rafmacalaba/datause-encoder-data huggingface dataset:
# **Text references**

(References to the database used in this compilation are found on-line in Annex 3.<sup>2</sup> )

- Ashenfelter, O. and A. Krueger. 1994. “Estimates of the economic return to schooling from a new sample of twins.” _American Economic Review_ 84(5): 1157-1173.

- Becker, G.S. 1964. _Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education_ . New York: National Bureau of Economic Research.

- Becker, G.S. and B.R. Chiswick. 1966. “Education and the distribution of earnings.” _American Economic Review_ 56(2): 358−369.

- Card, D. and A.B. Krueger. 1992. “Does School Quality Matter? Returns to Education and the Characteristics of Public Schools in the United States.” _Journal of Political Economy 100_ (1): -40.

- Card, D. 2001. “Estimating the return to schooling: Progress on some persistent econometric problems.” _Econometrica_ 69(5): 1127−1160.

- Chirwa, E. W., and M.M. Matita. 2009. “The Rate of Return of Education in Malawi.” University of Malawi Working Paper 2009/01.

- Chiswick, B.R. 2003. “Jacob Mincer, Experience and the Distribution of Earnings.” _Review of Economics of the Household_ 1: 343-361.

- Clark, D. and P. Martorell. 2014. “The signaling value of a high school diploma.” _Journal of Political Economy_ 122:2: 282-318.

- Damodaran, A. 2016. “Annual Returns on Stock, T.Bonds and T.Bills: 1928 – Current”. ~

- <u>http://pages.stern.nyu.edu/ adamodar/New_Home_Page/datafile/histretSP.html</u>

- Duflo, E. 2001. “Schooling and labor market consequences of school construction in Indonesia: Evidence from an unusual policy experiment.” _American Economic Review_ 91(4): 795−813.

- Fiszbein, A., P.I. Giovagnoli H.A. Patrinos. 2007. “Estimating the returns to education in Argentina using quantile regression analysis: 1992-2002.” _Económica_ 53.

Goldin, C. and L.F. Katz. 2010. _The Race between Education and Technology_ . Belknap Press.

- Griliches, Z., 1977. “Estimating the returns to schooling: Some econometric problems.” _Econometrica_ 1-22.

Harmon, C., H. Oosterbeek and I. Walker. “The returns to education: Microeconomics.” _Journal_

> 2 <u>http://datatopics.worldbank.org/education/files/GlobalAchievement/ReturnsEdAnnex3.docx.</u>

15
- [37] World Bank, 2005. Bangladesh Growth and Export Competitiveness. Poverty Reduction and Economic Management Sector Unit, South Asia Region, Report No. 31394-BD.

- [38] Yi, K. M., 2003. Can Vertical Specialization Explain the Growth of World Trade? Journal of Political Economy 111, 52–102.

# 8 Appendix

This Appendix contains the detailed derivations of the equilibrium conditions for the model described in the paper as well as the way of solving the model numerically. As usual, the model is solved backwards. We begin with Stage 3:

## 8.1 Model Derivations

### 8.1.1 Stage 3: Production Decisions

Exporting to the US Consider a Bangladeshi exporter with productivity ' and demand shock v. The US does not give tari¤preferences to Bangladeshi garments, and the presence of country-speci...c quotas in most categories makes meeting ROOs mandatory for exports. This means that Bangladeshi ...rms exporting to the US have no choice but to meet ROOs. (Recall that we assume that documentation costs are zero in the US and that as only assembly is required for origin, meeting ROOs is costless so � = 1:) They have to pay the tari¤ of 20%. As a result, the ...rm sells quantity and earns the following revenues and pro...ts: The price set by a ...rm does not depend on its market speci...c shock v. However, v a¤ects 47
|Shareholding company (non-traded) or||-0.048|-1.288|
|---|---|---|---|
|Partnership Y:1 N:0||(0.108)|(1.390)|
|R&D and New product/process||0.069|-0.183|
|||(0.243)|(0.628)|
|Constant|||-14.303|
||||(9.536)|
|**Total or Aggregate effect**||**7.956*****|**4.262*****|
|||**(1.927)**|**(1.215)**|
|Number of observations|5,197|5,197|5,197|

Huber-White robust standard errors clustered on country-industry pairs. *** (1%), ** (5%), * (10%). Without much loss of generality, only the combined contribution of R&D and new product/process development is shown.

