rafmacalaba/datause-encoder-data huggingface dataset:
Respondents were probed on whether they believe the following donors and non-state actors exert too much, too little or just the right amount of influence over their government: domestic civic organizations and NGOs; and, international donors and NGOs. These two variables indirectly capture citizens’ normative views of non-state service provision, specifically, whether donors and non-state actors are helpful or harmful for their country.

I would expect citizens who believe that donors and non-state actors have too little influence over their government to be less likely to willingly defer to their government. These respondents probably believe that their government needs the assistance of donors and non-state actors to provide essential goods and services. A perception that donors and non-state actors exert too much influence over their government may not necessarily translate into a greater willingness to defer to the government. Some may view the influence donors and non-state actors exert over their government positively and believe that the more influence donors and non-state actors exert over their government, the better off their country.

Others may view the influence donors and non-state actors have over their government negatively. If citizens view the influence donors and non-state actors exert over their government negatively, it is not clear whether they are more or less likely to be willing to pay taxes. Citizens have multiple strategies for confronting the state. On the one hand, some may want to bolster the strength of the state to collect revenue from, and provide public goods to, citizens. An increase in a state’s extractive capacity should correspond to less dependence on donors and NGOs and an increase in state autonomy. On the other hand, others may want to try and replace the regime with a stronger government that is committed to standing up to donors and non-state actors and re-claiming the state’s sovereignty.

Finally, I also include several country-level measures of donor and nonstate actor activities. First, I include a measure of the number of intergovernmental organizations (IGOs) and NGOs of which a country or territory is a member, whether directly or through the presence of members in that country for 2007 (Union of International Associations (UIA), 2005). I excluded dead, inactive or unconfirmed organizations. Second, I include a measure of the amount of foreign aid a country receives from bilateral and multilateral donors. Specifically, I include the natural log of net Official Development Assistance (ODA) received per capita (current US $) for 2007. Third, I include a measure of food aid. The Food and Agricultural Organization (FAO) compiles food aid shipments, which represents a transfer of food commodi15
**Figure 1.  Electricity, Outages and Generators**

<!-- Start of picture text --> Electricity<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>NPB LGD<br>AFG<br>PAK<br>LKA<br>IND<br>MDV<br>BTN<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>Power Cuts >1 per mon.<br>Constraint level, conditional means; quadratic fit; 5% robust CIs<br>AFG<br>NPB LGD<br>IND<br>PAK<br>BTN<br>MDV<br>L KA<br>6 7 8 9 10 11<br>Log(GDP p.c.) at PPP<br>4<br>3<br>2<br>1<br>0<br>1.5<br>1<br>.5<br>0<br><!-- End of picture text --> 64
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The CAR already spends around $134 million per year on infrastructure, equivalent to about 7 percent of its GDP. Almost $37 million a year is lost to inefficiencies of various kinds, associated mainly with underpricing in the power, water, and road sectors; poor financial management of utilities; and inefficient allocation of resources across sectors. If the CAR could raise the tariffs to cost-recovery levels, and manage aligning operational inefficiencies with reasonable developing-country benchmarks, it would increase existing flows to the infrastructure sectors.

Assuming that the inefficiencies are fully captured, comparing spending needs against existing spending and potential efficiency gains leaves an annual funding gap of $183 million per year. By far the largest gap exists in transport. The CAR has the potential to close this gap by raising additional public funding for infrastructure from increased fiscal receipts of various kinds. Furthermore, the CAR has not captured as much private finance for infrastructure (measured as a percentage of GDP) as many of its neighbors. This scope for improvement, coupled with the prospect of an economic rebound and prudent policies, could lift the country from it fragile state back to and beyond the prosperity standards it once enjoyed.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in more than 40 Sub-Saharan countries, including the Central African Republic. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for the CAR, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that the CAR is a fragile state trying to catch up with other low-income countries (LICs) in the region, both fragile-state and LIC African benchmarks will be used to evaluate the CAR’s situation. Detailed comparisons will also be made with immediate regional neighbors in Central Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2009. Most technical data presented are for 2008 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, to make comparisons across countries, indicators were standardized so that analyses were done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

Between 2002 and 2009 the CAR’s GDP grew at a rate of 2.2 percent per year. The effect of the global economic crisis was notable: the economy grew 3.7 percent in 2007, 2.2 percent in 2008, and 1.7 2
Researchers have used international assessments to analyze the determinants of learning (Hanushek and Luque 2003; Hanushek and Kimko 2000; Barro 2001; Lee and Barro 2001; Afonso and Aubyn 2006; Bedard and Ferrall 2002; Hanushek and Woessmann 2006; Alvarez, Garcia-Moreno and Patrinos 2007; Nabeshima 2003; Fertig 2003; Fertig and Schmidt 2002; Woessmann 2003; Fuchs and Woessmann 2007). While most analyses are cross-country, there is an increasing trend to look at individual countries in depth.

