rafmacalaba/data-use-mentions-extended huggingface dataset:
ALLOCATION AND MANAGEMENT OF PUBLIC SPENDING 64. Also, such guidelines would have to aim at a gradual change In the - composition cf expenditure the "Ideal" pattern could not be achieved in one step but would have to be sought over a period of years. Guidelines could only work at all In a context where funds actually released correspond closely to the provision In the Estimates, and It would be essential to have timely expenditure records from which compliance with the guidelines could be monitored. In many cases, the decentrallsation that Is Implied may be hampered by the fragmentation of sectoral responsibilities among a number of ministrles. The approach also underlines the Importance of the development of economic planning skills within sectoral ministries. (Again the development of effective minlsterial planning units may be hampered by the Institutional fragmentation of sectors.) 65. Derivlng Expenditure Guldelines. There remains the question of how to set such guidelines. The problem is probably not as difficult In practice as It may seem in principle. It would be very difficult to determine a single "correct" level of resources to allocate to any activity, but all that is practically required is a sense of the qualitative directions for change. Thus, If It is apparent that, say, primary health care has been underfunded, expenditure guidelines should be set that allow a faster rate of growth for primary health care expenditures than for others; this should be continued (probably over several years) until the Imbalance ceases to be obvious, but, since the process takes time, it Is not necessary to know at the outset exactly what Its outcome will be - guidelines can be continually revised In the light of experience.l141 66. All the same, It would be Interesting to know, at least approxlmdtely, what would be the cost of operating Government's existing services properly. Indeed, thils Is a crucial piece of Information If Government is to plan Its public expenditures rationally over the medium term, and it has a direct bearing on the balance that should be struck between development
comparability both across countries and within countries over time using methodologies developed by the World Bank, the Luxembourg Income Study, OECD, and country statistical offices, as described in greater detail in Hassine (2015). All expenditure data used in this analysis have been deflated by the CPI of the respective economy and year, and adjustments for spatial price differences have been made for Egypt, Syria, and the Palestine territories.<sup>12</sup> The PPP conversion factor for private consumption (LCU per international dollar), obtained from the World Development Indicators database (World Bank, 2015), is employed to facilitate comparison of consumption expenditure levels across countries. Only in the case of the Palestine territories, the PPP conversion factor for GDP is used instead.

The household surveys cover different years for different countries. The surveys for Egypt, Jordan, the Palestine territories, and Tunisia have been conducted in the mid-to-late 2000s, but in the cases of Syria and Yemen they were implemented in the late 1990s to the mid-2000s. Appendix Table 1.1 shows details on each country, including the names of the surveys, the survey years, and the headcount poverty rates for the first and last years in the survey period.

In order to assess welfare dynamics with alternative, subjective welfare measures, we employ data on subjective well-being from the Gallup World Poll. The annual Gallup World Poll contains nationally representative country samples of at least 1,000 randomly selected respondents who are 15 and older. Since most of the variables (including education achievement) that are employed in the construction of the synthetic panels are only collected in the Gallup Poll in 2009 or after, we focus on the whole period 2009-2012 to obtain a longer term perspective and include all Arab countries for which we have subjective well-being information. But we also offer stylized graphical analysis of life satisfaction for several countries for the period immediately preceding

> 12 The absence of spatial price differentials prevented adjustments for regional price differentials in Jordan, Tunisia, and Yemen.

11
Gazola Hellmann, 2015). Applicants report their information, including information on household income, expenditures, assets, socioeconomic characteristics, and demographic characteristics, to interviewers at Cadastro Unico<sup> ́</sup> agencies, which are program offices spread across Brazil’s 5,570 municipalities. Information is entered by interviewers into the Cadastro Unico<sup> ́</sup> registry. Beneficiaries of the BF program are required to update their information once every 2 years to maintain their benefits.

Eligibility for the BF program is based on reported household per-capita income. Household per-capita income is calculated in five steps. First, the applicant is asked to report labor income for the last month as well as average monthly labor income over the past year for each member in the household (the applicant must present a government-issued ID for herself and for each family member thus making it difficult to register fictitious family members). Second, the applicant is asked to report average monthly income received from five additional sources for each household member (see Appendix B.1, which shows the questionnaire used to calculate monthly income). Third, for each individual, the computer calculates the minimum between the average monthly labor income over the past year and last month’s labor income. Fourth, the computer sums this minimum monthly labor income along with income from the five additional sources to get a measure of government spending under policy **p** (exclusive of spending on BF), and ∆ _R_ = _R_ ( **p**<sup>_′_</sup> ) _− R_ ( **p** ) captures the fiscal externalities of the reform. In this case, the lower and upper bounds for the MVPF are given by:

> <sup><u>[</u></sup><sup>_G_</sup><sup><u>(</u></sup><sup>_τ ′_<
2003. The first survey involved an original sample of 200 firms randomly drawn from the 1987 Ghana manufacturing census. The sampled firms were broadly representative of the entire national economy across size distribution and the four major industries: food processing, textiles and garments, wood and furniture, and metals and machinery.

