rafmacalaba/datause-encoder-data huggingface dataset:
29

# B:ibliogVahy

- Barro, Robert _3.,_ 1991, "Economic Growth in a Cross Section of Countries," _Quarterly Journal of Economics,_ 106, pp. 407-44.

- Barro, Robert, and Jong-Wha Lee, 1993, "Intemational Comparisons of Educational Attainment," Journal of Monetarq Economics, 32(3), pp. 363-394.

- Birdsall, Nancy, 1988, "Economic Approaches to Population Growth," in _Handbook of Development Economics, Volune I_ ed. by Holls Chenery and T. N. Srimnvasan. Amsterdam: North-Holland, 1988, 478-540.

- Brauder, James A., and Steve Dowrick, 1994, "The Role of Ferdlity and Population in Economic Growth: Empirical Results from Aggregate Cross-National Data," _Journal of Population Economics,_ 7:1, pp 1-25.

- Cardoso, Eliaina, and Albert Fishlow, 1989, "Latin American Development: 1950-1980, NBER Working Paper #316 (November).

- Cassen, Robert, 1994, Popuiaioh and Development: Old Debates. New Conclusions. Transactions Press: Oxford.

- Cutler, David M., James M. Poterba, Louise M. Sheiner, and Lawrence H. Summers, 1990, "An Aging Society: Opporamity or Challenge?" _Brookings Papers on EconomicAcivity -:1990,_ 1-73.

- De Long, J. Bradford, and Lawrence H. Summers, 1991, "Equipment, investment, relative prices and economic growth," Qurllely Journal of Economics, 106(2), pp 445-502.

- Diamond, J., 1989, "Government Expenditre and Economic Growth: An Empirical Investigationt," mimeograph, Fiscal Affairs Department, nernational Monetary Fund, (May).

- Easterly, William, Michael Kremer, Lant Pritchett, and Lawrence Summers, 1993, 'Good policy of good luck? Country growth performance and temporary shocks," Journal of Monetary Economics, 32(3), pp. 459484.

- FAO, 1984, Lnd, food, and pegople, United Nations: Rome.

- Grier, Kevin and Gordon Tulock, 1989, "An Empirical Analysis of Cross-Country Economic Growth," _Journat of Monetary Economics,_ 24, pp 259-76.
Selective interventions such as special economic zones and WTO accession have helped overcome various barriers to reform.

# **A. Special Economic Zones**

Just like in other countries, the PRC’s reformers faced opposition from those benefiting from the status quo. The PRC managed to reform its economy by using selective opening and regulatory reforms, first in SEZs located to maximize export potential. This reduced rent seeking and opposition to reforms in non-SEZ areas. By the time SEZ privileges were phased out and larger parts of the economy were reformed, there was enough pressure from export-oriented firms and entrepreneurs to counter rent seekers and reform opponents.

SEZs can be an instrument for the reform, and their purpose is explicitly to showcase conditions for generating a dynamic market economy and to create a strong constituency for expanding those conditions countrywide. The PRC’s SEZs are effectively early reform zones that demonstrate and spread the benefits of good governance. They provide an environment of excellent infrastructure, enabling services, market-friendly institutions, and good governance. Productivity gains eventually spilled over from the SEZs to SOEs outside the zones. Expansion of local competition among firms also reduced scope for rent seeking among government officials. With a more dynamic and market-oriented economy, managers of private firms demanded greater safeguards for private property rights and more independent courts to enforce contracts without government interference. Taxation preferences were phased out by 1994 in the PRC when SEZs’ business conditions had essentially expanded to the rest of the economy.

Investors in the PRC’s SEZs during the early days were foreign firms that brought in new technology, global standards, and international managerial practices, and were often found to be more productive than domestic firms. Indeed, Xu, Zhu, and Lin (2005) find evidence that foreign ownership is strongly associated with higher firm productivity in the PRC.<sup>5</sup> Similarly, <mark>World Bank (2006)</mark> also confirms, with a larger sample of firms, that foreign firms tend to have significantly higher productivity in the PRC.

