rafmacalaba/datause-encoder-data huggingface dataset:
from a Cobb-Douglas production function in capital and labour, with a capital share of 0.5.<sup>18</sup> Each panel also shows the estimated relation between the variables assuming both a simple linear specification and a cubic polynomial to allow for the nonlinearities required to generate multiple equilibria. In the first panel we observe a linear relation between (log) TFP and (log) capital per worker. It is apparent that the polynomial specification does not offer a significant improvement on the fit with respect to the linear specification. Overall, the estimated relationship suggests a constant elasticity γ of about 0.2. Both the finding of a constant elasticity and its magnitude are inconsistent with the requirements of multiple equilibria. A constant elasticity γ implies that the function _Af_ '( _k_ ) is either always decreasing or always increasing, which implies a unique equilibrium (or none). Also, as determined above, the lower bound for γ (obtained by assuming a very large capital share) is 0.4, which is almost twice the estimated value. The relation of TFP with total capital, shown in the second panel, does not fare better. In fact, the figure shows practically no relation between the two variables. Even if we take the econometric results at face value, they also suggest a constant and small elasticity (about 0.06). As explained above, a constant elasticity is inconsistent with multiple equilibria, and the implied value of the elasticity is way below the magnitude required for multiplicity. Finally, the bottom panel shows the relation of TFP with total output. It is apparent that the situation is almost identical to that obtained for total capital.

The panels of **Figure 10** repeat the exercise performed in **Figure 9** , but this time using data from Colombian plants.<sup>19</sup> The graphs plot the relations between the log of TFP at the plant level and the log of the different measures of activity at the industry level after partialling-out plant, industry, and year fixed effects. The conclusion that emerges from the three figures is similar, and suggests that the scale factor is independent of these aggregate variables. The linear specifications show a clearly flat relation between the log of firm level TFP and the aggregate variables, and the non-linear specifications are undistinguishable from the linear ones. Again, although these results should be taken with caution because of the caveats mentioned above, the 18 Results are similar using other values for the capital share. Note also that we make no effort to adjust for the quality of labour input, as is often done in development accounting exercises. It could be that the nonlinearity in productivity comes precisely from sharp improvements in worker skills at some level of development, and so we would like our estimates of productivity to capture this.

19 The data on Colombian plants comes from Fernandes (2003). Industry level data was obtained from UNIDO (2002).

30
15

# 3. **The Data**

The data utilized in this study cover the time period 1981-88 and pertain to three broad areas: domestically-produced apparel, imported apparel from Hong Kong and license holdings for apparel imports from Hong Kong. We did not attempt to obtain data on all categories of apparel. There are severe difficulties in assembling a consistent panel of data due to the different and changing classification systems used in reporting information on imports and domestic production. Therefore, we chose groups of apparel such that these consistency problems were minimized. Our objective was to get as many relatively consistently-defined, disaggregated groups that we could find or develop concordances for between the different classification systems employed.

We identified ten such groups. They are: (1) dresses; (2) skirts; (3) playsuits; (4) sweaters; (5) trousers; (6) men's coats; (7) women's coats; (8) woven shirts; (9) knit shirts; and (10) underwear. We obtained data for these groups for the following variables between 1981 and 1988. The variables are defined below. In our notation, the subscript i indexes the apparel group, and t indexes the year.

Pit us - Unit value of US production. Pit HK . F.O.B. Hong Kong price. This includes the license price. tit - Ad valorem tariff in the US.

Tit - Pit HK _ Adjusted Hong Kong price, where: Transport cost per unit from Hong Kong to the US.

Pi HK _ PitHK (1 + tit) + Tit. Qit us - US sales of US production. Qit HK _ Imports from Hong Kong.
behaviors. Pregnant mothers may also be vulnerable to infections, which can impair their nutritional status and endanger the health and development of the children. Mothers with hookworm infections are at higher risk for pre-term delivery and low-birth weight of children, which increases the odds of childhood undernutrition (Dreyfuss et al 2000).

EED is the newest hypothesized disease link between WASH and nutrition. The hypothesis suspects that chronic exposure to feces could inflame the gut and cause physical intestinal deformations, where the impaired gut cannot absorb or retain essential nutrients needed for healthy development (Humphrey 2009; Ngure et al 2014). More importantly, those children affected with EED may not exhibit symptoms of enteric infection such as episodes of diarrhea. However, victims of EED may be at high risk for vaccine failure, reduced immunity, and growth faltering. EED is difficult to diagnose due to its complex pathogenesis and limitations in testing methods; however, EED is suspected to be most prevalent in areas of poor WASH (Guerrant et al 2013).

