rafmacalaba/datause-encoder-data huggingface dataset:
26

**TAIUL** L

SUMMARY OF **ICAL INCINTrVI SCHEMU** IN 24 **DEVOILNO COINThE** - **TYPI AZ) (1) AN ) 103MM**

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|---|---|---|---|---|---|---|---|
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|Ecuador<br>Fiji<br>G"|31.61.62(d<br>21(1), St.66<br>_A_<br>14 IS 32|)<br>-<br>20% on P+M<br>4/PM|i. (Sys)<br>At taxpayer'<br>disceMioa|-<br>sI.A._SS/._|-<br>-|P+MExL<br>-<br>PME|<br>-<br>Up toSO/.t<br>(hotels only)|
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|Paraguay<br> <br>Pakistan|62(c)<br>3,6.19.20<br> <br>21(d), 29,53|-<br>10/30% on<br>P+M|-<br>15%pa.|-<br>IT.C 00%<br>|---<br>D.S-5/% D<br>**T.S.m **+**100**<br>D|P+M+R EH.<br>**%**||
|---|---|---|---|---|---|---|---|
|Peru<br>|65<br>|-|-|-|-|P+M +-parts<br>**Ex.**||
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t with effcc. atom June 1974. tt up to and including May, 1974.

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# **Tables**

## **Table 1: Key elements of intervention arms**

||Grant provided|Management training<br>provided|Management manual<br>provided|
|---|---|---|---|
|WSD Schools|Yes|Yes|Yes|
|Grant-only Schools|Yes|No|Yes|
|<br>Control Schools|No|No|Yes|

**Table 2: Timeline of intervention and evaluation**

|Date|Activity|
|---|---|
|10/2007 – 4/2008|Sensitization and coordination between stakeholders|
|4/2008 – 6/2008|Assignment to interventions and baseline data collection|
|5/2008 – 12/2008|Grant distribution and training in the WSD schools|
|5/2009 – 6/2009|Collection of first follow-up data|
|5/2010 – 6/2010|Collection of second follow-up data|
|5/2011 – 6/2011|Collection of third follow-up data|
|Throughout|Monitoringof implementation|

**Table 3: Description of the data**

|Year|Data type|Respondent|Obs|Notes|
|---|---|---|---|---|
||School data|Principal, deputy|273||
||Student test|3<sup>rd</sup>& 5<sup>th</sup>grades<br>|8,856||
|2008|Classroom visit|4<sup>th</sup>& 6<sup>th</sup>grades|528||
||Student interview|3<sup>rd</sup>& 5<sup>th</sup>grades|2,688|Administered to a subset of tested<br>children|
||School data|Principal, deputy<br>|176||
||Student test|4<sup>th</sup>& 6<sup>th</sup>grades|5,660||
|2009|Classroom visit|3<sup>rd</sup>& 5<sup>th</sup>grades|346|No data in Grant-only schools|
||Student interview|4<sup>th</sup>& 6<sup>th</sup>grades|1,755||
||Teacher test|About 6 teachers|1,049||
||School data|Principal, deputy|276||
||Student test|3<sup>rd</sup>& 5<sup>th</sup>grades|9,022||
|2010|Classroom visit|4<sup>th</sup>& 6<sup>th</sup>grades|502||
||Student interview|3<sup>rd</sup>& 5<sup>th</sup>grades|2,678||
||Parent interview|Parent or caregiver|567|Of two interviewed students|
||School data|Principal, deputy|274||
||Student test|4<sup>th</sup>& 6<sup>th</sup>grades|5,230||
||Classroom visit|3<sup>rd</sup>& 5<sup>th</sup>grades|534||
|2011|Student interview|4<sup>th</sup>& 6<sup>th</sup>grades|2,579||
||SMC interview|Committee (minus|249|Mostly PTAs, in controls and|
|||principal)||Grant|
||Teacher interview|4<sup>th</sup>& 6<sup>th</sup> grades|517|Teachers of tested students|