> 5The three authors examine the effectiveness of privatization or corporatization and ownership structure on firm performance post-SOE reform using a 1998 national survey data set on ownership reform of industrial SOEs conducted by the National Statistical Bureau. They find that individual ownership (by the employees) has a negative impact on firm performance, partly due to the dispersed nature of individual ownership and limited number of strategic owners, but foreign ownership has a positive impact on firm performance.

13
**Table 3 Productivity regression results: baseline specifications**

|Dependent Variable: Log Output<br>_O_<br>(1)|_LS_<br>(2)|_I_<br>(3)|_V_<br>(4)|
|---|---|---|---|
|Bribe Tax<br>-0.031||-0.608||
|[0.069]||[0.252]**||
|Time Tax|0.001||-0.009|
||[0.002]||[0.008]|
|L<br>0.319|0.311|0.320|0.309|
|[0.045]***|[0.046]***|[0.048]***|[0.048]***|
|M<br>0.420|0.418|0.423|0.425|
|[0.030]***|[0.031]***|[0.031]***|[0.031]***|
|K<sup>Equipment</sup><br>0.070|0.074|0.072|0.075|
|[0.017]***|[0.017]***|[0.017]***|[0.016]***|
|K<sup>Land</sup><br>0.003|0.011|-0.003|0.004|
|[0.013]|[0.013]|[0.013]|[0.013]|
|Exporter<br>0.219|0.206|0.233|0.209|
|[0.050]***|[0.054]***|[0.052]***|[0.052]***|
|Age<br>-0.000|-0.000|-0.000|-0.000|
|[0.001]|[0.001]|[0.001]|[0.001]|
|SizeM<br>-0.440|-0.454|-0.406|-0.458|
|[0.104]***|[0.109]***|[0.113]***|[0.113]***|
|<br>SizeL<br>-0.254|<br>-0.257|<br>-0.237|<br>-0.242|
|[0.060]***|[0.064]***|[0.066]***|[0.066]***|
|Foreign Owned<br>0.175|0.183|0.205|0.169|
|[0.067]***|[0.073]**|[0.073]***|[0.072]**|
|Innovator<br>0.068|0.072|0.066|0.048|
|[0.054]|[0.056]|[0.054]|[0.055]|
|Competition<br>-0.022|-0.016|-0.009|-0.011|
|[0.015]|[0.015]|[0.016]|[0.017]|
|Courts<br>-0.039|-0.065|0.048|-0.046|
|[0.052]|[0.055]|[0.067]|[0.056]|
|Political Stability<br>0.056|0.037|0.071|0.024|
|[0.047]|[0.048]|[0.048]|[0.047]|
|Constant<br>YES|YES|YES|YES|
|Country Dummies<br>YES|YES|YES|YES|
|Sector Dummies<br>YES|YES|YES|YES|
|R squared<br>0.86|0.87|0.79|0.80|
|Wu-Hausman (p-value)||0.03|0.17|
|<br>Hansen J (p-value)||0.66|0.45|
|F-Stat||43.11|27.24|
|Observations<br>1666|1519|1629|1490|
|_Note_<br>_: Standard errors clustered by country-industry in brac_<br>_at 5%; *** significant at 1%. Instruments: bribe tax: countr_|_kets. * signific_<br>_y-industry bri_|_ant at 10%; *_<br>_be tax, dumm_|_* significant_<br>_y for female_|
|_owner, dummy for electricity connection application. Time_<br>_forfemale owner. * significant at 10%; ** significant at 5%_|_tax: country-i_<br>_; *** significa_|_ndustry time t_<br>_nt at 1%_|_ax, dummy_|