Much of the literature on WASH and nutrition derives from observational studies, particularly on the correlation between poor sanitation on nutrition outcomes. There are limited experimental studies that have evaluated the effectiveness of WASH interventions on improving nutritional outcomes. Further, the few experimental evaluations that exist have shown mixed results on the ability of traditional WASH to improve growth. For example, a couple of randomized controlled trials (RCTs) have evaluated improved sanitation for promoting childhood nutrition. In rural Indonesia, it was found that a sanitation marketing intervention reduced diarrheal prevalence and improved growth in wealthier households that did not have sanitation at baseline (Cameron et al 2013). Pickering and colleagues (2015) found that community-led total sanitation interventions did not reduce diarrheal disease incidence but did improve child growth. However, two RCTs on pit latrine building campaigns to eradicate open defecation in rural India showed that increased latrine coverage had minimal impact on disease outcomes including diarrhea and undernutrition, though both areas still had notably high rates of open defecation (Clasen et al 2014; Patil et al 2014). Nevertheless, a meta-analysis of observational and experimental studies found that sanitation interventions overall had a positive effect on at least height-for-age (Freeman et al 2017).

There have been fewer experimental evaluations on the effectiveness of improvements in household microbial water quality on improving childhood nutrition, but there is some promising evidence for potential impact. Indicators of E. coli in household drinking water are associated with diarrheal disease and are an appropriate marker of fecal contamination (Gruber et al 2014). Systematic reviews have 5
# Annex **1  Industry Groupings: WIFO Taxonomies**

Industry groupings are differentiated by various criteria, including a combination of factor inputs and market strategy criteria (taxonomy 1), and labor skills (taxonomy 2), borrowed from the Austrian Institute of Economic Research (WIFO)<sup>60</sup> taxonomy which provides a whole variety of different classifications (Peneder 1999, 2000).<sup>61</sup> The list of criteria is derived from trade and growth theories and includes labor intensity, capital intensity, skill intensity of workers, and technology intensity. All the distinct industry types are summarized in Table 12, below.

**Table 12 The WIFO taxonomies of manufacturing industry**

|_Taxonomy I_: Factor input combinations|
|---|
|•Marketing driven industries (MDI)<br>•Labour intensive industries (LI)<br>•Technology driven industries (TDI)<br>•Capital intensive industries (CI)<br>•Mainstream manufacturing (MM)<br>|
|_Taxonomy II_: Skill requirements|
|•Low-skill industries (LS)<br>•Medium-skill blue-collar industries (MBC)<br>•Medium-skill white-collar industries (MWC)<br>•High-skill industries (HS)|

The taxonomies were initially intended to offer a coherent set of empirical tools that facilitates inquiries into industrial performance with respect to the intangible sources of competitive advantage of European industries.

_Taxonomy I_ is based on US data for wages and salaries, investments in physical capital, advertising outlays and R&D expenditure. These are assumed to span four independent dimensions of productive inputs for revenue generation. Ratios to total value added have been calculated for wages and physical capital. Expenditures on advertising and R&D are represented by their ratio to the total sales. The latter are directly derived from balance sheet data.

_Taxonomy II_ is based on occupational data, distinguishing first the two types of whitecollar and blue-collar workers, and then for each, the shares of respectively high- and low-skilled labour. The data source stems from the OECD and covers employment shares for a sample of developed economies.

> 60  WIFO ( _Österreichische Institut für Wirtschaftsforschung_ ) analyzes national and international economic trends and supplies short- to medium-term economic forecasts. Most of its activities include commissioned research and consulting for domestic and international decision-making bodies, the European Commission, OECD, major business and financial institutions.

> 61  Peneder, M. (2000), _External Services, Structural Change, and Industrial Performance_ , paper prepared for the Competitiveness Report 2000 on behalf of the European  Commission. Peneder, M. (1999), ‘Intangible Investment and Human Resources. The New WIFO Taxonomy of Manufacturing Industry’, _WIFO Working Papers_ No. 114.

71
health-related intentions into health outcomes. In consequence, these potentially-critical variables are generally excluded from statistical assessments of relationships between DHS-recorded variables and health outcomes .