This paper documents the assent of Jordan in international assessments. The process involved in preparing for the numerous assessments Jordan takes part in is described, along with a review of the steps involved. The change in scores over time is analyzed by using the decomposition methodology that is usually applied in wage regressions research, but in this case used to measure the effects of resources versus efficiency in explaining score changes. It is shown that a significant part of the overall increase in scores is associated with Jordan’s educational inputs becoming more efficient.

# **Background**

Jordan’s assessment of the status of student learning outcomes through international comparisons started in the early 1990s. Jordan’s first participation in international studies was in 1991, as the first Arab country to participate in such studies. At the same time that the International Assessment of Educational Progress (IAEP II) was launched Jordan began its review of the education system and a comprehensive reform.

Jordan has been participating in the major international exams: 1991 in International Assessment of Educational Progress (IEAP), 1999 in Trends of International Mathematics and Science Study (TIMSS-R), 2003 TIMSS, 2006 Program of International Student Assessment (PISA), 2007 TIMSS, and 2009 PISA. Using these tests and their national assessments they have benchmarked their performance: with IAEP they assess performance at the end of the primary cycle in science and math; with TIMSS the focus is on science and math, for students in grade 8, in parallel to education reforms; and with PISA and NAfKE they assess structural diagnostics of skills at the end of the compulsory school stage.

Early IEAP results in 1991 were alarming, as Jordan ranked 18 among 19 countries. IAEP II provided crucial data on educational performance but also allowed the country the opportunity to learn assessment techniques (sample selection, test administration, implementation monitoring). Thus IAEP was instrumental in building national capacity for conducting surveys of student achievement. Jordan’s students ranked near the bottom in IAEP II. The results came as a shock. Almost 75 percent of students in mathematics and 67 percent of students in science scored lower than the international average. Jordan ranked third from the bottom in both subjects among the 20 participating countries.

While the impact of the assessment results of the education reforms and projects was expected to take time before showing results, after serious interventions and follow-ups on the gaps in their 3
Rozenfeld, H. D., D. Rybski, X. Gabaix, H. A. Makse. (2011). The Area and Population of Cities: New Insights from a Different Perspective on Cities. American Economic Review 101(5):2205-2225.

Sanyal, K., and Jones, R., (1982). The theory of trade in middle products. American Economic Review 72(1): 16–31 Soo, K. (2005). Zipf ’s law for cities: A cross-country investigation. Regional Science and Urban Economics, 35(3): 239–63.

Soo, K.T. (2007). Zipf Law and Urban Growth in Malaysia. Urban Studies, 44(1):1-14.

UNCTAD. (2013). World Investment Report 2013. Global Value Chains: Investment and Trade for Development, New York & Geneva: United Nations.

Venables, A. J. (1999). Fragmentation and multinational production, European Economic Review 43: 935-945.

World Bank. 2008. World Development Report 2009: Reshaping Economic Geography. Washington, DC: World Bank.

World Trade Organization (WTO). (2011). Trade patterns and global value chains in East Asia: From trade in goods to trade. Available at: <u>https://www.wto.org/english/res_e/publications_e/stat_tradepat_globvalchains_e.htm</u> WTO and IDE-JETRO. (2011). Trade Patterns and Global Value Chains in East Asia: From Trade in Goods to Trade in Tasks,” World Trade Organization and Institute of Developing Economies, Geneva and Tokyo, 2011 (http://www.ide.go.jp/English/Press/pdf/20110606_news.pdf).

Zipf, G.K. (1949). Human Behaviour and the Principle of Least Effort. Addison-Wesley: Reading, MA.