Note firstly that the coefficient on the production factors capital, labour, land and materials are all positive as expected. Furthermore, exporters and foreign-owned firms are more productive, _ceteris paribus_ , as expected. Strikingly, larger firms tend to be less productive, perhaps a sign of 23
**Table S7.** Robustness analyses: Impacts of Personal Initiative and Interpersonal Initiative interventions on economic and psychosocial outcome indices, controlling for age and head of household <u>status, at endline in Mechanism Experiment</u>

|||**Control**|**Personal Initiative**|**Interpersonal Initiative**|
|---|---|---|---|---|
|**Outcome**|df|Mean<br>(SD)|**Coefficient**<br>(SE)<br>Robust_p_-value<br>Cluster robust_p_-value|**Coefficient**<br>(SE)<br>Robust_p_-value<br>Cluster robust_p_-value|
|Economic Composite<br>Index|2472|0.00<br>(1.00)|0.07<br>(0.05)<br>0.167<br>0.262|0.12<br>(0.05)<br>0.012<br>0.021|
|Food Security Index|2471|0.00<br>(1.00)|0.07<br>(0.05)<br>0.150<br>0.260|0.11<br>(0.05)<br>0.029*<br>0.059†|
|Business Omnibus<br>Index|2472|0.00<br>(1.00)|0.04<br>(0.05)<br>0.391<br>0.470|0.09<br>(0.05)<br>0.070†<br>0.107|
|Psychosocial<br>composite index|2485|0.00<br>(1.00)|0.10<br>(0.05)<br>0.039*<br>0.058†|0.12<br>(0.05)<br>0.014<br>0.029|
|Intrapersonal<br>composite index|2485|0.00<br>(1.00)|0.14<br>(0.05)<br>0.003**<br>0.010*|0.12<br>(0.04)<br>0.009**<br>0.023*|
|Relational composite<br>index|2485|0.00<br>(1.00)|0.05<br>(0.05)<br>0.333<br>0.337|0.09<br>(0.05)<br>0.072†<br>0.118|

Note: Columns 4 and 5 show regression output comparing the Personal Initiative and Interpersonal Initiative conditions, respectively, to the control condition in the Mechanism experiment. Regressions control for randomization strata, age, and head of household status. Standard errors are robust.<sup>†</sup> p<.10, *p<.05, **p<.01, ***p<.001.

49
economic and political change for 29 transition countries and five comparator countries in Western Europe (France, Germany, Italy, Sweden and the United Kingdom).<sup>3</sup> A key question in the survey addresses the normative basis of social exclusion: “Which of the following groups do you prefer to _not_ have as your neighbor?” (see Table 1). We analyze the responses to this question to assess the extent to which negative attitudes toward different groups are held in the different countries covered by LiTS and to identify some of the correlates of those attitudes. We interpret respondents’ answers to reveal not only their housing preferences but also their broader attitudes toward the inclusion and exclusion of specific groups in social life and beyond.

**Table 1: Response categories and % responding yes (across all sample countries) to the LiTS question: “On this list are various groups of people. Could you please mention any that you would not like to have as neighbors? Please just read out the letter that applies”.**

|Response category|% not wanting category as neighbor<br>(all countries)|
|---|---|
|Families with children|4.0|
|Pedophiles|81.0|
|Drug addicts|83.3|
|People of a different race|14.7|
|People who have AIDS|43.2|
|Elderly people|4.2|
|Immigrants / foreign workers|15.9|
|Homosexuals|54.9|
|Gypsies|49.3|
|People of a different religion|10.2|
|Poor people|5.9|
|Heavy drinkers|68.8|

3 The Life in Transition survey was conducted jointly by the European Bank for Reconstruction and Development and the World Bank in 2006 and 2010. Nationally representative samples of between 1,000 and 1,500 households were interviewed in every country. In total, almost 39,000 households were surveyed in 2010 in 34 countries of Europe and Central Asia.

6
substantial and not limited to manufacturing, thanks to propagation channels that operate in local economies.

Finally, we find that the BTA has a more positive impact on employment of females, and poor and rural households. The findings seem to suggest that the BTA carries important distributional implications: it helps reduce income gaps between poor and rich households, and between rural and urban ones.

# **References**

Acemoglu, D., Autor, D., Dorn, D., Hanson, G, H., Price, B. (2016) “Import competition and the great US employment sag of the 2000s”, _Journal of Labor Economics_ , 34(1).

Allendorf, Keera (2007) "Do Women's Land Rights Promote Empowerment and Child Health in Nepal?" _World Development_ , Elsevier, vol. 35(11), pages 1975-1988, November.

