rafmacalaba/datause-encoder-data huggingface dataset:
pursuing export-led industrialization strategies relative to those committed to import substitution. They use a CGE model to examine the impact of externalities that arise not only from exporting but also from importing capital equipment which embodies developed-country technology.

# **Existing Empirical Analysis of Asian Integration**

As noted in the above, the literature applying multi-country CGE models to evaluation of the impacts of economic integration is growing rapidly. The earlier models were used to analyze alternative tariff-cutting proposals in multilateral trade negotiations under the GATT (including the recently completed Uruguay Round) and other international trade policy issues such as North-South trade. More recent studies have concentrated on European Community market integration initiatives and the likely effects of the North American Free Trade Agreement (NAFTA) on current or future participants. 16 The extensive CGE modeling efforts on Europe and North America economic integration reflect the demand for quantitative assessments of regional initiatives from policymakers in those regions, and these models have played an important role in the public debate over these efforts. The growing interest in economic cooperation in the Pacific Rim, along with East Asia's emergence as the principle engine of world economic growth, has led as well to increased demand for research similar to that conducted on Europe and North America. Until recently, however, contemporary analytic efforts on Asia remain rather limited relative to those on Europe or North America.

For example, Harrison and Kimbell (1986) developed a 12-region, 20-sector model to investigate the impact of trade liberalization and economic interdependence among Pacific Rim countries, although they did not explicitly include Hong Kong, Taiwan, and China in their study. Martin, Petri, and Yanagishima (1994) built a 19-region, 7-sector model to estimate the impact of concerted trade

> _16_ Early surveys of such models can be found in Shoven and Whalley (1984) and Fretz, Srinivasan, and Whalley (1986), while a more contemporary survey is available in Brown (1992).

26 _Lewis, Robinson, and Wang_
**Appendix 5** : _Commercial Insurance Enrollment, Consolidation, and Antitrust Concern Levels in US State Health Insurance Markets, 2002-’03 (cont.)_

|State|Commercial<br>Insurance<br>Enrollment<br>(000’s)|Share of<br>Largest<br>Health Plan<br>(%)|Share of<br>three largest<br>health plans<br>(%)|Herfindahl-<br>Hirschmann<br>Index of<br>Market<br>Concentration|Antitrust<br>concern<sup>42</sup>|
|---|---|---|---|---|---|
|Downstate New York|8,191|26|51|1,497|Moderate|
|Upstate NewYork|3,659|26|69|1,786|Moderate|
|North Carolina|4,799|50|91|3,353|High|
|North Dakota|395|51|N/a|N/a|N/a|
|Ohio|7,859|33|62|1,677|Moderate|
|Oklahoma|1,957|36|48|1,441|Moderate|
|Oregon|2,195|43|66|2,282|High|
|Pennsylvania|8,797|33|63|1,718|Moderate|
|Rhode Island|894|56|100|5,071|High|
|South Carolina|2,266|44|74|2,444|High|
|South Dakota|462|56|69|3,305|High|
|Tennessee|3,451|43|62|2,217|High|
|Texas|11,116|32|55|1,428|Moderate|
|Utah|1,571|32|70|1,767|Moderate|
|Vermont|342|44|71|2,316|High|
|Virginia|4,642|57|80|3,519|High|
|Washington|3,764|27|66|1,796|Moderate|
|West Virginia|819|43|58|1,972|High|
|Wisconsin|3,405|17|39|689|Low|
|Wyoming|275|44|64|2,105|High|

_Source:_ Robinson (2004)

> 42 Antitrust concern is based on the Federal Trade Commission (FTC) & Department of Justice (DOJ) thresholds.  An HHI below 1,000 denotes a low level of antitrust concern, between 1,000 and 1,800 denotes a moderate level of antitrust concern, and higher than 1,800 denotes a high level of antitrust concern.

35
- Nkhata, R., Jumbe, C., & Mwabumba, M. (2014). _Does irrigation have an impact on food security and poverty: Evidence from Bwanje Valley Irrigation Scheme in Malawi_ (IFPRI Working paper No. 04). Lilongwe, Malawi. Retrieved from

- http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/128180/filename/128391.pdf

- Nkhoma, P. R. (2018). The evolution of agricultural input subsidy programs: contextualizing policy debates in Malawi’s FISP. _World Development Perspectives_ , _9_ (December 2017), 12– 17. https://doi.org/10.1016/j.wdp.2017.12.002

- NSO, & ICF. (2017). _2015-16 Demographic and Health Survey: Key Findings_ . Zomba, Malawi and Rockville, Maryland, USA.

