rafmacalaba/datause-encoder-data huggingface dataset:
|_Woman occupation_<br>|||||
|---|---|---|---|---|
|Earns cash|0.90|0.059**|0.89|0.007|
|Works all year|0.88|-0.047|0.88|0.023|
|Not working|0.25|-0.021|0.24|-0.015|
|Agriculture|0.19|0.055|0.25|0.011|
|Service & sales|0.39|0.057|0.35|0.016|
|Professional|0.05|-0.028|0.04|-0.010|
|Manual|0.11|-0.063***|0.12|-0.003|

_Note:_ Column (1) is a sample at 20 to 100 km from a nonactive mine. Column (2) difference for sample at 0-20 km from an nonactive, compared with column (1) Column (3) is a sample within 20 to 100 km of an active mine. Column (4) difference for sample at 0-20 km of an active mine, compared with Column (3) *** p<0.01, **p<0.05, *p<0.1. Univariate regression model.

Appendix Table A2 also shows selected child health outcomes as summary statistics across the four treatment groups. We note that, once again, the sample looks quite balanced in the first three columns, although children seem to be worse off in communities close to mines that have not started producing, evidenced by the fact that infant mortality is 8 percent compared to 7 percent farther away, and 6 percent in communities with active mines. The anthropometrics height-for-age (stunting or chronic malnutrition), weight-for-age (wasting or acute malnutrition), and weight-for-height (underweight) show that the children living in mining communities before the mine started operating have the lowest scores of all four groups. The outcomes seem to improve with mining, although not enough to offset the initial adverse situation.

To test for exogeneity, we run regressions using baseline individual-level data to explore changes in observable characteristics among women (the main part of the sample). Table 3 shows that there are no significant effects of the mine opening on the age structure, migration history, marital status, fertility, or education, using the difference-in-difference specification with a full set of controls. If anything, it seems that women in active mining communities are marginally older, more likely to never have moved, and more likely to be or have been in a cohabiting relationship or married. Given the women’s slightly higher age, it is not surprising to find that they have higher fertility and lower schooling (assuming that schooling has increased over time in Ghana). All these estimates are, however, insignificant.

**Table 3 Observable characteristics in the DHS individual data**

|non-<br>ever|currently|ever|total|any schooling|
|---|---|---|---|---|

16
**Pnnh, Polay and Ru"ch**

**Edueetlon and Empboymont**

Which is better: school-based training (vocational education), center-based training, or onthe-job training (such as apprenticeship schemes)? Controversy about choice of training mode has been aggravated by three factors: Failure to recognize that vocationaltechnical training covers a broad spectum, from applied science education to job-readiness training. Advocates and critics of a particular training mode who find themselves arguing at cross purposes often have different points of this spectrum in mind.

Insufficient appreciation of the training that - is, or could be, provided for example, by the private sector irn the form of in-service training (apprenticed or otherwise) and training provided by proprietary schools, suppliers of equipment, and other sources.

* A tendency by planners to overestimate greatly the need for extended pre-employment training for entry-level jobs. This bias is reinforced by wishful thinking that training can provide an easy solution to the problem of mass youth unemployment.

Drawing on a comprehensive survey of intemational experiences and issues, the author concludes that:

* Under favorable conditions (including adequate financing), any training system can be effective. Considering the time and money it takes to execute radical change, it makes more sense to improve the performance of existinr training systems than to try replacing them. The design of a training system is not so important as its ability to evolve in light of experience.

* Planners should consider dividing responsibility between schools, employers, and training centers. Schools should be responsible for basic skills and the theoretical end of the vocational training spectrum, and employers should be responsible for the practical end - after the individual has entered the labor force.

* Ideally, for some occupations, employertrained trainees should attend training centers (which may or may not be schools) part-time to add to their theoretical knowledge - both during initial on-the-job training and later in their working life, as the need arises.

* Full-time institutionalized training aimed at bringing the trainee to job readiness is likely to be less cost-effective for a number of reasonsand in many countries would place an intolerable burden on public funds.

This paper is a product of the Education and Employment Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cynthia Cristobal, room S6035, extension 33640.