<mark>Autor, David H, David Dorn and Gordon H. Hanson. (2013) "The China Syndrome: Local Labor Market Effects of Import Competition in the United States."</mark> _<mark>American Economic Review</mark>_ <mark>, 103(6): 2121-68.</mark> <mark>Brambilla, Irene, Daniel Lederman, and Guido Porto (2012) "Exports, export destinations, and skills."</mark> _<mark>American Economic Review</mark>_ <mark>102 (7): 3406-3438.</mark> <mark>Costa, Francisco J.M., Jason Garred, and João Paulo Pessoa (2016) "Winners and Losers from a Commodities-for-Manufactures Trade Boom."</mark> _<mark>Journal of International Economics</mark>_ <mark>102: 50-69.</mark> Ebenstein, Avraham & Ann Harrison & Margaret McMillan & Shannon Phillips, (2014) "Estimating the Impact of Trade and Offshoring on American Workers using the Current Population Surveys," _The Review of Economics and Statistics_ , MIT Press, vol. 96(4), pages 581595.

36
Policy Research Working Paper 10448

# **Abstract**

This paper estimates worker, firm and assistance programs’ responses to foreign shocks in Brazil exploiting quasi-experimental variation in firms’ foreign demand resulting from the Global Financial Crisis. It shows these transitory shocks have permanent effects in a setting characterized by a large informal sector. While the informal sector provides a buffer mitigating long-term scarring, it is not sufficient to compensate worker and firm scarring. Scarring occurs for incumbent workers, not just displaced workers. Linking employer-employee data with the national registry of low-income households, unemployment insurance disbursement records, and worker-level training, the paper finds that assistance programs fail to mitigate adjustment. Training does not respond, unemployment insurance compensates 4 percent of workers’ wage losses and welfare programs 2 percent. Firms in highly concentrated sectors or state-owned do not bear the burden of adjustment. Other firms bear the burden and either exit or restructure, downsizing employment and productivity, scarring incumbent workers and increasing long-run inequality.

This paper is a product of the Development Research Group, Development Economics and the Office of the Chief Economist, Human Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at afernandes@worldbank.org and jsilva@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
Figures

**Figure 1:  Employer skill preferences and greatest skill gaps among youth, %**

|theoretical training in discipline<br>basic math<br>leadership<br>English proficiency|
|---|
|hands-on training in discipline<br>problem solving<br>written communications<br>creativity<br>computer literacy<br> <br>gap<br>importance|
|work ethic<br>teamwork<br>local language<br>oral communications<br>|
|0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80|

Question: “importance” is defined as the percentage of respondents ranking the skill as 8 or higher out of ten. “gap”is defined as (% of respondents who rank a skills as highly important) – (% of respondents who feel youth are highly competent in that skill). _Source: adapted from Mourshed, Farell and Barton (2012), Exhibit 15, page 12_ .

**Figure 2: Employer demand for each skill set, by distribution of 100 points to each set based on degree of importance, %**

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50 mean, total<br>40 mean, 10% highest scores<br>30 mean, 10% lowest scores<br>20<br>10<br>0<br>specific knowledge socio-emotional<br><!-- End of picture text --> _Source: Derived from Graph 6.7, page 150 in Bassi et al. 2012._ Question: distribute 100 points among the three skills sets, based on the importance of each in the respondents’ firms 42
<!-- Start of picture text --> irs)“ openseeeseeee<br>Raseeer"” tee coessere ee<br>on cere —_—<br>T reel Meee aoe<br>T ~ caneeeeere<br>x wae<br>y re<br>+<br>wTt<br>20 30 40 50<br>Woman's age at the time of the survey<br>~-----=-- 95% Cl === Uneducated ********= Educated<br>Pooled NDHS 1990, 99, 03, 08. Survey weights used. First births from women’s fertility histories.<br><!-- End of picture text -->
# **1. INTRODUCTION**

A major obstacle in regional economic analysis and empirical economic geography is the lack of consistent, reliable regional data, especially data on interregional trade and interindustry transactions. Despite decades of efforts by regional economists, data analogous to national inputoutput accounts and international trade accounts, which have become increasingly available to the public today, still are generally not available even for well defined sub-national regions in many developed countries. Therefore, economists have had to develop various non-survey and semisurvey methods to estimate such data. In earlier years, quotient based, gravity based and regional purchase coefficient based non-survey methods were popular but lacked logical and theoretical structures, and so have been deemed as ‘deficiency methods’ (Jensen, 1990).