- Nuss, E. T., & Tanumihardjo, S. A. (2010). Maize: A paramount staple crop in the context of global nutrition. _Comprehensive Reviews in Food Science and Food Safety_ , _9_ (4), 417–436. https://doi.org/10.1111/j.1541-4337.2010.00117.x

- Ortega, D. L., Waldman, K. B., Richardson, R. B., Clay, D. C., & Snapp, S. (2016). Sustainable Intensification and Farmer Preferences for Crop System Attributes: Evidence from Malawi’s Central and Southern Regions. _World Development_ , _87_ , 139–151. https://doi.org/10.1016/j.worlddev.2016.06.007

- Pace, N., Daidone, S., Davis, B., Handa, S., Knowles, M., & Pickmans, R. (2017). One Plus One can be Greater than Two: Evaluating Synergies of Development Programmes in Malawi. _Journal of Development Studies_ . https://doi.org/10.1080/00220388.2017.1380794

- Poole, N., Donovan, J., & Erenstein, O. (2021). Viewpoint: Agri-nutrition research: Revisiting the contribution of maize and wheat to human nutrition and health. _Food Policy_ , _100_ , 101976. https://doi.org/10.1016/j.foodpol.2020.101976

- Ragasa, C., & Mazunda, J. (2018). The impact of agricultural extension services in the context of a heavily subsidized input system: The case of Malawi. _World Development_ , _105_ , 25–47. https://doi.org/10.1016/j.worlddev.2017.12.004

- Ricker-Gilbert, J, Jayne, T. S., & Chirwa, E. W. (2011). Subsidies and crowding out: A DoubleHurdle Model of Fertiliser Demand in Malawi. _American Journal of Agricultural Economics_ , _93_ (1), 26–42. Retrieved from https://doi.org/10.1093/ajae/aaq122

Ricker-Gilbert, Jacob, & Jayne, T. S. (2017). Estimating the Enduring Effects of Fertiliser 28
and Mubende. Overall we find that the “pull” of agglomeration economies is strong and reduces the impact of complementary investments to decentralize manufacturing activity.

This simple simulation exercise uses results from the empirical analysis and identifies that private returns to public infrastructure investments, measured by new industrial development, is highest in areas that offer “preconditions” for success. In particular, the stock of human capital and an existing mix of diverse economic activities are important ingredients in a successful growth “recipe”. Incidentally, these preconditions are offered in the country’s main urban agglomerations.

# **5. Conclusion**

In this paper we find that entrepreneurs in Uganda value agglomeration economies, human capital, and infrastructure conditions in deciding where to locate manufacturing establishments. The effects of infrastructure improvements to promote industrial development and accelerate national economic performance are highest in areas that offer external scale economies from agglomeration and availability of skilled workers. These ‘preconditions’ are relatively abundant in main urban agglomerations of the country – thus, improving infrastructure in these places provides the highest private return to public investment. On the other hand, using infrastructure to support economic growth in areas which are deficient in these ‘preconditions’ is likely to yield low returns. Investments to link peripheral regions to markets are also likely to be more expensive in absolute terms. Policymakers should consider these spatial efficiencyequity tradeoffs in deciding the spatial allocation of infrastructure investment.

The results from the location choice analysis are consistent with very detailed cost benefit analyses of transport improvement projects in Uganda (World Bank 2004). The World Bank’s HDM model allows for the modeling, through time, of the interaction between traffic volume and composition, and road condition and vehicle operating costs. The cost benefit analysis using the HDM model shows that the net present value (NPV) of improving 67Kms of roads between Kampala - Gayaza- ZirobweWobulenzi, connecting the capital to agriculture rich areas was US$23.3million. In comparison, improving 114 Kms of roads between the Northeastern towns of Soroti and Lira produced a NPV of US$9.9million.

22 -
<!-- Start of picture text --> 0.045<br>0.04<br>0.035<br>0.03 By fee income share<br>0.025 By non-deposit funding share<br>0.02<br>0.015<br>0.01<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>20 Bins<br><!-- End of picture text -->

**Figure 7. Return on assets by fee income and non-deposit funding share.**

Return on assets is pre-tax profits divided by assets. The fee income share is the share of non-interest income in total operating income. The non-deposit funding share is the share of non-deposit short-term funding in total deposits & short-term funding. The plots display the rate of return on assets by fee income share and non-deposit funding share. The horizontal axis represents the 20 bins of the fee income share (the non-deposit funding share), with each bin containing 5 percent of observations of one of these two variables in increasing order. The vertical axis shows the return on assets. Data are from Bankscope.

