rafmacalaba/datause-encoder-data huggingface dataset:
4

|Title|Author|Date|
|---|---|---|
|WPSl Imports Under a Foreign Exchange<br>Constraint|Cristian Moran|March 1988|
|WPS2 Issues in Adjustment Lending|Vinod Thomas|March 1988|
|WPS3 CCE Models for the Analysis of Trade<br>Policy in Developing Countries|Jaime de Melo|March 1988|
|WPS4 Inflationary Rigidities and<br>Stabilization Policies|Miguel A. Kiguel<br>Nissan Liviatan|April 1988|
|WPS5 Comparisons of Real Output<br>in Manufacturing|Angus Maddison<br>Bart van Ark|April 1988|
|WPS6 Farm/Nonfarm Linkages in Rural Sub-<br>Saharan Africa|Steven Haggblade<br>Peter B. Hazell<br>James Brown|April 1988|
|WPS7 Institutional Analysis of Credit<br>Cooperatives|Avishay Braverman<br>J. Luis Cuasch|April 1988|
|WPS8 Prospects for Equitable Growth in<br>Rural Sub-Saharan Africa|Steven Haggblade<br>Peter B. Hazell|April 1988|

For information on obtaining copies of papers, contact the Research Administrator's Office, 473-1022.
# **Figure 9: Poverty impact graph for both participants and the whole population**

Round 1 <!-- Start of picture text --> .01<br>0<br>-.01<br>-.02<br>-.03<br>-.04<br>-.05<br>Poverty line<br>PW only sample as a whole<br>Round 2<br>.01<br>0<br>-.01<br>-.02<br>-.03<br>-.04<br>-.05<br>Poverty line<br>PW only sample as a whole<br>0 5000 6988 10000 15000 20000 25000<br>0 5000 783610000 15000 20000 25000<br>Impact on poverty (change in CDF)<br>Impact on poverty (change in CDF)<br><!-- End of picture text --> _Source:_ Authors’ estimates based on BREGS Survey.

33
Table 7: Domestic Implicit Subsidy Provided by Gazprom

||Via arxears|Via barter|Total as % of GDP|
|---|---|---|---|
||($ billion)|($ bilon)||
|1993|1.4|1.4|1.4|
|1994|1.6|1.7|1.3|
|1995|3.4|3.7|1.8|
|1996|3.6|3.7|1.8|
|1997|3.3|3.5|1.6|
|Total|**12.7**|13.5||

Source: staff calculations

# _Power Utilities - RAO UES (UES)_

Like Gazprom, the electricity sector is heavily involved in the nonpayments web, and serves as a major channel for large implicit subsidies to domestic industries. Table 8 shows that the electricity generation sector is also plagued by low collection rates and by low cash collections, i.e., by both arrears and NCS. These phenomena are unique to FSU countries, as a detailed multi-country World Bank study of the energy sector shows.<sup>25</sup> The problem is virtually absent in the transition countries of central Europe and has been largely solved in the Baltic Republics.

Table 8: Composition of Power Utilities Sales by Means of Payment*

||199.|1997|
|---|---|---|
|Sales|100|100|
|Cash & liquid equivalent|20|20|
|Bank bills|11|6|
|Offsets and barter|49|62|
|Unpaid arrears|20|12|

* _Including intra -industry transactions_

Source: UES, MFK Renaissance, Brunswick Warburg Table 9 presents our estimate of the implicit subsidy flow from the electricity generation sector. According to it, the average annual net subsidy extended by power utilities to other sectors amounted to 2.3 percent of GDP per year over 1993-1997, a cumulative total of $36.8 billion.<sup>26</sup> Consequently, the energy sector as a whole has been providing economy with an annual implicit subsidy equal to 4 percent of GDP - a hefty $63 billion over the five years, 1993-1997.

> 25 World Bank (1999).

