rafmacalaba/data-use-mentions-extended huggingface dataset:
unique challenges in these fields. Effective approaches should focus on enhancing women’s skills, providing exposure to better-paying sectors, connecting them with supportive role models, and challenging prevailing gender norms and attitudes.

# **2 Context**

## **2.1 Labor Market In Cˆote d’Ivoire**

Data from the World Bank<sup>4</sup> sheds light on gender dynamics within the labor market of Cˆote d’Ivoire. As of 2023, the labor force participation rate for females stands at 56.5%, compared to 72.2% for males. These disparities also apply to young people aged 15 to 24. Notably, female labor force participation has shown an upward trend since 1990, indicative of a gradual narrowing of the gender gap. On a regional scale, the labor force participation rate for women in Sub-Saharan Africa is reported at 60.7%, marginally higher than Cˆote d’Ivoire’s 56.5%. The incidence of vulnerable employment, characterized by informal work arrangements lacking social protection and safety nets, remains a significant issue. In 2022, 80.8% of employed women and 63.4% of employed men in Cˆote d’Ivoire were engaged in vulnerable employment. Despite high rates, there has been a discernible improvement in reducing vulnerable employment among females since 1991, reflecting ongoing structural changes within the labor market. Business ownership also reveals pronounced gender disparities. In 2022, the proportion of female business owners in Cˆote d’Ivoire was 26%. This underscores the continuing challenges women face in achieving parity in entrepreneurial activities. Women in Cˆote d’Ivoire face significant barriers to entrepreneurship, including limited access to finance and business networks. These barriers are exacerbated by socio-cultural norms that often discourage female entrepreneurship. Additionally, women’s participation in high-growth sectors remains low, further limiting their economic opportunities (The African Development Bank Group, 2021). In summary, while Cˆote d’Ivoire has made strides in increasing female
situations where state owned enterprises were subjected to very little or no financial discipline - an unlikely goal of any industrial policy in the region.

Here we do not intend to discuss specific details of any industrial policy in transition economies. Neither do we focus on the role the conventional determinants of industrial policy (e.g. trade and exchange rate regimes, investment and capital accumulation, etc.) played in shaping the outcomes of those policies. Our intention here is to introduce yet another channel through which industrial policymakers In Transition Economies are likely to be able to affect industrial growth.

Following recent studies on economic growth, and in particular those exploring the link between institutional quality and _economic growth in general_ (e.g. Knack and Keefer 11995], Clague _et al._ [1995]) we attempt to test the relationship between _industrial growth_ and institutional quality indicators. Owing to the lack of sufficiently long time series and relatively poor quality of data, we did not base our analysis on transition economies. Instead we used a sample of 27 developing countries of Asia and Latin America containing data from 1982 to 1997 to derive our conclusions . 2 We present evidence of a strong effect of various indicators of institutional quality and financial development on industrial growth. We then hypothesize that the results obtained in the paper will hold for a wide range of transition/developing countries.

The paper is structured in the following way. Section II establishes the theoretical link between quality of institutions and growth. Section III goes on to describe the institutional quality indicators to be used in the study. We then outline the basic 2 Latin American countries are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Jamaica, Mexico, Panama, Paraguay, Uruguay. Asian countries are: Bangladesh, China, Hong Kong (China), India, Indonesia, South Korea, Malaysia, Nepal, Philippines, Singapore, Sri Lanka, Thailand 3
income elasticity of demand for a variety of goods and services can support business decisions and economic policy planning. Similarly, own- and cross-price elasticities help identifying households’ preferences and vulnerabilities, highlighting market signals and opportunities, and where there is a need for governmnet intervention. For example, the income elasticity of demand for supermarket products can reveal consumer preferences and, therefore, support supply chain actors to adjust to changing preferences and regulators to define quality and safety standards (Mergenthaler et al., 2009).

