rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Survey methodology**

The surveys were designed to elicit the perceptions of public sector officials. All employees employed in the organizations being surveyed were eligible and no control group was included. As no 'before and after' information was sought, a cross sectional design was selected.

# _3.1 Questionnaire design_<sup>_36_</sup>

In drafting and pre-testing the questionnaires, care was taken to avoid: ambiguous words and phrases; questions asking two or more opinions simultaneously; words or phrases that could be expected to trigger off emotional responses; and information that could manipulate the respondent.<sup>37</sup> Most pre-testing exercises pointed to excessive length of the questionnaires. The rule of thumb applied was that it would be unlikely that a public official could afford to spend longer than 45 minutes with an interviewer even assuming that the surveyor could hold the official's attention for longer.

Although the responses had to be converted to numbers for analysis, verbal response scales were selected over numeric scales in most questionnaires. This happened because during pre-testing, responses on a numerical scale tended to show responses clustered close to the middle of the scale.<sup>38</sup> The advantage of using descriptive words for each category of response was that each category could be relatively well defined. The number of categories was kept low, to avoid loss of meaning in translation. The middle category was excluded to force respondents to express preferences even when they are indifferent. Pre-testing showed that including a middle category biased responses towards the middle category even when respondents had a slight preference one way or other. 'Don't know' as a response category did not appear in any survey questionnaire except in Indonesia. Thus the number of respondents expressing an opinion was biased upwards.

Filters and branches were used. Although filter questions added to the length of the questionnaire, they were used to ask whether or not something was a problem before asking the degree of the problem. Most questionnaires began with 'easy' questions like how long the respondent had been working with government or in the same position, or details about the personnel management system in place in the respondent's organization. Questions on the same theme were grouped together. Listing all categories in the same order minimized any existing order effect within questions. In most questions categories were listed from low to high, and from bad to good.

Most surveys asked about corruption and other sensitive questions such as what an official did when he or she did not agree with a supervisor's decision. Recognizing that respondents could be unwilling to admit incriminating behavior in face-to-face

> 36 The survey instruments are available on http://wwwl .worldbank.org/publicsector/test/civilservice/survey.htm. 3' **/** See Rea and Parker (1997).

> 38 See Recantini, Wallsten, and Xu (2000).

14
that period life expectancy is identical to cohort life expectancy.<sup>7</sup> Based on studies from South Africa, the average age of death from TB is 40 (Statistics South Africa 2006), so years of life lost are calculated by region as life expectancy at 40. Life expectancy for TB patients co-infected with HIV was assumed to be 10 (Morgan and Whitworth 2001). World Bank data on GDP levels for 2004 and projected growth rates for 2006–2015 were used.

The value of statistical life-years (VSLYs) represents annuitized, age-specific VSLs based on age-specific years of life expectancy and a three percent discount rate<sup>8</sup> as per the equation below (Moore and Viscusi 1988).

where _r_ is the discount rate and _L_ is life expectancy at birth. VSLYs are calculated for the United States using U.S.-specific life-expectancy estimates. Constant VSLYs were calculated for the two Africa regions (high HIV cases and low HIV cases) and for each of the 22 high-burden TB countries relative to a U.S. baseline VSLY of $200,310 and using an elasticity of VSL with respect to income of one and varied in a sensitivity analysis from 0.8 to 1.2.<sup>9</sup>

# **Full-Income GDP Growth Rate Calculations**

Baseline estimates of full-income growth rates were based on the assumption that these were consistent with a Sustained DOTS scenario. Starting from this baseline, we calculated the reduction in full-income growth if DOTS was not sustained at the 2005 level and the increase in full-income growth associated with implementing the Global Plan. Life expectancy data are from the United Nations Statistical Database

> 7 This is a good approximation for interventions that have little effect on overall life expectancy or the cohort survival curve. It has the effect, however, of making every intervention appear less effective when overall mortality is high; effectiveness is inversely correlated with disease burden.

> 8 The constant discount rate of 3% per year recommended by Gold and colleagues is routinely used in evaluating health interventions in the United States (Gold, Siegel et al. 1996).

