rafmacalaba/datause-encoder-data huggingface dataset:
whether cash transfers have significant impacts on the subset of households exposed to shocks. We test whether the combined effect 2 3 is significantly different from 0 to assess whether transfers fully mitigate the effect of shocks. As for equation (1), our preferred specification is a ββ + ββ matched-DID estimation that compares actual cash transfer beneficiaries to non-beneficiaries in the control group with similar propensity score (computed from the variables that enter the PMT score calculation; see section 3.1).

As a complementary approach, we apply the methodology from Cissé and Barrett (2018) to construct an indicator that proxies the resilience level of each household. We consider per capita consumption as the main welfare indicator ( ). We first estimate the resilience level of each household , as the probability to have welfare above a threshold ii,ii in the next period, ii,ii WW ρρ� given its characteristics : ii WW<sup>�</sup> ii,ii−1 XX ) (4) ii,ii ii,ii ii,ii−1 ii,ii−1 To estimate the probability that household ρρ� = PP(WW remains above a consumption threshold, we use the ≥WW<sup>�</sup> |WW , XX control group to estimate the conditional mean and variance of consumption per capita in 2015 ii as a function of baseline (2012) consumption per capita ( ) and other household characteristics ( ) .<sup>27</sup> Based on this, we then predict the resilience level ii,2012 for each WW household in our sample, i.e. both in the control and treatment groups. Second, we forecast the ii,2012 ii,2015 XX ρρ� resilience level in the next period based on current consumption per capita and household characteristics and ii,f iiffs . This indicates whether changes in characteristics ρρ� induced by the cash transfer program translate into higher resilience to future shocks. Third, we ii,2015 ii,2015 WW XX compute FGT-like resilience measures which indicate whether households are resilient or not (resilience headcount ratio, ) and their distance to desirable resilience levels (resilience αα RR gap, ). For this, we define a normative probability which is the resilience threshold,<sup>28</sup> αα= 0 and measure as: αα= 1 PP<sup>�</sup> αα RR

> 27 We use two thresholds: median consumption and the 10th percentile of the consumption distribution (to capture very low levels of consumption). This second threshold is closer to a humanitarian objective. The household characteristics used to predict endline consumption from the baseline survey are the variables used for the PMT formula and a few additional baseline variables (assets, education, household characteristics, commune fixed effects). We follow Cissé and Barrett (2018) and include shock variables to predict endline consumption.

> 28 We use a probability when is the median consumption. We use when is the humanitarian objective of remaining above the 10<sup>th</sup> consumption percentile. Thus, we measure whether i) households have a PP�= 0.5 WW� PP�= 0.9 WW�

17
47 forecast, as military operations against IS had badly affected farmers in major grain‐ producing areas, and IS levies heavy taxes on farmers in the areas under its control<sup>57</sup> .

The long‐term outlook for Syria’s agricultural sector is slightly more positive, according to the newest view on Syria economy by EIU. There has been an improvement in conditions in rebel‐held areas, which could potentially lead to higher production and increased economic activity.

# Social Services

The infrastructure damage is enormous. In some instances, humanitarian aid sometimes cannot reach the affected populations. Aid agencies have to go through extensive controls by the Syrian government to deliver life‐saving medicines. The UN reported that the government prevented 80,000 medical treatment items, “including diarrhea kits, emergency health kits, antibiotics, and other medicines from going into besieged areas.”

# **<u>Somalia</u>**

# Security

Civilians in Somalia are suffering from abuses by all warring parties, and have limited access to basic services. Citizens are living in the fear of mass security sweeps by Somalia’s national intelligence agency, even though it has no legal mandate to arrest or detain people. Children and women are in worst danger. Security forces arbitrarily detain and recruit children and use them as informants to identify Al‐Shabab members. Internally displaced women are extremely vulnerable to rape by armed men, “including government soldiers and militia.” Protection for them basically does not exist<sup>58</sup> . According to UNHCR, Somalia has over 1.4 million internally displaced people. In the year 2016, some 24,500 refugees and asylum‐ seekers were registered in Somalia. Refugee return from Kenya began in 2014. Voluntary repatriation number is close to 40,000 Somali nationals from 2014 to December 2016<sup>59</sup> .

