rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10028

# **Abstract**

Women’s labor force participation in the Kurdistan Region of Iraq is very low, at 14 percent. This paper investigates a number of social and psychological barriers to participation, using recent methods in the measurement of social norms and cultural beliefs and primary data collected from all three governorates. Furthermore, since greater growth in employment generation is expected in the private sector, the paper explores women and men’s perceptions toward working in the private sector in detail. The findings show that while 70 percent of women and men support women’s participation in the private sector. Several challenges remain in both information about the sector, as well as perceived risks and discrimination. More broadly, the findings show that traditional gender role expectations may still impede women’s labor force participation. Perceptions of common societal practices and beliefs of other members from the same household are all correlated with women’s work. The paper explores additional mental barriers using a smaller sample of younger and more educated female job seekers, who are registered with a jobs agency, and finds that both perseverance in the job search process and trust and engagement with formal institutions are additional behavioral barriers.

This paper is a product of the Mind, Behavior, and Development (eMBeD) Unit,Poverty and Equity Global Practice. 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 zafif@@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
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determined is a major question, both in principle and in actual measurement. As far as the principle goes, the value needs to capture the effect of providing the consumer with more complete information. The value would be the marginal benefit under this better set of information. Certain kinds of information are known to medical professionals such as the change in the probability of disease incidence with and without vaccination or the kinds and degrees of health improvements which may be expected from alternative treatments. Other kinds of information are known to the patient, such as tolerance for pain or uncertainty of outcome or burdens put on family members due to disability, death or financial cost. The appropriate information set to consider is the combination of personal situation and preferences on the one hand and professional knowledge on the other. Practically speaking, there could be two different ways of handling this problem. One would be to try to approximate the social value of a service under the augmented information set by 1) guessing (or doing research into) how many more people would use a given service if they were fully informed of the consequences of using it 2) determining the elasticity of demand for the service (either using demand studies for the service under the status quo or using studies from better informed (better educated?) populations or subpopulatiotis within the same area and 3) inferring how much higher the price under the new demand curve would be at the old, status quo, level of demand. Alternatively, an explicit valuation of the service under the new information could be avoided by allowing there to be more than one dimension of valuation for the project giving the decision maker the freedom of using different weighting systems between them. Monetary changes (or things which may be easily converted into monetary values) could be added up in one dimension with various sorts of health outcomes left in a separate account.

Finally, social and private price may diverge indirectly due to the lack of information on the part of consumers as a result of having to rely on medical professionals to suggest treatment. This "principalagent" problem revolves around the problem that medical professionals may have incentives to provide care which are different froii those that would be chosen by a perfectly informed consumer. This problem, identified 30 years ago by Arrow (1963), is at the core of attempts to model behavior in markets for medical care. A number of researchers (Ellis and McGuire (1986, 1991), Selden (1990), Pauly (1988))have advanced models of this phenomenon though there is little consensus as to the most salient features to include or how these models might best be adapted to the conditions faced in developing countries. The often mentioned "supplier induced demand" problem may be thought of in these terms. This is the hypothesis that an increase in the supply of doctors may not reduce the price of
> POLICY RESEARCH WORKING PAPER 2381

# Summary findings

Agenor and Aizenman examine the extent to which permanent terms-of-trade shocks have an asymmetric effect on private savings.

Using a simple three-period model, they show that if households expect to face binding constraints on borrowing in bad states of nature (when the economy is in a long trough rather than a sharp peak), savings rates will respond asymmetrically to favorable movements in the permanent component of the terms of trade-in contrast with the predictions of conventional consumption-smoothing models.

They test for asymmetric effects of terms-of-trade disturbances using an econometric model that controls for various standard determinants of private savings. The results-based on panel data for nonoil commodity exporters of Sub-Saharan Africa for 1980-96 (a group of countries for which movements in the terms of trade have traditionally represented a key source of macroeconomic shocks)-indicate that increases in the permanent component of the terms of trade (measured using three alternative filtering techniques) indeed tend to be associated with higher rates of private savings.

