rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>References</u>**

Banerjee, Abhijit and Esther Duflo. 2008. “What is Middle Class About the Middle Classes Around the World?” _Journal of Economic Perspectives_ 24(3): 61-80.

Banerjee, Abhijit and Esther Duflo. 2011. _Poor Economics_ . PublicAffairs.

Banerjee, Abhijit V, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan. 2010. “The Miracle of Microfinance? Evidence from a Randomized Evaluation.” _MIT mimeo_ .

Banerjee, Abhijit and Andrew Newman. 1993. “Occupational choice and the process of development.” _Journal of Political Economy_ 101 (2): 274– 298.

Berge, Lars Ivar Oppedal , Kjetil Bjorvatn and Bertil Tungodden. Forthcoming. “On the role of human and financial capital for microenterprise development: Evidence from a field experiment in Tanzania”, _Management Science._ Blanchflower, David G. and Andrew J. Oswald. 1998. “What Makes an Entrepreneur?” _Journal of Labor Economics_ 16(1): 26-60.

Bloom, Nicholas, and John Van Reenen. 2010. “Why do management practices differ across firms and countries?” _Journal of Economic Perspectives_ , 24(1): 203-224.

Bruhn, Miriam, Dean Karlan, and Antoinette Schoar. 2011. “What Capital is Missing in Developing Countries?” _American Economic Review Papers and Proceedings._ Buera, Francisco J. Joseph P. Kaboski, and Yongseok Shin. 2011. “Finance and Development: A Tale of Two Sectors.” _American Economic Review_ 101(5): 1964-2002.

Calderón, Gabriela, Jesse M. Cunha and Giacomo De Giorgi, 2013. Business Literacy and Development: Evidence From a Randomized Controlled Trial in Rural Mexico, _Barcelona GSE Working Paper No. 742_ .

De Mel, Suresh, David McKenzie and Christopher Woodruff. 2009. “Are Women More Credit Constrained? Experimental Evidence on Gender and Microentreprise Returns” _American Economic Journal: Applied Economics_ , 1(3):1-32.

De Mel, Suresh, David McKenzie and Christopher Woodruff. 2008. “Returns to Capital: Results from a Randomized Experiment.” _Quarterly Journal of Economics_ 123(4): 132972.

de Mel, Suresh, David McKenzie and Christopher Woodruff. 2014. “Business training and female enterprise start-up, growth, and dynamics: Experimental evidence from Sri Lanka.” _Journal of Development Economics_ 106: 199-210.

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Figure A3: Unspecified Places of Purchase by Good <!-- Start of picture text --> (a) Percentage of Total Expenditure (b) Percentage of Goods’ Expenditure<br>FoodAlcoholClothesUtilities & FuelsFurnishing & Equipment HealthTransportCommunicationCultureEducationRestaurantsMisc. Goods FoodAlcoholClothesUtilities & FuelsFurnishing & Equipment HealthTransportCommunicationCultureEducationRestaurantsMisc. Goods<br>5<br>60<br>4<br>3 40<br>2<br>20<br>1<br>0 0<br><!-- End of picture text --> This figure shows the share of expenditures with an unspecified place of purchase by goods categories (COICOP-2 digit level) on average, across the 31 countries of the core sample, discussed in section 2.2. Panel (a) shows this share as a percentage of total expenditures and Panel (b) as a percentage of each goods’ total expenditure.

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# <u>Table A5. Learning over time in Pakistan</u>