# Economic Opportunity

According to Berlin‐based Transparency International, Somalia is one of the world’s most corrupt countries. Improved governance could enable Somalia’s economy to grow on the basis of its oil and gas reserves. Ongoing droughts continue to drive hungry and thirsty refugees to surrounding countries, and large parts of the population are in need of humanitarian aid. The agriculture sector contributes to over two‐thirds of its GDP while industry only makes up for 7% in 2013<sup>60</sup> . According to the IMF, Somalia has a very high youth

> 57 EIU Syria economy: Quick View ‐ Wheat harvest set to fall short of government forecast, July 2017

> 58 Human Rights Watch, Somalia Events of 2016, <u>https://www.hrw.org/world‐</u> ‐ <u>report/2017/country chapters/somalia</u>

> 59 UNHCR, Somalia, <u>http://reporting.unhcr.org/node/2550?y=2016#year</u>

> 60 CIA The World FactBook, <u>https://www.cia.gov/library/publications/the‐world‐ factbook/geos/so.html</u>
from politics, in practice, these dimensions are closely tied. It is the nature of the bureaucratic – political bonds in India that drive the emergence and continuity of parallel systems.<sup>22</sup> Students of Indian public administration suggest that because local politicians in India must draw on their own financial resources to fund political activities they face strong incentives to raise financing through managing and intervening in HRM functions.<sup>23</sup> There remains much less public outrage than may have been expected given the low performance of the public health care system observed in public health service delivery. While it may be debated whether patients in poor areas have ‘exited’ from using the public system to private providers (Hirschman, 1970), the current reality remains that a large section of the poor – the intended beneficiaries of public services - seek health care from private providers, some of which are less than fully qualified. Household surveys show that over 80 percent of outpatient care is provided by private providers (MSPI 2004). This may partly explain why local politicians, with very few exceptions across Indian states, do not make health into an electoral issue or held accountable for the low system performance.

Given that some physicians perceive themselves as victims of parallel systems, it would be logical to assume that there could be instances of collective action to bring about HRM reforms. With the exception of one state (described below), this has not been the case. Part of the reason may stem from the fact that the physicians are themselves divided as a group, with many invested in maintaining the status quo (whether the older generation in senior administrative positions or those who are able to make the system work in their favor).

Another reason may relate to instances of positive deviations in which parallel systems are used to post qualified and motivated physicians where need is greatest. A small subset of interviewees corroborated such arguments and would signal more positive conclusions from the workings of the current system. For example, those in favor of the continuation of the current parallel practice argue that it allows physicians to rally community support behind ostensibly ‘good service’ to pressure a local political leader to use his influence on the health department to block transfers. Good doctors, or those perceived to be good doctors, laboring in PHCs enjoy immense popularity among the community and getting or retaining highly performing doctor posted in their constituency can contribute to the popularity of local politicians.

22
Policy Research Working Paper 10801

# **Abstract**

Although female labor force participation in the Islamic Republic of Iran is among the lowest in the world, there is a lack of studies on the effects of the COVID-19 pandemic on the country’s female labor force participation. This paper finds that female labor force participation decreased during the pandemic years by around 1 percentage point in 2021 and 2022. When controlling for excess mortality rates, the declines increase by as much as 3.9 and 8.7 percentage points in late 2021 and early 2022, respectively. Compared to the modest, pre-pandemic female labor force participation rates, these figures translate into 5 percent and 18–40 percent decreases, respectively. There is heterogeneity, with more educated individuals being more likely to work. Compared to married individuals, divorcees were more likely to work, and those who were widowed or never married were less likely to work. The results offer relevant inputs for labor policies, particularly those aimed at reducing gender inequalities.

This paper is a product of the Development Data Group, Development Economics. 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 hdang@worldbank.org and salehi@vt.edu.

_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
Policy Research Working Paper 8596

# **Abstract**

The shares of manufacturing in value added and employment across a range of developing economies peaked at lower levels of per capita income compared with their high-income, early-industrializer precursors. Based on the statistical analysis of input-output tables and firm-level data, the paper contributes to the discussion on whether this “premature deindustrialization” matters by showing that: a) the premature declining share of the manufacturing sector is largely not driven by a statistical artifice whereby what was earlier subsumed in manufacturing value added is now accounted for as service sector contributions; b) Some features of manufacturing that were thought of as uniquely special for development, such as scale economies, exports, and innovation, are increasingly shared by services sector firms. Yet, a given service subsector is unlikely to provide opportunities for productivity growth and job creation for unskilled labor simultaneously; c) Some high-productivity services serve final demand or derive demand from several sectors, while others are more closely linked to a manufacturing base.