63
# **III. Data Sources and Descriptive Statistics**

# **_Survey data on adults in middle-income countries_**

Our study uses a survey database on nine middle-income countries collected over the period 2012-13 under the Skills toward Employment and Productivity (STEP) program of the World Bank.<sup>38</sup> The countries are Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Serbia, Ukraine and Vietnam. One adult aged 15-64 years was randomly selected as the respondent from about 3,000 randomly selected households, but we restrict our analysis to the subsample of adults aged 25-54 as our focus is labor market behaviors and outcomes.<sup>39</sup> With a few exceptions, the same household survey instruments were administered in all countries and the data have been harmonized. The survey collected details on skill acquisition (i.e., early childhood education, schooling attainment, training and apprenticeships), measures of skills (cognitive, noncognitive and other job-relevant skills), labor force participation and occupation, family background and socioeconomic status.

Cognitive skills have been measured through a literacy assessment, developed specifically for use in the context of developing countries.<sup>40</sup> These values are not assessment scores of individuals in themselves, but are values imputed from a conditional distribution of assessment scores based on population characteristics. Thus, our estimated returns to cognitive skills indicate

> 38 The STEP project included a total of 14 countries as of 2016, but data on our preferred measure of cognitive skills are not available for five countries (Lao PDR, Macedonia, the Philippines, Sri Lanka, and China (Yunnan Province)), so we drop them in this paper.

> 39 Survey respondents aged 15-24 are more likely to be enrolled in an educational institution or training course or looking for a first job, while respondents 55 and over may have stopped working for pay for a variety of reasons, including voluntary retirement and ill health. To address estimation issues associated with outliers, we estimate our models using 1%-trimmed samples.

> 40 The assessment includes sets of questions taken from the OECD’s International Program for Assessing Adult Competencies (PIAAC), the International Adult Literacy Survey, and the Adult Literacy and Life Skills to produce Reading Literacy Assessment Scores and derived “plausible values” of literacy proficiency (see Pierre et al., 2014).

22
# **4.5 Stylized Fact 5: The persistence of the pandemic and the induced mobility disruptions accelerated digital technology diffusion and product innovation but did so in an uneven manner.**

The social distancing measures to curb the spread of the pandemic generated a profound shock to which firms had to respond. These restrictions restrained workers’ mobility, introduced bottlenecks in supply chains and made it difficult for firms to reach consumers in person. These constraints in turn induced changes in production and demand patterns.<sup>26</sup> Apedo-Amah et al. (2020) find evidence of accelerated digitalization and product innovation among firms in response to the pandemic in the short-term.<sup>27</sup> Using a subsequent wave of data, we document the medium-term patterns (for more details on digital adoption see Cirera et al., 2021a). Our results suggest that, _first_ , firms responded by accelerating the use of existing digital technologies, investing in new digital solutions and by altering their product-mix. This expansion in the use of digital technologies allowed firms to better reach consumers (or reach new ones) and organize production more flexibly and efficiently. Nearly 44% of businesses have increased the use of digital technologies, compared to 31% in the first wave (Figure 16 panel a). This shift towards the acceleration of digitization is confirmed in Figure 16 panel (b), showing that 29% of firms had invested in new digital solutions, nearly doubling from wave 1.

Figure 16: Probability of digitization <!-- Start of picture text --> (a) Increased use of digital tech. (b) Investment in digital solutions<br>60 40<br>44 29<br>30<br>40<br>31<br>20<br>17<br>20<br>10<br>0 0<br>Wave 1 Wave 2 Wave 1 Wave 2<br>Probability of investing in digital<br>Probability of starting/increasing use of digital<br><!-- End of picture text --> _Second_ , the extent of digitization and product innovation is contingent on the severity of the initial shock to mobility, although causality is difficult to establish. Firms are more likely to accelerate digital adoption and product-innovation when facing larger disruptions to mobility (Figure 17). These differences are especially large when it comes to the use of digital technologies compared to 26For example Watanabe and Omori (2020) find that in the first few months of the pandemic Japanese consumers familiar with online channels switched to products and services that were sold online perhaps in an attempt to avoid face-to-face interactions.