To maintain the representative nature of the sample, new firms were resampled from the census database to enter the survey in subsequent waves in order to make up for firms that dropped out of the survey. The final dataset used in this article is an unbalanced panel, containing 262 individual firms with 1,955 total observations. The primary dataset contains information on output and inputs necessary to estimate production function, as well as other firm-level characteristics. In addition to the primary dataset, S ̈oderbom and Teal (2004) computed physical capital from the investment flows information and firmspecific output and input prices, which were used to deflate monetary values. Both the primary data, the derived physical capital, and firm-specific prices data were made freely available by CSAE at the University of Oxford.

The long time span of the dataset, which includes the two episodes of trade liberalization (1991 and 1995), represents an advantage for this study. On the other hand, a common 6
- 5 -

electricity demand. In practice, however, the price structure is often complex and reflects various policy considerations, such as affordability of the poor and promotion of energyintensive industries. Therefore, the impacts of raised tariff may not be straightforward. An increasing block tariff mechanism is, for instance, a common practice to encourage largevolume customers to use electricity wisely, while keeping the prices for the poor relatively low. However, this discriminatory pricing may adversely affect the households’ incentives to install a proper meter or falsify metering readings; people do not want to be applied to higher rates. Hence, the net impacts may remain debatable.

The current paper analyzes household data from the Albania’s Living Standards Measurement Studies (LSMS) for 2002–2005. Because there are multiple endogeneity issues, it applies the Nelson-Olsen (1978) two-stage estimator, which models a system of censored and non-censored equations. The remainder of the paper is organized as follows: Section II summarizes the theoretical and practical issues related to metering. Section III discusses the possible effects of climatic conditions on the electricity demand, in particular in the context of Albania. Section IV develops an empirical model and discusses various econometric issues to estimate the electricity demand. Section V describes our data, and Section VI presents the main estimation results. Section VII discusses robustness of the estimation results and some policy implications. Section VIII then concludes.

# **II. METERING IN THEORY AND PRACTICE**

Metering is an essential policy instrument to motivate customers to use electricity wisely. In theory, a simple model can show that people would overconsume electricity in the absence of metering. There is no incentive for overconsuming customers to install a meter, even if the cost of meter installation is small. By contrast, unmetered customers who are consuming less than the norm will have strong incentives to install a meter, if the installation cost is small enough. This self-selective process would end up with the overconsumption situation where unmetered customers would overconsume the utility services. One might think that
# **Table A1.D. Construction of Matched Daughters-Fathers in India**

||**IHDS Wave 2**<br>**Azam (2016)**|**IHDS Wave 2**|
|---|---|---|
|Total Surveyed women in age 20-49|45,319|45,319|
|Total Surveyed women in age 20-49, with non-missing<br>education information|45,276|45,276|
|Father's Edu from household co-resident|4,416|4,957|
|Father's edu from Women's Module|34,290|34,290|
|Total Women whose father's Edu is available|38,706|39,247|
|% of surveyed women for whom father's Edu is<br>available|85.49%|86.68%|

Notes: Column 1-2 are directly obtained from Table 1 in Azam (2016) using IHDS 2012 while column 3 is based on authors’ own calculation using IHDS 2012.
WPS3837 The Impact of Regional Trade Agreements and Trade Facilitation in the Middle East North Africa Region

# Allen Dennis<sup>1</sup>

# **Abstract**

_The Middle East and North Africa regions (MENA) trade performance over the past two decades has been disappointing. Efforts to boost trade through a plethora of regional trade agreements (RTAs) are underway. This study examines the potential contribution of regional trade agreements, as well as trade facilitation improvements in enhancing the development prospects of the region. Using the Global Trade Analysis Project (GTAP) model and database, both intra-regional integration and integration with the EU are observed to have a favorable impact on welfare in the MENA region. The welfare gains from integrating with the EU are observed to be at least twice as much as intra-regional integration. Furthermore, these welfare gains are observed to at least triple when the implementation of the RTAs are complemented with trade facilitation improvements._ World Bank Policy Research Working Paper 3837, February 2006

> _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Comments received from Andrew Stone, Hamid Alavi, Zoubida Allaoua (MNSIF) have been helpful in the revision of this paper. Ernesto Valuenzuela and Maros Ivanos (DECRG) have also been helpful on the modeling aspects of the study.