In the literature, there is a relative abundance of demand analyses for Vietnam, dating back from the Đổi Mới reforms in 1986. However, this literature has almost exclusively focused on food demand analysis (e.g. Niimi, 2005; Gibson and Kim, 2013a; Bairagi et al., 2020; Vu, 2020), with a few exceptions looking at electricity (Meyerhoefer et al., 2007) and health care services (Phu, 2020). A more detailed analysis looking at the main economic sectors that can contribute to further the economic development of Vietnam is therefore lacking and this paper aims to fill this gap. More specifically, in this paper we use micro-level household consumption data to estimate a Quadratic Almost Ideal Demand System (QUAIDS), obtaining income, own- and cross-price elasticities. We focus on 10 goods and services, namely food, foodstuffs, beverages and tobacco, household equipment, clothing and accessories, transport, telecommunication, education, medical and health, and electricity. These groups are broad aggregates, therefore the degree of unobserved quality substitution can be potentially high, generating overestimated demand elasticities. In order to control for this potential bias, and to reduce the bias potentially generated by using endgogenous unit values as proxies of market prices, we adopt an instrumental variable (IV) approach to the QUAIDS.

Our results show that all the goods and services considered are normal. However, household equipment, post and communication, clothing and accessories, transport, and medical and health services are
,Clark et al. (2004), and Byrne et al. (2008) show that rms producing homogeneous goods tend to be relatively less aected by exchange rate volatility than rms producing dierentiated products.

This paper studies the impact of exchange rate volatility on the structure of trade using detailed sectoral trade data. It starts from the simple premise that nominal exchange rate volatility should aect relatively less the trade of those rms and industries whose prots do not uctuate much as a result of exchange rate movements. In particular, industries exporting goods whose international price co-moves negatively with nominal exchange rate uctuations, which I refer to as industries with a natural hedge against exchange rate volatility, should be relatively less aected. Firms exporting goods with this characteristic are naturally protected against exchange rate risk. Therefore, if this risk matters they should be relatively less aected by exchange rate volatility.

The possibility that this natural hedge oers some protection to rms is supported by existing evidence that rms whose income is positively correlated with the exchange rate, such as those in

> 1McKenzie (1999) surveys the traditional literature that used time-series methods to estimate the impact of a country's exchange rate volatility on its total trade and exports. The most recent literature has exploited detailed panel data on bilateral trade ows and used gravity equations to estimate the impact of exchange rate volatility, exchange rate regimes, and currency unions on bilateral or multilateral trade. See, for instance, Frankel and Wei (1993), Frankel and Rose (2002), Rose (2000), Rose and Van Wincoop (2001), Klein and Shambaugh (2006), and Tenreyro (2007), among others.Clark et al. (2004) provides a comprehensive recent survey.

1
groups; calculate simple averages or weighted tariffs rates; calculate effects of tariff reductions using the simulation function; and extract data in Excel format.

# _<u>Global Monitoring Report 2004; Overall Trade Restrictiveness Index (OTRI)</u>_

Global Monitoring focuses on how the world is doing in implementing the policies and actions for achieving the MDGs and related development outcomes. It is a framework for accountability in global development policy. The 2004 Global Monitoring Report is part of a five-year stocktaking effort to monitor progress towards achieving the Millennium Development Goals by 2015.

When the focus is on measuring the trade effects of policies on an importing economy, the trade restrictiveness index can be defined as the equivalent uniform tariff that would keep imports constant. When the focus is on an exporting country’s perspective (market access), the index is the equivalent uniform tariff implied by the set of policies maintained by each country (or all importers) on that economy’s export bundle. The OTRI is calculated as the weighted sum of nominal tariffs and the ad valorem equivalent of nontariff measures at the tariff line level. OTRIs are negatively correlated with GDP per capita—the higher is a country’s GDP per capita, the lower is its trade restrictiveness as measured by the OTRI. For developing countries for which such data are available, OTRIs are positively correlated with poverty headcounts: the higher is a country’s trade restrictiveness, the poorer it tends to be.

11 ____________________________________________________________________________________________________________
fund ( _R_<sup>_RF_</sup> _−_ 1) is set at 2 _._ 7 percent.