> 27Relying on the first wave data collected between April and August 2020.

22
Climate Survey (PICS), respectively.

These surveys enable us to contribute to the literature. To start with, the rural-urban comparison documents novel empirical results. The only similar study of rural-urban disparities in enterprise performance we are aware of is the 2004 Sri Lankan investment climate assessment, conducted by the World Bank (World Bank, 2004). In addition, most of the available evidence on rural entrepreneurship is based on household surveys. The rural Ethiopian RICS contains very detailed information on both firms and the business environment in which they operate and thus constitutes an improvement over previous surveys. The RICS also enables us to fill a knowledge gap, since information on even the most basic characteristics of the Ethiopian non-farm enterprise sector was uncertain hitherto (Günter et al., 2007). Secondly, increased variation in key investment climate variables enables us to better estimate to what extent differences in enterprise performance are driven by variation in the investment climate: since most rural firms hardly grow, it would be difficult to identify the determinants of enterprise success on the basis of the rural data alone. The rural-urban comparison thus serves as a method to examine the impact of the investment climate and market integration on firm performance. Thirdly, by studying firms and assessing the empirical relevance of some of the mechanisms often highlighted in theoretical models of spatial disparities in economic outcomes, such as increasing returns to scale and agglomeration effects (see e.g. Romer, 1987, Krugman, 1998), we aim to provide a complementary perspective on the New Economic Geography Literature.

Ethiopia provides a very relevant context to examine the impact of market integration on firm performance, since the Ethiopian economy is characterized by exceptionally high levels of market fragmentation and limited international economic integration. According to the World Bank _“Sheer remoteness and isolation epitomizes life in rural areas”_ (World Bank, 2005c, p.69). In addition, reducing remoteness through improvements in transport

-4-
- Torrisi, O. (2020). Armed conflict and the timing of childbearing in azerbaijan. _Population and Development Review_ 46(3):501–556.

- Torrisi, O. (2022). Wedding amidst war? armed conflict and female teen marriage in azerbaijan. _European journal of population_ 38(5):1243–1275.

- Torrisi, O. (2023). Young-age exposure to armed conflict and women’s experiences of intimate partner violence. _Journal of Marriage and Family_ 85(1):7–32.

- Valente, C. (2011). What did the maoists ever do for us? education and marriage of women exposed to civil conflict in nepal. _Education and Marriage of Women Exposed to Civil Conflict in Nepal (July 1, 2011). World Bank Policy Research Working Paper_ (5741).

- van Noort, S. and Goyal, T. (2020). Is Ethnic Violence Self-Perpetuating? QuasiExperimental Evidence from Hindu-Muslim Riots in India. _Quasi-Experimental Evidence from Hindu-Muslim Riots in India (April 20, 2020)_ .

- Varshney, A. (2002). _Ethnic Conflict and Civic Life: Hindus and Muslims in India_ . Yale University Press.

- Varshney, A. (2004). Understanding gujarat violence. _Contemporary Conflicts_ 26:1–5.

- Varshney, A. (2008). _Ethnic Conflict and Civic Life_ . Yale University Press.

- Varshney, A. (2020). Understanding Gujarat Violence. Social Science Research Council https://items.ssrc.org/from-our-archives/understanding-gujarat-violence/.

- Varshney, A. and Gubler, J.R. (2012). Does the State Promote Communal Violence for Electoral Reasons? _India Review_ 11(3):191–199.

- Wilkinson, S.I. (2004). _Votes and Violence: Electoral Competition and Ethnic Riots in India_ . Cambridge Studies in Comparative Politics. Cambridge University Press.

- Wilkinson, S.I. (2009). Riots. _Annual Review of Political Science_ 12(1):329–343.

- Williams, N.E., Ghimire, D.J., Axinn, W.G., Jennings, E.A. and Pradhan, M.S. (2012). A micro-level event-centered approach to investigating armed conflict and population responses. _Demography_ 49(4):1521–1546.

- World Bank Blog (2023). Tackling gender based violence in fragile contexts. _The World Bank:_ https://www.worldbank.org/en/news/feature/2023/03/07/ tackling-gender-based-violence-in-fragile-contexts.