18
33 this case, the coefficients on the other bins in Figure 1 are over-estimated relative to bin 0-4. The coefficients on workers in the 5-9 year bin are not over-estimated relative to bin 0-4, however, since both groups train. There are 8 bins so, abstracting from mortality, training is concentrated among one quarter of the workers. This implies that workers in the 0-4 and 5-9 experience bins spend 13% of their time training in developed economies, compared to 8% in developing economies. What does this imply for the returns? According to our benchmark estimates, the gap in experience returns between developed and developing economies was 2 _._ 4% for workers without college, or 1 _._ 4% percent for workers with college. If we adjust the returns in Figure 1 based on the above scenario and recompute the gap, it declines to 2 _._ 2% for workers without college and 1 _._ 2% for workers with college. These are declines of 8 _._ 3% and 14 _._ 3% respectively. Thus, while the magnitude of our estimated gaps are somewhat sensitive to assumptions regarding worker training early in their careers, the difference is small. See Web Appx. Table A.11.

Suppose instead that all training is performed by workers in the lowest experience bin, i.e. 0-4. In this extreme case, the coefficients on the other bins in Fig. 1 are overestimated relative to bin 0-4, including the 5-9 bin. There are 8 bins so, abstracting from mortality, training is concentrated among one eighth of the workers. This implies that workers in the 0-4 experience bin spend 25% of their time training in developed economies, compared to 15% in developing economies. If we recompute the gap, it declines to 2 _._ 2% for workers without college and 1 _._ 2% for workers with college. These are declines of 25% and 35 _._ 7% respectively. Thus, even under a scenario where all training is complete within 4 years, a significant gap in returns remains.

# **7. Conclusion**

In this paper, we provide additional global evidence that experience matters.

First, we studied wage-experience profiles and obtained returns to experience, and education, using data from 1,084 household surveys and census samples across 145 countries. Across many specifications, we found that returns to experience are strongly correlated with economic development and that workers in developed countries may accumulate twice more human capital at work than workers in developing countries.

Second, exploiting the fact that we were able to estimate returns to experience for more than 100 countries and multiple years of data during the period 1990-
Table 8b: Estimations of rural manufacturing <u>production functions and local 1-person establishment shares</u>

||Full sample<br>|Full sample<br>|Full sample<br>|Tradables<br>industries<br>|Col. 4<br>excluding 1-<br>person firms<br>|Non-tradables<br>industries<br>|Col. 6<br>excluding 1-<br>person firms<br>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||DV: Log outpu|t in manufactur|ing establishmen|t||
|Log employment in establishment|0.372<br>[0.013]***|0.373<br>[0.013]***|0.375<br>[0.013]***|0.382<br>[0.020]***|0.611<br>[0.034]***|0.373<br>[0.016]***|0.463<br>[0.025]***|
|Log capital in establishment|0.128<br>[0.006]***|0.126<br>[0.006]***|0.126<br>[0.006]***|0.095<br>[0.010]***|0.117<br>[0.013]***|0.143<br>[0.008]***|0.149<br>[0.009]***|
|Log materials in establishment|0.555<br>[0.009]***|0.554<br>[0.009]***|0.554<br>[0.009]***|0.594<br>[0.013]***|0.528<br>[0.020]***|0.533<br>[0.012]***|0.517<br>[0.013]***|
|Informal manufacturing employment||-0.199|-0.217|-0.148|-0.229|-0.242|-0.243|
|share in district||[0.033]***|[0.040]***|[0.072]**|[0.094]**|[0.047]***|[0.053]***|
|Informal manufacturing 1-person firm|||0.067|-0.196|-0.327|0.203|0.206|
|employment share in district|||[0.089]|[0.175]|[0.234]|[0.088]**|[0.100]**|
|Observations|266,718|266,718|266,718|86,983|51,865|179,735|139,321|
|Adjusted R-squared|0.925|0.925|0.925|0.929|0.942|0.924|0.929|
|State-industry-year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: See Table 8a.
# **VII. References**

Anderberg, M.R. (1973), _ClusterAnalysisfor Applications,_ Academic Press, NY.

Anderson, K. H., J. S. Butler and F. A. Sloan (1987), "Labor Market Segmentation: A Cluster Analysis of Job Grouping and Barriers to Entry. " _Southern Economic Journal,_ 53:3 January.

Aronson, R.L.(1 99 1), _Self-Employment, A Labor Market Perspective._ ILR Press, Ithaca.

Balan, J. and H.L. Browning, E. Jelin (1973), _Men in a Developing Society,_ Institute of Latin American Studies, U.T. Austin.

