rafmacalaba/data-use-mentions-extended huggingface dataset:
domestic ones, implying that the intensive margin effect dominates and in net tariffs reduce the market shares of exporters and thus their incentive to engage in PTM. In the empirics, we will verify that this is indeed the case using our large dataset of developing and emerging countries.

# **2.3 Discussion**

Note that our assumption is that NTMs increase fixed compliance costs, however in principle NTMs could also affect variable costs, for instance if firms need to pay a higher price for their inputs. In that case the effect becomes similar to that of a tariff. This would decrease both the number of firms and their size. However, contrary to tariffs, because NTMs are non-discriminatory, the extensive effect might dominate and market power might increase for non-exiting firms – we would observe in this case more PTM. NTMs could also take the form of per unit costs, for instance if firms need to pay inspection costs per unit sold in the destination country. In that case, assuming as in Corsetti and Dedola (2005) that these additional per unit costs are paid in the currency of destination 2, the presence of NTMs would now also reduce the firms’ perceived elasticity of demand in market 2. The extensive margin effect together with the effect on the elasticity of demand may likely dominate, leading to more PTM for non-exiters. In both cases, because NTMs increase _variable_ costs, they should affect market power and PTM through the intensive margin of trade. However, in Section 6, we will verify that NTMs are mostly associated with changes at the extensive margin of trade.

7
# **Table Sup 6: Estimating equation 1 using original data used as inputs in Kummu, Taka and Guillaume**

# **(2018)**

This table presents robustness checks for our main result presented in Table 1, column 2. Instead of using filledin disaggregated data provided by Kummu, Taka and Guillaume (2018), we use the inputs data coming from Genaioli et al. (2013). Column 1 uses the sub-sample of observed data available at the sub-national level (no national data, no extrapolated or interpolated data). Column 2 uses sub-national and nationally observed GDP data (no extrapolated or interpolated data). Column 3 uses sub-national data with both observed, extrapolated and interpolated data (no national data). Column 4 uses sub-national and national data, including interpolated data. Results for precipitation remain robust.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Rainfall|0.571**|0.555**|0.462***|0.430***|
||(0.211)|(0.195)|(0.112)|(0.114)|
|Rainfall<sup>�</sup>|-0.166*|-0.161*|-0.095**|-0.091**|
||(0.073)|(0.065)|(0.030)|(0.030)|
|Temperature|0.017|0.046|0.051|0.076*|
||(0.043)|(0.044)|(0.034)|(0.035)|
|Temperature<sup>�</sup>|-0.000|-0.001|
between 2005 and 2014. It also had a positive effect on several nonmonetary measures of well-being, such as access to services (electricity, sewerage, drinking water), the quality of the dwelling (floor materials, access to toilets), education, and health care. Overall, it finds a general, positive effect on access to services, which is markedly higher in urban areas. Yet, it does not find evidence of a significant impact on extreme and moderate poverty rates. This is likely because of the fact that, while the fund did have a positive and significant income effect in rural and semi-urban areas, the effect was only found among the highest income deciles, that is, it was concentrated among people who ran the lowest risk of poverty. This was not the case in urban areas, where the fund had a significant, positive, and greater effect at the lowest level of the distribution (deciles 1–7). A possible explanation for the greater equalizing effect of the fund in urban areas is the fact that resources are usually invested in the periphery of cities, which tends to attract higher numbers of low-income people. Simultaneously, because of factors such as economies of scale, lower unitary costs of investment, and less government capacity, FAIS resources in rural and semi-urban areas tend to be invested in cabeceras (the county capitals). Because the cabeceras typically attract people with relatively higher incomes and fewer deprivations compared with the rest of the county, the program could be exacerbating inequalities.

After 20 years of implementation, the FAIS has not delivered the expected results on welfare, at least not where they are most direly needed. To improve the targeting of FAIS resources, the program could be reformed to reflect consideration of an indicator of gaps in local social infrastructure as a component in the allocation of funds to municipalities, instead of the index of poverty and social deprivation gaps. Separating FAIS allocations from the results the program aims to achieve (multidimensional poverty) is key to minimizing potential perverse incentives,
tors. Despite the growing concern about the potential impacts of climate change, however, not much is known about the impacts of extreme weather events on incomes and poverty. Existing studies have either focused on the potential impacts of climate change on specific economic sectors at a macro-level of analysis (CEPAL, 2014) or on more micro analysis estimating the potential impacts of climate change on food security (Ervin & Gayoso de Ervin, 2019).