The PPR Working Paper Series disseminates the findings of work under way in the Banks Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
Alesina, Hausmann, Hommes, and Stein (1999) and Fabrizio and Mody (2006) include MTEFs in aggregate indexes of budget institutions using data from Latin America and Eastern Europe, respectively.<sup>7</sup> Our results complement this empirical literature by providing evidence of MTEF impacts from a worldwide sample. While previous studies rely on small samples and either cross-sectional or static panel models our empirical methodology takes advantage of the time variation to estimate dynamic panel models. We also propose a new classi...cation of MTEF phases, based on the level of disaggregation of the central government’s ...scal objectives.

Our paper is also related to the broader empirical literature on budget institutions.<sup>8</sup> The most studied institutions have been balanced budget amendments, debt ceilings, tax and expenditure limitations, and supermajority requirements for tax increases. Examples include: Bayoumi and Eichengreen (1995), von Hagen and Eichengreen (1996), Stein, Talvi, and Grisanti (1999), Hallerberg and von Hagen (1999), Perotti and Kontopoulos (2002), Fatas and Mihov (2003), von Hagen and Wol¤(2006), and Hallerberg and Ylaoutinen (2010), all using country-level data; and Poterba (1994), Bohn and Inman (1996), Kiewert and Szakaly (1996), Poterba and Rueben (1999), Knight (2000), Knight and Levinson (2000), and Fatas and Mihov (2006) using state-level data. Important lessons from this literature are that numerical constraints have limited e¤ectiveness because they can be circumvented, that the e¤ect of reduced ...scal discretion on macroeconomic volatility remains an open question, and that the political environment matters for the e¤ectiveness of budget institutions.

We contribute to this literature by proposing a dynamic panel approach (di¤erence GMM) that models the ...scal adjustment process while at the same time addressing the issue of institutional endogeneity. Apart from employing standard internal instruments, we also propose external instruments that improve estimation e¢ ciency, based on the time-varying degree of MTEF penetration in a country’s geographic region. Also, in addition to macroeconomic e¤ects, which have been the focus of this literature, we provide evidence of sectoral e¤ects.

The structure of the paper is as follows. Section 2 provides a background discussion of the MTEFs, their adoption trends, and their expected e¤ects on ...scal performance. Section 3 discusses the data and the empirical strategy. Section 4 presents the empirical results. Section 5 summarizes the paper and suggests directions for future research.

> Schiavo-Campo (2009) puts forward conceptual arguments supporting a gradual introduction of these institutions and emphasizes the potential downsides of instant reform.

> 7In these two papers the MTEF component of the index is weighted by 1/10 and 1/12, respectively.

> 8See the NBER volume edited by Poterba and von Hagen (1999), as well as von Hagen (2006), for reviews of the budget institutions literature.

5
- [17] Sheffield, J. R. (1973), _Education in Kenya – An Historical Study_ , Teachers College Press, Columbia University.

- [18] Thias, H., and Carnoy, M., (1972), _Cost-Benefit Analysis in Education: A Case Study of Kenya_ , World Bank Staff Occasional Paper No. 14, IBRD, Washington, D.C.

- [19] United Nations (2004), _World Population Prospects_ , Online Database: http://esa.un.org/unpp/

- [20] Wambugu, A. (2003), “Essays on earnings and human capital in Kenya”, School of Economics and Commercial Law, Goeteborg University.

- [21] Yamano, T., and Jaynes, T.S. (2005), “Working-Age Adult Mortality and Primary School Attendance in Rural Kenya”, _Economic Development and Cultural Change_ , : 616 – 653.

- [22] Young, A. (2005), “The Gift of the Dying: The Tragedy of AIDS and the Welfare of Future African Generations”, _Quarterly Journal of Economics_ , 120(2): 423 – 466.

47
- 13 -

is estimated at 32 percent in the road sector. The electricity sector has the similar level of incumbent bidders’ presence (36 percent). In the water sector, however, the concentration of incumbent bidders is relatively low at 16 percent.

