rafmacalaba/datause-encoder-data huggingface dataset:
- _A) Teacher content knowledge in subject k is uncorrelated with all unobservables conditional on controlling for student and teacher invariant heterogeneity; i.e.;_

- _B) If a student is taught by a general teacher, who teaches both subjects_ k and k’ _in primary grade_ g _, then she is taught by a general teacher in grades_ g<sup>�</sup> �g _._

Assumption 1 implies that teacher content knowledge does not have to be measured at the same time at which it is applied; for example, measuring content knowledge of a student’s grade 3 teacher at the time when the student is in grade 4 gives an accurate measure of the teacher knowledge the student would have been exposed to in the prior year. Our data can therefore be used to estimate a cumulative production function.<sup>20</sup> Assumption 2 allows us to simplify the production function to: (4) y��� �α� �αx���,��� �αγx���,��� �αγ<sup>�</sup> x���,��� �αγ<sup>�</sup> x���,��� �ν��� Reducing the number of structural parameters (from seven to two) is a necessary condition for them to be identifiable from the reduced form effects of teacher knowledge in grades 3 and 4 on student learning in grade 4.<sup>21</sup> Assumption 3A is our core identifying assumption. It allows for teacher knowledge to be correlated with student unobservables (such as the child’s aptitude for learning or parental support), for instance because better students sort into better performing schools, but it rules out that students systematically sort based on subject-specific abilities into schools with subject-specific teacher knowledge. That is, the assumption would be violated, if, for example, students in lower primary with relatively higher motivation for mathematics systematically sort into schools (or classrooms) with relatively more knowledgeable mathematics teachers.

Assumption 3A also places some restrictions on what parents and schools do. For example, while our identifying assumption does allow for parents (or schools) to respond to their children’s low mathematics aptitude by providing additional teaching (or hiring a private tutor), they cannot do this to compensate for insufficient teacher mathematics knowledge.<sup>22</sup>

> 20 This assumption is also frequently made in the teacher value-added literature (see Kane and Staiger, 2008). Chetty et al. (2008) show that estimates of teacher value added with and without an assumption of timeinvariance give very similar results.

> 21 As shown in Todd and Wolpin (2003), assumption 1 is also required in order to derive the lagged-score valueadded model from a linearized cumulative student achievement function.

> 22 Using data from kindergarten students in Ecuador, Araujo et al. (2016) find that while parents recognize better teachers, they do not change their behaviors to take account of differences in teacher quality. Note that our

12
# household income and expenditure.

Our results suggest the following sequential channel through which financial literacy training may be effective. Financial literacy education may first increase awareness of and change attitudes towards financial products, then improve numerical ability to compare financial options, and subsequently change financial behavior. The results of this paper provide strong evidence for the first step in this causal framework. We also have follow up surveys currently ongoing in the field to measure differences in financial behavior as well as longer-term numeracy skills. These surveys will allow us to test whether changes in financial awareness and attitudes result in better financial decisions, or whether there are constraints, other than financial knowledge, to changing financial behavior.

Our results also have important implications for how financial literacy is measured and evaluated. The finding that financial literacy programs do not immediately make recipients better financial product evaluators suggest the use of a broader measure of financial literacy, one that does not focus exclusively on questions that require high numeracy skills or calculating financial tradeoffs, but instead includes a healthy combination of basic financial awareness and attitudinal questions. Our paper provides a startling list of such questions to complement existing measures of financial literacy.

