rafmacalaba/datause-encoder-data huggingface dataset:
||**Table 3: Gin**|**i coeffici**|**chane**<br>**ent by geogr**|**aphy**|**chane**|
|---|---|---|---|---|---|
||**2002**|**2010**|**g**<br>**(02-10)**|**2008**|**g**<br>**(08-10)**|
|Kingdom|0.345|0.329|-0.016|0.318|0.012|
|Urban|0.350|0.336|-0.013|0.321|0.015|
|Rural|0.300|0.271|-0.029|0.260|0.011|
|Amman|0.362|0.368|0.006|0.347|0.021|
|Balqa|0.350|0.317|-0.033|0.282|0.035|
|Zarqa|0.296|0.276|-0.020|0.242|0.034|
|Madaba|0.281|0.276|-0.005|0.226|0.050|
|Irbid|0.290|0.277|-0.013|0.286|-0.008|
|Mafraq|0.282|0.257|-0.025|0.261|-0.004|
|Jarash|0.327|0.227|-0.100|0.225|0.002|
|Ajlun|0.229|0.252|0.022|0.227|0.025|
|Karak|0.298|0.299|0.000|0.266|0.032|
|Tafiela|0.244|0.236|-0.008|0.232|0.005|
|Ma'an|0.296|0.260|-0.036|0.286|-0.026|
|Aqaba<br>Source: World B|0.319<br>ank Staff Calculations u|0.244<br>sing Official|-0.075<br>Household Inco|0.258<br>me and Expendit|-0.014<br>ure Surveys.|

# _III.4. Winners and Losers_

It can be argued that the welfare growth over the past decade has benefited the poor in Jordan but not necessarily the most vocal. To endorse this claim, the paper refers to the growth incidence curve calculated for per capita household consumption for the period 2002-2010 and portrayed in Figure 1 (left-hand side). The negative slope observed reveals that the poorest in Jordan have benefitted from the rise in welfare over the past decade relatively more than the rich. This is evident as the aggregate annual growth in real per capita consumption for households up to the 45<sup>th</sup> percentile of the population distribution is higher than the mean welfare growth in the Kingdom. It is also higher than the welfare growth of all other households from the upper percentiles.

Despite the rise in consumption, these figures indicate that the middle and upper-middle class Jordanians are now relatively worse off, and that the consumption gap with the poor has been shrinking. This might create a perception of impoverishment among those groups of the population, pushing them to express their discontent vis-à-vis the overall economic situation. This category of the society is often the most influential and has evidently better access to the political establishment and to the media. It is therefore surely the most vocal and their discontent is often made public and quickly reaches policy makers. Delving further into the results and observing growth incidence curves for both urban and rural areas (Figures 2a and 2b), the study portrays the winners and losers from household welfare growth on a regional basis. Those figures confirm the previous results and indicate that the middle class across all Jordan saw 12
most recent edition was completed in 2021 for 123 economies (Demirguc-Kunt et al., 2022). It includes indicators of access to and use of formal and informal financial services that have been collected in previous editions of Findex, as well as indicators of the use of cards, mobile phones, and the internet to make and receive payments, including digital merchant payments and payments from the government. In addition to wider country coverage than other data sources, the key advantage of the Findex dataset is that it provides nationally representative demand-side indicators of DFS usage. In contrast, studies that have used supply-side indicators not only have used data for a smaller sample of countries (in which advanced economies were more heavily represented), but also relied on indicators for a subset of DFS providers. For example, Claessens et al. (2018) rely on data collected from surveys of fintech platforms and other public sources by the Cambridge Center for Alternative Finance (CCAF) that explicitly excludes digital credit provided by commercial banks, focuses on the larger fintech platforms, and excludes other providers of digital credit such as online mortgage lenders, big tech firms, and e-commerce platforms.<sup>9</sup> In the empirical analysis that follows, the dependent variable indicates the share of the adult population in a country that made or received a digital payment in 2020-2021 using any type of provider (see Table 1 for a detailed variable description).