> 26 Net power utilities' subsidy excludes share of gross subsidy coming from Gazprom.
and large firms are more severely affected by stringent labor regulations, while small firms are less affected, probably because they are partially exempt from such regulations or can more easily circumvent them. Moreover, stringent labor regulations have more of an impact on job flows for small and medium entering and exiting firms, as well as continuing firms of all sizes, whereas product market regulations are more important for shaping the job flows of large entering and exiting firms, and do not play much of a role for continuing firms.

The remainder of the paper is organized as follows. Section 2 presents our harmonized firm-level dataset and discusses the different concepts we have used to characterize labor reallocation. Section 3 analyzes the main features of job flows, highlighting the role of firm dynamics, industry and size compositions. Section 4 presents the results from the analysis of variance. Section 5 introduces the difference-in-difference approach used in the econometric analysis and discusses the empirical results for the baseline and policy augmented specifications of the job flow equations. It also describes a battery of robustness tests. Finally, section 6 provides our concluding remarks.

# **2 Data**

Our analysis of job flows draws from a harmonized firm-level database that involves 16 industrial, developing and emerging economies (Germany, Finland, France, Italy, Portugal, the United Kingdom and the United States, Estonia, Hungary, Latvia, Slovenia, Argentina, Brazil, Chile, Colombia and Mexico) and covers the 1990s (the time period covered varies by country - see Table 1).<sup>2</sup> The data collection was conducted by an active participation of local experts in each of the countries, and involved the harmonization of key concepts to the extent possible (such as entry and exit of firms, job creation and destruction, and the unit of measurement), as well as the definition of common methods to compute the indicators (see Bartelsman et al. [2005] for details).<sup>3</sup> The key features of the micro-data underlying the analysis are as follows:

**Unit of observation** : Data used tend to conform to the following definition:“an organizational unit producing goods or services which benefits from a

> 2The database also includes Indonesia, South Korea and Taiwan (China) as well as the Netherlands, Canada, Denmark, Romania and Venezuela, but annual data on job flows are not available for these countries or are not fully reliable.

> 3Micco and Pages [2004] compiled a dataset from different country sources covering 2-digit manufacturing sector information for 18 countries. Their dataset does not include transition countries, and does not allow differentiating job flows by firm status and firm size for all the countries.

5
The evaluation literature of such programs is more abundant in developed countries. Importantly, a large portion of these studies are also correlational analyses for which there are obvious challenges to causal interpretation of the findings. Two studies in the U.S. written by the National Center for Educational Statistics (2001a and 2001b) found a positive relationship between availability of computers in schools and test scores. For the US, Wenglinsky (1998) measured the amount of computers that were used in math classes and scores on math tests and found a positive relationship between use of computers and learning in both 4<sup>th</sup> and 8<sup>th</sup> grades. Rouse and Krueger (2004) undertook a randomized study of a popular instructional computer program, known as Fast ForWord, which is designed to improve language and reading skills. They concluded that while the program may have improved some aspects of students’ language skills, the gains do not translate into a broader measure of language acquisition or into actual readings skills.

Similar positive relationships have been found in OECD countries between computer use and test scores for mathematics (NCES 2001a, Cox et al., 2003), science (NCES, 2001b, Harrison et. al. 2002) and reading (Harrison et. al. 2002). Kulik (2003) reviews 75 impact evaluations of technology applications in the US, finding the following results, among others: (i) students who used computer tutorials in mathematics, natural science, and social science score significantly higher on tests in these subjects; (ii) the use of computer-based laboratories did not result in higher scores; and (iii) primary school students who used tutorial software tutorial in reading scored significantly higher on reading scores, while very young students who used computers to write their own stories scored significantly higher on measures of reading skills.