This paper is a product of the Finance, Competitiveness and Innovation 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/research. The authors may be contacted at gnayyar@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
_5_

**highly** arbitrary. This direct consumables criteria was undoubtedly one reason why products in the crude material group (SITC 2) were dropped from GATT and World Bank definitions of manufactures, although the relatively light degree of processing of some products in this group was no doubt a contributing factor.' What are the implications of these conceptual points for the different statistical series on manufactures production and trade compiled by various international organizations? First, the degree of processing an item has received should be a key factor in determining whether a specific product is classified as a commodity or manufacture -- the greater the degree of processing the greater the case for classifying an item in the latter group. However since there is no completely objective standard for determining exactly when a product has passed the threshold between "commodities" and "manufactures" reasonable disagreements may exist as to how some items should be classified. These disagreements may be expected to center on "lightly processed' items falling in SITC 2' and 68. However, a more conceptually questionable and empirically important practice involves the exclusion of "disappearing consumables" from definitions of manufactures. Indeed, many items in the excluded groups experience a high degree of processing and also have very similar counterpart products that are included in every defnition of manufactures used by international organizations.8 The present study argues tbat the conceptual basis for excluding these items is weak and the

**_'_** This study focuses on the distinction between manufactures and other types of physical goods, but a point that has received relatively little attention is that the distinction between some manufactures and service industry outputs may be equally blurred. For example, some "manufactures" that are often made to order (ike many machine tools or specialist semiconductors) could be argued to be essentiaDly "services" in nature as a large part of the final purchase price consists of the specialist design. Indeed, where the design is carried out separately it is a "service" and where it is carried out by the same enterprise that produces the final good it is a "manufacture".

> **_8_** For example, (human) consumable alcoholic beverages falling in SITC 112 are excluded from World Bank and UNCrlAD defrnitions of manufactures while industrial alcohols are included. Refined petroleum products (gasoline, lubricants, jet aviation fuels and kerosene) in SITC 332 are excluded while mineral tars and crude petroleum based chemicals (SITC 52) are included. Tobacco manufactures (SITC 12) along with petroleum refining, which are among the most capital intensive sectors in the U.S., are also excluded.
-10-

FiRure 2 FISCAL DEFICITS, REAL Ouwr GOWH AND REAL INEREST RATES FOR GIVD CURRENr ACCOtMr TARGETS.

<!-- Start of picture text --> qh~~~~~~~~~~~~~~~~~~~~e . . ------<br>X . 0<br>R<br>/<br>co 0~~~~~~~<br>" 5-2s t<br>RM. IfnTuT Si!C cU POINT!)<br>a<br>545<br>CAS a Current Account Surplus<br>FCA * Feasible Orent Acount Surplus<br>TCA * Targ6v, Owrent Accoat<br>Surplus<br>FS * Fiscal Surplus * inims Fiscal Deficit<br>NPS * Net Private Savig Surplus<br>Private Savings - Private Investment<br><!-- End of picture text -->
Post-WTO periods, it stopped short of describing the dynamics in details. Figures 2 and 3 fill this gap by presenting trends in markups to shed more light on yearly variation. In panel (a) of fig. 2, three sectors showed an immediate drop in markup level between 1992 and 1993, while the metal sector extended its drop to 1994. The food sector had the lowest level of markup on materials during the study period; despite some increases in the early years, the markup level began to drop noticeably from 1998. Average markups for the food sector decreased by 28 percent from 1992 to 2002. The textile sector markup dropped significantly by 26 percent from 1992 to 1995. Although there was a slight increase afterward, the yearly variations did not reach pre-reform levels. Over the whole study period, the average markup for the textile sector shrank by 25 percent.

The wood and metal sectors exhibited some volatility in yearly variations of markup levels. The metal sector variations can be divided into two phases: 1992-1996 and 19972002. After dropping significantly in the first period (despite a slight increase in 1994) average markups started an upward trend with some volatility. Notice that there was a decrease of 22 percent between 1992 and 1996, while the sector recorded a decrease of 15 percent over the whole study period. The wood sector was the most volatile; after dropping sharply by 23 percent between 1992 and 1994, the average markup started to increase, with the final figure almost reaching the initial levels.

<!-- Start of picture text --> 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Food Textiles Food Textiles<br>Wood Metals Wood Metals<br>(a) (b)<br>2 6<br>4<br>1.5<br>Average Markup Average Markup 2<br>1 0<br><!-- End of picture text --> Figure 2: Trend in Markups Level, By Sector

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Markups level aggregated at 2-digit International Standard Industrial Classification (ISIC) level. Panel (a) shows markups computed on material input, while Panel (b) shows markups computed on labor.