This paper is a product of Economic Policy and Poverty Reduction, World Bank Institute. Copies of the paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contact Tanya Loftus, room J4-282, telephone 202-473-63 17, fax 202-676-98 10, email address tloftus@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Pierre-Richard Agenor may be contacted at pagenor@worldbank.org. June 2000. (38 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas ahout 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_ **_view_** _of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
corn and let households be able to adjust. If consumption responses are sufficiently strong (because households can substitute easily to other food products) and/or if supply responses are large enough, then a net-consumer can in principle become a net-producer and thus benefit from the increase in corn prices. The standard argument against this caveat is that second-order adjustments of the type described here are often small and thus unlikely to change the implications of the first-order calculations. An example is shown in the fourth panel of Table 6, where we estimate consumption substitutions and supply responses. Allowing for consumption and production second order effects does not, in fact, affect the results. The gains for net producers are slightly larger and the losses for net consumers are slightly smaller, but the welfare impacts are not affected much.

# **6 Conclusions**

This paper provided an overview of the impacts of price changes on household welfare. We began by exploring commodity dependence in the data and by looking at expenditure and income dependence in the household surveys from Latin American and Africa. Our analysis suggests a strong dependence on commodity prices, a dependence that is biased towards the poor, especially on the consumption side.

The paper then tackled the conceptual issues related to commodity dependence. It did so by laying out a theoretical framework, which described how changes in commodity prices affects consumption, production, wages, and household adjustments. Each of these aspects of the analysis of commodity dependence is illustrated with evidence from existing literature.

Our review tells the story of heterogeneous responses to commodity price changes. Most shocks to commodity prices are international in nature and the pass-through to domestic prices is imperfect (that is, changes in international prices are not translated one to one to domestic prices). The pass-through is also heterogeneous across countries and depends on infrastructure, institutions and market structure (the nature of imperfect competition in domestic markets).

Faced with a price shock, households are affected on both the consumption and income 25
To estimate the effects of different transfer modalities on a variety of outcomes, we use a regression specification that takes the following form: (1)

> The variable _Yi_ represents the outcome of interest (uses of the transfer, purchases, food expenditures, food security and assets) of household _i_ after the transfer. The indicator variable _cash_ i is equal to one if the household was assigned to an unconditional cash transfer, zero if the household was assigned to the voucher.  _N_ are neighborhood fixed effects, the level at which we stratified prior to randomization. To improve precision, we include a vector of household baseline covariates, **X** _’i_ 0, such as household size.<sup>22</sup> The error term consists of  _i_ , which captures unobserved individual or household characteristics or idiosyncratic

> shocks. The coefficient of interest is _α_ , the intent-to-treat (ITT) effect of the cash transfer (as compared with the voucher) on the outcome of interest, under the assumption that _cashi_ is orthogonal to  _i_ . Since take-up was nearly perfect, the ITT is equivalent to the average treatment effect on the treated (ATT). Given the high rate of attrition in the second survey round, we use the data from the third survey round for all specifications. However, we also present the results using the pooled data across all post-transfer survey rounds in appendix S4.

Equation (1) is our preferred specification for most outcomes, as much of the data were not collected during the baseline. For those outcomes where baseline data are available, we also estimate the treatment effect using the Analysis of Covariance (ANCOVA), which controls for baseline values of the outcome variables. In cases where the outcome variables have high variability and low autocorrelation, as is the case in our context, the ANCOVA model is preferred over difference-in-differences (McKenzie 2012).

As is the case with unconditional cash transfer programs, there are a number of potential causal pathways. Throughout this paper, we examine the differential impact of the transfer modality on over sixty

> 22 Including household size in the regression also ensures that we are not capturing the effect for a subset of the population, e.g., small households whose per capita value of the transfer was much higher than larger households. Results are robust to excluding household size.

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to sell their goods overseas without facing the difficulties of overseas collections. In addition, the factor in the buyer’s country must conduct a credit check before agreeing to factor the buyer, which reduces the sellers need to do due diligence on potential buyers. Because the trade credit extended by the seller can be easily converted into cash, the seller is able to offer more competitive terms to its customers. Finally, the seller is able to improve its own risk management, by reducing its credit and exchange rate risks.