Based on our division of the sample period, bidders seem to have been awarded only a few contracts on average. The sample mean is 0.6 contracts in the road sector. This is the highest among the three sectors. The reason is simply that the total number of contracts in our preperiod is largest. The average numbers of past contracts are only 0.2 and 0.27 in the water and electricity sectors, respectively.

|Table 4. S|ummary<br>|Statisti<br>|cs<br>|||
|---|---|---|---|---|---|
||Obs.|Coef.|Std.Dev.|Min|Max|
|Roads||||||
|Bid amount (million US$)|188|27.02|22.27|2.03|144.38|
|_D_(Fringe bidder)|188|0.67|0.47|0|1|
|_D_(Fringe bidder facing incumbents)|188|0.38|0.49|0|1|
|Share of incumbents in that auction|188|0.32|0.31|0|1|
|Number of contracts awarded|188|0.60|1.04|0|4|
|Number of bidders|188|6.63|3.75|2|16|
|Total road length (km)|188|43.27|60.89|3.50|427.00|
|Number of lanes|188|2.84|1.16|1|6|
|_D_(New roads)|188|0.53|0.50|0|1|
|_D_(Rehabilitation work)|188|0.37|0.48|0|1|
|Contract duration (month)|188|33.80|9.81|11.00|48.00|
|Water and sewage||||||
|Bid amount (million US$)|236|15.95|26.61|0.33|276.66|
|_D_(Fringe bidder)|236|0.83|0.37|0|1|
|_D_(Fringe bidder facing incumbents)|236|0.28|0.45|0|1|
|Share of incumbents in that auction|236|0.16|0.24|0|1|
|Number of contracts awarded|236|0.22|0.68|0|4|
|Number of bidders|236|6.36|3.81|2|16|
|_D_(Water)|236|0.27|0.44|0|1|
|_D_(Treatment plant)|236|0.45|0.50|0|1|
|_D_(Network)|236|0.60|0.49|0|1|
|Treatment capacity (million m3)|236|0.10|0.18|0|0.60|
|Total concrete tunnel length (km)|236|0.33|2.32|0|24.85|
|Total iron pipe length (km)|236|36.11|56.59|0|258.70|
|Contract duration (month)|236|26.11|13.03|3.00|72.00|
|Electricity||||||
|Bid amount (million US$)|99|51.97|71.89|0.22|435.49|
|_D_(Fringe bidder)|99|0.64|0.48|0|1|
|_D_(Fringe bidder facing incumbents)|99|0.38|0.49|0|1|
|Share of incumbents in that auction|99|0.36|0.29|0|1|
|Number of contracts awarded|99|0.27|0.71|0|3|
|Number of bidders|99|4.71|2.38|2|12|
|_D_(Generator)|99|0.26|0.44|0|1|
|_D_(Trans. lines)|99|0.21|0.41|0|1|
|_D_(Substation)|99|0.34|0.48|0|1|
|_D_(Civil work)|99|0.68|0.47|0|1|
|Installed capacity (MW)|99|69.47|193.50|0|1,200|
|Number of generators|99|1.36|3.12|0|12|
|Transmission line capacity (kV)|99|32.03|94.52|0|500.00|
|Total transmission line length (km)|99|20.59|55.92|0|230.00|
|Contract duration(month)|99|27.28|12.19|3.00|48.00|

Source: Author’s calculation.
Moreover, modem labor practices are new to Vietnam. In 1995, a labor law was passed and a labor code was published that covers a wide range of labor issues including labor contracts, collective bargaining, social insurance, working conditions and training (for a full description, see World Bank 1995b).

In 1990 the government began the process of dismantling the old public sector wage structure. No longer would workers be paid mainly according to length of service, nor guaranteed employment for life. A plan was announced to place all government workers on contract, but this process has been slow (Hiebert 1993; Norlund 1993).

In 1993 the wages of public servants were partly monetized. Prior to this only a fraction of the salaries of civil servants or employees of state-owned enterprises was paid in cash. Up to 90 percent was received in bonuses and in-kind payments (ILO 1994). State workers no longer receive access to rationed goods at artificial prices, nor do new state employees receive fringe benefits such as subsidized housing, health services, education and social insurance. The new structure rewards workers according to skill, education, responsibility and job performance. The government also announced plans to decompress public sector wages. The full impact of these reforms may not be seen for years, however, since those hired prior to 1994 are largely exempted.