55
In all regressions with female data, unless noted otherwise, the individual characteristics included in _Xict_ are age, highest grade completed, and binary indicators for the frequency of communication with mothers about SRH topics and whether the female’s household (i.e., parents) owns the house in which she lives. We included these controls because they are strongly correlated with sexual activity and relationship status and improve the precision of the estimates (Bruhn and McKenzie, 2009); however, the results are qualitatively similar if we do not include them. We estimate equation 1 for the whole analysis sample and for sub-populations of interest, such as females who had ever had sex at baseline and females who had partners in the last two years at baseline.

# **4.1 Baseline Balance and Follow-up**

The underlying identification assumption needed to interpret the results from an RCT as causal is that sample characteristics are balanced at baseline. We show balance for our baseline survey sample of 3,178 females for our primary outcomes in Table 1. In columns 1–2, we show balance for the cluster randomization, and we show balance for the individually-randomized _Goal_ intervention in columns 3–4. Overall, the RCT appears to be balanced across observed outcomes at baseline. Appendix Table B2 replicates Table 1, including the _Supply_ arm differences. Of the 3,178 females in our baseline sample, 2,591 were successfully tracked to the endline survey, an overall tracking rate of 81.5%. This tracking rate is similar across survey treatments (81% in the control arm, 85% in _Boys_ , and 80% of females invited to _Goal_ ). Table B3 presents baseline balance for this sub-sample. The treatment balance is maintained within this sub-sample. We discuss attrition in more detail in section 7.

In addition, we show balance for the IPV outcomes for the sub-sample of females who reported having ever had sex at baseline (25.7% of the sample) in Table B4, and we report balance for demographic characteristics and additional outcomes in Tables B5–B7.

15
9 four countries combined. As ouch, Table 2 indicates the extent to which Madagascar's annual import prices exceeded other countries (positive numbers show such a percentage price premium) while any negative values (a discount) would show the percentage by which they were lower.

Table 2 indicates Madagascar always paid a substantial price premium for imports--irrespective of whether comparisons **are made** with induetri **1** or other developing countries. The lowest observation, based on 1985 French prices, indicates a Madagascar premium of 8 percent over similar goods shipped to developing countries, but in most years the relative prices are far higher. In fact, several comparisons, such as those for Germany's or Japan's 1582 and 1983 exports show Madagascar's import prices were more than dcuble those of other developing countries, while Japan's 1981 (trade-weighted) prices were 200 percent higher.

liSpecifically, Madagascar's unweighted average relative import price was derived from: <!-- Start of picture text --> Vmi Qci<br>x<br>(2) Um r Qmi V<br>N<br><!-- End of picture text --> where the V and Q terms are defined in equation (1) and N is the number of fivedigit goods shipped.

The trade-weighted average (Wi) is defined by: Vmi Vmi Qci Vmt Q^ Vci (3) Wm = N where Vt represents Madagascar's total imports of the five-digit products.
significant price and/or knowledge elasticities and high adjustment speeds, suggesting a good "capacity to deliver", in the sectors where RCA are present;

**Table 3. Point estimates of adjustment parameters. Asymptotic standard errors in parenthesis**

||Germany|France|Italy|Japan|UK|
|---|---|---|---|---|---|
|a]|-|-|-|-|-|
|a2|1,325|0.912|0.828|0.432|1.796|
||(0.323)|(0.137)|(0.201)|(0.105)|(0.370)|
|a3|0.563|0.808|1.072|2.484|0. 704|
||(0.427)|(0.122)|(0.384)|(0.058)|(0.398)|
|_a_4|1.624|0.388|2.243|0.280|1.908|
||(0.427)|(0.122)|(0.384)|(0.058)|(0.398)|
|a5|0.561|0.956|1.780|0.260|0.261|
||_(0.155)_|(0.173)|(0.486)|(0.093)|(0.094)|
|a6|0.322|0.267|0.248|0.686|0.452|
||(0.079)|(0.049)|(0.071)|(0.172)|(0.133)|
|ac,|1.651|0.951|0.869|1.011|1.332|
||(0.393)|(0.221)|(0.142)|(0.288)|(0.309)|
|a8|1.236|0.772|0.618|1.240|0.395|
||(0.329)|(0.203)|(0.219)|(0.293)|(0.173)|

iii) in several cases sectoral price and knowledge elasticities appeared to be not significantly different and were constrained to take on the same value in final stages of estimation<sup>3 5</sup> , this indicates that the sectoral disaggregation followed is not always fully relevant, however aggregation differs across countries; iv) drawing on results discussed in para 3 significantly different knowledge elasticities -when present- suggest that in all country cases there is a tendency to a modification in the trade specialization