# **3.  The Benefits and Challenges to Factoring in Emerging Markets**

Factoring is quite distinct from traditional forms of commercial lending where credit is primarily underwritten based on the creditworthiness of the borrower rather than the value of the borrower’s underlying assets. In a traditional lending relationship, the lender looks to collateral only as a secondary source of repayment. The primary source of repayment is the borrower itself and its viability as an ongoing entity. In the case of factoring, the borrower’s viability and creditworthiness, though not irrelevant, are only of secondary underwriting importance.

In some countries, borrowers can use receivables as collateral for loans. The difference is that the lender secures the working capital assets as collateral, rather than taking legal ownership of the assets. Therefore, this type of financing requires good secured lending laws, electronic collateral registries, and quick and efficient judicial systems, which are often unavailable in developing countries. However, factoring only requires the legal environment to sell, or assign, accounts receivables. Factoring does not require good collateral laws or efficient judicial systems.

6
7 benefits provided through preferential utility prices and rates of asset lease, etc.<sup>6</sup> It is estimated from data of the State Tax Service that regional governments provided more than 1% of GDP in tax exemptions in 1995, which constitute about 20% of their funded budget subsidies (Table 3). In Tatarstan, which is probably an extreme case of regional government intervention, a case study revealed that in 1995 off-budget subsidies, i.e. the most implicit part of the overall subsidy program, exceeded 50% of those which were reflected in the budget (Table 4)7.

_Table 4. Tatarstan: various subsidies in 1995._

||In trln. rbl.|As % of total|
|---|---|---|
|Subsidies from the regional|3.65|64.83|
|budget, o/w:|||
|-consumer<br>-producer|1.95<br>1.70|34.64<br>30.20|
|Subsidies to enterprises from regional<br>extrabudgetary funds|1.65|29.31|
|Tax benefits granted to local enterprises|0.33|5.86|
|TOTAL|5.63|100.00|

Source: the World Bank In addition, a number of case studies suggest that the regional allocation of transfers to industrial enterprises is biased towards regionally dominating "crisis" industries and strong sectoral interest groups. For example, in Kemerovo oblast subsidies for the regional coal company Oblkemerovougol amounted to 6.6% of all expenditures in the 1996 draft oblast budget, and budgeted subsidies per ton of coal produced were higher than the average per ton federal coal subsidies provided by the federal budget (Bodnar et al., 1996). Correspondingly, there is evidence that some regions in Central Russia, depending on peculiarities of their industrial structure, provide relatively more in budget support for local textile industry (Cherniavski and Smimov, 1994) and defense enterprises (Freinkman and Titov, 1994). At the same time, regional governments disburse very little in strategic subsidies which would be targeted at sectors with long-term comparative advantages and, in particular, there is no significant support for new private entry (Alfandari et al., 1996; Halligan et al., 1996).

All types of regional subsidies presented in this paper can be divided into two major groups. Most regional subsidies (on average about 80% of the total) are consumer focused, that 6 A relatively new tendency in this area is the introduction of various import restrictions, including a special sale tax on goods imported from other regions, in order to help local producers (Lavrov, 1996b). This kind of off-budget subsidies is funded through an implicit taxation of final consumers.

7 Halligan et al. (1996) provided detailed estimates for tax benefits granted by regional govermnents in Yaroslavl and Novgorod oblasts and found substantial inter-regional variation.
Holland, S. P., Mansur, E. T., Muller, N. Z., & Yates, A. J. (2021). The environmental benefits of transportation electrification: Urban buses. _Energy Policy_ , 148, 111921. <u>https://doi.org/10.1016/j.enpol.2020.111921</u> Horváthová, E. (2012). The impact of environmental performance on firm performance: Shortterm costs and long-term benefits? _Ecological Economics_ , 84, 91-97. <u>https://doi.org/10.1016/j.ecolecon.2012.10.001</u> Hottenrott, H., Rexhäuser, S., & Veugelers, R. (2016). Organisational change and the productivity effects of green technology adoption. _Resource and Energy Economics_ , _43_ , 172-194. <u>https://doi.org/10.1016/j.reseneeco.2016.01.004</u> Hrovatin, N., Dolšak, N., & Zorić, J. (2016). Factors impacting investments in energy efficiency and clean technologies: empirical evidence from Slovenian manufacturing firms. _Journal of Cleaner Production_ , _127_ , 475-486. https://doi.org/10.1016/j.jclepro.2016.04.039