Private firms are now free to set wages without government interference. In the parastatal sector, subsidies were reduced in 1989 and further privatization measures introduced in 1990.

3
## 5.3.4.   Regional heterogeneity

Next, we run models to test whether the effects of conflict events on our main outcome variables of interest—food insecurity and consumption—vary across the different geopolitical zones. We have reason to believe this could be the case because the nature and intensity of conflict varies across the three zones.

In table 9, the zone indicator is interacted with the conflict event variables. We can see that the effect of conflict on consumption is driven by the North East (column 5). We also find that the relationship between conflict events and food insecurity is strongest again in the North East (column 2), and it is also statistically significant at the 5 percent level in the North Central (column 1).

# 5.4.   Robustness checks

## 5.4.1.   Alternative CSI

In the main analysis, we have used the CSI with five items, a measure considered to be valid across different contexts. The GHS food insecurity module has a sixth item, “Limit the variety of foods,” that can be included in the index. We have therefore conducted the analysis with an index that includes this additional item (in the construction of the index, this item takes the lowest weight, 1). The results are presented in appendix C, table C.3. We can see that the results are statistically significant at the 5 percent or 1 percent level, depending on the specification, and they are similar to those in table 2. The results on the additional item separately (columns 5 and 6) are also statistically significant.

## 5.4.2.   Alternative samples on food insecurity

A second telephone survey round focusing on food insecurity was conducted between August 15 and September 8, 2017. During this second round, 581 of the 717 households in the conflict telephone survey were re-interviewed (only the 717 were attempted to be reached).<sup>27</sup> Due to the attrition, we present the results using this sample as additional results instead of as part of our main analysis. However, because the additional survey round was collected in autumn 2017, this sample allows us to employ the conflict information from 2016 and spring 2017 in the analysis, which provides an interesting robustness check.

> 27 The attrition rates from the first to the second telephone survey rounds are 16 percent, 21 percent, and 19 percent for the North East, the North Central, and the South South, respectively. The geographical distribution of this sample is the following: 147 households in the North East, 219 in the North Central, and 216 in the South South.

24
resulting two treatments (individual benefit and household benefit) are in Table 1.

**Table 1: Design of randomized survey experiment**

|**Group**|**Question**|
|---|---|
|**Control**|Are you willing to get a COVID-19 vaccine?|
|**Individual Beneft**|The best choice you can make to protect yourself from|
||COVID-19 is to get fully vaccinated. This greatly reduces the|
||risk of severe illness, hospitalisation and dying from COVID-19.<br>Are you willing to get a COVID-19 vaccine?|
|**Household Beneft**|The best choice you can make to protect your household|
||and family from COVID-19 is to get fully vaccinated. This will|
||help to keep the people you love healthy, safe and strong.|
||Are you willing to get a COVID-19 vaccine?|

In total, 10,169 people completed the online survey, of which 2,937 people took part in the online experiment. As the experiment focused on ways to increase willingness to get vaccinated, only people who had not received a COVID-19 vaccine were eligible (i.e., all respondents to the Facebook advertisement who stated they were already vaccinated for COVID-19 did not participate in the experiment). Survey experiment participants were randomly allocated into three groups (the two treatment groups and the control group). Forty percent of participants were allocated to the control group and the remaining 60 percent were split between the two treatment groups. The sample sizes for the treatment and control groups were informed by best practice in the literature (Haaland et al., 2022). Randomization ensured there were very few statistically significant differences between treatment and control groups across demographic characteristics (see Table A4.1 in Appendix). As such, we present graphs showing treatment effects by comparing means across groups (with survey weights) in the body of this paper. In Appendix A4, we present tables showing the regression analysis of the treatment effects with controls added. Results from both methods are effectively the same.

20
# Sub-S-hdran Africa

ANGOIA: Both the IMR an, life expectancy at birth are from _World Population Prospects 1990._ The U.N. estimates are based on the 1940 census as analyzed by Brass et al. (1966). BENIN: The IMR is a Bank projection from a 1961 national demographic survey. The 1981 World Fertility Survey est.mates for 1977-81 are also considered. Life expectancy at birth is nrrjected from the 1961 survey.