26
# _U.S Environmental Regulations_

Another barrier to exports derives from environmental concerns in the U.S. In the 1960s a large public debate began over phosphate effluent from detergents and the resulting eutrophication of U.S. rivers and lakes. By the late 1960s, almost 10,000 lakes were reported to be suffering from excessive nutrient enrichment by human activities (ReVelle and ReVelle, 1988). As early as 1985, jurisdictions which had enacted phosphate bans included New York, Michigan, Indiana, Vermont, Minnesota, Dade County, Florida, Akron, Ohio, and Chicago Illinois (Knud-Hansen, 1994). By 1996, more than 19 states and counties in 15 other states were limiting the phosphorous content of household laundry products to 0.5 percent.<sup>14</sup> In 1999, many regions of the U.S. had adopted phosphate limitations or bans, affecting about 40 percent of the U.S. population (Kostick 2004). The effects of these phosphate bans on consumption are apparent in figure 7 below.

Zeolites (hydrated aluminosilicates of the alkaline and alkaline-earth metals) are an environmentally friendly alternative to phosphates and are commonly used in liquid detergents as builders.<sup>15</sup> The introduction of phosphate-reduced (1976) and later phosphate-free detergents (1983) marked the beginning of the era of builder systems containing zeolites. Many U.S. suppliers are also researching the building properties of soluble silicates. These can be used in both liquid detergents or in powders (after being spray-dried).

> 14 Source: published summary of phosphate legislation from the Soap and Detergent Association. March 20 1996. <u>http://www.ledizolv.com/LearnAbout/LeadDustCleaning/lszsixarg.asp</u>

> 15 “Builders” serve to tie up mineral (hard water) ions, specifically magnesium and calcium, so surfactants can work better. They emulsify oil stains, reduce the re-deposition of dirt during washing and rinsing, provide alkalinity for cleaning and soften laundry water.

24
percent of GDP in 2030 (Table A3.3); the need for government services is higher as they have to make up for the loss in private consumption, which also influences the U5MR. A comparison between urban and rural poverty changes across simulations indicates that rural poverty responds more strongly to changes in GDP growth; this is related to the fact that its labor force is more heavily represented in non-government sectors, for which growth rates of which are positively correlated to GDP growth; in these simulations, government growth rates are either fixed or, given the use of government health services as the policy tool for MDG targeting, inversely related to GDP growth since more health service growth is needed to make up for less growth in GDP and private incomes and consumption.<sup>13</sup> From a different angle, given inefficiencies in the government health sector in many LICs it may be feasible to reduce the need for extra financing by raising government efficiency. A set of scenarios were constructed to learn about what difference this may make: in these, the rural U5MR is gradually raised to reach the urban level by 2030 (as in the preceding scenarios) with different rates of additional health sector efficiency growth combined with marginal financing from foreign grants. The rates of efficiency growth were raised up to the point where there was no need for foreign grants above the level included under the base. (Total foreign grants are here simply defined as the sum of grants in 2014-2030.) Drawing on the results for these simulations, Figure 3.5 maps out combinations of (a) average per capita foreign grant increases (in 2009 US$) 2014-2030; and (b) additional annual growth in government health service efficiency, with efficiency defined to cover investment efficiency (measured by the quantities of goods and services needed per unit of new capital), as well as efficiency of labor and capital use but without assuming efficiency gains in intermediate inputs (such as medicines). In the absence of a gain in efficiency, the grant increase in an average year is around US$22 per capita (at 2009 prices; from the scenario _mdg-eq+fg_ ). The need for additional grant aid would be eliminated if efficiency in the health sector reached close to 5 percent per year. While such rapid gains may be infeasible, additional growth of at least 1-2 percent per year may be within the realm of the possible.<sup>14</sup>

> 13 An additional simulation showed that the domestic resources that could be mobilized via increased domestic government borrowing were insufficient to finance the health policy of the last two scenarios ( _mdg-eq+fg_ and _mdg-eq+tx_ ).