- Huang, Y. H., & Wu, J. H. (2021). Bottom-up analysis of energy efficiency improvement and CO2 emission reduction potentials in the cement industry for energy transition: An application of extended marginal abatement cost curves. _Journal of Cleaner Production_ , _296_ , 126619. <u>https://doi.org/10.1016/j.jclepro.2021.126619</u>

- IEA (2019). _Energy efficiency 2019_ . Paris: International Energy Agency (IEA). Available online <u>https://iea.blob.core.windows.net/assets/8441ab46-9d86-47eb-b1fccb36fc3e7143/Energy_Efficiency_2019.pdf</u>

IEA (2020a). _World Energy Investment 2020_ . Paris: IEA.

IEA (2020b). _Energy efficiency 2020_ . Paris: IEA.

IEA (2020c). _Energy efficiency indicators: Highlights_ . Paris: IEA.

IEA (2021a). _Net Zero by 2050: A roadmap for the global energy system_ . Paris: IEA.

IEA (2021b). _Data and statistics: Data tables_ . Paris: IEA. Available online <u>https://www.iea.org/data-and-statistics/data-tables</u>

- IFC (2019). _The Market Opportunity for Productive Use Leveraging Solar Energy (PULSE) in Sub-Saharan Africa_ . Washington, DC: International Finance Corporation (IFC). Available online https://www.lightingglobal.org/wp-content/uploads/2019/09/PULSE-Report.pdf

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|||**Table 31: I**|**V Estimatio**|**n(6)**|||
|---|---|---|---|---|---|---|
||Bicycle ow<br>IV Probit|nership<br>|Motorcycle<br>IV Probit|ownership<br>|Car owners<br>IV Probit|hip<br>|
|D|0.366|(0.399)|0.508|(0.398)|-0.168|(0.525)|
|lnHH size|0.410|(0.127) ***|0.397|(0.129) ***|0.369|(0.156) **|
|Adult share|0.051|(0.256)|0.405|(0.263)|1.421|(0.340) ***|
|HH head sex: Male|-0.181|(0.160)|-0.514|(0.172) ***|-0.457|(0.229) **|
|HH head education: Elementary|0.004|(0.108)|0.420|(0.113) ***|0.106|(0.143)|
|HH head education: Secondary|0.023|(0.175)|0.964|(0.172) ***|0.373|(0.216) *|
|HH head education: University|0.077|(0.360)|0.917|(0.332) ***|0.896|(0.388) **|
|Appliance ownership: Refrigerator|0.313|(0.172) *|-0.203|(0.166)|0.013|(0.224)|
|Appliance ownership: Color TV|0.007|(0.163)|0.157|(0.167)|0.134|(0.220)|
|Appliance ownership: Radio|0.212|(0.101) **|0.079|(0.101)|0.210|(0.134) *|
|Appliance ownership: Gas stove|-0.177|(0.165)|0.427|(0.182) **|-0.340|(0.219)|
|Appliance ownership: Washing machine|-0.096|(0.116)|-0.026|(0.114)|0.164|(0.144)|
|Gas use for cooking|-0.102|(0.115)|0.054|(0.112)|0.264|(0.141) *|
|Power use for lighting|-0.231|(0.171)|0.480|(0.184) ***|0.016|(0.239)|
|Tap water for cooking|-0.148|(0.107)|-0.036|(0.108)|0.048|(0.143)|
|Constant|-1.063|(0.601)*|-2.219|(0.617)***|-2.411|(0.788)***|
|Obs.|911||911||911||
|Wald-stat|42.05||76.06||41.37||
|Amemiya-Lee-Newey overidentifying restriction|||||||
|test|0.62||0.85||0.98||
|Hausmanexogeneity test|14.94|***|7.10|***|13.85|***|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

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changes by specifying the following regression: where 1 _{s_ = _t}_ is an indicator variable that equals 1 if the time is _t_ and 0 otherwise. All the rest is the same as before.