> 35 This is shown in table 4 whenever the parameter symbol replaces the point estimate.

21
(b) the same data as in (a) above but focusing on the switches from centralized to decentralized provision for the case of Korea from 1968-92 from the IRF source; and (c) data on roads disaggregated by activity at state and local government levels obtained from seven German states (Baden-Wuerttemberg, Hessen, Bayern, Niedersachsen, SchleswigHolstein, Nordrheinwestfalen, Saarland and Rheinlandpfalz). The data covers the period from 1980-1993. The state government data was provided by the Deutsche Strassen Liga, a nongovernmental institute. **13** The local government data was collected from the yearly Finanz Bericht and from interviews with road experts and officials in seven states.

In addition, technical experts at the World Bank and in Germany were contacted and interviewed to obtain additional information.

From the first data set we can investigate the relative impact of fiscal decentralization across the three main road functions, giving an indication of the optimal fiscal-functional allocation of responsibilities. With the second data set, we can measure the specific contribution of changes in degrees of decentralization across time, as we are looking at one country which has changed the relative shares of local government contributions to road expenditures. The third data set allows us to conduct a two level (horizontal and vertical) analysis of the impact of decentralization on road performance in addition to the functional and fiscal analysis possible with the first and second data sets, as well as a spatial analysis. The measures of decentralization differ depending on the level of analysis. At the horizontal level, the analysis takes the number of local governments per capita GDP to measure spatial decentralization. At the vertical level, decentralization is measured through the conventional variable percentage local government spending versus central government expenditure. This analysis is limited to measuring performance in terms of resource cost efficiency. An analysis of performance with respect to preference costs measured as is not possible since almost all roads are paved and an inventory of road quality is only conducted in only two out of the seven states.

# 5. **Country Case Studies**

Empirical analysis of the optimal structure for decentralized provision of roads suggests that the impact of fiscal decentralization on road performance is partially driven by country specific factors (see a companion paper by Humplick and Moini-Araghi, 1995). But these country effects are generally fixed and unobservable. In this paper, we 13The Deutsche Strassenliga brings together professionals working in different areas on roads. Members come mainly from the private sector but the agency is in close cooperation with the state and federal governments. It promotes the development of roads in Germany.

11
parts to their Chinese subsidiaries and a large share of the final output exported from China to the United States.<sup>50</sup> In a prescient discussion of a U.S. external imbalance that was already massive and still rising in 2004, Mann (2004) writes, “There is a real possibility that the entanglements created by this co-dependency cannot be undone by anything short of a global economic crisis.” Indeed, the global recession that began in 2008 did reduce the saving/investment imbalances underlying the huge overall U.S. trade deficit and the bilateral trade deficit with China. U.S. gross saving as a share of GNP reached a minimum in the second quarter of 2008 and then trended upward, while gross domestic investment began to fall in the third quarter. Although both imports and exports fell, exports fell by less; the U.S. trade and current-account deficits both narrowed in early 2009.

Meanwhile, slow or even negative growth of income in most countries worldwide caused demand for Chinese exports to fall sharply. To maintain the pace of its economic growth, Chinese policy makers implemented a major domestic stimulus, and saving fell relative to investment. In early 2009, China’s trade surplus also fell from the record level attained in 2008. Accordingly, China’s international reserves grew more slowly than in recent years. China sold a substantial volume of U.S. Treasury securities and other foreign bonds in early 2009 before resuming purchases in March.

