rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 4: Education and experience premium by year.**

<!-- Start of picture text --> Panel A. Education. Panel B. Potential Experience.<br>2.0<br>0.8<br>1.5 0.6<br>1.0 0.4<br>0.5 0.2<br>0.0 0.0<br>0 2 4 6 8 10 12 14 16 18 0 4 8 12 16 20 24 28 32 36 40 44<br>Years of Education Potential Experience<br>1995 2003 2012 1995 2003 2012<br>log(Earnings). 2005 Reals log(Earnings). 2005 Reals<br><!-- End of picture text --> Note. Each line represents the predictions of a yearly regression that includes a quartic polynomial in education, a quartic polynomial in potential experience, a dummy variable for workers below the minimum wage, two dummies for formality status, two race dummies, a gender dummy, four region dummies and an indicator of work in rural areas (see Table 2). The year 1995 includes 1995 and 1996 PNAD samples, the year 2003 includes 2002 and 2003 PNAD sample, and the year 2012 included 2011 and 2012 PNAD sample. Source: _Pesquisa Nacional por Amostra de Domicílios_ PNAD.

29
We categorize firm size by dividing them into three (terciles) based on the firm’s export shares in its initial year of the sample. Firms with exports in the 3rd tercile are classified as large, those whose exports fall within the 2nd tercile are considered medium-sized, while the rest (1st tercile) are considered small-sized firms. We then interact our main variable of interest with two different dummy variables indicating whether a firm is large ( _LFi_ ) or medium ( _MFi_ ). The reference group is thus the small-sized firms. The results presented in the last three columns of Table 3 are in line with the predictions of the heterogeneous firm models, given that the coefficient on the interaction term is positive. When considering all sectors together, we observe that the trade-reducing effect of NTMs is decreasing in firm size with the observed trade effects being lower for large and medium-sized firms compared to small firms (column 7). However, within the agriculture sector, the size of the firm does not moderate the tradereducing impact of NTMs, as indicated by the lack of statistical significance in the interaction terms (column 8). Conversely, we find that NTMs have a relatively milder impact on trade for large and medium-sized firms compared to small firms in the manufacturing sector (column 9).

# **5.2.4 Characteristics of destination and origin**

As we observe in Panel B of Figure 2, when it comes to the use of NTMs to regulate trade, there is a clear developed-developing country divide, with the number of NTMs imposed by the destination increasing with income level of the destination country. According to World Bank (2020), the higher the income level of a country, the lower the level of tariffs and the higher the likelihood of making extensive use of standard-like NTMs. This suggests that the impact of NTMs on the intensive margin could differ by the income status of the destination country. To test this conjecture, we define a dummy variable ( _NHId_ ) that takes the value of one if the destination is not classified as a high-income country according to the World Bank income classification, and zero otherwise. We then interact this variable with our measure of NTM. The results are shown in columns 1 to 3 of Table 4. The result suggests that the tradereducing effect of NTMs does not depend on the income status of the destination country. Thus, the effect of NTMs on the intensive margin of firm level trade holds irrespective of the income level of the destination country.

16
<!-- Start of picture text --> Figure 4.2: Sectoral Composition of GDP<br>1985–98<br>Agriculture Industry Services<br>1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>Source: IDEMA<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998<br><!-- End of picture text --> <!-- Start of picture text --> Table 4.2: Sectoral GDP, 1985-1998<br>Annual<br>1985 1998<br>Sector Growth<br>(R$ million) (R$ million)<br>(Percent)<br>Industry 2047 2340 1.0<br>Services 1629 4052 7.3<br>Agriculture 375 448 1.4<br>TOTAL 4051 6840 4.1<br>Source : Contas Regionais, IBGE<br><!-- End of picture text --> The service sector has been the most dynamic in the last two decades. It increased its share of GDP from 40 percent in 1985 to 59 percent in 1998 while the shares of industry and agriculture declined from 50 to 34 percent and from 9 to 7 percent, respectively.

Economic growth experienced in the last decades in RN is far from homogeneous throughout the state. The large regional differences within the state are associated with the expansion of economic activities in a few of the areas away from the Sertão. Of the eight zones in RN only two, Litoral Oriental and Mossoroense, experienced an aboveaverage expansion of output during 1970–96. In the past three decades these two veryfast-growing zones on the coast also experienced by far the largest population inflow of the eight zones (Table 4.3). The largest real GDP growth, above 500 percent during 1970–96, took place in the capital region (Litoral Oriental) where the industry and service sectors are particularly strong. The second-largest GDP expansion, 312 percent during 1970–96, occurred in the zone of Mossoró, where, in particular, oil, gas, and irrigated agriculture have expanded rapidly in some municipalities and created opportunities for the population. Litoral Oriental and Mossoroense had the largest **–** population growth, 151 and 79 percent, respectively, during 1970 2000 (Table 4.3).