Finally, from a practical and policy perspective, our findings provide information for designing more effective financial education programs. Our results suggest that rather than emphasizing financial numeracy skills such as adding expenses or estimating interest rates, familiarizing individuals about the financial products or financial planning tools available to them may be more effective in enhancing financial knowledge. Furthermore, although many financial choices we face in the real world, like comparing the costs of loans for example, do require some form of arithmetic, our findings suggest that increasing an individual’s financial sophistication through financial literacy training would be a daunting task; even monetary incentives for performance do not induce better performance, suggesting that cognitive constraints rather than lack of attention are a key barrier to improving financial knowledge. Other complementary approaches, 5
Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
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|WPS2953 Health Facility Surveys. An<br>Introduction|Magnus Lindeléw<br>Adam Wagstaff|January 2003|H. Sladovich<br>37698|
|WPS2954 NeverToo Late to Get Together<br>Again: Turning the Czech and Slovak<br>Customs Union into a Stepping Stone<br>to EU Integration|Bartlomiej Kaminski<br>Beata Smarzynska|January 2003|P. Flewitt<br>32724|
|WPS2955 The Perversity of Preferences’ The<br>Generalized System of Preferences _—<br>and Developing Country Trade<br>Policies, 1976-2000|Caglar Ozden<br> Eric Reinhardt|January 2003|P. Flewitt<br>32724|
|WPS2956 Survey Compliance and the<br>Distribution of Income|Johan A. Mistiaen<br>Martin Ravallion|January 2003|P Sader<br>33902|
|WPS2957 Mexico: In-Firm Training for the<br>Knowledge Economy|HongTan<br>Gladys Lopez-Acevedo|January 2003|H. Tan<br>33206|
|WPS2958 Globalization and Workers in<br>Developing Countries|Martin Rama|January 2003|H. Sladovich<br>37698|
|WPS2959 Wage Differentials and State-<br>Private Sector Employment Choice<br>in the Federal Republic of Yugoslavia|Michael M Lokshin<br>Branko Jovanovic|January 2003|P. Sader<br>33902|
|WPS2960 The Poverty/Environment Nexus in<br>Cambodia and Lao People’s<br>Democratic Republic|Susmita Dasgupta<br>Uwe Deichmann<br>Craig Meisner<br>DavidWheeler|January 2003|Y. D'Souza<br>31449|
growth of world trade in the second half of the twentieth century. Conversely, Rubin and Tal (2008) and Rubin (2009) have built on this theory to conjecture that rising oil prices will lead to a major slowdown in the growth of world trade, and especially intra-GPN trade. In this section, we will attempt to gain insights into this latter conjecture, by using the panel data structure of the processing trade data to analyze the sensibility of intraGPN trade to changes in oil prices.

# **5.1. Trends in oil prices and transportation costs**

Oil prices have risen dramatically in the last decade. As it is shown in Figure 10, while crude oil prices were relatively stable and even declined over the period 1980-1999, they have rapidly increased over the period 1999-2008, growing at an annualized rate of 20.6%. During the Great Recession of 2008-2009, the oil prices have retreated (see Figure 10), but this is likely a temporary phenomenon. As the global economy comes out of the recession and as peak oil is reached, oil prices are expected to return to and even exceed its pre-crisis levels. Peak oil refers to the attainment of the maximum conventional oil output (i.e. excluding heavy oil from tar sands, oil shale etc.), expressed in terms of millions of barrels of crude oil extracted per day (Deffeyes, 2001). There is a general consensus among oil experts that peak oil will be reached prior to 2015 (De Almeida and Silva, 2009). When this occurs, the gap between oil production and demand is expected to increase. As a consequence, the price of oil is expected to both rise significantly and become more volatile.

# **[Figure 10 about here]**

Rubin and Tal (2008) have argued that the rise of oil prices is likely to lead to significant hikes in international transportation and thus will lead to a major slowdown in the growth of world trade. As supporting evidence, they highlight that, hand-in-hand with the oil price hikes, the cost to ship a standard 40-foot container from Shanghai to the U.S. eastern seaboard has risen from US$3,000 in 2000 to US$8000 in 2008. Other studies, however, have estimated that the sensitivity of shipping freight rates to oil prices remains relatively low, thus limiting the threat that rising oil prices will lead to a significant reduction in the growth of intra-GPN trade. Hummels (2007) and UNCTAD (2010) 24
this region is Rs 40 and that all the days lost due to bad weather are likewise valued. Then, at this opportunity cost, the total annual savings for the whole village amount to Rs 0.20 million, whereby the limit _e_ ̄ will not reached until 20 years will have passed. The Orissa Survey data suggest that while, in the short-run at least, the road will have little effect on morbidity, there is substantial willingness to pay for treatment. Putting a value on the latter is difficult, however, so we leave it aside for the moment. Turning to mortality, the assumption that the road will reduce the rate among primeage adults by 10 percent (see Section 4.3), while conservative in Orissa, may be on the optimistic side for Himachal Pradesh. Suppose, therefore, that a reduction of only half as much will result. Since the changes are quite small, the components making up the EV do so approximately linearly; so that, in the first 20-year period, the two-to-one ratio will shrink to four-to-one – in the extreme case that the estimated Rs 0.20 million associated with school attendance is also halved. The aggregate ‘measurable’ annual benefits at this stage are, therefore, Rs 2.69 (= 1 _._ 25 _×_ (2 _._ 05 + 0 _._ 10)) million, leaving us still Rs 0.59 million short.