In South Asia, many empirical studies based on the DHS and local sampling exercises have found that health outcomes are improved when parents (usually mothers) are better-educated, wealthier and more mature. Supporting evidence for wasting can be found in Vollmer et al. (2017), Harding et al. (2018), Martorell and Young (2012), Black et al. (2018), IFPRI (2014), Frongillo et al. (1997) and Fernandez et al. (2002). Similar results for maternal anemia can be found in Dutta and Sengupta (2017), Mangla (2016), Gogoi et al. (2016), Chowdhury et al. (2015), Mithra et al. (2014), Arlappa et al. (2014), Singh and Chaudhary (2015), and Tayade et al. (2018).

Other studies have suggested that culture-based preferences can also play a significant role. For example, associations between child gender, birth order and wasting have been explored by Harding et al. (2018), Hill and Upchurch (1995), and Pillai and Ortiz-Rodriguez (2015). In a study of child feeding practices, Dasgupta et al. (2018) find a perverse “education bias”: Cultural preferences seem to dictate a negative association between maternal education and the share of fish among animal protein sources, even though fish is both less expensive and nutritionally superior.

With these studies as background, we specify regression models for child wasting and maternal anemia in equations (1) and (2) below. All regression variables are based on identical measures in the DHS surveys for India and Bangladesh. We posit that child wasting probability is a function of mother’s education,<sup>3</sup> household wealth, mother’s age, child birth order and child birth month.<sup>4</sup> We also incorporate controls for survey months, and fixed effects for Indian states [Bihar, Jharkhand, West

> 3 We have also tested husband’s education, which is statistically insignificant in all cases.

> 4 We include birth month to incorporate the potential effects of seasonal food supply fluctuations on mothers’ post-partum nutrient intake.  This follows Dasgupta et al. (2018), who find that child health measures in Bangladesh are higher for birth dates in September - December, when monsoon floods sharply increase the availability of fish protein.

5
# **Table 2: Evasion Gaps**

## **Descriptive Statistics Evasion Gaps**

(Log Exports Reported by Partner minus Log Imports Reported in Tunisia at the HS6-source-country-year level)

|||**All**|||**By D**<br>|**ominant Impor**<br>|**ter**<br>||
|---|---|---|---|---|---|---|---|---|
|||||**_Ben Ali_**|**_Offshore_**|**_Onshore_**|**_Public_**|**_Residual_**|
||Mean|Std.Dev.|N|Mean|Mean|Mean|Mean|Mean|
||_(Log E_<br>|_xport Value_<br>|**_Eva_**<br>_Reported by P_<br>|**_sion Gap - Va_**<br>_artner – Log I_<br>|**_lues_**<br>_mport Value Re_<br>|_ported in Tunis_<br>|_ia)_<br>||
|_All_|-0.001|1.875|49347|0.356|0.071|-0.036|-0.234|0.098|
|High Tariff|0.099|1.939|24896|0.797|0.090|0.094|-0.101|0.170|
|Low Tariff|-0.104|1.801|24451|-0.108|0.033|-0.136|-0.293|0.048|
|N||||760|16068|29692|2106|721|
||_(Log Exp_<br>|_ort Quantity_|**_Evasi_**<br>_Reported by P_|**_on Gap - Quan_**<br>_artner – Log I_|**_tities_**<br>_mport Quantity_|_Reported in Tu_|_nisia)_||
|_All_|-0.015|2.172|48724|0.134|0.080|-0.083|0.129|0.087|
|High Tariff|0.080|2.173|24646|0.318|0.104|0.037|0.389|0.127|
|Low Tariff|-0.112|2.167|24080|-0.056|0.033|-0.176|0.014|0.059|
|N||||749|15,908|29,303|2,059|705|
||_(Lo_<br>|_g Unit Price_<br>|**_Evasio_**<br>_Reported by P_<br>|**_n Gap - Unit_**<br>_artner – Log U_<br>|**_Prices_**<br>_nit Price Repo_<br>|_rted in Tunisia)_<br>|||
|_All_|0.020|1.026|48724|0.216|-0.006|0.056|-0.357|0.028|
|High Tariff|0.022|0.951|24646|0.465|-0.017|0.065|-0.473|0.048|
|Low Tariff|0.018|1.098|24080|-0.042|0.014|0.049|-0.306|0.014|
|N||||749|15,908|29,303|2,059|705|

Note: Sample is confined to product-source-year combinations(i) in which imports reported in Tunisia and exports reported in partner countries are strictly positive, (ii) from countries which are among the top 15 source countries in terms of cumulative import value (either as declared by partners or as declared by Tunisian customs) over the period 2002-2009, and (iii) products which account for at least 0.01 percentage points of import value (either as declared by partners or as declared by Tunisian customs) over this period.In this sample, Ben Ali dominated goods account for 4.75% of aggregate import value, offshore dominated goods for 44.17%, onshore dominated goods for 35.63%, state dominated goods for 14.56% and mixed goods for 1.04%.