|**Subject**|**What is the question**|**% correct 2003**|**% correct 2011**|**% correct 2018**|**% correct 2018**<br>**(college)**|**N**|
|---|---|---|---|---|---|---|
|**Panel A. Res**|**pondents with test scores in 2003, 2011, and 2018**||||||
|Mathematics|Tick box next number that matches the number of objects|49 %|89 %|94 %|99 %|467|
|Mathematics|678+923|56 %|83 %|77 %|94 %|467|
|Mathematics|7/3=__|3 %|16 %|7 %|18 %|467|
|English|Match picture: Banana|63 %|94 %|91 %|99 %|467|
|English|Missing letter to match picture: Flag|27 %|76 %|70 %|96 %|467|
|English|Use word in sentence: deep|1 %|29 %|22 %|63 %|467|
|Urdu|Match picture: Book|74 %|98 %|95 %|98 %|467|
|Urdu|Join letters and write word: m-a-l-k|36 %|76 %|64 %|83 %|467|
|Urdu|Fill blank in the story by selecting the correct word 3|NA|NA|NA|NA|0|
|**Panel B. Res**|**pondents with test scores in 2011 and 2018**||||||
|Mathematics|Tick box next number that matches the number of objects|NA|82 %|85 %|100 %|1643|
|Mathematics|678+923|NA|71 %|62 %|93 %|1643|
|Mathematics|7/3=__|NA|13 %|5 %|20 %|1643|
|English|Match picture: Banana|NA|88 %|82 %|99 %|1643|
|English|Missing letter to match picture: Flag|NA|69 %|60 %|97 %|1643|
|English|Use word in sentence: deep|NA|21 %|18 %|61 %|1643|
|Urdu|Match picture: Banana|NA|90 %|86 %|100 %|1643|
|Urdu|Join letters and write word: m-a-l-k|NA|66 %|53 %|85 %|1643|
|Urdu|Fill blank in the storybyselectingthe correct word 3|NA|44 %|36 %|74%|1643|

_Notes_ . This table shows a sample of questions for the three subjects tested on paper: Mathematics, Urdu, and English. We show the same sample questions as in Table 4 (Part 1). For each question, we indicate the proportion of respondents who answered the question correctly in 2003, 2011, and 2018, as well as the number of respondents. Panel A is restricted to respondents who were tested in 2003, 2011, and 2018, while Panel B is restricted to respondents who were tested in 2001 and 2018. The number of respondents for “% correct 2018 (college)” is restricted to the sub-sample of respondents who went to college (82 respondents in Panel A and 234 respondents in Panel B).

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The WFP program included two bi-weekly vouchers for a total value of 24 JD per person per month. The voucher was provided to the principal applicant and it could be spent via a network of 652 stores distributed across all governorates of Jordan. In this paper, we focus on the food voucher program but the approach proposed can be equally used for any cash program.

By the end of 2013, the food voucher program reached almost universal coverage and was assessed positively by both its administrator and external organizations. In addition to the benefits accrued to the direct beneficiaries, the WFP found that “ _The program has also led to some US$2.5 million investment in physical infrastructure by the participating retailers; created over 350 jobs in the food retail sector; and generated about US$6 million in additional tax receipts for the Jordanian government. In terms of indirect effects, this study finds a predictive multiplier ranging from 1.019 to 1.234. In other words, WFP’s plan to distribute US$250 million in vouchers during 2014 would lead to some US$255-US$308 million of indirect benefits for the Jordanian economy._ ” (WFP, 2014a, p. 1) The World Bank and the UNHCR jointly evaluated the poverty reduction and targeting capacity of the program and found it to be effective on both fronts at a time when targeting was universal (Verme et al., 2016).

Towards the end of 2014, the humanitarian community started to face budget shortages that forced the WFP to scale down its food voucher program. The organization found itself with the difficult choice of how to prioritize beneficiaries and opted to follow a welfare approach whereby income or consumption would be used to assess welfare and target households. Welfare targeting is a well-developed methodology in welfare economics and has produced important tools like Proxy Means Targeting (PMT) that help organizations such as the World Bank or governments to target cash-based programs. However, this is not an exact science and it is known that this approach invariably results in sizeable leakage and undercoverage errors. In the case of humanitarian organizations, this task is even more daunting because these organizations are not typically staffed by economists and the adoption itself of these methodologies requires a lengthy process.

# 3. Data

The paper uses a combination of the UNHCR proGres (PG) registry data and the UNHCR Home Visits (HV) data. The PG database is the official global registration system of the UNHCR. Most of the published statistics on refugees derive from this database and any survey or home visit targeting refugees is usually based on this database. ProGres contains information collected from refugees at different stages starting from the first brief interview administered when refugees cross the border to more extended interviews 5
spouse counts as a dependent, even if the couple do not have children); if both work only one can claim the dependent deduction so the other implicitly faces higher effective tax rates; a couple with one worker claims the deduction and faces lower effective tax rates. Thus, there is a fiscally relevant difference between couples with one or two workers. However, given Jordan’s low rate of female labour force participation, relatively few couples have two workers. Consequently, the final fiscal categories combine couples with one or two workers for the sake of parsimony. These five categories represent at least a tenth of all Jordanian households (no workers with school children, 12 percent; pensioners, 12 percent), increasing to one-seventh (no workers and no dependents, 14 percent) and then around a third (workers and no dependents, 26 percent; workers and school children, 36 percent)(Figure 3).

