rafmacalaba/datause-encoder-data huggingface dataset:
- 19 -

relatively low.<sup>7</sup> There must be significant economic potential to explore business opportunities in the regional and global markets. To support such potential, port accessibility is critical.

33. One unexpected result may be that the coefficient of labor costs turned out positive, meaning that firms prefer to be located where labor costs are high, not low. There seems to be high correlation between wages and city population. Local wages tend to be higher in more urban areas where more people live, partly because they may be more educated and have higher skills. To avoid this problem, the secondary school enrollment rate is used to control for possible heterogeneity in labor quality. It is found that firms prefer to have access to the quality labor market, regardless of potentially high wages. The coefficient is estimated at 0.012. When both factors are incorporated, the last column model shows that higher school enrollment has a positive impact to attract firms, while the coefficient of local wages is still positive. It can be interpreted to mean that labor quality is more important than wages. Firms may prefer to pay high wages if the quality of labor is also high.

**<u>Table 7. Conditional logit estimation: Firm location</u>**

||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|ln No. of existing firms|0.451|(0.060) ***|0.137|(0.052) ***|0.091|(0.051) *|
|Road density|0.045|(0.003) ***|0.063|(0.004) ***|0.063|(0.004) ***|
|Transport cost to port|-0.005|(0.003) *|-0.014|(0.003) ***|-0.016|(0.003) ***|
|ln Population|-0.225|(0.084) ***|0.556|(0.093) ***|0.604|(0.092) ***|
|ln Population density|-0.417|(0.048) ***|-0.826|(0.076) ***|-0.856|(0.074) ***|
|ln Local wage|0.300|(0.081) ***|||0.305|(0.078) ***|
|Secondary school enrollment|||0.012|(0.001)***|0.011|(0.001)***|
|Obs.|43808||43808||43808||
|Wald chi2|426.15||643.33||676.14||
|Pseudo R-squared|0.066||0.095||0.099||

7 According to the 2007 BEEP data, about 17 percent of the firms surveyed import materials and equipment from abroad. About 5 percent of the firms export their products.
27 unsuccessful “Big Bang” reforms: the radical nationalization and collectivization after the Communist take-over in the late 1940s, the Great Leap Forward 1958 to 1962, and the Cultural Revolution from the mid-1960s to the mid-1970s. Moreover, the reformers in China probably did not regard a Big Bang strategy as necessary to block a subsequent reversal of the economic reforms. Both the authorities themselves and economic agents were probably confident that the new regime would continue to be in political control for a considerable period of time. This point is important since confidence that the reforms would stay, and indeed continue, is crucial for agents contemplating investment in real and human capital. In this sense, political stability contributed to making gradualism feasible.<sup>39</sup> The actual growth performance of the Chinese economy has been impressive regardless of whether we rely on the official figures of about 9.5 percent GDP growth per year since the start of the reforms (8.3 percent per capita), or whether we believe in somewhat more conservative calculations.<sup>40</sup> When evaluating China’s success in terms of GDP growth, it is, however, also important to take into account the resource costs of the chosen growth strategy – i.e., the _efficiency_ of the growth path. There are several reasons to expect that the growth path has not been very efficient. One reason is the previously mentioned malfunctioning of factor markets, in particular credit and capital markets. While the financial markets in China have succeeded in intermediating the large volume of household saving to firms, the allocative efficiency of the process seems to have been poor. Obvious indicators are that the geographical mobility of capital and credit is low, that firms plow back profits rather than return them to the capital market, and that publicsector firms are favored as compared to private firms. There are also indications that the government tends to reallocate funds from regions with high returns to regions with low returns.<sup>41</sup>

> 39 An alternative interpretation of the gradualism would be that it was an unintended result of the political process. The experimental nature of the process suggests, however, that gradualism was quite intended. An often quoted slogan among the political leaders was that “the river should be crossed with the feet solidly touching the stones”.