# **Bank Regulation and Supervision Survey Dataset**

The Bank Regulation and Supervision Survey (BRSS), collected by the World Bank Group, provides comparable economy-level data on how banks are regulated and supervised around the world. The most recent edition was completed for 160 jurisdictions in 2019 (Anginer et al., 2022; World Bank, 2019).<sup>10</sup> In the analysis that follows, the BRSS indicator is the index of overall restrictions on banking activities. Following Barth, Caprio, and Levine (2013), it is

> 9 See Claessens et al. (2018), p. 31, Box A.

> 10 For more details on the survey itself and the BRSS dataset, see: <u>https://www.worldbank.org/en/research/brief/BRSS.</u>

9
# **_Cost block equations_** :

# Variable cost (vcost) = ∑ inic*prinpi *Xc

Miscellaneous cost (mcost) = ∑ Xc * miscostc Temporary labor cost (labcost) = ∑ tlabt* twage Family labor cost (rescost) = ∑ flabt * rwage Total cost = vcost + mcost + labcost + rescost

# **_Production process_**

National rice production (natprod) = X*yield*conf _Where conf is the conversion factor from paddy to milled rice._

# **_Demand balance_**

National rice consumption (natcon) = natprod + imports - exports

# **_Policy_**

Fiscal revenue (govrev) = imports *pm *itax Where pm is the price of imports and itax is the rate of import tariff on rice

# **_Variables of policy interest_**

Value of national production (valpro) = ∑ natprodc * pricec Farm income (yfarm) = revenue – tcost + rescost Employment (employ) = ∑ ∑ (Xc *ltc)/dpm/12 _Where ltc is the labor used per time per crop and dpm is the number of work days per month_ Trade balance (tradebal) = ∑ (exports *pe – imports * pm) _Where pe and pm are the prices of export and import, respectively_

# **a. Data**

# _Land availability and use_

The country covers a geographical area of about 72,300 km2 (72 million hectares)<sup>10</sup> and nearly threequarters of the land is suitable for crop production on a sustainable basis. Of this total, 4.3 million hectares (ha) are uplands of low fertility and an estimated 1.06 million hectares of fertile lowlands with considerable potential for food crop production. There are five main types of cultivable land in Sierra Leone: (i) The Uplands account for 80 percent of arable lands, highly leached with low fertility status, suitable for a variety of food and cash crops; (ii) Inland Valley Swamps (IVS) account for nearly 12 percent of arable lands and are highly fertile from hillside runoff; (iii) Mangrove Swamps, which accounts for

> 10 (2015 Population and Housing Census)

10
related to MES in both a regression and pairwise correlation framework.

In summary, both total volatility and the systemic risk exposures are higher for banks with a more specialized (less diversified) sectoral business loan portfolio. The results for our herding measure are, as with the market-based similarity measures, opposite (whenever significant) to the theoretical predictions. The more the bank’s sectoral loan portfolio differs from the average bank in the country the riskier the bank is perceived to be. Our results are based on a very broad cross-section of countries and banks, especially the large sample with return-based exposure data. Pooling these banks and countries and testing for a linear relationship between banks’ sectoral specialization and their performance might mask significant variation across banks, across countries and over time. We will explore such variation in the next section.

# **4 Lending Specialization and Bank Performance: Cross-country Heterogeneity**

of banks. Most previous studies relied on proprietary data from a central bank and were hence restricted to one country. This feature of the sample creates the opportunity to directly examine how the impact of lending specialization on bank performance varies with bank and country characteristics. Moreover, we will also exploit that our return-based sample covers a long time period (of ten years) including the global financial crisis. In particular, we will examine whether the relationships are different in (i) the pre-crisis versus (post-)crisis period, (ii) developed versus developing countries and (iii) countries with and without explicit guidelines on bank asset diversification, (iv) banks above and below the median size, (v) banks above and below the median Lerner index of market power, (vi) banks below and above the loan-asset ratio, and (vii) banks below and above the median of non-interest income share, where the last two are indicators of the extent to which banks engage in traditional intermediation business.

22
# **4 Conclusions and Policy Implications**

Empirical studies have experienced difficulties in finding evidence about the positive effects of ICT on productivity. Furthermore, the wide variation in terms of the elasticities for developed countries, mainly the U.S. and Europe, creates an expectation of heterogeneity in the effects by country, industry and type of firm (Draca et al., 2007).