Panel (b) of fig. 2 displays average markups computed on labor input over time. The dynamics generally appear to be the same for all sectors. However, the average markup computed on labor tends to increase over the years. The food, textiles, wood, and metal sectors grew by 43 percent, 25 percent, 32 percent, and 92 percent respectively, from their 14
capita income, even on a purchasing power parity basis, more than ten times higher than Ghana’s.<sup>8</sup> In other countries such rapid house price increases have been identified as the likely culprit for both the problems of housing affordability and an inability to exploit many of the most productive employment opportunities, besides the Gyourko et al (2006) study of superstar cities, see Glaeser and Gyourko (2003) and Green et al (2006) for the U.S., Cheshire and Sheppard (1989) for the U.K, and Malpezzi and Mayo (1997) for Korea and Malaysia. Williamson (1988) also provides a discussion of these effects during England’s Industrial Revolution as well as for developing countries generally. Moreover, in a country like Ghana, where one city may account for 20 percent or more of GDP, it would not be surprising if housing cost increases in that city alone could affect the country’s growth rate.<sup>9</sup>

**Evidence on Housing Costs and Conditions in Accra.** Housing costs in some of

Accra’s lower income neighborhoods are considerably lower than they are in Accra’s exclusive neighborhoods. But then, so too are the income levels. To get a sense of how housing conditions in Accra affect households with different income levels it is necessary to either make detailed surveys of conditions across the city, or to compare Accra’s lowincome housing conditions with those of similarly situated households in other cities.

Because of data availability we choose the latter approach. This approach has the added

> 8 The rankings are from _Global Cost of Living Rankings 2006/7_ by Finfacts: Business and Finance Portal. The per capita income comparisons are from the _World Development Indicators, 2006._ As for the cost of real estate, a recent World Bank study (2007) indicates that the rents of Class A offices in Ghana are 40 percent higher than the average for 10 other Sub-Saharan countries, Table 9, p.48. Of course the recent increases in utility costs, related to the country’s energy problems, contribute to these costs. Nevertheless, even with adjustment for this effect the costs noted remain relatively high, as the discussion of rental payments in the text will show.

> 9 This assumption about Accra’s share of GDP would seem to be conservative as the Greater Accra area has a population of almost 15 percent of the national population and incomes there are considerably higher than elsewhere as discussed later. Below we explore the channels through which these prices increases could affect growth.

6
the decline in Accra, but helped the improvement elsewhere. The farming sector seems to have spearheaded the decline in rural poverty through an increase in mean incomes as well as better distribution. But a larger proportion of the growing income of farming families came from the non-farm. The growth of farm and non-farm income is in fact bound together in the process of economic development and growth.

Table 6. Income shares by locality (share from other incomes not included)

||_Agriculture_|_Informal/Non-farm_<br>_Wage_|
|---|---|---|
|Accra|||
|1988||34.9<br>41.5|
|1992||41.1<br>42.2|
|_O_<br>_cange_||_-7.7_<br>_L.6_|
|_Other Cities_|||
|1988|23.8|39.8<br>24.3|
|1992<br>|15.6<br>|48.8<br>24.4<br> <br> <br>|
|_ehange_|_-34.2_|_22.7_<br>_0._<br>_2_|
|Rural Areas|||
|1988|71.7|15.6<br>7.5|
|1992|57.4|27.9<br>9.0|
|_%Achange_|_--20. 0_<br>_-_|_78.7_<br>_20.7:_|

19
Recently the view has emerged that a large part of migration to the Northeast is retummigration. If this is the case, we would expect that NE-SE migrants are significantly older than SE-NE migrants. While NE-SE migrants tend to be older than SE-NE migrants, the difference is not very pronounced (see Figures 3.1 and 3.2). The Southeast-to-Northeast ever-migrated age distribution shows the typical bimodal behavior of most migration studies, which is less pronounced for Northeast-to-Southeast migrants (Figure 3.2). Average family size for Southeast-to-Northeast migrants is 3.6 compared to 3.4 for migrants in the opposite direction.

**Figure 3.1: Age distributions of Migrants over last 10 years - age at time of migration (Household heads only)**

<!-- Start of picture text --> o- SE to NE migrants - -NE to SE migrants<br>.04-<br>.02 -<br>0<br>0 20 40 60 80<br>Age'<br><!-- End of picture text -->

> _Source:_ Author's own calculations based on PNAD 1999. Estimates based on Epanechnikov kernel density estimates with a width of approximately 20.

> The PNAD contains limited information on return-migration. We adopt the following simplified definition for retumrn-migrants. A migrant is classified as returning if he/she

> were bom in the same region as he/she is currently residing but has a history of living in a different region. Interestingly, return migration is an issue for migration to the NE, but

> less important for migration to the SE. Around 25 percent of all migrants from the SE to the NE are retum-migrants, and the proportion of retum-migrants from the NE to the SE

> is only 3 percent (Table 3.1).3

> 3 One caveat to keep in mind is that the actual number of returning migrants in Table 3.1 might be understated since children of return-migrants who are born before returning home should effectively also be classified as return-migrants and not migrants.

8