Beneria, L.(1989) Subcontracting and Employment Dynamics in Mexico City, in Portes et al., _The Informal Economy,_ Johns Hopkins.

Castells, M. and A. Portes (1989), "World Underneath: The Origins, Dynamics, and Effects of the Informal Economy," in A., M. Castells, and L. A. Benton, eds., _The Informal Economy: Studies in Advanced and Less Developed Countries,_ Baltimore, Maryland: Johns Hopkins University Press.

Davis, S. J., J. Haltiwanger, and S. Schuh (1994), "Small Business and Job Creation: Dissecting the Myth and Reassessing the Facts," in L. C. Solmon and A. R. Levenson, eds., _Labor Markets, Employment Policy, and Job Creation,_ Boulder, Colorado: Westview Press.

de Soto, H. (1989), _The Other Path: The Invisible Revolution in the Third World,_ (New York: Harper and Row).

Dunne, T., M. J. Roberts, and L. Samuelson (1989), "The Growth. and Failure of U.S. Manufacturing Plants," _Quarterly Journal of Economics,_ 104:4, 671-698.

Elizondo, N. (1992), "Illegality in the Urban Informal Sector of Mexico City," in V.E. Tokman, ed., _Beyond Regulation, The Informal Economy in Latin America,_ Lynne Rienner, Boulder, Colorado.

Esfahani, H. and D. Salehi-Isfahani (1989), "Effort Observability and Worker Productivity: Towards and Explanation of Economic Dualism," _Economic Journal,_ 99, 818-836.

Evans, D. S. and L. S. Leighton (1989), "Some Empirical Aspects of Entrepreneurship," _American Economic Review,_ 79:3, 519-535.

Everitt, B. (1974) _Cluster Analysis,_ John Wiley and Sons, NY.

Fairlie. R. W. and B.D. Meyer, "Trends in Self-Employment Among White and Black Men: 1910-1990," Mimeo, U.C. Santa Cruz.

39
- 33 -

production and external balances. At the **sam** time, little liberalization was achieved, accordlng to others. Table 4 Indicates that during 1982-86 the SAL countries have increased their export shares significantly, while their import shares In GDP have increased modestly. Liberalization seems to have been selective, applicable in the first instance to inputs needed In production. Although there are individual examples of excessively rapid liberalization, this by no means seems to have happened In general.

Table 4: lMD3 SHAS IN GOP (in perent)

||19|65-73|197|4-61|198|246|196|5-81|196|546|
|---|---|---|---|---|---|---|---|---|---|---|
||ECK|P DIP|CP|DP|ECP|DC|No?|Do|U|DIP|
|Africa|23.4|22.9|26.2|26.9|22.7|23.7|24.7|24.8|24.3<br>|24.6<br>|
|Africa (excl Nigeria)|24.4|24.0|26.1|28.2|26.3|27.2|25.2|26.0|25.4<br>|26.2<br>|
|Aia|12.5|13.1|21.0|21.5|27.3|26.5|16.5|17.1|19.0|19.2|
|EM3tI|16.6|22.0|20.2|29.7|24.7|31.1|18.3|25.6<br>|19.8<br>|26.9<br>|
|Latin<br>Aerica|12.2|12.1|13.7|15.0|16.0|13.1|12.9|13.5|13.6|13.4|
|tU- unKmeGtries<br>a/|6.9|7.5|8.4|10.8|9.7|13.0|7.6|9.0<br>|8.1<br>|9.9<br>|
|High LIdebted cotries<br>b/|12.8|12.8|15.0|16.6|16.6|14.2|13.8|14.6|14.5|14.5|
|35 SAL Countries c/|12.8|13.8|15.9|18.1|19.1|18.2|14.2|15.9|15.3|16.4|
|15 Major SAL Countries d/|13.2|15.0|16.6|20.3|23.3|22.0|14.8|17.5|16.7|18.5|
|Dereloping auntry<br>Average e/|13.2|14.4|17.6|20.1|20.7|21.1|15.3|17.1|16.5|18.0|

Based on XDR 30 _low_ ln cointries. ased on Wrld Debt T&blm classification.

Based on the Ibrld **_Bsiks_** stnctural adjustat lendiv camtries. Chile, Calaobia, Coe d'Ivoire, Jaulca, Kenya, Korea, Malawi, llritius, Mmdco, Mbrocco, Psm, Philippies, Seg1, Tailad, and lnbiey. e/ _Basd_ on WIR 90 amuntries.