39
**Table 2a:  The impact of Local Elections (PILKADA) on District Government Expenditures per capita by Region** Treatment: T =1 if district held direct election in 2005; T = 0 if district did not have direct election during 2005-07

|_Variable_|_Coeffi-_<br>_cient_|_Administration_|_Public Works_|_Transport_|_Health_|_Education_|_Housing_|_Agriculture_|_Industry_|_Total_<br>_(all sectors)_|
|---|---|---|---|---|---|---|---|---|---|---|
|**Districts o**|**utsideJ**|**ava and Bali vs.**|**Comparison**|**Districts**|||||||
|T*D06||378,894***|86,316***|7,178|35,983***|135,161***|108,830**|38,318***|26,828**|905,669***|
|||(138,985)|(27,941)|(12,396)|(11,819)|(43,910)|(43,298)|(9,890)|(12,888)|(298,317)|
|# obs||607|542|592|599|599|583|599|577|607|
|# districts||130|128|130|130|130|130|130|130|130|
|**Districts i**|**nJava a**|**nd Bali vs. Com**|**parison Distr**|**icts**|||||||
|T*D06||-28,573**|-44,367***|2,925|-13,667***|-22,708**|-5,871|-4,664**|766.6|-123,761***|
|||(14,085)|(11,426)|(4,476)|(4,032)|(9,734)|(3,956)|(1,860)|(2,184)|(38,177)|
|# obs||569|535|551|564|564|555|564|555|569|
|# districts||118|118|118|118|118|118|118|118|118|

Notes:

*: 10% level of significance; **:  5% level of significance; ***: 1% level of significance Standard errors in parentheses estimated based on fixed-effects model with an AR(1) disturbance (Baltagi and Wu, 1999) Districts that held direct elections during 2006 and 2007 are dropped from the sample used in the regressions

33
# **5 Empirical Estimation**

From the Barro-Lee database, we observe the share of individuals that enrolled in or completed a given level of schooling _s_ ̄, in country _i_ and time _t_ . We denote the enrollment or completion rate _xit_ , which is the empirical measure of _P_ ( _._ ) in our theoretical framework. From the enrollment or completion rates by gender available in the data, we can also calculate the female-to-male ratio among those that enrolled or completed a given level of schooling in country _i_ and time _t_ , denoted _yit_ . While the original Barro-Lee database consists of 146 countries, we exclude countries that have inconsistent or missing time-series data from our estimation sample.<sup>18</sup> We also exclude countries that have a female-to-male ratio in tertiary enrollment or secondary completion above 4, and countries with age group populations under 10,000 for males or females for any of the years. These sample restrictions result in a balanced panel of 98 countries which we use as our baseline estimation sample. To assess the robustness of our results, we also estimate the model parameters with less restrictive sample restrictions.

We estimate the model parameters by pooling observations of _x_ and _y_ available every five years from 1950 to 2010 for each country in our balanced panel. Our baseline pooled specification therefore exploits variation in educational attainment and gender ratios over time and between countries. Table IV reports the countries with the highest and lowest female-to-male ratio for the three levels of educational attainment. It shows wide cross-country variation in the gender ratio for tertiary enrollment and secondary school completion. As of 2010, the 10th-90th percentile range of the female-to-male ratio in tertiary enrollment spans from 0.59 to 1.73, with a standard deviation of 0.45. For secondary completion, the 10-90th percentile range of the gender ratio goes from 0.80 to 1.24, with a standard deviation of 0.23. We observe less cross-country variation for the gender ratio in primary completion, with a 10th-90th percentile range spanning from 0.93 to 1.13, and a standard deviation of 0.11.

Figure I and Table V show that relative to cross-country variation, time variation in gender ratios for tertiary enrollment and secondary completion is also substantial. The average female-to-male ratio for countries in our sample has increased from about 0.40 in 1950 to about 1.22 in 2010 for

> 18Many countries in the original sample exhibit a very large drop or increase in the gender ratio in specific years.