Figure 5: Dynamic Treatment Effects: Manufacturing <!-- Start of picture text --> 2013 2014 2015 2016 2017 2018 2019<br>Dynamic Treatment Effects<br>.08<br>.06<br>.04<br>.02<br>0<br>Log Employment -.02<br>-.04<br>-.06<br>-.08<br><!-- End of picture text --> _Notes:_ Manufacturing only. The figure plots coefficient estimates on _βs_ ’s from the dynamic treatment effects analysis as specified in equation (3) where the dependent variable is the log of employment.

Figure 5 plots the coefficient estimates from regressions in equation (3) with lead indicators for the three to the 2016 and indicators for the three years leading up year lag years after 2016. We do not detect any differential pretrends in that employment growth for the two groups shows a similar trend leading up to 2016. Moreover, the effect of the minimum wage increases grew over time: following the minimum wage change announcement in July 2017, the employment drops by 2 percent immediately. In the year 2018, the first effective year of Moon Jae-in’s regime, employment declines by 4.5%; it then drops by 5.3% in 2019.<sup>25</sup>

> 25In Appendix Figure B.1, we estimate equation (3) in non-manufacturing firms only and find no negative employment effects.

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_Table 4. Linear regression for essential medicines for children and pregnant women availability_

||(1)<br>|(2)<br>|(3)<br>|
|---|---|---|---|
||% Medicine available|% Medicine available|% Medicine available|
|Stable Electricity vs No Access|4.88***||4.50***|
||(1.34)||(1.33)|
|Unstable Electricity vs No Access|0.55||0.76|
||(1.29)||(1.29)|
|Uses ICT for supply chain||7.33***|6.75***|
|||(1.56)|(1.55)|
|# Outpatient last 3 months|0.00***|0.00***|0.00***|
||(0.00)|(0.00)|(0.00)|
|Second quantile road distance|2.93**|2.98**|2.91**|
||(1.26)|(1.24)|(1.24)|
|Third quantile road distance|0.58|0.61|0.76|
||(1.26)|(1.26)|(1.25)|
|Fourth quantile road distance|0.81|0.98|0.93|
||(1.21)|(1.20)|(1.20)|
|2G vs No Access|-5.32***|-5.61***|-5.37***|
||(1.62)|(1.61)|(1.61)|
|3G vs No|-11.97***|-11.97***|-12.07***|
||(2.51)|(2.49)|(2.50)|
|Catchment pop.|0.18**|0.18**|0.18**|
||(0.09)|(0.09)|(0.09)|
|Constant|45.63***|45.71***|42.76***|
||(4.75)|(4.62)|(4.69)|
|Observations|2,539|2,530|2,530|
|R-squared|0.40|0.41|0.41|
|Controls|Yes|Yes|Yes|
|Fixed Effects|County|County|County|
|Mean|44.69|44.69|44.69|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# 4.4. Availability of Antenatal Care Tests

Promptly detecting potential risks to children's health in utero and/or pregnant women is essential to avoid fatal consequences. In Kenya, Maternal Mortality Ratio (MMR) is 362 maternal deaths per 100,000 live births (DHS Program, 2014),<sup>19</sup> and the stillbirth rate is between 18.32 and 21.09 per 1,000 births according to UNICEF estimates (UNICEF, 2020). Although the indicators have indeed improved in the last decade, they remain far from their targets, which are 147 and 12, respectively for maternal deaths per 100,000 live births and stillbirth rate per 1,000 births (Lang’at, Mwanri, & Temmerman, 2019). Antenatal care (ANC) tests are essential to diagnose possible complications that require special attention during pregnancy, which is critical to attain those goals. Therefore, facilities must provide ANC tests to pregnant women and have them readily available.

19 The latest available data is from 2014.

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