S9urce: M, World Bark
Table A.10: Heterogeneous treatment effects on type of peers sought, by mother-in-law opposition to family planning

||**Sought comp**|**any to visit the ADC**<br>**from:**|
|---|---|---|
||Sister-in-law|Non-relative|
||(1)|(2)|
|Own Voucher|0.053**|0.019|
||[0.022]|[0.018]|
|BAF Voucher|0.167***|0.101***|
||[0.034]|[0.031]|
|Own Voucher _×_ MIL opposed to FP|-0.014|-0.011|
||[0.051]|[0.024]|
|BAF Voucher _×_ MIL opposed to FP|0.131|0.020|
||[0.106]|[0.075]|
|Observations|583|583|
|Endline control mean|0.003|0.009|
|p-values:|||
|Own = BAF|0.003|0.009|
|Own _×_ MIL opposed = BAF _×_ MIL opposed|0.205|0.672|
|Own + Own _×_ MIL opposed = 0|0.409|0.625|
|BAF + BAF _×_ MIL opposed = 0|0.003|0.078|

_Notes:_ All columns are fully-interacted regressions, where all the covariates are interacted with MIL opposed to FP. The main effect of MIL opposition to FP at baseline is also included as a control variable. All columns include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. MIL denotes mother-in-law, FP denotes family planning, and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

58
the country have electricity connections as observed in most developed countries, then electricity connections should not be a source of spatial disparity any more though intensity of electricity use may still vary with households’incomes.

## 2.3 Empirical Estimation

In the empirical estimation, we follow the practice of existing literature and use log of per capita real expenditure as an indicator of welfare (Vij):Per capita household expenditure is de‡ated by the spatial cost of living index. We estimated spatial disparity indices for two years (2000 and 2010). The indices are also estimated by including or excluding di¤erent sets of controls for household attributes (sorting) and locational attributes (infrastructure and geo-climatic conditions). Suppose �(s) denotes the spatial disparity arising from di¤erential provision of infrastructure, and �(H) from sorting of household attributes. Estimates of �(s) [�(H)] for two years can be compared to ...nd out how much of the spatial convergence or divergence is due to change in provision of infrastructure and services [sorting of household attributes]. Because of the fundamental di¤erences in the economic structure of urban and rural areas, estimations of spatial disparity are done separately for rural and urban areas along with that for the entire country ("pooled sample").

# 3. Data

The main data sources for our empirical analysis are the two rounds of the Household Expenditure Survey (HIES) of Bangladesh. The survey rounds utilized in this paper are 2000 and 2010.<sup>3</sup> Both of the surveys were carried out by the Bangladesh Bureau of Statistics with assistance from the World Bank. Both surveys utilized a two-stage strati...ed sampling strategy to select a nationally representative sample. There were 14 strata in 2000 survey and 16 in 2010 survey. The total sample sizes are 7,440 households from 442 primary sampling units (psus) in

> lead to more spatial concentration followed by regional convergence at even lower level of transport costs.

> 3 The HIES was done for 2005 also. The results based on 2005 data are very similar to those using 2010 data. We omitted analysis for 2005 round of HIES for brevity.

11
**Table A.3.2: Results of Simulation 2 by region, income group and lending type. Effect on mean (score)**

||||Post-COVID-19<br>||
|---|---|---|---|---|
|Aggregate|Baseline|Optimistic|Intermediate|Pessimistic|
|EAP|461|455|445|435|
|ECA|461|455|445|434|
|LAC|402|396|386|376|
|MNA|400|393|384|374|
|NAC|513|507|495|483|
|SAR|||||
|SSF|||||
|Grand Total|440|433|424|413|
|HIC|482|476|464|452|
|UMC|410|403|394|385|
|LMC|362|355|349|342|
|LIC|-|-|-|-|
|Grand Total|440|433|424|413|
|Part I|487|481|470|458|
|IBRD|413|406|397|388|
|IDA/Blend|334|327|320|313|
|Grand Total|440|433|424|413|

Source: Authors’ calculation. Results expressed in mean score (PISA points). Simulation 2 results based on latest available PISA and PISA-D mean score of 92 countries. Unweighted average. Student coverage as share of lower secondary enrollment: 100% NAC; 95% LAC; 94% EAP; 91% ECA; 76% SAR; 39% MNA; 3% SSA.

46