> 14 For example, on the basis of surveys in 6 low- and middle-income countries, Chaudhury _et al_ . (2006) find that, on average, primary health workers were absent 35 percent of the time. Other things being equal, a gradual reduction in their absenteeism to 17.5 percent of their time by 2030 would correspond to an annual increase in their productivity by around 1.5 percent per year. In the simulations, similar improvements are assumed for capital use and investments.

16
findings also indicate that inflation persistence plays an important role in explaining both the average level of inflation and its variation.

# **2.2 The Case of Turkey**<sup>5</sup>

The Turkish economy has been undergoing chronic high inflation since the 1970s. Indeed, starting from the 1970s, the inflation rate displayed an upward trend and reached its peak of 120 percent in 1994 in the aftermath of an exchange market crisis.<sup>6</sup> The evolution of inflation in Turkey can be divided into four sub-periods: (i) the period during which price developments were influenced by financial liberalization and the deteriorating current account outlook (1989-1993); (ii) the 1994 currency crisis and worsening debt dynamics (1995-1999); (iii) the exchange-rate based stabilization program and its collapse (2000- February 2001); and (iv) the adoption of the floating regime in February 20001, which was followed by the inception of the Economic Program for Strengthening the Turkish Economy in May 2001.

Between 1989 and 1993, prior to the 1994 crisis, inflation rate hovered around 60 percent. After the 1994 crisis, the inflation rate moved to an upper plateau in the 80 percent range. Inflation began to fall back towards the 60 percent range at the outset of 1998 due mainly to the fiscal retrenchment along with the sharp contraction in economic activity.<sup>7</sup> In an attempt to stabilize the Turkish economy, which was plagued by chronic high inflation and real interest rates as well as deteriorating debt dynamics, the authorities launched an exchange rate based stabilization program in January 2000. The program

> 5 This section draws mainly upon Bahmani-Oskooee and Domaç (2003). 6

> For a more extensive discussion of the studies on inflation in Turkey see Kibritçioğlu (2001) and references therein.

9
# **Resource Misallocation and Productivity Gaps in Malaysia**

Lay Lian Chuah, Norman V. Loayza, and Ha Nguyen<sup>*</sup> Development Research Group, World Bank JEL Classification Code: D24, O12, O40 Keywords: Total factor productivity, resource misallocation, distortions, efficiency, growth, Malaysia.

> * We thank the Economic Planning Unit (EPU) and the Department of Statistics of Malaysia (DOSM) for kindly providing the firm-level data from the manufacturing census. We thank Ayberk Yilmaz for his valuable inputs and technical support, and Mohammad Amin, Kevin Chua, Kevin Garcia Cruz, Rafael Munoz Moreno, Rong Qian, Julio E. Revilla, Luis Serven, and Ulrich Zachau for their valuable comments. We would also like express appreciation to discussants Rudolfs Bems and Jesus Felipe, as well as other participants at the 2017 National University of Singapore Productivity workshop, the 2017 Singapore Economic Review conference, and the World Bank firm productivity workshops in Kuala Lumpur (July and October 2017) for insightful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent. Authors’ e-mail addresses: llchuah@worldbank.org; <u>nloayza@worldbank.org;</u> and <u>hanguyen@worldbank.org.</u>
<!-- Start of picture text --> HM >0.55<br>HN 0.45-0.55<br><0.45 Zé ti<br>(a) Wave 1: ~~ gy<br>1994-1998 %<br>ss<br>MM >0.< pe a NW%<br>MN 0.5-0.6 es<br><0.5 } a<br>(b) Wave 2: j ?<br>1999-2004<br>i ts : ><br>, a pe _TMp< iy‘}<br>|<br>MM >o.s . ty? ‘ve<br>MN 05-06. w» ee:oy;x4<br>(c) Wave 3: 4 is<br>2005-2008 7<br><!-- End of picture text -->
This can be seen as a reduced-form model in which income is exclusively determined by circumstances and effort, such that all individuals having the same circumstances and the same effort obtain the same income. Roughly speaking, the source of unfairness in this model is given by the effect that circumstance variables (which lie beyond individual responsibility) have on individual outcomes.