- _Private production_ . Consistent with the evidence on Sub-Saharan Africa (SSA) surveyed in Buffie _et al._ (2012), the labor income shares in the nontraded and traded good sectors correspond to _αN_ = 0 _._ 45 and _αT_ = 0 _._ 60. In both sectors private capital depreciates at an annual rate of 10 percent ( _δN_ = _δT_ = 0 _._ 10). Following Berg _et al._

18
Month of interview is known for each household in the survey. Given that for all expenditures (except durables) the recall period is last month, shifting the time of interview by one month gives the actual period of expenditures reported. Monthly rebased CPI is then applied to deflate the components of welfare aggregate within each year.

Across year deflation is done to keep welfare aggregates in similar prices. Given that expenditure per capita is transformed into USD in 2011 PPPs, welfare aggregates in each year are inflated or deflated to be in 2011 prices using SCI CPI.

# 4.3 Spatial deflation

Spatial deflation is another important step to make sure the welfare aggregate is corrected for differences in prices across space. Two different deflators were constructed for this purpose: food and rent. The food spatial deflator is constructed from unit values of purchased food products (excluding tobacco) and is based on Paashe price index following Deaton and Zaidi (2002). Paashe index is calculated using the formula below where ��� is the share of household h's budget devoted to good k. P0 is reference price vector. Ph is vector of prices household face. For spatial deflator, food unit prices are used to calculate household level deflators, which are then averaged to the regional level separately for urban and rural areas (called subregions further in the text). The whole process is described below.

Unit food prices are calculated by dividing inter-temporally (within-year) adjusted expenditures by purchased quantities. To make sure prices are calculated for consistent units, all quantities are transformed to kilograms. Products with zero or missing quantities are dropped. Products observed less than six times at the national level are dropped as well. Finally, outliers in unit prices at sub-regional level - lower or higher than 2.5 standard deviation from the mean – were identified and replaced by missing values. Missing values were replaced by median values at the lowest possible level starting from sub-regional level, followed by rural/urban and finally the national level. Households with zero expenditure were
6 therefore advised to treat all of these figures -and especially those in the last column, with a large dose of salt.

The main findings are:

- Globally, the central government wage bill absorbs about 5.4% of GDP. With wage rates for local government roughly estimated from information for a few countries at about three-fourths of central government wages, and based on the employment information shown in Table 1, total civilian government salaries can be estimated to absorb about 9% of GDP as a worldwide average;

- the heaviest fiscal weight of the central government wage bill is in the Middle East and North Africa (9.8% of GDP), followed by Africa at 6.7% of GDP. Asia, the OECD countries and Latin America are all between 4.5% and 5% of GDP, and the lowest percentage of GDP is accounted for by the central government wage bill in Eastern Europe and former USSR (although from the greater incidence of benefits in kind);

- when measured as a multiple of per capita GDP, central government wages appear higher in Africa than in any other region, apparently contrary to widespread evidence concerning deterioration of public wages in Africa. This finding, however, is largely due to the lower participation rate in African countries (see Table A-1) and the higher (although unquantifiable) degree of underestimation of informal production and thus of GDP per capita. Furthermore, the African average is influenced by comparatively high salaries in franc-zone countries, which are 7-11 times per capita GDP. It is, thus, entirely possible that government wages may still be higher than needed to attract and retain qualified government personnel in franc-zone countries. In most of the rest of Africa, instead, the widespread consensus about wage inadequacy is most probably right;

- conversely, the relatively low multiplier for OECD countries is related to the higher participation rate as well as the much higher GDP and smaller skill differentials between public employees and the rest of the working population. As noted earlier,
TIOLI sample. Education levels are high, with 88 percent having completed at least secondary education in the BDM sample and 95 percent in the TIOLI sample. This compares to only 59 percent of the overall Jamaican population aged 25 to 44 having completed at least secondary education in 2010 (Barro and Lee, 2013), and reflects positive selection into entrepreneurship on education. In the TIOLI sample entrepreneurs are significantly wealthier than in the BDM sample as measured by a wealth index.<sup>7</sup> In both samples, the firms cover a wide mix of industries, with the majority of businesses in the BDM sample being in services (38 percent), retail (30 percent) and manufacturing (23 percent), while in the TIOLI sample we have a larger share in services (53 percent), a

> 6For the BDM sample we removed 17 firms with more than 10,000 dollars in monthly sales. From the TIOLI sample we removed 3 firms with more than 35,000 dollars in monthly sales, allowing in the sample firms with more than 15 employees and more than 10,000 dollars in sales.