> 9 Although there is some intuitive appeal to the idea that each year of life should be worth the same, recent estimates of VSLs from labor-market hedonic studies indicate that VSLYs may vary with age (Aldy and Viscusi 2006). However, they disagree on the shape of the VSLY curves with respect to age.

14
- Akerlof, G. A. (1984): “Gift Exchange and Efficiency-Wage theory: Four Views”. _The American Economic Review_ , vol. 74, issue 2, Papers and Proceedings of the Ninety-Sixth Annual Meeting of the American Economic Association, May, pp. 79-83.

- Akerlof, G. A. and Yellen, J. Y. (1987): “Rational models of irrational behavior”. _The American Economic Review_ , vol. 77, issue 2, May, pp. 137-142.

- Akerlof, G. A. and Kranton, R.E. (2003): “Identity and the Economics of Organizations”. Typescrit.

- Arrow, K.J. (1963): “ Uncertainty and the welfare economics and medical care”. _American Economic Review_ 53, pp. 941-973.

- Bakerma, H.G. (1995): “Do Job Executives Work Harder When they are Monitored?” _Kyklos_ 48, pp.19-42.

- Beer, M., Bert, S., Paul, R., Lawrence, D., Mills, Q. and Walton, R. E. (1984): _Managing Human Assets_ .  Macmillan, New York.

- Benabou, R. and Tirole, J (2003). “Intrinsic and Extrinsic Motivation”. _Review of Economic Studies_ vol 70(3), pp.489-520.

- Besley, T., and Ghatak, M.(2003): “Competition and Incentives with Motivated Agents”. Working paper, London School of Economics. November.

- Bewley, T. F. (1995): “A Depressed Labor Market as Explained by Participants”. _American Economic Review_ , vol. 85, No. 2, pp. 250-254.

- Congleton, R. D. (1991): “The economic role of a work ethic”. _Journal of Economic Behavior and Organization_ , 15, pp. 365-385.

- Chang,  J. and Lai, C. (1999): “Carrots or sticks? A social custom viewpoint on worker effort” _European Journal of political economy_ , vol.15., pp. 297-310.

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Jondrow, James and Lovell, Ca and Materov, Ivan and Schmidt, Peter, 1982. “On the Estimation of Technical Inefficiency in The Stochastic Frontier Production Function Model.” Journal of Econometrics. 19. 233-238.

Kaufmann, Daniel and Kraay, Aart and Mastruzzi, Massimo, 2011. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” Hague Journal on the Rule of Law, 3(2), 220-246. doi:10.1017/S1876404511200046 Kayser, Mark A. and Peress, Michael, 2012. “Benchmarking across Borders: Electoral Accountability and the Necessity of Comparison” American Political Science Review, Volume 106, Issue 3August 2012, pp. 661-684.

King, Gary and Honaker, James and Joseph, Anne and Scheve, Kenneth, 2001. “Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.” American Political Science Review, 95, Pp. 49–69. Copy at https://j.mp/2oOrtGs Kumbhakar, Subal C. and Lovell, C.A. Knox, 2000. “Stochastic Frontier Analysis.” Cambridge University Press.

Kumbhakar, Subal C. and Lien, Gudbrand and Hardaker, J. Brian, 2014. “Technical efficiency in competing panel data models: A study of Norwegian grain farming,” Journal of Productivity Analysis 41: 321–337.

Kumbhakar, Subal C. and Wang, Hung-Jen, and Horncastle, Alan P., 2015. “A practitioner's guide to stochastic frontier analysis using Stata.” Cambridge University Press.

Kumbhakar, Subal C. and Lien, Gudbrand, 2017. “Yardstick Regulation of Electricity Distribution – Disentangling Short-run and Long-run Inefficiencies,” Energy Journal 38:17-37.

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Loayza, Norman and Fajnzylber, Pablo and Calderón, César, 2005. “Economic Growth in Latin America and the Caribbean: Stylized Facts, Explanations, and Forecasts.” Washington, DC: World Bank.

Organization for Economic Co-operation and Development (OECD), 2020. “Reading performance (PISA) (indicator).” doi: 10.1787/79913c69-en Ravallion, Martin, 2012. “Benchmarking Global Poverty Reduction” World Bank, Policy Research Working Paper 6205.

_______________, 2016. “Toward better global poverty measures” The Journal of Economic Inequality, 2016, Vol.14(2), pp.227-248.