# **7. Conclusions**

A goal of this paper is to provide a framework that allows us to make sense of the U.S.-Japan and U.S.-China trade relationships over the past thirty years, seeing similarities and differences as well as implications for evolution of the rules-based GATT/WTO system. The central similarity in the two bilateral relationships is the huge bilateral trade imbalance, a reflection of the outward-oriented growth strategy followed by Japan and China but also of underlying macroeconomic conditions. In both cases, the result has been a strain on the reciprocity-based trading system. We have looked at the imbalance as the result of exports that grew “too fast” from the U.S. perspective and imports that grew “too slowly.” In both cases, the U.S. public and government officials chose to interpret the imbalance as a symptom of non-market considerations; in both cases, the official response included both policies that addressed the symptoms as well as efforts to remedy features of the partner economy that were perceived as underlying causes. But in both cases the U.S. public and government officials were slow to

> 50 The full export price of these goods is reflected in the statistics on U.S.-China trade even when Chinese value-added is only a fraction of this price.

25
economies with respect to their estimated contribution to GDP (see Table below from Vuletin, 2006). While Paraguay is one of the countries in our sample in which firms identify informal competition as a top constraint, Nicaragua and Honduras are not. In addition, Uruguay, the country in which the largest percentage of firms that identify informal competition as a top constraint, the informal economy is comparatively small (36.2 percent of GDP). The correlation between the percent of firms citing informal competition as a top three constraint and the estimated size of the informal economy gives us a low correlation of 0.019.

**Size of the informal economy (% of** **<u>GDP)</u>**

|_Country_|_Percent of GDP_|
|---|---|
|Mexico|28.2|
|Brazil|28.4|
|Chile|32.1|
|Argentina|32.9|
|Uruguay|36.2|
|El Salvador|36.4|
|Peru|38.1|
|Guatemala|42.3|
|Colombia|43.5|
|Panama|44.4|
|Ecuador|50.7|
|Honduras|54.1|
|Nicaragua|64.4|
|Paraguay|68.2|

Source: Vuletin (2006): Table 3.

The results of this correlation may seem counterintuitive since we would expect a strong relationship between larger informal sectors and greater competition stemming from it. The argument is that the size of the informal sector is good indicators of the level of competition formal firms contend with in their markets. As is shown above, we do not find that and it is clear in our data, the size of the informal sector does not determine the level of competition it presents to firms in the formal sector. But if size of the informal sector is not a determinant of competition, what is?
We posit three hypotheses for the absence of positive mean impacts on student exam scores. First, nonschool or school factors outside the control of teachers may make raising student exam scores difficult even with greater teacher effort. Targeting schools with the lowest mean student exam scores in the province places the program in circumstances that may be especially handicapping. Second, teachers may lack the knowhow to raise student exam scores. Third, teachers may optimally choose to direct their effort at those incentivized margins that maximize payoffs net of effort costs. In this case, it appears that the net payoff-maximizing margin is school enrollment in urban areas, exam participation rates in rural areas, and not student exam scores anywhere.

The rest of the paper is organized as follows. Section II describes the context and the program. Section III discusses the data, sample, and empirical strategy. Section IV reports the empirical results. In concluding, Section V summarizes the main results and discusses potential explanations for the absence of program impacts on student exam scores.

# **II. Context and program**

# _A. Context_

Punjab, the site of the teacher performance pay program, is the largest of Pakistan’s four provinces, accounting for three-fifths of the country’s population and income. In 2012/13, the estimated net enrollment rate at the primary school level was 66 percent (2012–13 Pakistan Social and Living Standards Measurement [PSLM] survey report, Pakistan Bureau of Statistics, Government of Pakistan). In 2007, third-grade students in a school sample in rural Punjab scored, on average, 31 percent in English, 27 percent in Urdu, and 34 percent in mathematics on independently administered, competency-based tests. These results are significantly below official grade-level standards.<sup>4</sup> The public school system is the main provider of education in Punjab. In 2012–13, 61 percent of primary school students were in public schools (2012–13 PSLM report, Pakistan Bureau of Statistics, Government of Pakistan). The system is large, composed of 51,504 functional schools. Seventy percent of these schools are primary schools (preschool to fifth grade) with 105,300 assigned teachers and 2.7 million children enrolled in first through fifth

> 4 Statistics provided by Jishnu Das.