In this paper, we analyze to what extent weather shocks have had an effect on (labor) incomes earned and poverty in Paraguay. Towards this goal, we combine yearly household survey data from the Permanent Continuous Household Survey ( _Encuesta Permanente de Hogares Continua_ (EPHC)) with ERA5 temperature data and CHIRPS precipitation data between 2004 and 2019. We merge these datasets at the lowest administrative level possible (districts) to account for large variations in weather across the country and construct short-term weather shock variables characterized as anomalies from long-term means. Then, our empirical strategy consist of exploiting variations in weather shocks across districts and time, following closely Letta, Montalbano, and Tol (2018), Sedova and Kalkuhl (2020), and Aggarwal (2021) through pooled OLS cross-sectional regression models.

Our main findings indicate heat shocks lead to average reductions of households’ incomes of 5% in urban areas, and up to 8.8% in rural areas, over the period of study. Drought shocks show similar negative impacts on rural areas. Our results also show that short-term weather shocks are associated with increases in poverty, with heat shocks increasing poverty levels by 4.2 percentage points in rural areas, and 1.7 percentage points in urban areas, on average. Flood shocks primarily affect urban areas, increasing poverty by 1.9 percentage points, on average. The results presented in this paper also evidence the regional heterogeneity of the impacts of short-term weather shocks: while heat shocks and flooding are most detrimental in urban areas, heat shocks and droughts have the largest negative effects on
POLICY RESEARCH WORKING PAPER 2178

# Summary findings

Adams uses household-level data from a nationally representative survey to analyze the impact of nonfarm income on income inequality in rural Egypt. After pinpointing the importance of nonfarm income to the rural poor, Adams decomposes total rural income among five sources: nonfarm, agricultural, livestock, rental, and transfer.

He shows that while nonfarm income represents the most important inequality-reducing source of income, agricultural income represents the most important inequality-increasing source.

A 1 percent marginal increase in nonfarm income will cause the Gini coefficient of overall income to fall by 12.8 percent. But a 1 percent marginal increase in agricultural income will cause the Gini coefficient to rise by 15.8 percent.

The reason for this difference has to do with land, which is distributed very unevenly in this study.

Regression analysis of the determinants of income shows that land ownership is positively and statistically related to the receipt of agricultural income but has no statistical relationship to the receipt of nonfarm income. This leads Adams to three conclusions:

* If policymakers are interested in reducing poverty and improving income distribution in rural Egypt, they should focus on nonfarm income - which not only accounts for almost 60 percent of total income for the rural poor but also favorably affects income distribution.

* Nonfarm income is an inequality-reducing source of income in a land-scarce setting such as rural Egypt because inadequate land "pushes" poorer households out of agriculture and into the nonfarm sector.

Agricultural income contributes most to rural income inequality because it is highly correlated with land ownership and with total rural income.

- - This paper a product of the Human Development Sector Group, Middle East and North Africa Region is part of a larger effort in the region to identify the sources of income for the rural poor. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Moira Coleridge-Taylor, room MC3-797, telephone 202-473-3704, fax 202-522-3283, Internetaddressmcoleridgetaylor@worldbank.org. Policy ResearchWorking Papers are also posted on the
set of distinct damage functions. These functions may then be modified using empirical and analytical methods to account for the presence of secondary characteristics that increase or decrease the base vulnerability of the class. Such secondary characteristics include roof pitch for wind damage, foundation type for earthquake shaking hazard and minimum floor height for flood damage (see (World Bank, 2013) for a list of secondary characteristics modelled by AIR for PCRAFI).

**Table 2.1: Global Earthquake Model examples of parameter types characterizing building seismic response**

|**Type of Parameter**|**Examples**|
|---|---|
|**Mechanical**<br>**Characteristics**|Strength of the material of the lateral load‐resisting system|
|**Dimension**<br>**Characteristics**|Total height / Storey height<br>Number of storeys<br>Plan dimensions ‐ Bay length|
|**Structural Detailing**|Tie spacing at the column<br>Reinforcement ratio at the column<br>Hardening ratio of steel|
|**Geometric**<br>**Configuration**|Perimeter Frame Building ‐ Space Frame Building<br>Rigid Roof / Deformable Roof<br>Column orientation|

Source: Global Earthquake Model from D’Ayala et al, 2014.

The ability to differentiate the response of a building (or other asset) to hazard intensity according to vulnerability‐determining characteristics is an important facilitator of the ‘external validity’ of damage functions. For example, damage functions developed for buildings in one region can be modified for use in another, if sufficient detail around differences in construction types and quality is available. This out‐of‐context application is more challenging when considering the impact of hazards on poverty, rather than physical damage, outcomes.

