based on a sample of 24 advanced economies during 1995H1-2018H1.

E.F. Based on a sample of 23 EMDEs during 2000H1-2018H1.

22
A growing number of studies have taken various approaches to try to identify the relationship between firm performance and working conditions. These studies show that profits, productivity, and firm survival are positively associated with improvements in working conditions and increased compliance with labor standards (World Bank Group, 2015). A discrete‐time survival analysis model of 595 Cambodian garment factories by Jetha and Linsen (2015) found that increased compliance with social protection standards was associated with reduced odds of factory closure. A survey of management and laborers in the Lao PDR garment sector found that failure to increase worker well‐being led to dissatisfaction, higher labor turnover, lower productivity, and impaired firms’ opportunities to strengthen participation in garment GVCs (World Bank, 2012). Brown et al. (2016) demonstrate that garment factories with higher compliance with international labor standards have higher labor productivity and higher profitability. Oka (2012) examines the link between labor standards and supplier competitiveness. The study finds that better labor standard compliance increases suppliers’ likelihood of retaining buyers that are reputation‐conscious, though it does not drive buyers’ sourcing decisions. Other criteria such as price, quality, and delivery time matter more.

Firm participation in monitoring and training programs, in particular ILO‐IFC Better Factories or Better Work programs, has been more widely studied. These programs combine monitoring, remediation and training for participating factories. For example, Better Factories Cambodia was shown to be successful at improving compliance with national law and international standards since its implementation in 2001 (Robertson 2017). Other studies have shown a positive relationship between program participation and working conditions in Cambodia (Adler and Woolcock 2010, Beresford 2009, Berik and van der Meulen Rodgers 2010, Miller et al. 2009, Oka 2010a, Oka 2010b, and Polaski 2006). Shea et al. (2010) and Brown et al. (2016) find that the improvements in working conditions and factory compliance come through engagements with multiple stakeholders as part of the program in Cambodia. Bair (
# **1. Introduction**

India’s low female labor force participation, especially in urban areas, is now extensively documented. There has been a swathe of studies over the last decade, especially since the National Sample Survey’s (NSS) 68th Round established a decline in women’s employment over the period 2004-05 and 2011-12. Some have called this a ‘conundrum’ (Das and Zumbyte, 2017: 2), asking why women should opt out of the labor market, despite being better educated in recent years and during a period when India had registered high levels of growth that would indicate better job opportunities. Increased urbanization itself is associated with better job opportunities across the board, and one would expect younger women to benefit from market work more than their mothers’ generation had, at least in cities and towns. But in fact, women’s employment in cities and towns in India has never crossed the 25 percent mark, as Das and Zumbyte (2017) point out, and urban women’s labor force participation has historically been much lower than that of their rural counterparts.

The large number of studies conducted since 2010 have focused almost entirely on the supply of female labor. The onus by implication, is on female workers and their families, who decide whether women should stay home or go into paid work. Some of the research has highlighted the role of the demand side in exerting an influence on women’s employment (Chatterjee, Murgai and Rama, 2015:8). Smaller studies and qualitative work have alluded to active discrimination against women in hiring and if hired, of their poor treatment in the workplace (Kumari, 2014; Tripathi and Yadav, undated). Still others have argued that in fact, the demand and supply of female labor may be mutually reinforcing (Das and Desai, 2003; Das, 2006). However, in the absence of large-scale surveys that can capture the demand for female labor, it is difficult understand what may be happening
# 1. Introduction

Household budget surveys (HBSs) are commonly used to collect information about household expenditure, consumption, income, and other aspects of social development. National governments and international organizations use these multipurpose surveys to design public policies to improve socioeconomic well-being (Deaton, 2018) and to monitor progress in achieving many Sustainable Development Goals (Ferreira et al., 2016). Household budget surveys are also exclusively used by the World Bank to track the goals of ending extreme poverty and promoting shared prosperity.

Availability and access to household budget surveys along with regular reporting of monetary poverty are important components of the overall statistical capacity of a country. The Middle East and North Africa region (MENA) stands out for its low statistical capacity, lagging behind other regions in 2019 according to the statistical capacity indicator developed by the World Bank. According to Arezki et al. (2020), loss of data transparency and capacity has cost the MENA region about 7-14 percent in GDP per capita losses since 2005.

Although availability and access to household budget surveys has been improving in selected countries in the MENA region during the last decade, many challenges and data gaps remain (Serajuddin et al., 2015). For example, household budget surveys (HBSs) are not collected regularly in many countries and are often not comparable across years, limiting the ability to monitor development indicators. As it stands today, more than half of the countries in the region conducted two or fewer surveys between 2004 and 2018. This results in about 46 percent of MENA’s population living in data deprived countries.

Partially this is related to violent conflicts in some countries, including Iraq, Libya, the Syrian Arab Republic, and the Republic of Yemen. Conflicts prevent any traditional data collection due to security issues. Moreover, conflicts may affect poverty quickly and make existing data outdated and unable to capture the changes. There are also many countries in the MENA region which do not collect HBSs regularly or do not share the microdata with the public. All