There is some overlap between the care and fiscal categories, but not enough to warrant a single typology for analytical purposes. Where there is a significant degree of overlap in categories, it is not perfect, while others avoid clustering (Table 3). For example, half of the households in the ‘married couple with dependents’ (a care category) are also classified as ‘1-2 workers with children in school’ (a fiscal category), but at the same time, a third, while also having one/two workers, enter in the no dependents type (because their children are pre-school age or not in school, or because they have no children). Other care and fiscal categories have no significant overlaps where over then percent of the households belong simultaneously to the same fiscal and care categories.

<!-- Start of picture text --> Figure 2. Frequency of care categories Figure 3. Frequency of five fiscal categories<br>No workers, no<br>No dependents dependents<br>11 14<br>26 No workers, children<br>in school<br>Single adult, 36<br>12<br>dependents<br>Pensioners<br>5<br>Married couple, 12<br>dependents One/two worker, no<br>58 dependents<br>26<br>Other care One/two worker,<br>categories children in school<br>Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations<br><!-- End of picture text -->

**_Table 3. Fiscal and care category comparison (percentage of total households)_**

|||No<br>workers, no<br>dependents|**FIS**<br>No<br>workers,<br>children<br>in school|**CAL CATEGOR**<br>Pensioners|**IES**<br>Worker(s),<br>no<br>dependents|Worker(s),<br>children in<br>school|
|---|---|---|---|---|---|---|
|<br>**IES**|No dependents|5|4|7|7|4|
|**ARE**<br>**GOR**|Single adult, dependents|2|0|1|1|0|
|**C**<br>**ATE**|Married couple, dependents|1|6|2|18|31|
|**C**|Other|5|2|2|1|1|

Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations.
increase in per capita income (US$ in constant prices) is associated with a 0.05 percent decrease in the prevalence of diarrhea and weight-for-height malnutrition in a region. Collectively our climate zone dummy variables are not found to be significant but our country dummy variables are significant. We would expect the country dummy variables to represent significant differences in public health regimes and conditions net of per capita income differences.

# **7. Health impact simulation for 2020**

In this section, we simulate the impact of an increased frequency of extreme temperature and rainfall weather events on health, focusing only on the incidence of diarrhea across regions of Sub-Saharan Africa by 2020.

The simulation is based on three working assumptions. First, the frequency of excess rainfall events and the average maximum and minimum temperatures would increase in Sub-Saharan Africa as a result of climate change. The probability of excess rainfall (two or more months) is assumed to increase from the current level of 43 percent to between a lower bound of 60 percent and an upper bound of 80 percent, and the maximum and minimum temperature both to rise by 1 degree centigrade by 2020.

The second assumption is that the level of public investment in social and environment sectors in the coming decade would be such as to maintain the current levels of population access to basic services. We essentially assume the rate of investment in access to basic services is the same as the population growth rate in Africa. That is, all other covariates in the model would be kept at the current level in the simulation.

World Bank data from the SIMA database show that between 1995 and 2006, the proportion of the population with improved water access increased from 40 to 51 percent, and access to improved sanitation from 30 to 50 percent, indicating the speed of improvement in safe water and sanitation access in Sub-Saharan Africa of between 2 percent and 6 percent per annum, respectively. Our assumption of the rate of investment in environmental infrastructure seems plausible in the light of the UN projected population growth rate of 2.4% between 2000 and 2020 in Sub-Saharan Africa.

The third assumption is that the population growth rate for the population aged under five in Sub-Saharan Africa between 2000 and 2020 will be in line with UN medium variant population projections (UN, 2006). As shown in Table 5, the UN projected population aged under five increases from 116 million in 2000 to 157 million in 2020 in Africa.

# 7.1 Projection of health burden and economic cost of diarrhea by 2020

The health impact of diarrhea is estimated using the incidence of diarrhea obtained from the econometric model presented in Section 3, and the UN population projections listed in Table 5. The results of this estimation are that diarrheal incidence would increase from 14
of the MFA. Similarly, high-skilled wage premiums relative to low-skilled workers decreased during the same time, from 70 percent in 2005 to 52 percent in 2016. These results align with our expectation that positive export shocks have an aggregate and economy-wide effect in Bangladesh labor markets, benefiting females and low-skilled workers across all sectors.