The pathway of impact of disasters on household welfare outcomes is complex and involves many indirect mechanisms. In economics, much research has
br>|l<br>|
|---|---|---|---|---|---|---|
|Panel I|Male|Female|Male|Female|Male|Female|
|1993-94 to 1999-00|13,643,608|3,865,132|12,046,864|1,272,055|25,690,472|5,137,186|
|1999-00 to 2004-05|19,997,430|18,487,154|15,608,202|7,442,918|35,605,632|25,930,072|
|2004-05 to 2009-10|14,392,954|(18,148,143)|11,477,387|(982,351)|25,870,341|(19,130,494)|
|2009-10 t o2011-12|3,756,646|(2,780,432)|5,011,517|2,750,767|8,768,163|(29,665)|
|Panel II|||||||
|1993-94 to 2004-05|33,641,038|22,352,286|27,655,066|8,714,973|61,296,104|31,067,258|
|2004-05 to 2011-12|18,149,600|(20,928,575)|16,488,904|1,768,416|34,638,504|(1,916,0159)|

Source: Authors’ own estimate based on NSSO and census data. Numbers within parentheses indicate the drop in FLFP.

8
2007, Fang et al. 2019, Gehlbach and Keefer 2011), such accountability is likely limited and non-stable. Without strong accountability, local officials retain large discretion, making data manipulation feasible. _Second_ **,** the literature agrees that local governments face strong political incentives in promoting local economic growth (Li and Zhou 2005; Xu 2011). Such incentives could lead to exaggerating GDP growth to gain advantages in promotions. Given the information asymmetry along the administrative hierarchies (Hayek 1945), when facing the choices between growing the local economy and inflating growth figures, the career-concerned subnational leaders find it easier and tempting to choose the latter over the former.<sup>1</sup> _Finally_ , Weberian merit-based promotion incentives of local officials are thought to be a key factor behind China’s strong growth over decades (Maskin, Qian and Xu 2000; Li and Zhou 2005; Xu 2011). If anything, China has been viewed as the prototype of strong bureaucracy throughout history (Fukuyama 2014, p. 370). Whether such merit-based promotions are reality or myth is thus critically important for understanding China’s growth in particular and the nature of bureaucracy in general.

In this paper, we study how the performances of top local bureaucrats are measured and manipulated, and the underlying incentives of such behavior, using data of Chinese cities from 2001 to 2013. We first provide evidence that local GDP growth data are susceptible to manipulation. Using data of growth-target setting in economic plans, an annual practice for Chinese governments at all levels, we graphically show a sharp discontinuity in the distribution of over-performance above the growth target—i.e., the gap between the reported growth and the targeted growth--at the threshold of just achieving the target, which indicates “sorting” of reported figures around the target. This, combined with increasingly frequent adjustments of official GDP figures, is suggestive of potential manipulations of annual GDP growth rates. Following Henderson, Storeygard, and Weil (2012),
# 2.3 How do GVCs shape the trade-jobs nexus?

A country’s type of GVC engagement matters for employment growth through exports. The World Bank (2020) proposed a GVC taxonomy that differentiates between four types of GVC participation: (1) commodities; (2) limited manufacturing; (3) advanced manufacturing and services; and (4) innovative activities. The classification is based on the extent of backward GVC participation of the manufacturing sector, the types of goods and services exported, and measures of innovation (see Appendix 3). Our analysis finds that the way in which countries engage in GVCs seems to matter for the magnitude of export-employment elasticities in the sample of 48 OECD and non-OECD countries (Figure 4). The link between exports and employment for countries participating in commodities (0.06 percent) and innovative tasks (0.02 percent) is less than half the magnitude of countries at the advanced manufacturing and services stage (0.14 percent). A similar and even stronger relationship is found for labor incomes.

**Figure 4: Export-employment, export-income elasticities and skills supply, by taxonomy group**

<!-- Start of picture text --> Export-employment and export-income elasticities Employment shares by skills, 2018<br>0.25 100%<br>16 20<br>30<br>0.20 80% 43<br>0.15 60%<br>72 64<br>0.10 40% 56<br>48<br>0.05 20%<br>0.00 0% 13 17 14 9<br>Commodities Limited Advanced Innovative Commodities Limited Advanced Innovative<br>manufacturing manufacturing activites manufacturingmanufacturing activities<br>and services and services<br>Employment Total labor income Low-skilled Medium-skilled High-skilled<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE and TiVA data, covering 48 countries (see Appendix 2) and
. Although a few studies such as Crozet and Milet (2015) and Breinlich et al. (2014) study servitization using representative firm-level data from France and the United Kingdom, there is no previous firm-level analysis of servitization in a developing country. We fill this gap by studying the patterns of servitization and its correlates for India. We go beyond existing work by exploring whether servitization can be seen as a transitional phase in structural transformation.