**<u>Table 4.3: Real GDP, Population, And Poverty in RN’s Eight Zones, 1970–2000</u>**

9
find that, while public school teachers tend to be better paid and have higher levels of education, training, and teaching experience than their private school counterparts, the teacher absenteeism rate in public schools is almost double that in private schools (15% vs. 8%). They also find that, while private school teacher salaries are increasing in teacher _and_ student test scores and decreasing in teacher absenteeism, public school teacher salaries are largely unresponsive to these variables and are mainly a function of teacher credentials (education, training, and experience). The greater accountability in the private sector is likely due to market competitive forces which the public sector is not subject to, at least directly.

Three: in the wake of the public sector failure to adequately address these issues, the private sector has emerged as a major alternate provider of education, growing dramatically in size and reach. In particular, the rapid growth of a private sector that offers schooling opportunities to low-income households has created a major policy opportunity. Using data from 2000, Andrabi et al. (2006) find that there was an exponential increase in the number of private schools over the 1990s, with over 50% of existing private schools established on or after 1996. They also find that the birth rate of private schools in the recent period is higher in rural areas. These changes in the patterns of growth in private schools are also reflected in changes in the patterns of participation growth among students. Data from 2004/05 show that 18.7% of children ages 6–15 years were enrolled in private schools in Punjab, a 36% increase from 1998/99, with this increase largely attributable to private school participation increases among children from rural and poor households. The demand for private schooling is likely driven by the fact that fees in private schools tend to be low, accounting for a small percentage of mean annual household expenditure (Andrabi et al. 2008). It is also likely driven by the perceived higher quality of private schooling—evidence from rural Punjab shows that the learning levels in private schools are significantly higher than in public schools, even after controlling for a range of village, household, and school characteristics (Andrabi et al. 2007).