Our paper proceeds as follows. Section 2 outlines our conceptual framework, which includes some simple but illustrative simulation results. Section 3 describes the trade data, trends in trade and production, the labor market data, and relevant labor market characteristics. Section 4 describes the estimation data set, local labor market definition, and empirical methodology. Section 5 presents our results, including the short-run effects on wages and informality and the economywide changes that we observe in the long run. Section 7 concludes.

# 2. Conceptual Framework

Consider a two-sector model in which output, _y,_ is produced using a fixed factor (“capital”) and two types of workers (“males” and “females”) that are indexed by _j_ . Assuming perfect competition and constant returns to scale, decisions of identical atomistic firms within each sector can be represented by the usual profit maximization decision in which firms take wages (determined in aggregate factor markets) and output prices (determined in global markets) as given. These decisions are often represented by cost functions in which the production cost _C_ for sector _i_ at time _t_ is represented by: (1) i i i i ) ii Shephard’s lemma allows us to derive factor demand curves from the cost function. Holding CC = ff(pp yy , ww capital fixed, the demand for males and females are derived as a function of output prices and factor prices. Using φ _itj_ to represent the demand for factor _j_ at time _t_ in industry _i:_ (2) <u>i</u> i i i ) i ∂∂CC ∂∂ww ii = φφ ii(pp , ww ii Note that equation (2) represents the marginal revenue product of each factor, which, of course, is the factor demand curve. Under normal assumptions: In the absence of adjustment costs, this model reduces simply to the Heckscher-Ohlin-Samuelson (HOS) model. Bernard, Redding, and Schott (2007) show that in the presence of firm heterogeneity, such as that described by Melitz (2003), the neoclassical predictions of the HOS model still hold. One of the well-established results of this model is the Stolper-Samuelson 5
**Table 8:  Radio access and Health Knowledge**

|Respondent knows:|Oral<br>Rehydration<br>Salts are<br>correct<br>treatment for<br>diarrhea|Who appoints<br>members of<br>Health Center<br>Mgt.<br>Committees<br>(COGECs)|Government<br>policy on<br>fee-setting<br>by health<br>facilities|Government<br>distributes<br>free bed<br>nets|Child<br>mortality<br>between 13<br>– 18 per 100<br>births|Number of<br>health<br>centers in<br>commune|Government<br>targets at least<br>one health<br>center per<br>arrondissement|
|---|---|---|---|---|---|---|---|
|Number of private|-0.011|-0.003|0.004|-0.005|**0.027**|-0.001|-0.022|
|non-commercial<br>stations|(0.51)|(0.82)|(0.80)|(0.46)|**(0.01)**|(0.93)|(0.28)|
|Number of children|0.009|0.0084|0.008|-0.025|0.009|0.003|0.004|
|listed|(0.29)|(0.23)|(0.43)|(0.00)|(0.15)|(0.61)|(0.65)|
|**Radio X children**|**0.005**|**0.004**|**0.007**|**0.004**|**-0.002**|**-0.0006**|**0.006**|
||**(0.04)**|**(0.08)**|**(0.02)**|**(0.00)**|**(0.34)**|**(0.75)**|**(0.04)**|
|Health Worker visit|0.020|0.178|-0.042|-.002|0.001|0.050|0.064|
|for malaria|(0.32)|(0.00)|(0.07)|(0.85)|(0.95)|(0.00)|(0.01)|
|discussion||||||||
|Percent correct|.48<br>(open)|.25 (2<br>possible<br>answers)|.59 (5<br>possible<br>answers)|.93 (open)|.15 (5<br>possible<br>answers)|.15 (open)|.46 (3 possible<br>answers)|
|Observations|3,663|3,649|3,663|3,663|3,621|3,663|3,637|
|R-squared|0.19|0.22|0.09|0.06|0.07|0.12|0.10|

Note: Ordinary least squares with commune fixed effects. P-values based on village-level clustered standard errors reported in parentheses. Regressions include all controls found in the full specifications of tables 4 and 5, plus whether household was visited by a health worker. Percent correct refers to fraction of respondents who answered the question correctly; the number of possible answers is indicated in parentheses (“open” means that the question was open-ended and no answers were prompted).

36
tax policies (re‡ecting, on average, a non-systematic reaction of taxes to cyclical ‡uctuations) imply � = 0.