One way of finding this missing sum is, first, to list what has not been measured and valued: in the commercial sphere, trade in commodities other than the main crop and substitution into more valuable crops when there is land to spare; in that of education, the fact that producing better educated citizens arguably also yields a public good; and in that of health, any reductions in pain and suffering or in infant and child mortality. The second step involves an assertion of belief, namely, that the sum of these benefits for the community in question is at least Rs 0.59 million a year. If doubts persist on this score, then recourse must be made to the existence of net external benefits that arise in the economic system as a whole (see Section 6 below).

The sceptical reader will stick with that which has been measured with some confidence, and on that basis establish the discount rate at which the project would just pass muster. Setting the annual stream of benefits at Rs 2.69 million, he or she will obtain _ρ_ = 7 _._ 0 percent, which also establishes how great the leap of faith must be when the bar is set at 10 percent. Does _gb_ = 0 _._ 02 supply the required boost? Eq. (2) yields _B_ 1<sup>+(</sup><sup>_ρ_= 0</sup><sup>_._10</sup><sup>_,_0</sup><sup>_._02) = Rs 2.77 million.This is still not quite enough,but now only very</sup> modest help from elsewhere is needed.

The poor villages in Orissa’s uplands are much less commercialized, but labor there is also arguably under-employed. Recall from Section 4.1 that in a sample of 30 such villages, the estimated aggregate annual commercial benefits average at about Rs 2.70 million. Proceeding as before, the Orissa Survey yields estimated savings in travel20
Figure 3: Size distribution by integration status <!-- Start of picture text --> Number of Firms<br>Not Integrated<br>Exporting<br>Importing<br>Fully Integrated<br>0 500 1,000 1,500 2,000<br>Micro Small<br>Medium Large<br>Source: EIA, 1997<br><!-- End of picture text --> In 1997, the great majority of the numerous non-integrated firms were micro or small plants.<sup>6</sup> Both exporters and importers have a similar composition in terms of firm size, with around 40 percent being small and 30 percent being medium firms. Finally, the fully integrated firms, that simultaneously export and make use of imported intermediate inputs, are the largest ones, with virtually no micro firms being part of this category. In 1997, three and four out of 10 firms had a medium or large size, respectively.

# 3.3 Trade Shock, Integration Status, Labor Productivity

As we mentioned above, integration was mainly brought about by a combination of NAFTA and the peso devaluation. We have also shown that non-integrated and exporting firms tend to be smaller than importing and fully integrated ones. In order to explore how the patterns of integration may have affected the size of the firms, Figure 4 shows the time trend in the proportion of integrated firms (all three integration status groups) and their average size (measured as total employees). According to Figure 4, the proportion of integrated firms increased steadily from 1993 to 1997 (continuous line). Regarding the size of the firm (measured as the number of employees), apart from the change occurring between 1993 and 1995, the average size of integrated firms increased throughout the period. It is interesting to note that 1994 is the only year

> 6Micro firms are defined as plants with less than 16 employees, small plants have between 16 and 100 employees, medium are those firms with more than 100 but less than 250 employees, while large have more than 250 employees.

10
**Consequences of Forced Displacement in Active Conflict: Evidence from the Republic of Yemen**<sup>_∗_</sup>

By Anna D’Souza<sup>_†_</sup> , Eliana Favari<sup>_‡_</sup> , Siddharth Krishnaswamy<sup>_§_</sup> , and Sharad Tandon<sup>_∗∗_</sup> Keywords: Conflict; Forced Displacement; Yemen JEL Classification: D12; I31; I38; O10; O53

> _∗_ The authors would like to sincerely thank Mr. Arif Husain and the VAM team of the World Food Programme. The views expressed here are those of the authors and may not be attributed to either the World Food Programme or the World Bank.

> _†_ Baruch College, City University of New York, 55 Lexington Ave, New York NY 10010, USA, anna.dsouza@baruch.cuny.edu.

> _‡_ World Food Programme, Regional Bureau- Middle East, North Africa, and Eastern Europe, 72 El-Nahda, Maadi as Sarayat Al Gharbeyah, Maadi, Cairo, Egypt, eliana.favari@wrp.org.

> _§_ World Food Programme, Regional Bureau- Middle East, North Africa, and Eastern Europe, 72 El-Nahda, Maadi as Sarayat Al Gharbeyah, Maadi, Cairo, Egypt, siddharth.krishnaswamy@wfp.org.

> _∗∗_ The World Bank, 1818 H St. NW, Washington, D.C. 20420, USA, standon3@worldbank.org.
that it too becomes insignificant.