> 7 The wealth index is the principal component of four variables: owning a vehicle, having access to the internet, being able to finance J$20,000 and reservation wage.

8
profile in equation 5, we compute each firm’s _TFPR_ and _TFPQ_ and calibrate _γ_ as the regression coefficient resulting from regressing _log_ � _TFPRTFPRsi_ � on _log_ � _TFPQTFP_ _<u>Qsi</u>_ �, where industry averages are computed at the 4-digit level. Equipped with an estimate of the elasticity parameter of the idiosyncratic distortion profile, we infer the value of the entry tax that equates Ghana’s average firm size in equilibrium with Ghana’s observed average firm size in the manufacturing formal-sector data.

The parameter values resulting from the first and second calibration steps are reported in table 4.

Table 4: Parameter Values

|Parameter|Values|Target|
|---|---|---|
|_δ_1_, δ_2|0.025,0.0125|Exit rate large formal frms, exit rate informal frms|
|_μ_<br>_δ_1|0.3|Employment share of the top 10 largest frms in the informal sector = 20%|
|_λ_|3|Bachas et al. (2021)|
|_θ_|4|Hsieh and Klenow (2009)|
|_ζ_|4.5|Average size of entrant relative to incumbent U.S. manufacturing|
|_η, φ_<br><br>|15,0.00056|Life-cycle growth and employment share of the top 10% largest frms in the U.S.|
|_fe_1<br>_fe_2 <sup>_,_</sup> <sup>_A_1</sup><br>_A_2|2.56, 7.15<br>|Average frm size formal relative to informal and informal employment share -8%<br>|
|_γ_1|0.285|Regression coefcient_log_(_TFPR_)vs_log_(_TFPQ_)<br>|
|_τ _<sup>_e_</sup>|1.6|Average frm size in Ghana’s manufacturingsector relative to the U.S.|
<!-- Start of picture text --> 15.0% Changes in sample composition based on<br>release of new census data and field level<br>challenges<br>Expansion of the rural<br>10.0% sample<br>5.0%<br>0.0%<br>-5.0%<br>-10.0%<br>% addition % deletion<br>-15.0%<br>Percentage of samples added or deleted<br><!-- End of picture text --> Figure 1: Percentage of samples added and deleted over survey waves. Notes: Based on Vyas (2020).

observe changes in socioeconomic variables since 2011. These are: (i) periodic labor force surveys (PLFS) of 2017-18, 2018-19 and 2019-20; (ii) the situation assessment of agricultural households (SAAH) of 2013 and 2019; and, (iii) the all-India Debt and Investment Surveys (AIDIS) of 2013 and 2019. The PLFS provides estimates of wage growth for casual and salaried wage workers, while AIDIS surveys track the evolution of physical and financial assets ownership overtime. The SAAH surveys allow us to study income inequality across agricultural (and predominantly rural) households. Following Himanshu (2019), we use these surveys to construct updated estimates of consumption, earnings, income and asset inequality.

The PLFS furthermore contains a single self-reported expenditure variable referred to as “usual household consumption expenditure”, which may serve as a proxy for the respondent’s monthly consumption. Mehrotra and Parida (2021) have used this “usual consumption expenditure” variable to document a large increase in headcount poverty in 2019-20. In Appendix 5, we examine this welfare aggregate and detect the presence of significant bunching of consumption around multiples of Rs. 1000 - consistent with theory of satisficing documented in Krosnick (2018). Our simulations suggest that these rounding off errors can have a considerable impact
**How Well is Humanitarian Assistance Targeted in Fragile Environments? Evidence from the Announcement of a Food Emergency in Yemen**<sup>_∗†_</sup>