Since the 1980s, various constrained matrix-balancing procedures have become increasingly popular for estimating unknown data based on limited initial information subject to a set of linear constraints. Attempts have been made to estimate regional and interregional transactions in a unified national accounting system of economic regions. Batten (1982) extended earlier work by Wilson (1970)<sup>1</sup> and laid out an optimization model based on information theory and linkages between national and regional input-output accounts to simultaneously estimate interregional deliveries in both intermediate and final goods. Batten and Martellato (1985) establish a simple hierarchical relationship among five classical models associated with authors such as Isard, Chenery and Leontief that address interregional trade within an input-output system. They find those models could be reduced to a statistical estimation problem based on varying degrees of available interregional trade data and demonstrate that the net effect of additional data or additional theoretical assumptions is similar in reducing the number of unknown variables in the underdetermined estimation problems. They also demonstrate such estimation problems are best undertaken with a closed system, i.e., when all the geographic components of the national or state data are estimated simultaneously. Following this philosophy, 3
Table 9.3 Fees for Service **at** Bokoro Hospital, 1989

|SERVICE CATEGORY<br>I|ZAIRES|SERVICE<br>CATEGORY<br>I|ZAIRES|
|---|---|---|---|
|AMBULATORY<br>CARE||INPATIENT<br>CARE||
|Consultation, referred<br>|300<br>|Deposit<br>|2,500<br>|
|Cons. non-referred<br>|500<br>|NiniamPayment<br>|1,250<br>|
|Cons.nurseonduty<br>|200<br>|Any additional day|250|
|Outpatient care* 1 week<br>|500<br>|||
|Diabetes (6 months)<br>|250<br>|Private room per day, standard<br>|500<br>|
|Privateconsultation<br>|1,000<br>|Private room per day, Luxury<br>|650<br>|
|Nedicalcertificate|500|Laboratory, cat. 4|250<br>|
|||Surgery,<br>cat. 1|2,100|
|Laboratory,<br>cat. 1|50|Surgor),<br>cat. 2<br>|3,150<br>|
|Laboratory,<br>cat. 2|100|<br>Surgery,<br>cat.3|4,050|
|Laboratory,<br>cat. 3<br>|150|<br>Surgery,<br>cat.4|4,850<br>|
|Laboratory,cat.4|250|Surgery,<br>cat. 5<br>|7,200<br>|
|||Surgery,<br>cat.6|8,650|
|X-Ray|1,000|Spinal<br>anesthesia|A50|
|Ultrasound,<br>extern,<br>w/ med.need|1,000|Ultrasound|500|
|Ulttasound,<br>extern,<br>on demnd|2,000|X-Ray|1,000|
|Echocardiogram|350<br>|||
|Surgery,<br>cat. 1|1,250|||
|Eye examination<br>.............................<br>.............|250<br>......................|...<br>.....|e....|