Therefore, it is important to extend this analysis to the case of a developing country such as Mexico which has the additional characteristic of being one of the countries directly affected by the sudden increase in Chinese competition in recent years. This fact led us not only to analyze whether previous results hold, but also to test the relation between ICT and competition.

Our results indicate that ICT alone is not going to improve firm performance unless appropriate incentives are provided through higher competition to use ICT more effectively by making complementary investments in reorganization and skills. That is, in order to cope with the pressure of Chinese competition, firms appear to either make more effective use of ICT or to be willing to make the organizational changes necessary to improve the returns of ICT use.

Thus, the implications for policy are straightforward and suggest going back to the basics and removing existing barriers to private sector competition. This does not mean just enforcing antitrust laws but designing policies aimed at opening markets, building the infrastructure to make them more connected, as well as eliminating red tape, among other strategies.

# **References**

- Aghion, P., Harris, C., Howitt, P., and Vickers, J. (2001). Competition, imitation and growth with step-bystep innovation. _Review of Economic Studies_ , pages 467–492.

- Beaudry, P., Doms, M., and Lewis, E. (2006). Endogenous skill bias in technology adoption: City-level evidence from the it revolution. Technical report, National Bureau of Economic Research.

- Bloom, N., Draca, M., and Van Reenen, J. (2015). Trade induced technical change? The impact of Chinese imports on innovation, IT and productivity. Technical report, (forthcoming), Review of Economic Studies.

- Bloom, N., Genakos, C., Sadun, R., and Van Reenen, J. (2012a). Management practices across firms and countries. _The Academy of Management Perspectives_ , 26(1):12–33.

- Bloom, N., Sadun, R., and Van Reenen, J. (2012b). Americans do IT better: US multinationals and the productivity miracle. _The American Economic Review_ , 102(1):167–201.

9
# **1 Introduction**

The role of women in the labor force has undergone a profound transformation over the past century, evolving from a largely marginalized position (Boserup, 1975) to one of increasing representation, influence, and leadership in the global workforce (Goldin, 2024). In the 1970s, for instance, there were, on average, fewer than four women for every ten men in professional and managerial positions across a large cross-section of countries. By the 2010s, this number increased to more than eight women for every ten men. This growing representation of women in the labor market not only reflects progress toward gender equality, but also has significant implications for countries’ aggregate employment structures, productivity, and economic growth.<sup>1</sup> Over the same period, economies around the world have undergone fundamental structural changes, with employment transitioning from agriculture to manufacturing and services (Kuznets, 1973).

In this paper, we study the evolution of gendered labor market opportunities, economic growth, and structural change empirically and theoretically. First, we use micro-data from 91 countries at different stages of development and over five decades to document new evidence on the link between economic development and gendered labor market outcomes. We then propose a model to study these patterns. Our framework integrates two broad explanations for the observed evolution of gendered labor market outcomes and economic growth: first, economic mechanisms such as sector- or occupation-specific technological change, income effects in consumption, and improved education; and second, gender-specific distortions arising from discrimination and social norms. We structurally estimate our model to six major economies, which offer high-quality gender-disaggregated data on _both_ wages and employment over five decades. We then use the estimated model to study the effects of changing gender distortions on structural change and aggregate growth in these countries. We find that gender distortions are important and have declined in most countries. On average, the decline in gender distortions in our sample countries accounts for a substantial share of overall economic growth and reallocation of employment in the labor market.

We begin by providing new evidence on gendered employment patterns across occupations and sectors. Our analysis draws on censuses and nationally representative household and labor force surveys from 91 countries, spanning five decades (1970-2018) and covering over 300 country-years. Using these data, we first show that the canonical process of

> 1See Cuberes and Teignier (2014); Hsieh et al. (2019); Ranasinghe (2024a,b); Chiplunkar and Goldberg (2024).