## 2.2 Biases in tax multipliers

Using equations (1)-(3), it is straightforward to show that there would be no bias if only exogenous tax changes were used to estimate (1). Formally, if we used �t<sup>exog</sup> t (and not �t<sup>endog</sup> t ) to identify tax changes in equation (2), the bias associated with the OLS coe¢ cient �<sup>[exog</sup> (where �<sup>[exog</sup> refers to the estimator for � in regression (1) when solely using �t<sup>exog</sup> t ) would be given by In contrast, if both �t<sup>exog</sup> t and �t<sup>endog</sup> t were used to identify tax changes as captured in equation (2), it is easy to show that the sign of the bias would depend on the sign of �: What would happen if �< 0? Recall that �< 0 implies that endogenous tax changes are procyclical. Then, Bias of �<<sup>b</sup> 0, or E �b < �. To ...x ideas, suppose that �< 0 (indicating that higher taxes h i b reduce output). Then, E � would be even more negative. Why would that be the case? Suppose h i that there is a tax hike enacted in response to a decline in tax revenues triggered by a fall in output. If such a tax change was wrongly included in the estimation sample, such observation would be taken as evidence of a larger contractionary e¤ect of tax increases on output (while it was actually the fall in output what caused the tax increase!).

What if �> 0? When �> 0, endogenous tax changes are countercyclical. Hence, Bias of �><sup>b</sup> 0, or b b E � > �. Suppose that �< 0; then E � would be less negative (or even positive). Intuitively, the h i h i wrong inclusion of countercyclical tax changes (i.e., taxes increases during good times or tax reductions in bad times) would lead to an underestimation of the true e¤ect of tax changes on output. Only in the case in which � = 0 (re‡ecting, on average, a non-systematic response of taxes to output ‡uctuations) would there be no bias.

# 3 Narrative analysis: Sample and sources

As the conceptual framework makes clear, we need to identify the motivation behind each tax change (i.e., whether it was exogenous to the business cycle or not). To this e¤ect, we ...rst need to identify the size and timing of legislated tax changes, in terms of their announcement and implementation. As in RR, we proxy the announcement of tax rate changes by using the time of approval of the corresponding 8
<!-- Start of picture text --> Exhibit 13 Exhibit 14<br>Patent Originality Index 2000-04 Patent Generality Index 2000-04<br>(As Ratio to US) (As Ratio to US)<br>1.00 1.00<br>Korea<br>Korea Taiwan (China) Japan<br>Taiwan (China)<br>0.90 0.90 Japan<br>0.80 0.80<br>0.70 0.70<br>0.60 0.60<br>0.50 0.50<br>Drugs & Computers & Electrical & Mechanical Drugs & Computers & Electrical & Mechanical<br>Medical Comm. Electronic Medical Comm. Electronic<br><!-- End of picture text --> <mark>Source: USPTO and Hu (2006)</mark>

# **_Knowledge Flows to, from and within East Asia:   Patent Citations_**

Section II noted that knowledge flows from abroad play a crucial role in facilitating domestic R&D and innovation. In that discussion the evidence for knowledge flows was adduced indirectly, by looking at correlations between activities that are expected to facilitate knowledge flows – for example, foreign trade, FDI or technology licenses - and economic productivity. Section III cited econometric evidence on the spillover benefits for productivity growth in an individual economy from R&D in foreign countries. This section looks at evidence from patent citations, which provide a unique and more direct window onto flows of knowledge between inventors, firms and economies.<sup>30</sup> Exhibit 15 shows the share of various foreign economies in patent citations made by seven East Asian economies (as a group). The United States remains by far the largest source of citations for East Asian innovators, providing close to 60 percent, this proportion having risen slightly between 1992-94 and 2002-04. Japan is the second largest source, contributing close to 20 percent. (Korea is an interesting exception to this general pattern: its reliance on US citations is substantially lower than other East Asian economies – around 45 percent – while its reliance on Japan is higher, around 33 percent). The share of other ‘G5’economies, defined here as comprising Canada, France, Germany, Italy and the U.K., is lower, less than 10 percent, having fallen over the last decade. Perhaps most interesting, the share of citations made by East Asian economies to other East Asian economies, while still low, is rising fast, picking up from 1.7 percent of citations in 1992-94 to 5.9 percent in 2002-04. Most of these intra-East Asian patent citations are to patents held by Korea and Taiwan (China), the two largest innovators in the

> 30 The discussion draws on data from the NBER Patent Citations Database.  See Footnote 30 for further details.

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