24
# **Table 3.  Indicators of education, laborforce participation, and female representation in national parliament**

**Table 3 (a) Gross tertiary enrollment ratio (%)** *

||19|90|20|11|
|---|---|---|---|---|
||Female|Male|Female|Male|
|Armenia|||55|43|
|Azerbaijan|19|28|20|19|
|Georgia|35|38|33|27|
|Euro area|32|34|65|55|

*expressed as a percentage of the total female population of the five-year age group following on from secondary school leaving.

**Table 3 (b) Laborforce participation rate** (% of population aged 15+)

||19|90|20|11|
|---|---|---|---|---|
||Female|Male|Female|Male|
|Armenia|61|77|49|70|
|Azerbaijan|54|71|62|69|
|Georgia|55|75|56|74|
|Euro area|41|68|50|65|

**Table 3 (c) Percent of employed age 15+, working in non-agricultural sectors, 2011**

% of employed age 15+, employed in

||Indu|stry|Serv|ice|
|---|---|---|---|---|
||Female|Male|Female|Male|
|Armenia|8|25|43|35|
|Azerbaijan|6|22|50|47|
|Georgia|4|17|40|33|
|Euro area|12|35|85|60|

**Table 3 (d) Seats held by women in national parliament (%):**

||1990|2011|
|---|---|---|
|Armenia|36|11|
|Azerbaijan||16|
|Georgia||12|
|Euro area|12|28|

**_Source:_** World Bank 2013a. The Little Data Book on Gender (compiled from the World Development Indicators), Washington DC: The World Bank.

26
- 12 -

**<u>Table 2. Summary statistics</u>**

|<br>Variable|Abb.|Obs<br>|Mean<br>S|td. Dev.|Min<br>|Max|
|---|---|---|---|---|---|---|
|Monthly salary (Ar million)|_W_|858|0.28|0.50|<br>0.0025|10|
|Dummy for commuters|_D_|858|0.33|0.47|<br>0|1|
|Distance to Taxibe routes (km)|_TAXIBE_|858|534.55|1067.97|<br>0.01668|6941.74|
|Distance to suburban bus routes|_SUBBUS_|858|484.43|437.47|<br>0.06477|2837.85|
|(km)|||||||
|Travel time to CBD (minutes)|_MINCBD_|858|29.57|13.69|<br>1.290293|79.85|
|Housing rent paid (Ar)|_RENT_|858|28153|103038|<br>0|2300000|
|Age|_AGE_|858|38.03|12.15|<br>12|78|
|Sex (male=1)|_MALE_|858|0.59|0.49|<br>0|1|
|Educational attainment:|||||||
|Dummy for primary education|_PRIM_|858|0.27|0.45|<br>0|1|
|Dummy for secondary education|_SECO_|858|0.25|0.43|<br>0|1|
|Dummy for tertiary education|_TERT_|858|0.31|0.46|<br>0|1|
|Household consumption:|||||||
|Dummy for 1st quantile|_Q1_|858|0.20|0.40|<br>0|1|
|Dummy for 2nd quantile|_Q2_|858|0.20|0.40|<br>0|1|
|Dummy for 3rd quantile|_Q3_|858|0.22|0.41|<br>0|1|
|Dummy for 4th quantile|_Q4_|858|0.21|0.41|<br>0|1|
|Number of family members|_HHS_|858|4.20|1.64|<br>1|16|
|Travel time to CSB (minutes)|_MINCSB_|858|3.44|2.80|<br>0.006001|15.31|
|Number of rooms|_ROOM_|858|1.96|1.27|<br>1|13|
|Dummy for home owner|_HOME_|858|0.40|0.49|<br>0|1|
|Access to infrastructure services:|||||||
|Electricity for cooking|_POWR_|858|0.06|0.24|<br>0|1|
|Private tap water|_TAPW_|858|0.14|0.35|<br>0|1|
|Toilet with seat|_TOIL_|858|0.23|0.42|<br>0|1|

**Figure 8. Distribution of wages by commuters and non-commuters**

<!-- Start of picture text --> Mean for non commmuters<br> Ar 258816<br> Mean for commmuters<br> Ar 309428<br>0 200000 400000 600000 800000<br>Monthly after-tax salary (cash or in-kind)<br>Non commuters Commuters<br>6.0e-06<br>4.0e-06<br>Density<br>2.0e-06<br>0<br><!-- End of picture text --> Sources: INSTAT; World Bank estimates.

**Figure 9. Estimated travel time to CBD**

<!-- Start of picture text --> 0 20 40 60 80<br>Travel time to CBD (minutes)<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text --> Source: World Bank estimates.