> 40 For instance, Maddison (1998 and homepage); Young (2003); and Garnaut and Song (1999) estimate that GDP growth during the reform period was about 7.5 rather than 9.5 percent per year. By contrast, official statistics on GDP growth during the last decades are, in fact, expected to be revised upwards somewhat in 2006, with a yearly growth rate since the early 1990s of slightly above 10 percent per year.

> 41 For various indications of inefficient allocation of capital in China, see, for instance, Levine (1997); Kahn and Senhadji (2000); Wen and Zhan (2001); and Boyreau-Debray and Wei (2005).
Policy Research Working Paper 10896

# **Abstract**

This paper contributes to the discussion on Africa’s pathways to economic transformation by examining the roles of trade patterns—specifically, South-South and NorthSouth trade—focusing on intermediate and capital goods sourced from both the Global North and the Global South. The paper relies on a panel dataset comprising 44 African countries from 2000 to 2022. To address endogeneity concerns, it uses a two-stage least squares method, employing instrumental variables that leverage exogenous changes in trading partner conditions. Findings from the analysis indicate that imported capital and intermediates significantly predict economic transformation in Africa. However, the impact of imports from the Global North and the Global South varies depending on the specific channel of economic transformation. Imports from the Global South are more influential in driving structural change, while those from the Global North are more effective in facilitating productivity convergence. This divergence highlights the distinct roles that North-South and South-South trade play in Africa’s economic transformation agenda. The findings underscore the importance of a nuanced trade policy that leverages the strengths of both regional and global trade partners to advance Africa’s economic transformation.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 wkassa1@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
6 _Tabke 3: Macroeconomic Indicators and Energy Price Changes_

|||||Pr<br>(% c|ice leve<br>hnnac)|l'<br>||(|GDP<br>0/o<br>chan|<br>)4|
|---|---|---|---|---|---|---|---|---|---|---|
|County|Rcfiorm|<br>Fucl|Price<br>change ex3|I<br>|II|III|Rcvcnuc<br>impact%/e2|I|H|m|
|Malaysia|1984485|Diescl<br>Kcrosenc|80.0<br> <br>69.5|4.9|2.1|1.1|2|6.3|3.3|3.3|
|Indonesia|1982-85|Diescl<br>Kcrosenc|21.8<br> <br>23.0|9.0|9.2|9.6|18|3.5|3.9|5.4|
|Ghana|1983-87|'Dicscl<br>Kerosenc|4.0<br> <br>3.0|82.4|99.6|32.3|38|-5.1|4.0|5.5|
|Zimbabwe|1983-84|Dicsd<br>Kerosene|39.7|11.3|13.2|8.1|6|5.7|0.7|7.6|
|Colombia|1985-88|Dicscl<br>Kerosene|36.22|-12.6|13.7|n.a.|4|2.5|4.8|3.9|
|Turkey|1987-88|Diesel<br>Kerosene|33.3<br> <br>23.5|45.5|56.6|61.5|20|6.9|2.6|8.2|

LI. = missing data. _Note:_ 1. Prie leve figures refcr to average amual gowth rates (percent) of Consumer Price Index CPI for three different periods: I = 2 years preceding the price reform, II = During the reform, and II = 2 years following the refomL 2. Revenue impacts of the pnc- refoam arc for the last year of thc reform period and reported as a percent of total central govemment revenues for the orrcsponding year. Dis. = Dicsel, Kcr.= Kerosene and Elec. = Electricity. 3. Nominl prices in the year prior to the begiuming of the reform (base year) exprcssed as a permcntage ofte price in dhe last year of 'he reform.

4. Outp figws rcfir to avea aumal gowth rates (percent) of GDP for three different periods: I = 2 years preceding the price refaom, II = During the reforn, and m = 2 years following the refrm.

caused by drought Another important factor was an increase in wages, which cannot be traced directly to the energy prices increase. Rising wages were the result of govrnment policy either to correct the racialy-based wage stncture (Zimbabwe) or to obtain relief from erosion of real wages, which occurred due to economic conditions prior to the period of energy price reforms (Turkey). An increase in eneW prices could have added to -hese inflationazy conditions. However, there is hardly any **evidence** in the analysis to support the hypothesis of energy price increases triggering inflation.