- 3 -
|P132216|2016|2019|8/6/2018|6 Active|Burkina Faso|
|---|---|---|---|---|---|
|P132216|2016|2019|2/2/2019|7 Active|Burkina Faso|
|P132216|2016|2020|9/29/2019|8 Active|Burkina Faso|
|P132216|2016|2020|4/22/2020|9 Active|Burkina Faso|
|P132231|2013|2014|2/24/2014|1 Closed|Bhutan|
|P132231|2013|2015|4/10/2015|2 Closed|Bhutan|
|P132231|2013|2017|10/19/2016|3 Closed|Bhutan|
|P132234|2014|2014|2/26/2014|1 Closed|Pakistan|
|P132234|2014|2015|9/21/2014|2 Closed|Pakistan|
|P132234|2014|2015|4/10/2015|3 Closed|Pakistan|
|P132234|2014|2016|10/16/2015|4 Closed|Pakistan|
|P132234|2014|2016|4/12/2016|5 Closed|Pakistan|
|P132234|2014|2017|10/25/2016|6 Closed|Pakistan|
|P132234|2014|2017|4/10/2017|7 Closed|Pakistan|
|P132234|2014|2018|1/22/2018|8 Closed|Pakistan|
|P132234|2014|2019|9/7/2018|9 Closed|Pakistan|
|P132234|2014|2019|1/15/2019|10 Closed|Pakistan|
|P132238|2013|2013||0 Active|Philippines|
|P132246|2013|2013|5/5/2013|1 Closed|Philippines|
|P132246|2013|2015|11/14/2014|2 Closed|Philippines|
|P132246|2013|2016|9/30/2015|3 Closed|Philippines|
|P132246|2013|2017|10/8/2016|4 Closed|Philippines|
|P132246|2013|2017|11/16/2016|5 Closed|Philippines|
|P132268|2013|2013||0 Closed|West Bank and Gaza|
|P132268|2013|2015|12/2/2014|1 Closed|West Bank and Gaza|
|P132268|2013|2015|6/4/2015|2 Closed|West Bank and Gaza|
|P132268|2013|2016|12/16/2015|3 Closed|West Bank and Gaza|
|P132268|2013|2016|6/8/2016|4 Closed|West Bank and Gaza|
|P132268|2013|2017|11/18/2016|5 Closed|West Bank and Gaza|
|P132268|2013|2017|5/29/2017|6 Closed|West Bank and Gaza|
|P132268|2013|2018|11/29/2017<br>|7 Closed|West Bank and Gaza|
|P132268|2013|2018|6/12/2018|8 Closed|West Bank and Gaza|
|P132268|2013|2021|7/23/2020|9 Closed|West Bank and Gaza|
|P132270|2014|2015|9/23/2014|1 Closed|Central Asia|
|P132270|2014|2015|4/27/2015|2 Closed|Central Asia|
|P132270|2014|2016|12/23/2015|3 Closed|Central Asia|
|P132270|2014|2016|6/30/2016|4 Closed|Central Asia|
|P132270|2014|2017|1/23/2017|5 Closed|Central Asia|
|P132270|2014|2017|6/20/2017|6 Closed|Central Asia|
|P132270|2014|2018|1/3/2018|7 Closed|Central Asia|
|P132270|2014|2019|10/15/2018|8 Closed|Central Asia|
|P132270|2014|2019|4/21/2019|9 Closed|Central Asia|
|P132270|2014|2021|7/22/2020|10 Closed|Central Asia|
|P132277|2014|2015|11/1/2014|1 Active|China|
|P132277|2014|2015|6/11/2015|2 Active|China|
|P132277|2014|2016|12/13/2015|3 Active|China|
|P132277|2014|2016|6/27/2016|4 Active|China|
**Table 4. Institutional determinants of fiscal policy volatility**