By Sharad Tandon<sup>_‡_</sup> and Tara Vishwanath<sup>_§_</sup> _Updated January 2021_ Keywords: Conflict; Humanitarian Assistance; Food Emergency; Yemen JEL Classification: D12; I31; I38; O10; O53

> _∗_ The authors thank the World Food Programme for sharing the data from the November 2017 Yemen mVAM survey and commend the team on the impressive data they have collected over the course of the conflict in Yemen. We thank the editor and two anonymous reviewers for excellent comments and suggestions. We also thank Chris Barrett, Eliana Favari, Dean Jolliffe, and Siddharth Krishnaswamy for helpful comments on earlier, preliminary results. The views expressed here are those of the authors and may not be attributed to the World Bank.

> _†_ Declarations of interest: none

> _‡_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, standon3@worldbank.org.

> _§_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, tvishwanath@worldbank.org.
and a marked decline in the poverty headcount from 0.43 to 0.31. Partly as a result of subdivision in the course of succession, the mean land endowment declined from 5.6 to 4.7, with a modest increase in the incidence of irrigation. The sharp increase in the share of land that inherited (from 82% to 95%) illustrates that limited importance of land markets in reallocating land from less to more productive farmers.<sup>15</sup> Control areas are characterized by higher levels of income and consumption, lower levels of poverty, a higher share of irrigated land, and marginally higher access to bullocks or tractors.

The period was characterized by significant increases in wages, from Rs 46 to Rs. 53 per day (for male it was 51 to 58 Rs per day and for female it was 36 to 41 Rs per day). The higher growth rate in non-agriculture compared to agriculture suggests that part of this increase may be MNREGS-induced although wage levels were higher in the control than the treatments. In terms of time use, we find a drop in those who did not work (15% to 11%) or attended to domestic duties only (26% to 24%). This was accompanied by increases in the share of self-employment in agriculture (28% to 33%) as well as non-agriculture (3% to 4%) as well as casual workers in agriculture (10% to 11%) and non-agriculture (6% to 10%) that may again be programinduced and a slight decrease in the share of those in salaried work (5% to 4%).

To compensate for the lack of information on MNREGS implementation at household level and allow us to make at least initial inferences on whether the consumption or the production channel is the most relevant channel for project impacts to materialize, we complement household-level with administrative data on the total number of days of employment provided under MNREGS, in total and for irrigation-related activities that could
would eliminate tariffs, quotas and other barriers to trade. Under PAFTA, all tariffs on goods of Arab origin were to be removed by January 1, 2005. Obstacles still hindering the full implementation of the great Arab FTA were to be cleared before 2010 and the freeing of service trade among Arab countries was expected to accelerate.<sup>13</sup> However, little is known about the extent to which the provisions of PAFTA and subsequent official declarations have been implemented.

**2.1 Goods – A largely completed agenda**

**Significant progress has been made in reducing barriers to trade in goods within PAFTA.** Over

the last decade, preferential liberalization under PAFTA and other PTAs has been complemented with reductions in MFN tariffs. As a result, the average uniform tariff equivalent of all tariffs (ad valorem and specific) for the region fell from 14.7 percent to 6.7 percent between 2002 and 2007. Despite mounting protectionist pressures during the global economic crisis, tariffs in the MENA region decreased by another 0.8 percentage points during 2008-09. The MENA region was actually the region where tariffs decreased the most during the financial crisis, especially on manufacturing goods (Figure 3). This improvement has been confirmed by private sector operators. In 2001, tariffs were ranked as one of the most important barriers to intra-regional trade in a survey of firms in the region (Zarrouk 2001). In a follow-up survey conducted in 2009, they were ranked last (Hoekman and Zarrouk 2009). A majority of respondent companies reported that tariffs on intra-regional trade have largely been removed, and that there has been a marked improvement in customs clearance-related procedures.

**_Figure 3.  Changes in tariffs regions, 2008–09_**

<!-- Start of picture text --> Percent<br>1.5<br>7.0<br>1<br>0.5<br>0<br>East Asia & Pacific Europe & Central