In the early 1980s ihe domestic prices of diesel, kerosene and electric were subsidized in most of the countries analyzed Toward the end of the 1980s the domestic price increases, combined with a
16

**as** privately-provided health and education services. Despite the inclusion of housing in the USSR data, making it broader, Soviet shares drop precipitously below OECD norms as income rises.

Public **services.** Figure 24 shows the sum of government, health, education, and science employment shares for the Soviet republics compared to the sum of the OBCD categories for government services and social and related community services (the latter is mainly health and education). The inclusion of science is problematic since it is not clear how much of this category is included in the OBCD figures; we therefore also show Figure 25, which excludes science in the Soviet data. Defense is included under government services in both sets of accounts; and the large Soviet defense establishment must be a major factor in explaining why Soviet employment shares are above those for the OECD at their income level in both cases.

# Ena Use

Another way to define the role of services in the economy is by the share of services in end use. For this analysis, we rely on comparisons of real activity levels generated by Heston and Summers (H&S) under the UN International Comparison Program (ICP). The focus here is on household consumption because H&S include all education and health services in household consumption and assume no services are used in investment, private or public. The H&S international comparison of household consumption of services is described in a recent paper (Heston and Summers 1992), which is also included in Griliches 1992; it is explained below and used as a reference comparator for the Soviet data. H&S only treat services at the aggregate level; so it is not possible to draw conclusions at the sub-sector level.
17 local governments too (Townsend 1999; Lin et al. 2002). Moreover, few developing country subsidies, including for farm inputs and to food consumers, are captured in the GTAP database.

# _Estimating the effects of current distortions and reform alternatives_

Enormous progress has been made in the past two decades in using economywide models to estimate economic effects of past and prospective trade-related policy regimes. Recent examples include the continuing disaggregation of the GTAP database regions to individual countries, and the provision of bound and applied bilateral tariffs at the same (HS6) level of disaggregation so as to know what cut in applied tariffs and subsidies to expect from commitments to reduce WTO-bound rates. Planned further disaggregation of the database’s products and regions over the next year or so will help, as will the inclusion of newly developed databases on bilateral international flows of labor and capital. With greater firm concentration in the value chain, it is becoming evermore important to include imperfect competition and scale economies in economy-wide models too.

To capture more of the prospective economic gains from trade reform in economy-wide models, better dynamics need to be introduced. Recent research on the aggregate links between exports and productivity has been carefully examining the potential endogeneity of that interaction. Firm-level studies report evidence of productivity growth associated with learning-by-doing after firms enter exporting (Melitz 2003, Blalock and Gertler 2005, Fernandes and Isgut 2006). Van Biesebrock (2004) finds that African exporting firms had higher productivity before entering export markets, and
policy relevance, which is that it could show how differences in camp location and administration affect refugees.

This paper aims to analyze how the living arrangement (camp vs. out-of-camp) affects Syrian refugees' QOL by studying the case of Syrian refugees hosted in Jordan. Besides its policy relevance, another reason for looking at Syrian refugees in Jordan is availability of data. The Syrian Refugee and Host Community Survey (SRHCS) implemented in 2015 in Jordan allows comparing living conditions of separate samples of out-of-camp and in-camp refugees, as well as a sample of a host population. While Jordan hosts Syrian refugees in two refugee camps, Zaatari and Al Azraq, most refugees live outside camps in urban, peri-urban, and rural areas of the country. How has the decision to live out-of-camp improved their QOL? Does living outof-camp reduce deprivations and vulnerability of female-headed refugee households? Moreover, does living in either of the two camps affect the refugees' QOL? This paper investigates these issues.

Data are analyzed using the difference-in-difference and propensity score matching methods. Combining these methods helps control selection bias and other unobserved variables that could bias estimates of causal effects. Multidimensional indicators are used to measure QOL to capture deprivations that cannot be measured by income indicators alone. These indicators include life satisfaction, satisfaction with access to services, household assets, risk of overcrowding, income and poverty levels.