BOTSWANA: Both lifc expectancy estimates and the IMR arc Bank projections from the 1988 Demographic and Health Survey.

BURKINA FASO: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ These estimates are model-derived, based on a 1976 post-enumeration survey.

BURUr' DI: Thi IMR is the direct estimate from the 1987 Demographic and Health Survey. Life expectancy a; birth estimates are from _World Population Prospects 1990._ The U.N. based its estimates on survey e..ima.es for 1965 and 1970-71, and the 1979 census.

CAMEROON: The IMR and life expectancy estimates are based on a Bank assessment of the 1978 WYorld Fertility Survey, and a projection of the IMR by the U.N.

CENTRAL AFRICAN REPUBLIC: The IMR is from _World Population Prospects 1990._ The U.N. based its estimate on a projection of data from a 1959-60 survey and the 1975 census. Life expectancy at birth estimates are Bank projections from the 1975 census.

CHAD: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on an analysis of a 1963 survey.

COMOROS: Both the IMR and life expectancy at birth are from _World Population Prospects 199v._ T he U.N. derived life expectancy estimates from the 1980 census and projected them to 1985-89.

CONGO: The IMR is a Bank projection from the 1984 census. Life expectancy at birth estimates are from _World Population Prospects 1990,_ and are projections from the 1974 census.

COTE DIIVOIRE: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The U.N. based its estimates on the 1978 World Fertility Survey.

DJIBOUTI: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ The UJ.N. based its estimates on levels in neighboring countries. EQUATORLAL GUINEA: The IMR is from _World Population Prompects 1990._ It is based on a projection from the 1983 census. Life expectancy at birth estimates are Bank projections from the 1983 census.

ETHIOPIA: The IMR is from _World Population Prospects 1990._ The U.N. based its estimate on a Coale-Demeney "North" model life table selection on the basis of the situation of the country. Lite expectancy estimates are based on a Bank assessment.

10
Our review of these _ex post_ evaluations yields some interesting observations. The presence of four regulations that have no environmental effect at all is particularly striking. Whether this is due to noncompliance or other factors in unknown. We also see that regulations with higher efficiency grades tend to have a higher effect or impact grade. In fact, regulations with an effect or impact grade of 3 or 4 seemed to have relatively high grades across the board. Further, we observe regulations for a single party (PEMEX), which tend to have high grades for all categories. This probably happens because of the large amount of consultation that takes place when planning regulations that will impose high compliance costs on PEMEX and other government controlled enterprises. Finally, despite the small sample size (16), we looked without success for evidence of a trend in quality over time. However, there is no evidence of either an increase or a decline in quality of the 10 year period, 1996-2006.

# **V. Conclusion**

The use of quantitative assessments of both the costs and benefits of new regulation to advance the international green growth agenda is clearly a work in progress. Especially in the case of new rules that could impose significant costs on public and/or private sectors, there is a strong impetus to develop an in-depth understanding of the expected outcomes before the rules are put in place. Yet, unlike the burgeoning efforts to evaluate anti-poverty efforts in developing countries, randomized experiments are not typically suitable to environmental/green growth initiatives. Thus, decision-makers are dependent on relatively simple estimates of the direct costs and benefits of proposed actions.

In our review of progress in the use of quantitative BCA assessments in the U.S. and Mexico, we observe a number of similarities and differences. Notwithstanding the relatively long U.S. track record in this area, both nations are seen as having embraced the BCA approach in support of the green growth agenda. Both nations have developed specific requirements and programs to advance their efforts, including the institutionalization of processes enforcing and managing the systems. Compared to most developing nations, Mexico has taken clear steps to strengthen its regulatory system.

Unsurprisingly, there are also important differences between the BCA activities in the two countries. With its advanced and internationally respected research base in both natural and social sciences, the U.S. is widely seen as a world leader in the quantification and monetization of both benefits and costs of environmental, health and safety regulations. It is also a leader in promoting the evolving work on retrospective analyses of both costs and benefits. It is, perhaps, comforting to know that the initial results of _ex post_ studies from the U.S. suggest that while the total costs of new regulations are often overestimated, unit costs tend to be generally accurate. That is, while regulators often anticipate that greater reductions will be achieved than actually occurs –the principal