The FAS program and PEF are direct outcomes of the government’s recognition of the importance of leveraging the potential of the growing low-cost private sector in addressing access, equity, and quality issues in primary and secondary education. PEF is a publicly-funded semi-autonomous statutory organization established in 1991 by the government. It serves as the main institutional conduit for PPP programs in education in Punjab. The organization’s primary aims are to provide affordable private school opportunities to socioeconomically-disadvantaged 7
Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1306 CapHal Flows and Long-Term<br>Equilibrium Real Exchange Rates<br>in Chile|Ibrahim A. Elbadawi<br>Raimundo Solo|June1994|R. Martin<br>39065|
|WPS1307 How Taxation Affects Foreign Direct<br>Investment (Country Specific Eviden|Joosung Jun<br>ce)|June1994|S. King-Watson<br>31047|
|WPS1308 Ownership and Corporate Control in<br>Poland: Why State Firms Defied the<br>Odds|Brian Pinto<br>Sweder van Wijnbergen|June 1994|M. Kam-Cheong<br>39618|
|WPS1309 Is Demand for Polluting Goods<br>Manageable? An Econometric Study<br>of Car Ownership and Use in Mexico|Gunnar S. Eskeland<br>Tarhan N. Feyzioglu|June 1994|C. Jones<br>37699|
|WPS1310 China's Economic Reforms: Pointers<br>for Other Economies in Transition|Justin Yifu Lin<br>Fang Cai<br>ZhouLi|June 1994|C. Spooner<br>30464|
|WPS1311 The Supply Response to Exchange<br>Rate Reform in Sub-Saharan Africa<br>(Empirical Evidence)|Mustapha Rouis<br>Weshah Razzak<br>Carlos Mollinedo|June 1994|J. Schwartz<br>32250|
|WPS1312 The New Wave of Private Capital<br>Inflows: Push or Pull?|Eduardo Femandez-Arias|June 1994|P.Tutt<br>31047|
|WPS1313 New Estimates of Total Factor<br>Productivity Growth for Developing<br>and Industrial Countries|Vikram Nehru<br>Ashok Dhareshwar|June 1994|M. Coleridge-<br>Taylor<br>33704|
|WPS1314 The Significance of the "Europe<br>Agreements' for Central European<br>Industrial Exports|Bartlomiej Kaminski|June 1994|M. Pateiha<br>37947|
|WPS1315 Global Tradable Carbon Permits,<br>Participation Incentives, and<br>Transfers|Bjom Larsen<br>AnwarShah|June 1994|C. Jones<br>37754|
|WPS1316 Preserving the CFA Zone: Macroeco-<br>nomic Coordination After the<br>Devaluation|Shantayanan Devarajan<br>Michael Walton|June 1994|C.Jones<br>37699|
|WPS1317 Estimating the Efficiency Gains<br>of Debt Restrucluring|Jeremy Bulow<br>Kenneth Rogoff<br>Ning S. Zhu|July 1994|R. Vo<br>33722|
|WPS1318 Exchange-Rate-Based Stabilization<br>in Argentina and Chile: A Fresh Look|Miguel A. Kiguel<br>Nissan Liviatan|July 1994|M. Divino<br>33739|
# Table 7: Legal Gender Disparities and Women Who Borrowed from a Financial Institution Relative <u>to Men</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
|**Dependent Variable**|**Bo**|**rrowed from**|**a financial i**|**nstitution, w**|**omen relati**|**ve to men (%**|**age 15+), 2**|**013**|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Legal disparities: Overall measure|-0.009**||||||||
||(0.004)||||||||
|Legal disparities: Accessing Institutions||-0.026**|||||||
|||(0.011)|||||||
|Legal disparities: Using Property|||-0.044**||||||
||||(0.020)||||||
|Legal disparities: Getting a Job||||-0.009<br>|||||
|||||(0.010)|||||
|Legal disparities: Going to Court|||||-0.137**||||
||||||(0.067)||||
|Legal disparities: Providing Incentives<br>to Work||||||0.062|||
|||||||(0.072)|||
|Legal disparities: Building Credit|||||||-0.025||
||||||||(0.025)||
|Legal disparities: Violence Protection||||||||0.003|
|||||||||(0.021)|
|Log of GDP per capita (constant 2005<br>US$)|-0.038|-0.034|-0.048|-0.045|-0.048|-0.045|-0.047|-0.048|
||(0.041)|(0.042)|(0.040)|(0.041)|(0.040)|(0.040)|(0.040)|(0.040)|
|Rule of law|0.000|0.022|0.032|0.022|0.032|0.046|0.031|0.043|
||(0.057)|(0.058)|(0.056)|(0.057)|(0.057)|(0.057)|(0.058)|(0.054)|
|region==East Asia & Pacific|0.120*|0.141**|0.118*|0.101|0.093|0.080|0.140*|0.110|
||(0.063)|(0.069)|(0.063)|(0.065)|(0.067)|(0.070)|(0.072)|(0.067)|
|region==Europe & Central Asia|0.019|0.042|0.027|0.022|0.026|0.046|0.064|0.041|
||(0.051)|(0.048)|(0.048)|(0.055)|(0.050)|(0.050)|(0.051)|(0.053)|
|region==Latin America & Caribbean|-0.024|-0.014|-0.017|-0.011|-0.025|0.011|0.037|0.002|
||(0.073)|(0.074)|(0.073)|(0.075)|(0.078)|(0.079)|(0.079)|(0.079)|
|region==Middle East & North Africa|0.103|0.140|0.078|-0.022|0.026|-0.058|0.001|-0.055|
||(0.134)|(0.171)|(0.115)|(0.111)|(0.143)|(0.128)|(0.133)|(0.126)|
|region==South Asia|-0.195|-0.180|-0.173|-0.259*|-0.214*|-0.249|-0.213|-0.264*|
||(0.138)|(0.136)|(0.142)|(0.156)|(0.123)|(0.157)|(0.164)|(0.160)|
|region==Sub-Saharan Africa|-0.000|0.035|-0.001|-0.052|-0.045|-0.038|0.015|-0.039|
||(0.087)|(0.091)|(0.092)|(0.087)|(0.088)|(0.084)|(0.094)|(0.087)|
|Constant|1.300***|1.156***|1.266***|1.313***|1.267***|1.212***|1.251***|1.237***|
||(0.368)|(0.360)|(0.355)|(0.382)|(0.353)|(0.349)|(0.354)|(0.349)|
|Number of observations|131|131|131|131|131|131|131|131|
|Adjusted R2|0.028|0.047|0.014|0.001|0.038|0.004|-0.001|-0.007|

note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported 31
Smarzynska, Beata and Shang-Jin Wei. 2000. “Corruption and Composition of Foreign Direct Investment: Firm Level Evidence” with Shang-Jin Wei, World Bank Policy Research Working Paper No. 2360.

- Wei, Shang-Jin. 2000. "How Taxing Is Corruption on International Investors?" _Review of Economics and Statistics_ . 82 (1): 1-11.