19
David Card and Orley Ashenfelter, 1043–1171. Elsevier: San Diego and Amsterdam, North-Holland.

- Acemoglu, D. and D. Autor. (2012). “What Does Human Capital Do? A Review of Goldin and Katz’s The Race between Education and Technology”. _Journal of Economic Literature,_ 50(2):426-463

- Autor, D., F. Levy, and R.J. Murnane. (2003). “The Skill Content of Recent Technological Change: An Empirical Exploration.” _Quarterly Journal of Economics,_ 118 (4): 1279–1333

- Barro, R. J. (2001). “Human Capital and Growth”. _American Economic Review_ , Papers and Proceedings 91(2):12-17.

Barro, R.J. and X. Sala-i-Martin (1995). Economic Growth. McGraw-Hill, New York Becker, G. (1964). _Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education_ (Chicago, IL: University of Chicago Press, 1964).

- Behrman, J.; R. de Hoyos, M. Székely. (2014). Out of school and out of work: A conceptual framework for investigating “ninis” in Latin America and the Caribbean. Mimeo. World Bank. Washington D.C.

- Ben-Porath, Y. (1967). “The Production of Human Capital and the Life Cycle of Earnings”. _Journal of Political Economy,_ 75(4, Part 1): 352-365

- Bontemps, C.; J.M. Robin; and G. Van Den Berg. (1999). “An Empirical Equilibrium Job Search Model with Search on the Job and Heterogeneous Workers and Firms,” _International Economic Review,_ 40: 1039–74.

- Burdett K.; C. Carrillo-Tudela; and M.G. Coles. (2011). “ Human Capital Accumulation and Labor Market Equilibrium”. _International Economic Review,_ 52(3):657-677

- Bynner, J. and Parsons, S. (2002) Social Exclusion and the Transition from School to Work: The Case of Young People Not in Education, Employment or Training, _Journal of Vocational Behavior_ , 60: 289-309.

- Cunha, F. and J. Heckman (2007). "The Technology of Skill Formation." _American Economic Review,_ 97(2): 31-47

- Cunningham W. and P. Villaseñor. (2016).  Employer Voices, Employer Demands, and Implications for Public Skills Development Policy Connecting the Labor and Education Sectors. _World Bank Research Observer_ 31(1): 102-134

- Croxford, L. and Raffe, D. (2000) Young People Not in Education, Employment or Training: An Analysis of the Scottish School Leavers Survey, Report to Scottish Executive, Edinburgh: CES, University of Edinburgh.

- De Hoyos, R., H. Rogers, H. and A. Popova. (2013) "Out of school and out of work: A diagnostic of Ninis in Latin America", background paper for the regional study "Out of school and

19
# **I. Introduction**

Can satisfaction-related questions be valuable in measuring the quality of public services, specifically in health and education? In examining this question, the paper develops a model for identifying the range of factors that influence satisfaction with services among users and examines how these determinants of satisfaction may vary by the type of service and economic status of regions within a country.<sup>1</sup> Measuring the quality of public services (or _changes_ in their quality over time) has become increasingly important, particularly to monitor or evaluate the impact of fundamental reforms in service delivery, such as decentralization. This has resulted in a range of initiatives attempting to measure “performance” indicators in service delivery. Many of such indicators are conceptually complex and expensive to collect information on. Satisfaction surveys can offer a cheaper and more convenient alternative, but _only_ if they can be shown to have information content that is meaningful to measure performance in service delivery.

In recent years, significant progress has been made in the realm of public service delivery measurement (see Amin, Das and Goldstein, 2007 for an overview of these instruments). An increasing number of the tools developed include subjective instruments that gauge citizen perceptions. These encompass citizen report cards, community scorecards, facility exit polls, and citizen satisfaction surveys.

We focus on one type of subjective tool for gauging citizen perceptions, namely citizen satisfaction with services after Indonesia’s “Big Bang” decentralization in 2001, which

> 1 This paper draws significantly from a companion paper by Amin, Dasgupta, and Skoufias (2008) that presents a detailed analysis of satisfaction with health services in Indonesia, using the same dataset. It is also complementary to the study of Lewis and Pattinasarany (2009) focusing on satisfaction with educational services but abstracting from the issue selection bias.

2