A parametric implementation of the model above,<sup>3</sup> which has been extensively used in the literature (see Bourguignon et al. 2007), considers estimating by OLS the following equation and computes inequality of opportunity as the value of a given inequality measure I�∙� applied to the distribution of the predicted values Y<sup>�</sup> �, where Y<sup>�</sup> � � a��b<sup>�</sup> C�. Hence the value of absolute inequality of opportunity is given by I�Y<sup>�</sup> � while the value of relative inequality of opportunity is given by I�Y<sup>�</sup> �/I�Y�.

A dynamic version of the model can be obtained by introducing the time dimension in alternative ways. We could consider a first expression, in which income is assumed to vary with time, while circumstances are assumed to be time invariant: Model (3) assumes that circumstances impact income in an identical way over the entire life. A variant of the same model considers the possibility of time-varying effects, possibly distinguishing between fixed and time-varying circumstances: Both models (3) or (4) are highly demanding in terms of data, because their longitudinal structure requires repeated observations of the same individual, possibly under alternative sets of circumstances which are independent from her will. In addition, implementing models (3) or (4) would provide a picture of the evolution of EOp over the life cycle of the specific birth cohorts that are present at the start of the analysis.

A less demanding approach in terms of data exploits the availability of repeated cross sections from the same population. If one is interested in understanding whether a society is experiencing changes in the EOp of its citizens, the relevant model considers where Y�� is the income of individual i sampled in survey t. The data generating process is allowed to change over time among random draws from the (same country) population. The implicit assumption is the over-time stability of the population, such that changes in EOp can be attributed to changes in the relevant parameters a and b. Model (5) is specular to cross-country analysis, once t is interpreted as a country indicator, but has the advantage of greater comparability of the underlying populations, originating from the same country.

> 3 In this paper we follow the ex ante approach. See Fleurbaey and Peragine (2013) for a comparison between the ex ante and ex post approaches to equality of opportunity.

3
Notes: Data pooled for all follow-up surveys, unless otherwise noted. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. Z score index constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q-values are reported below standard errors (q-values in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

a Z-score on access to financial services includes the six variables. Those are owner has a bank account, has a business bank account, owner use an account for business purposes, owner does not save at hombre or ROSCA or SACCO, owner saves at bank.

b Sample size on access to financial services is 9,438 due to businesses that have closed. For several savings outcomes for which it is possible that individuals are saving even without operating a business, we use the sample of firms still in business since we lack data on these outcomes for those whose businesses have closed. We code as 0 outcomes that are business-only for firms that have closed (business bank account and used an account just for business purposes, we imputed with 0 for those business which have closed).

51
# (b) Better occupational status than father

<!-- Start of picture text --> 1985-1995 1975-1985 1965-1975 Before 1965<br>.55<br>.5<br>.45<br>.4<br>Predicted probability<br>.35<br>.3<br><!-- End of picture text --> _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014.

Another dimension along which the examination of intergenerational mobility is interesting in Nepal is the caste/ethnicity status of individuals. The probability of doing better than one’s father conditional on one’s caste/ethnicity status shows considerable heterogeneity for both education and occupation. Newars, followed closely by hill Brhamins and Chhetris have the best odds of surpassing their father’s education and occupation levels. On the other hand, Nepalis in Muslim and other minority caste categories followed by Terai Brahmin Middle Caste, and Dalit categories have the lowest odds of upward mobility for education as well as occupation. Again, low sample size for some groups, such as Muslims and Others, means that the standard error around the point estimates are fairly large so the results have to be interpreted with caution. For example, even though the point estimates for Dalits and the Terai Brahmins and Middle Castes show slight differences, the 95 percent confidence intervals around these estimates have considerable overlaps, suggesting that it is hard to draw any conclusions distinguishing the two groups.