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17
across our OLS and IV specifications. Our point estimate on father’s income is substantially larger under our IV specification, suggesting that our OLS specification suffers from attenuation bias, as suspected. While our coefficient on father’s income is not significant in either specification, its point estimate in our IV specification is of a similar magnitude to the point estimate on _tu_ . This seems sensible as one may expect parents to react to an increase in unconditional grants and an increase in parental income in a similar manner. Finally, in column (3) of Table V we show that our results are robust to including controls for sibling ages and grades.<sup>28</sup> TABLE V: Constant Income and Price Effects

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|IV|IV|IV|
|tc|0.00838***|0.00820***|0.00823***|0.00814***|
||(0.002)|(0.002)|(0.002)|(0.002)|
|tu|0.00372*|0.00462*|0.00487*|0.00485*|
||(0.002)|(0.003)|(0.003)|(0.003)|
|y<sup>_f_</sup>|-0.000236|0.00379|0.00362|0.00362|
||(0.000)|(0.004)|(0.004)|(0.004)|
|tc<sup>(12+)</sup>||||0.000296<br>(0.002)|
|E[Enroll_|_control,year_>_97]|0.683|0.693|0.693|0.693|
|E[Enroll_|_treat,year_>_97]|0.767|0.778|0.778|0.778|
|E[tc_|_treat,year_>_97]|6.897|6.863|6.863|6.863|
|E[tu_|_treat,year_>_97]|2.867|2.896|2.896|2.896|
|E[famsize]|6.609|6.609|6.609|6.609|
|Observations|21321|17432|17432|17432|
|R-squared|0.153|0.116|0.121|0.121|

_Note:_ Standard errors are clustered at the locality level and are presented in parentheses. * _p <_ 0 _._ 10, ** *** _p <_ 0 _._ 05, _p <_ 0 _._ 01. Dependent variable: _Enrollit_ . Sample: unbalanced panel of children aged 12-15 years in 1997-1999 from eligible (poor) households with two parents and one parent reports to be the head of the household. tc denotes the weekly, per-capita transfer offered to child _i_ in year _t_ . tu denotes the weekly, per-capita transfer offered to child _i_<sup>_′_</sup> _s_ siblings under the age of 12. tc<sup>(12+)</sup> denotes weekly, per-capita transfer offered to child _i_<sup>_′_</sup> _s_ siblings aged 12-15 years. _y_<sup>_f_</sup> denotes father’s weekly, per-capita income. Regressors not shown: child age dummies, highest grade completed dummies, age interacted with highest grade completed, year dummies, and a child fixed effect. In addition, column (3) includes controls for number of siblings in various age brackets and grade brackets. Finally, in columns (2)-(4) we instrument for father’s income with the average wage in the locality (excluding the wage of the father in question).

Finally, because a child’s siblings aged 12 and above are also offered transfers, we estimate the following regression to investigate the effect of transfers to these siblings on child _i_<sup>_′_</sup> _s_ enrollment:

> 28. We include the number of siblings in age brackets [0-5], [6-7], [8-9], [10-11], [12-13], [14-15], and [16-18] and the number of siblings with highest grade completed in grade brackets [0-6], [7-9], and _≥_ 10.

30
curves; it has a functional form which is consistent with known household-budget data; it is simple to estimate, largely avoiding the need for non-linear estimation [...]” [Deaton and Muellbauer, 1980, p. 312]. Due to its desirable features, the AIDS system has been widely used in the analysis of expenditure behavior. The classical AIDS takes the form defined in equation 17 where _si_ is the share of commodity (i) in total expenditure on goods and services, (m) is total expenditure, _pi_ is the price of commodity (i) and P is a price index.

To be informative of household behavior under the null hypothesis, we replace total expenditure (m) in the original specification by income per capita (m). This adaptation reflects our interest in how expenditure shares change with increases in income. On the one hand, the adapted specification is not very different from the original specification based on total expenditure; households at the low levels of income have very little ability to save and spend most of their income. On the other hand, the derivation of AIDS/QUAIDS is based on a cost function, as an optimization of minimum expenditure on a given basket using an indirect utility. To dispel doubts, we keep the estimation based on income as our baseline result and provide the results for logarithmic expenditure per capita instrumented by logarithmic income per capita as a robustness check (see section 8.7 of the appendix).<sup>67</sup> To make this model consistent with utility maximization, three theoretical restrictions have to hold: the adding-up restrictions, homogeneity and symmetry.