||Pri<br>|mary Expendit<br>|ures<br>|Govern<br>|ment Consu<br>|mption<br>|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(3)|(4)|(5)|(6)|
|Political constraints|-11.27***|-3.596|-4.095|-8.497***|-0.714|-1.037|
||(2.502)|(3.224)|(3.298)|(2.834)|(3.220)|(3.284)|
|Control of corruption|-1.997***|-1.836***|-1.821***|-2.908***|-1.674**|-1.668**|
||(0.421)|(0.570)|(0.570)|(0.530)|(0.653)|(0.661)|
|EMDE||-2.381*|-2.418*||-1.397|-1.441|
|||(1.266)|(1.272)||(1.487)|(1.456)|
|Commodity exporters||0.661|0.843||2.062|2.199*|
|||(0.747)|(0.716)||(1.273)|(1.300)|
|Resource rents||0.281***|0.284***||0.253***|0.256***|
|||(0.0688)|(0.0699)||(0.0567)|(0.0551)|
|GDP per capita||-0.548|-0.349||-1.479**|-1.347*|
|||(0.464)|(0.496)||(0.617)|(0.739)|
|SWF|||-0.945|||-0.627|
||||(0.773)|||(1.198)|
|Constant|17.92***|19.44***|17.96***|19.73***|25.67***|24.69***|
||(1.305)|(5.025)|(5.114)|(1.615)|(6.616)|(7.544)|
|Observations|133|132|132|130|129|129|
|R-squared|0.394|0.595|0.599|0.363|0.543|0.544|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘EMDE’ is a dummy variable that takes the value “1” if a country is classified as an EMDE, and “0” otherwise. ‘Commodity exporters’ is a dummy variable representing whether a country is a commodity exporter or not. ‘SWF’ is a dummy equal to “1” if a country has a sovereign wealth fund and “0” otherwise.

34
# **1. Purpose and scope of review**

## **1.1 Background**

The aim of the paper is to review international practices for determining medium term resource needs of spending agencies or ‘bottom-up costing’ for MTEFs. The principal objective is to compile comparative information on practices and methodologies used by selected OECD countries to determine program costs as part of their medium term expenditure planning. The selected case study jurisdictions are Australia, Austria, Canada and the Netherlands.

This paper assembles case study data on costing practices for MTEFs and provides some preliminary observations on the strengths and weaknesses of current arrangements, as well as the status of costing within conventional understandings of MTEF guidance.

The focus of this paper is on the costing of budget programs as opposed to other approaches with different costing objects. Therefore, the methodology and the findings presented here are much more of an economic and managerial perspective rather than a pure cost accounting one. Nevertheless, most practical questions are common to both views, thus making this report of use to both cost accounting experts and policy makers.

The paper identified two key issues that were to frame the report and its approach. The first relates to the current treatment of MTEFs in the technical literature and the way this tended to emphasize general design principles rather than the practical detail of implementation. The second issue revolves around the interaction between general guideline methods for program costing that finance ministries have issued for use by spending ministries and the specific requirements for costing that finance ministries employ to standardize program costs for use in preparing forward estimates of expenditure.

## **1.2 Cost practices for MTEFs: A gap in the technical literature**

The first issue highlights the importance of the way the _design_ of MTEFs ‘scaffolds’ into their _implementation_ . Good practice guidance relating to MTEFs asserts the importance to medium term budgeting of rigorous program costing by spending ministries, and the disciplinary function of top-down expenditure ceilings. Two recent international surveys of MTEF experience help to illustrate the tension.

The first review is sourced from the World Bank and assesses the comparative effectiveness of MTEFs (World Bank 2013). The report emphasizes that an ‘effective MTEF’ depends on spending ministry capability to ‘cost programs’ using the ‘best available techniques for 2
- Krusell P, Ohanian LE, R ́ıos-Rull JV, Violante GL. 2000. Capital-Skill Complementarity and Inequality: A Macroeconomic Analysis. _Econometrica_ **68** : 1029–1054.

URL http://ideas.repec.org/a/ecm/emetrp/v68y2000i5p1029-1054.html

- Levy F, Murnane RJ. 1992. U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations. _Journal of Economic Literature_ **30** : 1333–81.

URL http://ideas.repec.org/a/aea/jeclit/v30y1992i3p1333-81.html

- O’Mahony M, Van Ark B. 2003. _EU productivity and competitiveness: an industry perspective: can Europe resume the catching-up process?_ Office for Official Publications of the European Communities Luxembourg.