4
The Asset volatility (book) variable reflects investment variability over time. We exclude firms with fewer than five asset growth observations, and trim this and other firm-level variables at the 5<sup>th</sup> and 95<sup>th</sup> percentiles. This yields 27,093 asset volatility observations with a mean of 0.344, as seen in Table 1.

An alternative volatility variable, Asset volatility (stock), is calculated as the annual average of market-based annualized asset value volatility measures based on Merton’s model following Anginer et al. (2014).<sup>5</sup> We use data from Datastream for the market value of equity and to estimate equity volatility. To ensure sufficient variability, we exclude firm-year observations with less than 90 days of nonzero stock returns. We further assume that the maturity of a firm’s debt is one year, noting that the results are insensitive to this particular assumption.<sup>6</sup> Finally, the dividend yield is taken from Worldscope, while the risk-free return is proxied by the yield on one-year US Treasury bills. Altogether, we have 24,615 Asset volatility (stock) observations with a mean of 0.024, as reflected in Table 1.

Looking at the association between firm volatility and the maturity structure of debt can be problematic since the direction of causality can go either way, with less volatile firms being able to attract more long-term debt. We deal with this identification problem by using the approach of Rajan and Zingales (1998) to construct a measure of firms’ “desired” debt maturity structure. This index, Maturity, is given by the use of long-term finance for sectors in the US on the assumption that firms in the US are not as constrained in their choice of short-term vs. long-term external finance. Specifically, Maturity is computed as the sectoral

> 5 See Appendix A1 of Anginer et al. (2014) for a description of Merton’s method to calculate firms’ asset volatility.

> 6 We checked the robustness of this assumption by changing the assumed maturity of one year to the firmspecific weighted average of the maturities of short-term debt and long-term debt approximated to 0.5 and 2 years, respectively. The estimated coefficients and standard errors in the regressions of Table 3 change only slightly (unreported).

8
level, however, the private sector plays a much bigger role. It accounts for close to half of the enrollment (46 percent). The educational system in Mexico is now so extensive that there are over 483,000 schools (excluding preschool) staffed by over a million teachers, of which 84.3 percent are from public schools. Teachers represent 2.8 percent of the full time labor force from which only 20.1 percent are private school teachers.

In 1999, the public school teachers’ share<sup>5</sup> was 42.82 percent of the total number of government personnel. All teachers in public basic education are affiliated with SNTE. All teachers in upper secondary and tertiary education have a union of professors and administrative workers also affiliated with SNTE or are independent (Autonomous or State Universities).

# **3. DATA AND METHODOLOGY**

The Primary Education Assessment Survey, second round 1997 ( _Encuesta de Evaluación de Educación Primaria_ [EEEP] _, segundo levantamiento_ 1997) from the Ministry of Education is representative by state level and by stratum (urban — public and private — schools, public rural schools, indigenous schools<sup>6</sup> and community schools). Students were given standardized achievement tests at the beginning of sixth grade that covered the subjects studied in the 5<sup>th</sup> grade. EEEP also collected information on schools, parents, teachers, supervisors, socioeconomic and academic backgrounds. Non-categorical are students' scores, age, amenities or facilities in the house, the number of rooms in the house, the number of teachers' updating courses, didactic material available to the teacher and school equipment. The survey design was stratified and multistage. In each stage the sample size was chosen randomly. Importantly, the final sampling unit was the school and not the student. The sample included 53,209 students and 3,645 schools. In matching students with their parents, 8,450 students were lost because their parents did not respond to the questionnaire. Another 30 percent of the sample (number of students) was also lost when matching students with their corresponding 5<sup>th</sup> grade teachers. The distribution of the scores of those students that were matched successfully suggests that there was not truncation in the sample. The Appendix shows the sample sizes by state and stratum and the list of variables employed in the analysis.

> 5 Federal, state plus autonomous school teachers. 6 The indigenous schools refers to schools offering services to populations which mother tongue is not Spanish.

6