1
***|(8.23)***|(3.71)***|(3.81)***|
|Observations|510|510|510|510|510|

Robust z statistics in parentheses

* significant at 5%; ** significant at 1%

Note: See text and Appendix for definitions of variables. The excluded regional dummy variable in model (5) corresponds to South Asia, which in this sample is India. The excluded Leamer commodity group in all specifications is mining and petroleum.

18
different ages, and localities that historically had different numbers of recruiting stations.

Recent work by Callaway et al. (2021) has highlighted that difference-in-differences with continuous treatments or different doses of a treatment results in additional complications to consider relative to the binary treatment case. In particular, a stronger parallel trends assumption needs to be employed, and if treatment effects are heterogeneous across units receiving different doses of the treatment, two-way fixed effects specifications can be hard to interpret, even when treatment is not staggered over time. They discuss several possible solutions, and migration work that relies on this differential exposure across areas will need to discuss these issues more in the future.

A final set of issues that can arise occurs when policies can get reversed or “turned off”. While this can raise further complications in the presence of dynamic effects and treatment heterogeneity, it may also increase the credibility of causal claims. For example, observing an effect in one direction when a policy is introduced, and then that the estimated effects go (as would be predicted) in the opposite direction after a policy is reversed, may serve as further proof that the policy is responsible for the changes in outcomes observed. Studies that take advantage of such reversals include Dinkelman and Mariotti (2016) and Abarcar and Theoharides (2022).

# **4.3 Instrumental Variables**

The instrumental variables (IV) approach exploits variation in the real world to establish causal relationships. When implemented credibly, IV deals with concerns about omitted variable bias and reverse causation.

Consider the following regression equation estimated using a cross-section of data on households, indexed by _i_ . The outcome of interest is _Yi_ , for example the level of household per capita consumption. We are interested in estimating the impact of an independent variable _Xi_ , such as whether the household has a member earning income overseas as a migrant worker.

In general, we worry that the coefficient _β_ on _Xi_ is biased due to omitted variables and/or reverse causation. When using the IV approach
that, according to our classification, Basel III has been implemented (for our sample of countries) mostly in 2013 (Argentina, Bolivia, Colombia, India, Indonesia, Kenya, Malaysia, the Philippines, Thailand, and Turkey) and 2012 (North Macedonia and Peru).<sup>6</sup> We label these countries as treated countries or Basel III implementors, interchangeably. Other EMDEs did not implement Basel III

> 5 However, G20 countries are not included in the FSI Survey. Consequently, for the G20 countries in our sample (Argentina, India, Indonesia, and Turkey), we use the data from the progress reports on adoption of the Basel regulatory framework, which are regularly prepared by the Basel Committee on Banking Supervision (BIS 2015).

> 6 We include Peru in the treated group of countries—although it did not yet implement Basel III definition of capital, Peru started with the introduction of capital buffers in line with Basel III (as well as a few other aspects of the Basel III framework; see table A.2).

6
# **References**

- Abay, K., Berhane, G., Hoddinott, J., and Tafere, K. (2020). Covid-19 and food security in ethiopia: do social protection programs protect?

- Abbas, J., Mubeen, R., Iorember, P. T., Raza, S., and Mamirkulova, G. (2021). Exploring the impact of covid-19 on tourism: transformational potential and implications for a sustainable of the travel and leisure _Current Research in Behavioral_ recovery industry.

- _Sciences_ , 2:100033.

- Adams-Prassl, A., Boneva, T., Golin, M., and Rauh, C. (2020). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. _Journal of Public economics_ , 189:104245.

- Albanesi, S. and Kim, J. (2021). Effects of the covid-19 recession on the us labor market: Occupation, family, and gender. _Journal of Economic Perspectives_ , 35(3):3–24.

- Amare, M., Abay, K. A., Tiberti, L., and Chamberlin, J. (2021). Covid-19 and food security: Panel data evidence from nigeria. _Food Policy_ , 101:102099.

- Basurto, M. P., Dupas, P., and Robinson, J. (2020). Decentralization and efficiency of subsidy targeting: Evidence from chiefs in rural malawi. _Journal of public economics_ , 185:104047.

- Berkouwer, S. B., Biscaye, P. E., Puller, S., and Wolfram, C. D. (2022). Disbursing emergency relief through utilities: Evidence from ghana. _Journal of Development Economics_ , 156:102826.