- Piketty T, Saez E. 2003. Income inequality in the united states, 1913-1998. _The Quarterly Journal of Economics_ **118** : 1–39.

URL http://ideas.repec.org/a/tpr/qjecon/v118y2003i1p1-39.html

- Rognlie M. 2015. Deciphering the fall and rise in the net capital share. In _Brookings Papers on Economic Activity_ . Conference Draft. URL http://www.brookings.edu/ ̃/media/projects/bpea/spring-2015/2015a_rognlie.pdf

- Ruggles S, Alexander JT, Genadek K, Goeken R, Schroeder MB, Sobek M. 2010. Integrated public use microdata series: Version 5.0 [machine-readable database]. Technical report, University of Minnesota, Minneapolis.

URL https://cps.ipums.org/cps/

- vom Lehn C. 2015. Labor Market Polarization, the Decline of Routine Work, and Technological Change: A Quantitative Evaluation. 2015 Meeting Papers 151, Society for Economic Dynamics.

URL http://ideas.repec.org/p/red/sed015/151.html

# Appendix A. Construction of Capital Income Shares

This appendix outlines our approach to measure income shares directly from the BEA’s nominal current cost values of detailed asset categories. As discussed in Section 3, our measurement exercise relies on two assumptions: First, no-arbitrage in capital markets requires the _gross return_ (8) to be equalized across all types of capital, which can be written more compactly as where (1 + _πi,t_ ) _≡ Pi,t_ +1 _/Pi,t_ is gross price inflation and _CGi,t_ = (1 + _πi,t_ )(1 _− δt_ ) are capital gains for capital of type _i_ .

Second, we impose constant returns to scale in aggregate production (equation (7)), and thus 37
# **<u>Appendix table 1: Village characteristics used in the regressions</u>**

||**Befo**<br>|**re**<br>|**Aft**<br>|**er**<br>|
|---|---|---|---|---|
||**Outside**|**Inside**|**Outside**|**Inside**|
|Total population|1,433|2,199|1,554|2,323|
|Share of households with access to rural pension|0.000|0.000|0.493|0.657|
|Share of households with access to cooperative health scheme|0.529|0.567|0.865|0.892|
|Amount of agricultural subsidy (yuan/mu)|68,491|90,275|201,608|268,608|
|Distance to the nearest county road (km)|8.382|6.375|8.231|6.141|
|Share of asphalt village road|0.046|0.127|0.054|0.123|
|Share of cement village road|0.463|0.651|0.757|0.786|
|Share of villages with primary school|0.448|0.307|0.272|0.175|
|Share of villages with medical stations|0.728|0.806|0.820|0.852|

_Source_ : Own computation from 2014 Chengdu village survey based on 529 villages.

23
because of pandemic-related restrictions. Resilience-building in the face of a crisis is a crucial next step to ensure that refugees can maintain their livelihoods in times of need.

Ongoing efforts will also be necessary on the research frontier, especially in providing highquality socioeconomic data that include refugee populations and are able to address existing data gaps and contribute to the design of inclusive evidence-based measures. With the continuous spread of the pandemic, new methods of collecting data such as phone surveys need to be evaluated relative to traditional face-to-face methods and improved upon. One particularly interesting strand of future research concerns the measurement of income which is notoriously difficult to capture precisely in in-person interviews due to the complexity of income streams in developing countries as well as recall and social desirability bias. It is likely these biases are aggravated during phone interviews. To evaluate the full extent of over and underreporting in phone surveys, more research will be necessary in the future.