If prices are known, equation 17 can be estimated by OLS, equation by equation. This procedure is equivalent to the estimation by maximum likelihood for the system [Deaton and Muellbauer,

> 67An important difference between using total expenditure or income per capita is linked to the assumptions related to savings. Usually the result of an AIDS/QUAIDS system are interpreted as a second stage of a twostage budgeting, in which the first stage determines the allocation of total consumption across time periods. Therefore, it is assumed that inter-temporal separability between taste shifters for savings or total expenditure and the expenditure of a certain good holds. In order to deal with this issue we instrumented consumption with income, following Banks et al. [1997]. The shapes of the Engel curves using consumption per capita instrumented by income per capita do not show substantial changes to the baseline model, particularly for the linear version. The exception is the quadratic version for health care expenditures. The overall consistency of the results with our baseline estimation suggests that our approach of estimating the Engel curves using income is a reasonable approximation for estimations using total consumption, which are theoretically supported in their functional form in the AIDS and QUAIDS approaches.

29
# PPR Working Paper Series

|Title<br>WPS105 Securing International<br>Market Access|Author<br>Richard H. Snape|Date<br>October 1988|Contact<br>J. Sweene,<br>31021|
|---|---|---|---|
|WPS1O6 Energy Issues In the Developing<br>World|Mohan Munasinghe<br>Robert J. Saunders|||
|WPS107 A Review of World Bank Lending for<br>Electric Power|Mohan Munasinghe<br>Joseph Gilling<br>Melody Mason|||
|WPS108 Some Considerations<br>in Collecting<br>Data<br>on Household Energy Consumption|L. Leitmann|||
|WPS109 Improving<br>Power System Efficiency in<br>the Developing<br>Countries Through<br>Performance<br>Contracting|Philip Yates|||
|WPS11O Impact of Lower Oil Prices on<br>Renewable Energy Technologies|E. Terrado<br>M. Mendis<br>K. Fitzgerald|||
|WPS111 Recent World Bank Activities in Energy|!ndustry and Energy<br>Department|||
|WPS112 A Visual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Policy Issues in Bank Lending: An<br>Interim Review of Experience|Barbara Nunberg|||
|WPS114 Africa Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor<br>31091|
|WPS116 Latin America and the Caribbean<br>Region Population Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor<br>31091|
|**Ta**|**ble 2: Robustne**|**ss for additional c**|**ontrols**||
|---|---|---|---|---|
|Dependent variable: Female<br>workers|**(1)**|**(2)**|**(3)**|**(4)**|
|Maternity leave (logs)|**2.124****|**2.491****|**2.285****|**2.238****|
||**(0.896)**|**(1.066)**|**(1.019)**|**(1.015)**|
|GDP per capita (logs)|26.987**|35.352***|23.952*|23.436*|
||(10.962)|(12.750)|(14.208)|(14.004)|
|Growth rate of GDP per|0.410**|0.350**|0.603***|0.633***|
|capita (%, annual)|||||
||(0.162)|(0.150)|(0.181)|(0.180)|
|Females in adult population|1.581***|0.951**|1.192**|1.224**|
|(%)|||||
||(0.459)|(0.470)|(0.566)|(0.572)|
|Number of workers (logs)|-0.495*|-0.621**|-0.449|-0.486|
||(0.278)|(0.291)|(0.318)|(0.320)|
|Female owner present Y:1|4.417***|3.997***|4.282***|4.258***|
|N:0|||||
||(0.481)|(0.514)|(0.550)|(0.553)|
|Top manager is female Y:1|13.059***|12.195***|13.213***|13.301***|
|N:0|||||
||(0.768)|(0.805)|(0.842)|(0.848)|
|Age of firm (logs)|-0.332|-0.464|-0.673|-0.695|
||(0.384)|(0.412)|(0.480)|(0.483)|
|Labor productivity (logs)|-0.194|-0.271|-0.069|-0.071|
||(0.186)|(0.190)|(0.195)|(0.196)|
|Exports (% of sales)|0.024*|0.018|0.015|0.015|
||(0.013)|(0.014)|(0.014)|(0.014)|
|Foreign ownership (%)|0.021**|0.014|0.011|0.011|
||(0.009)|(0.009)|(0.010)|(0.010)|
|Firm offers formal training|1.861***|1.365**|0.771|0.823|
|to workers Y:1 N:0|||||
||(0.548)|(0.555)|(0.583)|(0.584)|
|Inadequately educated|-0.109|-0.201|-0.289|-0.407*|
|workers: Obstacle severity<br>(0-4 scale)|||||
||(0.203)|(0.215)|(0.225)|(0.244)|
|Firm registered when started<br>operations Y:1 N:0|<br>-0.300|<br>-0.991|<br>0.426|<br>0.364|
||(0.998)|(1.012)|(0.815)|(0.801)|
|Firm experienced losses due<br>|0.767|0.713|1.812**|1.804**|
|to crime Y:1 N:0|||||
||(0.603)|(0.624)|(0.765)|(0.767)|