6
|**Table 1: Base**<br>|**Regression**|**Results: Log**|**odds ratios fr**|**om Logit esti**|**mation**||
|---|---|---|---|---|---|---|
|Dependent variable: R&D<br>Activity|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Informal Competition|1.880***|2.022***|2.076***|2.187***|2.181***|2.192***|
|(Country-level cell average)|(0.504)|(0.562)|(0.564)|(0.540)|(0.541)|(0.570)|
|GDP per capita (lagged, logs)||0.061|0.080|0.059|0.041|0.030|
|||(0.077)|(0.093)|(0.094)|(0.098)|(0.137)|
|Firm Size|||0.437***|0.391***|0.369***|0.360***|
||||(0.085)|(0.094)|(0.094)|(0.092)|
|HHI|||0.178|0.130|0.032|0.106|
||||(0.555)|(0.564)|(0.578)|(0.571)|
|Age of Firm (logs)||||<br>0.054|<br>0.059|<br>0.051|
|||||(0.093)|(0.094)|(0.092)|
|Manager Experience (logs)||||<br>-0.144|<br>-0.144|<br>-0.148|
|||||(0.103)|(0.105)|(0.105)|
|Exports (proportion of sales)||||0.764***|0.766***|0.753***|
|||||(0.260)|(0.251)|(0.251)|
|Female Top Manger Y:1 N:0||||0.059|0.051|0.074|
|||||(0.160)|(0.161)|(0.167)|
|Multi-firm Y:1 N:0||||0.132|0.085|0.090|
|||||(0.161)|(0.163)|(0.165)|
|Power Outages (day hours)|||||<br>-0.005|<br>-0.008|
||||||(0.014)|(0.015)|
|Finance Obstacle (0-4 scale)|||||0.040|0.027|
||||||(0.055)|(0.055)|
|Overdraft Y:1 N:0|||||<br>0.366***|<br>0.378***|
||||||(0.137)|(0.141)|
|Courts Obstacle (cell average)||||||0.169|
|||||||(0.143)|
|Population (lagged, logs)||||||0.031|
|||||||(0.060)|
|Inflation (%, annual)||||||-0.006|
|||||||(0.010)|
|Gross Tertiary Enrollment Rate||||||0.262<br>(0.539)|
|Gross Primary Enrollment Rate||||||<br>-0.243<br>(0.439)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-3.625***|-4.202***|-6.022***|-5.581***|-5.778***|-5.950***|
||(0.534)|(0.982)|(1.146)|(1.166)|(1.207)|(1.861)|
|Number of observations|12,980|12,980|12,980|12,980|12,980|12,980|
|Pseudo R-squared|0.065|0.066|0.076|0.083|0.087|0.089|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on the country. Significance is denoted by *** (1%), ** (5%), * (10%).

45
# 6. The Role of Panel Data: An Unavoidable Challenge?

The CILSS contained a rotating panel. This means that the longitudinal data derived from the survey did not span the four-year period, but consisted of three overlapping panel data sets, each covering two years. We have analyzed the panel data and compared the findings on poverty with the cross-sectional results (see Grootaert and Kanbur, 1992b). In general they confirm one another, but not entirely. The panel component was undertaken with only 10-15% attrition in the second year (some households could not be found or refused to participate in the survey a second time). While such attrition rate is not excessive, the households who dropped out of the panel were not a random subset. In particular, the main attrition occurred among better-off households. This was the opposite of the expectation a priori, that poor and more mobile households would be those most likely not to be found again.

The main usefulness of the panel data in the context of welfare and poverty analysis was to identify the extent of "permanent poverty" and the characteristics of the "hard-core poor". A surprising result was that mobility in and out of poverty was much greater than had been thought. But the characteristics of the hard-core poor were not significantly different from those groups where the poverty incidence had been found to be highest on a cross-sectional basis.

It is clear that the identification of people who remain in poverty over several years, as distinct from the transitorily poor who can escape poverty on their own means, is of key importance for targeting poverty alleviation policies. This information can only be learned fronm longitudinal data, but our experience suggests that two years may not be a long enough period for this. Also, if policy considerations provide a strong argument for panel surveys, the practical question is whether such panel data should be colle-ted as 2at of an otherwise cross-sectional survey. We feel that the limited gain in understanding poverty from a two-year rotating panel was not worth the added complexity in survey design, field implementation and data use which this feature

**16**