2
# **Liberalizing versus Facilitating Mode 4 Trade in Services**

# **Anirudh Shingal**<sup>**1**</sup>

**JEL classification:** F1, F10, F13

**Keywords:** Services trade, Mode 4, services suppliers, trade facilitation, STRI, movement of natural persons

> 1 ICRIER, New Delhi; EUI, Florence and WTI, Bern (anirudh.shingal@gmail.com). The paper is the result of a World Bank project on Trade Facilitation in Services that was led by Sebastián Sáez (Lead Economist, South Asia Region) and Ian Gillson (Lead Economist, Global Trade and Regional Integration Unit). The paper benefitted from support provided by the Umbrella Facility for Trade trust fund that receives contributions from the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. The paper was discussed at a ‘Trade Facilitation in Services’ workshop that was jointly organized by the World Bank and the Indian Ministry of Industry and Commerce in December 2019 in New Delhi. I would also like to thank Nora Dihel and the workshop participants for useful comments. The usual disclaimer applies.
historical conditions that unleash the integration process: the collapse of Japan after WWII and the discredit of a political system in Eastern Europe. In the absence of such historical conditions, regional integration tends to be a deeply protracted political process. The current political adjustment in the region coupled with the rebalancing of the world economy offer unique opportunities for the Arab world. The move towards multiple poles of growth is accelerating as middle classes grow in developing countries, billions of people join the world economy, and new patterns of integration combine regional intensification with global openness. For the Arab world, the rise of China, ASEAN and India offers great opportunities beside energy (Pigato 2009). Arab countries are an important source of capital for the rest of the world, and increasingly a business-service hub between Asia – East and South – and Euro-Africa. The Maghreb can soon be part of a Euro-Med integration linked to both the Mideast and Africa. The prospects of a Union for the Mediterranean encompassing 43 countries from Europe and the Mediterranean basin could become more favorable with the appropriate democratic transitions in North Africa, the Middle East and the Balkans.

**With appropriate flexibility and variable geometry, building on PAFTA to tackle border and behind-the-border barriers to trade and investment is the surest path for integrating the Arab world.** By removing barriers to trade among Arab countries, PAFTA would allow firms to obtain access to a larger regional market and, like in East Asia production networks in which hubs are increasingly sourcing intermediate inputs from the lowest cost spoke thereby increasing the scope for economies of scale to be realized, while consumers benefit from the price-reducing and variety-increasing effects of greater competition (Baumann and Ng 2011). PAFTA also helps to reduce the negative effects of the patchwork of overlapping trade agreements that has emerged – reducing the potential for trade diversion and negative hub-and-spoke effects associated with agreements signed by Arab countries with the EU and the United States (among others), and extending the benefits of free trade agreements between subsets of Arab countries (e.g., Agadir and GCC). PAFTA could also be critical in promoting innovation and trade diversification. A comparative analysis of dynamic exports of the Arab countries shows that for most Arab countries the number of dynamic products is higher in intra-regional trade than in exports to the EU (Zarrouk 2000). This indicates that the dynamic Arab products maintain differentiated export niches in intra-regional trade and suggests that there is scope for expanding intra-regional trade in processing activities.

# **2. The Status of Goods and Services Liberalization in the Arab World**

**Despite progress, the dismantling of intra-regional barriers to trade in goods and services remains an unfinished agenda.** PAFTA aims to enhance economic integration among its 17 Arab member countries through the establishment of a regional Free Trade Area (FTA) that

11
**Table 1B:** Rural Household Characteristics: Summary Statistics

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|
|Monthly per capita expenditure (INR)|323.88|219.73|0.69|39717.62|224368|
|Below Poverty Line (BPL)|0.3|0.46|0|1|224368|
|Household Size|5.12|5.16|1|601|224368|
|Any Literate|0.68|0.47|0|1|224368|
|Any Secondary +|0.45|0.5|0|1|224368|
|Marginal Landowner|0.49|0.5|0|1|224368|
|HH not in LF|0.04|0.19|0|1|224368|
|Any Non-Farm|0.34|0.47|0|1|224368|
|Any Farm|0.76|0.43|0|1|224368|
|Any Non-Farm Self-Employed|0.15|0.36|0|1|224368|
|Any Non-Farm Casual Worker|0.1|0.29|0|1|224368|
|Any Non-Farm Salaried Worker|0.1|0.3|0|1|224368|
|Any Manufacturing|0.1|0.3|0|1|224368|
|Any Trade|0.07|0.26|0|1|224368|
|Any Construction|0.05|0.22|0|1|224368|
|Any Service|0.13|0.34|0|1|224368|

_Notes_ : This table presents summary statistics for rural households using the four rounds of the NSS data. Household consumption is measured in 1993 values.