52
regionally concentrated multiplier effects of the price boom. As a consequence, rural households in coffee regions were significantly better off in 1995 than those in non-coffee regions.

In the second period, coffee farmers continued to perform well despite falling coffee prices, and other rural households in coffee regions grew at similar rates. However, non-coffee regions outperformed coffee regions, which brought rural consumption levels closer together again, rendering the regional differences insignificant. The very high growth rates in urban coffee regions as well as in both rural and urban non-coffee regions point to a strong autonomous growth process unrelated to the coffee sector.

Table 2: Per capita consumption growth by household type, 1992-99

|_Household type_|Growth in me<br>consump|an per capita<br>tion(%)|
|---|---|---|
||_1992-95_|_1995-99_|
|Coffee farmers|7.53|6.31|
|Rural in coffee regions|7.19|6.84|
|Rural in non-coffee regions|-1.15|13.92|
|Urban in coffee regions|6.52|15.31|
|Urban in non-coffee regions|13.61|13.14|
|Total|5.07|9.66|

Source: Authors’ calculations.

The basic thrust of the descriptive analysis is corroborated by a multivariate regression analysis that pools the observations from the three surveys. Per adult equivalent consumption expenditures are regressed against several variables including dummies for being coffee farmer and residing in coffee regions, which we interact with time dummies for each year, and time dummies for 1995/96 and 1999/2000. Additional right-hand-side variables comprise a standard set of controls, including education and land endowments, an urban dummy, as well as variables related to non-farm income. Some of these controls are also interacted with time dummies.

The results (ordinary least squares estimates are reported in Appendix A) correspond to expectations: controlling for other income determinants, in 1992/93 and in 1999/2000, neither coffee farmers nor people residing in coffee regions were better off than their counterparts in non-coffee regions. Yet, both the 1995/96 coffee region and the 1995/96 coffee farmer dummy are significantly related to higher expenditure levels. These effects are quantitatively 13
industry since the onset of the crisis. More than 60 companies have either wholly owned subsidiaries or majority owned joint ventures. All global first tier suppliers now have a presence in Korea.

• A substantial consolidation of the auto parts industry with large numbers of firms exiting the industry during the financial crisis. Many others remain hobbled with ongoing financial problems because of high debt ratios.

To the financial upheavals that the industry faced were added new pressures arising from technological change in the industry, most particularly the trend towards modularization. The result has been an (as yet incomplete) restructuring of the supply chain, with a dramatic decrease in the number of first tier suppliers. Moreover, in an industry that has become even more heavily dependent on international markets, with exports providing a market for more than 60 percent of Korean car production in 2003, issues of cost competitiveness and quality control in the components industry have assumed even greater importance.

# _Consolidation_

Despite the consolidation of the industry in the aftermath of the financial crisis, it continues to be dominated by small and medium sized companies. More than a quarter of all component producers have fewer than 50 employees; more than half have fewer than 100 (Table 1: these data are from the Korea Auto Industries Cooperative Association; not all components suppliers belong to the association). Consolidation continues among KAICA members: the total number of firms in 2003 is down from 915 in 2002.

29