Table S.4 Firnncial Situation of Bokoro Hospital, 1986 - 1989 <!-- Start of picture text --> Percent Percent Percent I Percent<br>of total of total t of total I 1.HALF of total<br>1986 exenses 1987 * ees 1988 exeses I M989 expenses<br>------------- ------.. ----- ------------------ 1- ---------------------- 1-----------------------<br>Exp. Rev. |Exp. Rev. | Exp. Rev. Exp. Rev.<br>OPEiRATING REVENUE<br>User fees and I I I I I I I<br>insurance plan 2,218,013 91.7X13,189,941 1 99.3115,220,314 1 96.91I4,147,044 1 98.3X<br>Others 201,598 i 8.3X1 21,398 | 0.7%1 164,501 i 3.1X1 71,782 | 1.7X<br>------.--- ... .. I ..--- I ----- 1--I----- .---------- --- --<br>TOTAL: 2,419,611 | 96.7813,211,339 56.3X15,384,815 16.3X14,218,826 | 62.1X<br>OPERATING EXPENSES I I I I I I<br>Persorrel (excl.expats) 846,222 I 49.61 12,049,358 I 52.81 12,768,706 46.4% 12,932,384 1 44.5X<br>Drugs and med.supplies 404,718 23.71X, 990,890 1 25.51 12,322,755 I 38.9X 12,834,017 1 43.01<br>Office siplies 16,220 1.01 I 29,483 I 0.8X 39,113 I 0.71 I 78,296 I 1.2X<br>Tranrport 610 | 0.0% 96,548 % | 2.51 | 141,861 | 2.41 | 31,334 | 0.5X<br>Maintenance 61,403 3.61 I 105,648 2.7S 126,212 2.1X I 127,772 1.9X<br>Fuel £ electricity 216,910 12,71 I 4,757 11.5X 429,037 7.2x 55S,316 I 8.4X<br>Food service & other 159,499 | 9.41 | 166,234| 4.2 | 140,943 2.4X | 34,282 | 0.51<br>.---.- ------ 1 .------- 1- ----- --.-.-.-- - ---------- -----<br>TOTAL: 1,705,582 |lOO.0 13.8a0.918 |100.01 O5,968,627 1100.01 16,593,401 1100.0|<br>OPERATING SUBSIDIES: 33,099 | 1.3112,497,14 1 43.7X12,742,143 | 33.7112,574,322 | 37.91<br>I --- 1 1 ---- 1 --- I ...<br>100.0O1 I 100.0O1 I 100.0X1 I 100.0l<br><!-- End of picture text -->

32
consequences of devaluation? How can the country go about devaluing the currency and what other policy actions would need to be considered to maximize gains from it?

The remainder of the paper is organized as follows: Section 2 briefly discusses the major economic challenges encountered by Ethiopia in recent years. Section 3 examines the country’s current exchange rate policy and recent movements in the exchange rate. Section 4 discusses why the real exchange rate is a potent instrument of development policy. Section 5 presents and discusses the macroeconomic effects of devaluation in Ethiopia. Section 6 assesses the major policy trade-offs that currency devaluation entails. Finally, Section 7 concludes with implications for policy.

**Figure 1. Annual real GDP growth rate, % (1982-2018)**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>Source: World Bank and Ministry of Finance.<br><!-- End of picture text --> <!-- Start of picture text --> Source: World Bank and Ministry of Finance.<br><!-- End of picture text -->

# **2. Major Challenges Facing the Ethiopian Economy**

Ethiopia has experienced sustained growth acceleration since 2004. Figure 1 shows the graph of annual real GDP growth rate for the period 1982-2018. Between 1980 and 2003, annual GDP growth averaged 3 percent. The pre-2004 growth trajectory was highly uneven, with growth hitting its highest level in times of good weather outturn and its lowest in dry (or turbulent) years. Unlike the erratic growth performance before 2004, the economy has staged a record-high growth of 10.3 percent per annum in 2004-2018, which is nothing short of impressive compared to the SSA regional average of 4.6 percent over the same period. The growth spell was, however, accompanied by inflationary pressures, with the inflation rate reaching an all-time high of around 40 percent year-on-year in 2011. Nonetheless, since 2013, inflation has been tamed to reasonable single-digit rates per year, except in 2018 when it crossed into double-digit territories.

However, the poor export performance in recent years has become a cause for concern. Exports have been on a downward slide since the early 2010s (Figure 2). Exports of goods and services as a percentage of GDP declined from its peak of 16.7 percent in 2011 to 8.9 percent in 2018. About 5 percent of Ethiopia’s total merchandise exports constitute manufacturing goods. Export as a share of national income is lower than the expected level of exports given Ethiopia’s GDP per capita, namely 24 percent (World Bank, 2014). It also bears noting that both goods and services exports have seen a decline. Exports steeply declined in recent years, which is very concerning taking into consideration the average annual export growth of about 20 percent registered over 2004–2014. Goods exports saw weak performance generally as a result of falling commodity prices, despite relatively solid volume growth.

3