The results show that living in a camp reduces QOL for refugees. On average, refugees in camps are 36 percent more likely to live below the national abject poverty line, meaning that they find it difficult to meet daily basic needs. They are 37 percent more likely to live in overcrowded shelters. They own fewer household assets than refugees living outside camps (-2.85 assets) and are less satisfied with water, electricity, and sewerage access. They also report lower life satisfaction by 0.76 step in a 10-step Cantril Ladder measurement of life satisfaction. The QOL indicators differ by gender. Female-headed refugee households living in camps were more deprived of material living conditions and subjective quality of life than male-headed households. However, moving out-of-camps tends to benefit them more in terms of poverty reduction. There are also noticeable differences in quality of life between refugees in different camps; indicators imply that refugees living in camps situated closer to the city enjoy higher quality of life.

4
decreased from 55 percent in 2013 to 19 percent in 2020. This reduction is the result of both new arrivals preferring to reside outside the camps, as well as the departure of refugees after some years in the camps.

However, several reports have shown that out-of-camp refugees often live in bad living conditions, work in informal jobs, and pay high rent (World Bank, 2020). Considering that they maintain lower contact with officials, they may lack access to information and may not be fully informed about their rights. These issues can put out-of-camp refugees at a disadvantage compared to camp residents, affecting their QOL (Verme et al., 2016).

Indeed, the debate on refugee QOL in and out of camps has received some attention. It is even more critical in Jordan, where many refugees can choose to live either in or out of camps. In the survey of refugees, Abdo et al. (2019) found that Syrian refugees scored significantly lower than Jordanians in the psychological health and social relationships domains. Krafft et al. (2019) explained that both camp and out-of-camp Syrian refugees living in Jordan face challenges in participating in the labor market, but refugees residing in camps suffer higher food insecurity. Elsayed (2019) showed that camp refugees suffer worse living conditions; they mainly live in smaller living areas, have worse access to public facilities, and have less ownership of durable assets. Ginn (2020) found that out-of-camp refugees are more likely to work and receive higher household earnings. Gender researchers have also shown that the Syrian refugee households headed by women in Jordan cope with increased risk of poverty, insecurity, and vulnerability compared to male-headed refugee households (Care, 2016; Hanmer et al., 2020; Mohammad, Abu Awad, Creedy, & Gamble, 2018; UNHCR, 2014c). While most of these papers were conducted using simple descriptive statistics, which does not allow for presumptive conclusions, this paper takes a broader perspective by looking at differences between in-camp and out-of-camp refugees using multidimensional QOL indicators and advanced statistical methods.

# 3. What do I mean by multidimensional quality of life?

"Quality of life" (QOL) refers to an individual's general well-being, including positive and negative everyday life experiences. It is a broader metric than economic participation and living standards, including various factors that influence human capabilities and functioning.

7
**Table 2 Proportion of Firms in Industries Included in the Survey**

|2 Digit Industries|Micro|Small|Medium|Large|Total|
|---|---|---|---|---|---|
|Food|0.22|0.42|0.26|0.10|130|
|Textiles|0.07|0.30|0.35|0.28|149|
|Garments|0.13|0.38|0.35|0.15|96|
|Chemicals|0.26|0.44|0.22|0.07|95|
|Plastics & rubber|0.19|0.37|0.33|0.11|27|
|Non metallic mineral|0.28|0.49|0.22|0.01|83|
|Basic metals|0.23|0.38|0.31|0.08|13|
|Fabricated metal products|0.23|0.57|0.10|0.10|30|
|Machinery and equipment|0.34|0.28|0.28|0.09|32|
|Electronics (31 & 32)|0.33|0.11|0.44|0.11|9|
|Services of motor vehicles|0.39|0.45|0.03|0.13|31|
|Wholesale|0.32|0.57|0.09|0.02|53|
|Retail|0.38|0.33|0.13|0.16|93|
|Others*|0.13|0.53|0.25|0.09|68|
|Total|208|375|223|114|920|