Table 3: Fixed effects <u>(within)</u> regressions with slope dummies

|Variables|1|2|3|4|5|
|---|---|---|---|---|---|
|I_GDPpc|-0.00003<br>(0.00002)|-0.00002<br>(0.00002)|-0.00003<br>(0.00002)|-0.00004<sup>∗</sup><br>(0.00002)|-0.00005<sup>∗∗</sup><br>(0.00002)|
|I/GDP|0.114<sup>∗∗∗</sup><br>(0.023)|0.120<sup>∗∗∗</sup><br>(0.022)|0.121<sup>∗∗∗</sup><br>(0.022)|0.115<sup>∗∗∗</sup><br>(0.023)|0.113<sup>∗∗∗</sup><br>(0.022)|
|Tax Rate|-0.071<sup>∗∗∗</sup><br>(0.020)|-0.072<sup>∗∗∗</sup><br>(0.020)|-0.072<sup>∗∗∗</sup><br>(0.020)|-0.068<sup>∗∗∗</sup><br>(0.020)|-0.078<sup>∗∗∗</sup><br>(0.020)|
|Tot. ODA|0.001<sup>∗∗</sup><br>(0.0004)|0.0007<sup>∗∗</sup><br>(0.0003)|0.0007<sup>∗∗</sup><br>(0.0003)|0.001<sup>∗∗∗</sup><br>(0.0004)|0.0005<br>(0.0003)|
|Polit. Stab.|0.378<sup>∗</sup><br>(0.194)|0.359<sup>∗</sup><br>(0.199|0.366<sup>∗</sup><br>(0.195)|0.415<sup>∗∗</sup><br>(0.199)|0.279<br>(0.196)|
|Latitude|0.010<br>(0.0077)|0.010<br>(0.0077)|0.010<br>(0.0077)|0.010<br>(0.0077)|0.011<br>(0.0077)|
|Latitude Sq.|0.001<sup>∗∗∗</sup><br>(0.0003)|0.001<sup>∗∗∗</sup><br>(0.0003)|0.001<sup>∗∗∗</sup><br>(0.0003)|0.001<sup>∗∗∗</sup><br>(0.0003)|0.0009<sup>∗∗∗</sup><br>(0.0003)|
|D_Africa|0.144<br>(0.386)|||||
|ODA_Africa|-0.0004<br>(0.0004)|||||
|ODA_Fragile||0.00006<br>(0.0005)||||
|ODA_AF_Frag.|||0.0002<br>(0.0007)|0.0001<br>(0.0007)|0.002<sup>∗∗</sup><br>(0.001)|
|ODA_AfNFrag.||||-0.0004<br>(0.0004)||
|Dmy_Af_Frag.|||||-1.483<sup>∗∗</sup><br>(0.615)|
|constant|0.400<br>(0.607)|0.352<br>(0.581)|0.316<br>(0.586)|0.391<br>(0.590)|0.882<br>(0.626)|
||N = 245<br>R<sup>2</sup>= 0.264|N = 245<br>R<sup>2</sup>= 0.262|N = 245<br>R<sup>2</sup>= 0.262|N = 245<br>R<sup>2</sup>= 0.265|N = 245<br>R<sup>2</sup>= 0.278|

To summarize, the most striking result is found in column 5 of Table 3; it indicates that ODA does not exert an independent effect on growth once African fragile countries are properly accounted for. In addition, Polit. Stab. ceases to exert an independent effect on growth once African fragile countries are controlled for (column 5 of Table 3). This suggests that ODA has a direct effect on growth as well as an indirect effect which runs through political stability. However, all these results are based on the strong assumption that all regressors are exogenous.

22
_35_ ultimate analysis, not only the quantty but the total value of xports is what dotermins the ability of a country to purchase imports. It is therefore importan **t** o consider the evolution of the value of exports in rlatlon to GDP. Ths also is a check on the growth rates in Table 8, u these are from national statlstics and are not very reliable. From the data in Table 9, it is immediately clear that despite an apparently robust real export growth, the price effect has been so negative in some cases that the share of exports in GDP actually decreas. Ihi is for example true for C6te d'Ivoire, Kenya and Uganda. Only in Ghana, Madagascr, Nigeria and Tanzania does real export growth in the post-r-form period, i.e. 1985-90, translate in a growing share of export in GDP. Moreover, except in Nigeria whose share of export Is strongly affected by the ups and downs of oil prica, and in Mali, in no other country does the share of exports in GDP in the late 1980s reach the historically high levels of the 1960s and 1970s.