# **References**

UNHCR Kenya. (2021). _Dadaab Refugee Complex_ . Retrieved from https://www.unhcr.org/ke/dadaab-refugee-complex

- UNHCR Kenya. (2021). _Kakuma Refugee Camp and Kalobeyei Integrated Settlement_ . Retrieved from https://www.unhcr.org/ke/kakuma-refugee-camp

- IFC. (2018). _Kakuma as a Marketplace. A Consumer and Market Study of a Refugee Camp and Town in Northwest Kenya._

- World Bank. (2021). _How Covid-19 continues to affect lives of refugees In Kenya._

Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., . . . Roser,

- M. (2020). _Our World in Data._ Retrieved from Coronavirus Pandemic (COVID-19): https://ourworldindata.org/coronavirus

- Dong, E., Du, H., & Gardner, L. (533-534.). An interactive web-based dashboard to track COVID-19 in real time. _The Lancet infectious diseases_ , 2020.

- UNHCR & World Bank. (2020). _Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume B: Kakuma Camp. Results from the 2019 Kakuma Socioeconomic Survey._

- UNHCR & World Bank. (2020). _Understanding the Socioeconomic Conditions of Refugees in Kenya. Volume A: Kalobeyei Settlement. Results from the 2018 Kalobeyei Socioeconomic Profiling Survey._

- UNHCR & World Bank. (2020). _Understanding the Socioeconomic Conditions of the Stateless Shona Community in Kenya. Results from the 2019 Socioeconomic Survey._

- WFP. (2020, December). _WFP Warns of Critical Food Shortages for Refugees in Kenya_ . Retrieved from https://www.wfp.org/news/wfp-warns-critical-food-shortagesrefugees-kenya

- Himelein, K. (2014). Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking. _Statistics and Public Policy_ , 40-45.

- Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., & Sanghi, A. (2018). Do refugee camps help or hurt hosts? The case of Kakuma, Kenya. _Journal of Development Economics, 130_ , 66-83.

- MacPherson, C., & Sterck, O. (2021). Empowering refugees through cash and agriculture: A regression discontinuity design. _Journal of Development Economics, 149_ .

21
**Table S11: Out-of-Pocket Expenditures Based upon Sibling Eligibility**

||All Providers|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Eligible Children|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.401|-0.161|0.041|-0.021|-0.275|
||(0.553)|(0.542)|(0.029)|(0.039)|(0.485)|
|Ineligible Sibling*Parent Enrolled|0.194|-0.101|-0.050|-0.010|-0.110|
||(1.526)|(1.493)|(0.036)|(0.065)|(1.140)|
|Ineligible Sibling|-0.271|-0.194|0.002|-0.009|0.014|
||(0.298)|(0.293)|(0.002)|(0.019)|(0.233)|
|Observations|2,172|2,172|2,172|2,172|2,172|
|R-squared|0.089|0.083|0.012|0.007|0.092|
|P-value of Enrolled + Enrolled*Ineligible|0.885|0.852|0.405|0.572|0.709|
|Panel B: Ineligible Children|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-1.093|-0.843|-|-|-1.573*|
||(1.154)|(1.101)|-|-|(0.863)|
|Eligible Sibling*Parent Enrolled|-1.180|-1.056|-|-|-1.112|
||(1.757)|(1.678)|-|-|(1.193)|
|Eligible Sibling|-0.246|-0.196|-|-|-0.322|
||(0.382)|(0.362)|-|-|(0.276)|
|Observations|824|824|-|-|824|
|R-squared|0.029|0.031|-|-|-0.008|
|P-value of Enrolled + Enrolled*Ineligible|0.090|0.138|-|-|0.001|

_Notes:_ The sample in Panel A is all children age 11 and under at baseline; the sample in Panel B is all children age 12 and over at baseline. "Ineligble Sibling" refers to a sibling in the household age 12 and over at baseline; "Eligible SIbling" refers to a sibling age 11 and under at baseline. Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and "Ineligible/Eligible Sibling*Parent Enrolled" is instrumented with "Inelgible Sibling" or "Eligible Sibling" times random assignment status. The dependent variables are the inverse hyperbolic sine of expenditures at various providers over the past year.Note that the regressions of EMP and public facility expenditures as dependent in Panel B could not be estimated due to insufficient variation. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

11