* Other industries include Other manufacturing, IT and Transportation Section

**Table 3 Proportion of Firms in Regions Included in the Survey**

|Regions|Micro|Small|Medium|Large|Total|
|---|---|---|---|---|---|
|Marmara|0.14|0.39|0.28|0.19|334|
|Aegean|0.18|0.39|0.29|0.14|156|
|Central Anatolia|0.28|0.39|0.26|0.08|145|
|South|0.29|0.45|0.19|0.07|196|
|Black Sea ‐ Eastern|0.40|0.44|0.12|0.03|89|
|Total|208|375|223|114|920|

**Table 4 Growth Rates of Firms at Different Size and Age Groups**

# ALL DATA

|Growth\Age|1‐5|6‐15|≥16|Total|
|---|---|---|---|---|
|Micro|0.29|0.14|0.14|0.18|
|Small|0.14|0.02|‐0.02|0.03|
|Medium|0.00|0.03|0.01|0.02|
|Large|0.26|0.14|‐0.04|0.06|
|P|ANEL FIR|MS ONLY|||
|Growth\Age|1‐5|6‐15|≥16|Total|
|Micro|0.22|0.13|0.24|0.19|
|Small|‐0.02|0.02|0.03|0.02|
|Medium|‐0.31|‐0.08|‐0.05|‐0.08|
|Large|‐0.09|‐0.15|0.00|‐0.03|

14
# How Unfair Is the Inequality of Wage Earnings in Russia? Estimates from Panel Data

Sailesh Tiwari<sup>1</sup> Gabriel Lara Ibarra Ambar Narayan ( _World Bank_ ) Keywords: Inequality; wages; earnings; inequality of opportunity; Russia JEL Classification: D31, D63, J62

> 1 Corresponding author: stiwari@worldbank.org.  We have benefitted from useful conversations with Carolina Sanchez, Francisco Ferriera and Maria Ana Lugo. Any remaining errors are our own. The findings, interpretations, and conclusions are entirely those of the authors. They do not necessarily represent the view of World Bank Group, its Executive Directors or the countries they represent.
in consumer credit is combined with low levels of financial literacy among borrowers. The share of bad consumer loans was a sizeable 12.25% in 2010 (Central Bank of Russia, 2011). It is within this rather unique context that our survey instrument was designed.

# **4. Data and Summary Statistics**

We use a panel dataset of Russian individuals in May/June 2008 and in June 2009. The 2008 sample was designed to be nationally representative at the individual level, and weighted by gender, age, education, 46 oblasts (i.e., administrative regions), and 7 federal region<sup>4</sup> for a total of 1,600 individuals interviewed face-to-face.<sup>5</sup> This is one of the very few panel surveys measuring financial literacy and other key variables over time. In 2009, 22% of individuals from the original sample either no longer resided at the same location or refused to answer the follow-up survey. However, analysis of the data between the two years does not show evidence of significant selection bias across the covariates used in the empirical work (available upon request).

These surveys collected information on individual levels of financial literacy (i.e., numeracy, knowledge of interest compounding, understanding of inflation), as well as use of financial services (e.g., the use of bank accounts and formal credit). The dataset also provides rich demographic and socioeconomic information, and measures of financial vulnerability. The primary respondent was the household head, without an age limit.

> 4 Since March 1, 2008, the Russian Federation consists of 83 federal subjects. Six types of federal subjects are distinguished: 21 republics, 9 krais, 46 oblasts, 2 federal cities, 1 autonomous oblast, and 4 autonomous okrugs. We exclude the North-Caucasian (Chechnya) district because civil unrest prohibited surveying. 5 Summary statistics by gender, age, and education (% with secondary degrees) are very similar to those found in the 2002 Russia Longitudinal Monitoring Survey (LSMS), as well as the 2002 Russian National Census. Relative to the census data, however, our survey appears to under-represent individuals in the highest income bracket.  This is likely the result of difficulty gaining access to the highest income individuals, many of whom live in gated housing communities, in order to conduct face-to-face interviews.

10