**Table 1C:** Summary Statistics: Non-Farm Unincorporated Enterprises

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|**N**|
|---|---|---|---|---|---|
|Revenues|15253.28|25981.57|0|800000|139182|
|Value Addition|4449.28|5027.72|0|151220|139182|
|Fixed Assets|117136.3|358223.3|0|6097000|139182|
|Own Account Enterprise|0.92|0.27|0|1|139182|
|Enterprise Expanding|0.32|0.46|0|1|139182|
|Enterprise Stagnating|0.53|0.5|0|1|139182|
|Age _<_ 3 years|0.15|0.36|0|1|139182|
|Any Credit|0.1|0.3|0|1|139182|
|Any Bank Loans|0.03|0.18|0|1|139182|
|Any Informal Credit|0.04|0.2|0|1|139182|
|Any Business Credit|0|0.07|0|1|139182|
|Enterprise Registered|0.21|0.4|0|1|139182|
|Female Owner|0.18|0.38|0|1|139182|
|SC/ST Owner|0.21|0.41|0|1|139182|
|Age|10.63|8.57|0|120|139176|
|Keeps Accounts|0.07|0.25|0|1|139182|
|Problem - power|0.05|0.22|0|1|139182|
|Problem - raw materials|0.03|0.18|0|1|139182|
|Problem - demand|0.12|0.32|0|1|139182|
|Problem - fnancial recovery|0.11|0.31|0|1|139182|
|Problem - credit|0.09|0.28|0|1|139182|
|Problem - labour|0.01|0.12|0|1|139182|

_Notes_ : This table presents summary statistics for rural unincorporated non-farm enterprises based on the NSS survey undertaken in 2010-11. _Revenues_ and _Value Addition_ refer to monthly enterprise revenues and value addition in current values. Own account enterprises are enterprises which do not hire any workers.

41
income; second, rural households with higher initial income had smaller financial constraints in sending members to other areas seeking better opportunities or in undertaking more investment with higher returns.

**Figure 9 – Growth incidence curves by average of completed years of formal education**

(All provinces, 1989-2004) <!-- Start of picture text --> Urban Rural<br>0 25 50 75 100 0 25 50 75 100<br>Percentile of the population (ranked by real per capita household income) Percentile of the population (ranked by real per capita household income)<br>0-3 years 3-6 years 0-3 years 3-6 years<br>6-9 years 9 years or above 6-9 years 9 years or above<br>500 500<br>400 400<br>300 300<br>200 200<br>100 100<br>0 0<br>Real per capita household income increase (%) Real per capita household income increase (%)<br>-100 -100<br><!-- End of picture text --> One common finding for the entire sample as well as for urban and rural sub-samples is that, households with in average primary middle school education or above gained significantly more than those with less education and income growth differential is higher within the richer households with better education. This offers support that the increasing difference in returns to education has been closely associated with the widening of income gaps, especially for households that face less constraint in capturing potential opportunities.

# **6. Sources of Widening Income Differentials**

We discuss in this section the role of different household characteristics (e.g. accumulation of human capital, occupation, mode of production, etc.) in income determination, and examine the interactions among changes in household characteristics, changes in returns to specific characteristics, and changes in income.

## **6.1. Estimating Household Income**

We estimate per capita household income determination for rural and urban households in 1989 and 2004 separately. We first control for household demographic characteristics, such as “proportion of male adults”, “average age of adults” and “average of completed years of formal education”, and for provincial fixed effects. For urban areas, we introduce “number of household members with different profession” to study the impacts of labor composition; and we introduce “number of household members who had a secondary occupation” to study the impacts of multiple job-holding. For rural areas, we introduce the “number of household members who participate in firms of different ownerships” with collective-ownership (mainly TVEs) as reference group and examine the impacts of farm and non-farm activities. We also introduce “amount of household’s farmed land” and dummy variables of “participation in various off-farm activities” to control for their impacts. Consistent with the findings in the previous section on the 14