We find that the share of manufacturing firms offering services has increased threefold from one-in-five manufacturing firms in 1994 to nearly three-in-five in 2013. At the same time, the intensity of servitization has also tripled: the average share of services revenue in total revenue increased from 7 to 21 percent. Consistent with the trends in advanced countries, firms in high-tech industries are the ones that are most servitized. Among the services provided by manufacturing firms, the share of firms offering financial and business services has increased dramatically over the sample period, while a large share of servitized firms continue to offer retail and wholesale services.

Our analysis shows that servitization of Indian manufacturers is positively associated with exposure to import competition. In addition, it also appears that there are fixed costs of servitization because the phenomenon is associated with the more productive firms. The positive link with import competition is stronger for servitization driven by consumers’ preference for bundling than that based on production economies of scope. That is, firms bundle goods with services preferred by consumers to protect their markets when exposed to global competition.

Our results suggest that servitization does not shield firms from competition. In fact, servitized firms most exposed to import competition see declines in their manufacturing revenue. These results are consistent with the proposition that firms are using servitization as a strategy to gradually flee manufacturing. In this context, we find that servitization in a prior period is associated with a higher probability of product innovation in services. Furthermore, many firms have switched from being primarily manufacturers to primarily service providers by way of servitization and incremental innovation in services. We find
Policy Research Working Paper 11083

# **Abstract**

The representation and significance of women in the labor force have grown significantly over the past five decades around the globe. Using nationally representative data from more than 90 countries, this paper documents distinct gender patterns in employment transitions across both sectors and occupations during this period. Using a model of occupational and sectoral choice and focusing on six major economies, the paper finds that declining gender barriers—defined as gender-specific distortions in employment and wages—were a key driver of the observed rise in female labor force participation, expansion of the service sector, and increases in real GDP per capita from 1970 to 2018, but with substantial variation across countries.

This paper is a product of the Development Research 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 attkleineberg@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
# **1. Introduction**

Economic growth in Sub-Saharan Africa has been slow for decades (Easterly and Levine, 1997; Ndulu and O'Connell, 2007). According to data from the Penn World Table (Heston et al., 2009), Sub-Saharan African real income per capita grew at less than one percent per year over the past half century. In this paper we examine whether and to what extent policy induced distortions to agricultural and non-agricultural production are responsible for Sub-Saharan Africa's dismal growth performance. The average share of GDP from agriculture in Sub-Saharan African countries during the past half century has been more than one-third (WDI, 2011). Even in recent years agricultural production in Sub-Saharan Africa has constituted about a quarter of total GDP and more than half of total employment (Sandri, Valenzuela and Anderson, 2007). Given these large shares, the question of how much policy induced distortions to relative agricultural prices have slowed the region’s economic growth is economically relevant -- both for the academic debate on the determinants of Africa's growth tragedy as well as for the longstanding debate in the development economics literature on whether the net positive growth externalities from industrial activity exceed those from agriculture.

Our estimation strategy to identify the causal effects that policy distortions to relative agricultural prices have on economic growth in Sub-Saharan African countries is based on a twostep estimation approach. In the first step, we estimate the response of these policy distortions to economic growth, using plausibly exogenous variations in rainfall and international commodity price shocks as instrumental variables.<sup>1</sup> The instrumental variables approach enables us to examine how distortions to relative agricultural prices respond to exogenous changes in GDP per capita growth. Importantly, beyond informing the political economy debate on the determinants of policy distortions, the results from this first step provide useful information on the extent to which these policy distortions are endogenous to changes in Sub-Saharan African countries' GDP per capita

> 1 We thus build on the prior literature that has shown that
.6pp in 2008 (Table A1, Panel iv). As a result, the Gini index increased by 6% between 1993 and 2008. This increase is slightly smaller than that observed in the main results but remains substantial.

> 30 These robustness checks are important. For example, Lakner and Milanovic (2015) who use a slightly different (and larger) sample of countries find a much smaller increase in the African Gini between 1993 and 2008 when they use the 2011 PPPs. Specifically, they tend to find a higher African Gini index than our estimates, while this gap diminishes over time. While it is difficult to determine the precise reasons for these differences, one difference is that Lakner and Milanovic use some income surveys, while we use consumption surveys throughout. Furthermore with the 2005 PPPs, the African Gini increases by 9% in Lakner and Milanovic, which is very similar to our findings.

> 31 To be precise, Yitzhaki (1994) shows that the (regional) Gini can be decomposed into (1) the between-country Gini and (2) a term which consists of the income share, the within-country Gini, and the overlap index between the country and the regional distribution. Replacing 100 points with ten points would reduce the last two components, while it would leave between-country inequality and the income share unchanged.

10