As an indicator of export diversification, Table 10 reports the share of nfacring in total exports. The data indicate that in the three Franc Zone countries, and to a lesser extent in Kenya and Madagascar, the share of manufacturing in total exports increased systematically over the period considered whereas In all othe countries the share remained either constant or actually decreased slightly. In order to find out to wbat extent the diversification in these countries is real and the result of a genuine increase in manufacUring exports rather than a result caused by a decrease in the price and hence the value of primary comnodity export, Table 11 reports the rate of growth in the dollar value of manufacturing exports over time.<sup>33</sup> In all countries manufacturing exports show positive growth in the second half of the 1980s. However, except for Ghana, this growth rare is smaller than the historically very high growth rates observed in the 1960s and 1970s. Moreover, because of the very small size of the manufacturing sector in the majority of the sampled countries, it will be quite sometimo before manufacuring exports will become a sizable portion of their total exports.

33Data limitation prevented the measurement of the real rate of growth of manufacturing export. The rates of gowth in Table 11 are therefore affected by changes in the intermational price of manufacturing goods as well as by fluctuations in the value of the dol!= C'hv --- cies.
<!-- Start of picture text --> Age profile for Employment - India<br>100<br>g 80<br>5<br>E 60 _<br>5 ; o_oza :ai ;Ay<br>ola a —— i is<br>D 2 8itt —t—1996 Lg<br>15 20 25 30 35 40 45<br>Age<br>@ 1940-44 1945-49 —— 1950-54 —— 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>—*> 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Employment - Bangladesh<br>100<br>.)s 80<br>5 1975<br>E 60 1985 1980, 1970<br>é 1990 , 4965<br>»<br>® en ae a<br>D£2000204i ne638 Airo ——_ 3 —<——fore——thes1945 19403<br>e<br>520 5 30 35 40 45<br>Age<br>@ 1940-44 1945-49 —— 1950-54 —— 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>> 2000-04<br><!-- End of picture text --> <!-- Start of picture text --> Age profile for Employment - Pakistan<br>100<br>=>4 80<br><7<br>£60<br>°<br>=<br>So 40<br>=2a<br>1986 1988 et | 672 —<br>2000<br>e<br>0<br>tp<br>15 20 25 30ops35 40 45<br>Age<br>@ 1940-44 1945-49 1950-54 — 1955-59<br>1960-64 1965-69 — 1970-74 — 1975-79<br>1980-84 1985-89 — 1990-94 — 1995-99<br>—® 2000-04<br><!-- End of picture text -->
13(1).

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_Journal of International Economics_ . https://doi.org/10.1016/0022-1996(88)90011-6 Gandelman, N., & Hernández-Murillo, R. (2015). Risk aversion at the country level.

Grauer, R. R., & Hakansson, N. H. (1987). Gains from International Diversification: 1968-85 Returns on Portfolios of Stocks and Bonds. _The Journal of Finance_ . https://doi.org/10.2307/2328383 Grinblatt, M., & Keloharju, M. (2001). What makes investors trade? _Journal of Finance_ . https://doi.org/10.1111/0022-1082.00338 Grubel, H. G. (1968). Internationally Diversified Portfolios: Welfare Gains and Capital Flows. _American Economic Review._ Laster, D. S. (1998). Measuring Gains from International Equity Diversification. _The Journal of Investing_ . https://doi.org/10.3905/joi.1998.408466 Leaño, M., & Pedraza, A. (2018). Ownership concentration and market liquidity: Evidence from a natural experiment. Economics Letters, 167, 56-59.

Levy, H., & Sarnat, M. (1970). International diversification of investment portfolios. _The_
# Productivity, Innovation and Growth in Sri Lanka: An Em irical Investi ation <u>p g</u>

<mark>Mark A. Dutz and Stephen D. O’Connell</mark><sup>1</sup>

## **Keywords: Economic growth, productivity, innovation, business environment.**

## **JEL Codes: O47, D24, O31, O53.**

This paper is a product of the Economic Policy and Debt Department’s innovation and growth work program, as part of the Poverty Reduction and Economic Management Network. The paper was cofunded by PRMED's budget and SASEP resources. The corresponding author may be contacted at mdutz@worldbank.org.

> 1 PRMED, The World Bank, and City University of New York, respectively. The paper benefited from helpful feedback from Francis Rowe. The authors thank the South Asia Labor Flagship team and David McKenzie for providing datasets used in this study.