- Cicala, S. (2020). Powering work from home. Technical report, National Bureau of Economic Research.

- Cong, L. W., Yang, X., and Zhang,
, we find very high cross-establishment correlations (above 0.93) in the log levels and growth rates of sales and employment, as well as in log labor productivity (0.73).<sup>14</sup> Additionally, the average adoption rate of ERP systems in Korean manufacturing establishments in FAT is similar to Chung and Kim (2021), who used a similar sampling frame (32% vs. 40% in Chung and Kim, 2021), and there is a strong cross-establishment

> 13The re-interviews produced 1,661 answers, 106 interviews times 3 business functions times an average of 5.2 technologies per function. Both the original and back-end interviews in the pilot are conducted by phone by different interviewers. The correlation between the binary responses in survey and pilot is 73% ranging from 65% in business administration to 77% in sales across business functions, and from 85% among the most basic technologies to around 61% in intermediate, and 77% at the most advanced technologies across functions.

> 14In Korea we merge FAT with the Korea Enterprise Data (KED), a leading supplier of business credit reports on Korean businesses. In Brazil, we merge the data with the _Relação Anual de Informações Sociais_ (RAIS), which is an administrative database maintained by the Ministry of Labor providing information on salaries for all formal workers in Brazil. The FAT survey asks about sales and the number of employees for two periods. The most recent year for which the information is available (i.e. the year before the implementation of the survey) and two years before that. For Korea, these reference years are 2019 and 2017.

14
# **II. THE BUSINESS ENVIRONMENT AND THE LABOR MARKET PERFORMANCE**

_Do countries with better business environment have better labor market performance? Which institutions are most important for a good employment record?_ Those are the two very important questions this section attempts to shed light on. We proceed in two stages. First, we perform a **graphical analysis** of the data in order to explore possible correlations between the investment climate and labor market outcomes. Second, **multivariate analysis** is used to estimate the contribution to employment of the different institutions considered here.

# **II.1. A first approximation to the data: bilateral correlations**

The first approximation to the data comes in the form of simple scatter-plots of the different dimensions of the business environment previously described against service employment rate, purged from the GDP per capita.<sup>7</sup> The rationale is that service employment rate is a good proxy for employment created by the new private sector since it is the only sector increasing its share of employment in all transition countries. It is also where new business opportunities can be mostly found. To separate the effect of economic development from the direct effect of the business environment institutions on the growth of the service sector, we use a corrected measure of service employment rate which controls for the GDP per capita.<sup>8</sup>

# **Box 3: Why do we use service employment instead of total employment rate as a labor market indicator?**

The focus of this paper is the employment generated by the new private sector. Hence the service employment rate is a better labor market indicator than the total employment rate.

The reason is that total employment is the sum of employment created by the public and by the private sector. Countries with a large public sector have, in general, a large total employment rate, which can be compatible with a bad business environment. That is because public employment does not respond to any
# **II. Data**

The Mexican National Institute of Statistics, Geography and Information (INEGI) tabulates official income data for Mexican 32 states GDP for 1970,1975,1980,1985,1988, and then annually for the period 1993-2002. We follow Esquivel (1999), in making several corrections to this data. First, most oil is pumped from the states of Tabasco and Campeche, but the attribution of oil revenues has changed without obvious cause over time. Though the revenues are in fact allocated to all states via a federal sharing formula, in some years they were entirely attributed to Tabasco, and in others to Campeche. We tried to correct for this, excluding the oil production as captured in the mineral production category of the state accounts, but still found the resulting growth series to be too erratic and exaggerated to be credible. We attribute this behavior to unresolved petroleum accounting issues since the remaining 30 non oil producing states behave more reasonably.<sup>21</sup> Though dropping these states clearly implies losing some of the spatial story, we find it preferable than contaminating the analysis with clearly unreliable series.

Second, we also corrected populations figures for Chiapas and Oaxaca for years 1975, 1980, 1985, 1988 as the 1980 census, when compared to other household census appears to have understated the state’s population induced distortions in the GDP per capita.<sup>22</sup> .

Finally we have merged the state of Mexico with Mexico DF (Federal District), the capital. The rationale for this aggregation stems from the fact that the two states have long been part of a common industrial belt and there exist strong labor market linkages between them which may lead to an overstatement of the capital city’s per capita income. This has led to reported population in the Mexico DF has remaining stable over the last 20 years while the population in the state of Mexico doubled. We run the analysis both with and without this aggregation (results available on