13
**<u>Table 6: Regression Results: IV Estimation (3)</u>**

||**Instr**|**umenting mig **|**ration variable**|**only**|**Instrume**|**nting both mig**|**ration and FD**|**variables**|
|---|---|---|---|---|---|---|---|---|
||**Rem/**|**GDP**|**Log o**|**f Rem**|**Rem**|**/GDP**|**Log o**|**f Rem**|
||**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|**(1)**|**(2)**|
|**Log of migrants abroad**|2.589***|2.586***|0.829**|0.854**|0.737|1.162**|0.559**|0.611***|
||[0.945]|[0.916]|[0.347]|[0.336]|[0.507]|[0.570]|[0.253]|[0.236]|
|**Bank deposit/GDP**|-0.132||0.280||5.469||0.678||
||[1.290]||[0.633]||[3.341]||[1.096]||
|**Bank credit/GDP**||-0.019||0.352||5.142*||0.648|
|||[1.144]||[0.483]||[2.906]||[1.062]|
|**Log of GDP**|-2.139***|-2.138***|0.279|0.262|-1.223**|-1.479***|0.417***|0.385***|
||[0.711]|[0.701]|[0.203]|[0.193]|[0.479]|[0.541]|[0.159]|[0.149]|
|**Log of GDP per capita**|0.176|0.162|-0.164|-0.188|-1.048**|-1.207**|-0.288|-0.310|
||[0.512]|[0.513]|[0.203]|[0.200]|[0.469]|[0.544]|[0.221]|[0.259]|
|**GDP growth**|-0.218|-0.220|-0.064|-0.065|-0.335|-0.332|-0.075|-0.074|
||[0.293]|[0.299]|[0.074]|[0.077]|[0.275]|[0.297]|[0.072]|[0.075]|
|**Diff. in the ratio of tertiary edu.**|0.038|0.037|0.002|0.003|-0.058|-0.046|-0.010|-0.008|
||[0.055]|[0.053]|[0.019]|[0.018]|[0.039]|[0.039]|[0.016]|[0.016]|
|**Constant**|21.062***|21.172***|3.867*|4.108*|31.656***|33.658***|4.999**|5.266**|
||[6.900]|[7.129]|[2.258]|[2.258]|[8.659]|[9.714]|[2.255]|[2.570]|
|**Observations**|80|80|80|80|80|80|80|80|
|**R2**|0.053|0.054|0.572|0.567|0.229|0.176|0.615|0.609|
|**F-statistic (p-value)**|3.02 (0.01)|3.03 (0.01)|18.20 (0.00)|18.07 (0.00)|3.64 (0.00)|3.37 (0.01)|21.49 (0.00)|21.01 (0.00)|
|**Overidentification χ2 (p-value)**|3.12 (0.37)|3.11 (0.38)|4.03 (0.26)|3.93 (0.27)|6.82 (0.23)|6.78 (0.24)|5.46 (0.36)|5.12 (0.40)|

Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level.

The estimation is based on the sample excluding Bangladesh, India, Pakistan, the Philippines, and Sri Lanka. For the first four columns, only the migration variables are instrumented. For the next four columns, both the migration and financial development variables are instrumented. The ratio of bank deposit to GDP is used in (1) and the ratio of bank credit to GDP is used in (2).

23
- 67 -

than ones obtained from sLngle country studies which exclude any dynamic factors. The problems of dynamic modelling, as explained by Mountain and Hsaio, cannot be easily overcome but this in general reduces the credibility of econometrically derived estimates of inter-fuel substitution.

One important issue that has received little attention in the econometric literature is the treatment of caves where one or more of the fuels is not used at all. As is clear from the international evidence, this is a common orc^irrence and is likely to be particularly important when using plant data (either in a cross section or a panel). The presence of zero observations in the dependent variable of fuel shares has been effectively ignored as a problem in most studies. Although the translog will always predict strictly positive shares if a finite fuel price is used as an explanatory variable, the error may not be too severe. However approaching the problem via the production function would lead to immediate difficulties since the fuel shares are related to the log of quantities (which are undefined for zero quantities). The problem with the translog cost function approach is to specify the price for the fuel where expenditure is zero. The simplest solution would be to use mean prices but this does not capture the reason for the non-use of the fuel. If prices really would be mean values for the plant in question then the fuel would be used, according to the model, to a substantial degree. Clearly the "true price" is so high that the fuel is above the switch point. Short of attempting to calculate the implicit price of the fuel at such sites, one solution would be to attach a high price (relative to the general country experience) and to experiment with values of this to see how closely the predictions of fuel share approach zero. Other methods would be to constrain the fuel shares by a Tobit type analysis - i.e. to hypothesize that there is a price above which the fuel would not be used and to attempt to recover this threshold from the data. The threshold can be made a function of own and competing fuel prices. Such an approach would be considerably more complex in computational terms than the simple translog fuel share equations.
cells are capped well below their true value and thus underestimated. Worryingly this may start as early as a value of 55, see Bluhm and Krause (2016).

More generally, one has to be very clear about what sort of economic activity for which nightlights are possibly a reasonable proxy. In this regard, it has to be realized that artificial light at night is likely to come from three sources: (a) commercial residences, (b) private residences, and (c) infrastructure. As such it is thus likely to capture and/or be correlated income/wealth with use of electricity of manufacturing and service industries, and household wealth, but it is less likely to be a good proxy for agricultural production. This may mean that it will be relatively poorer in capturing the impact of tropical cyclones in rural areas. Unfortunately, there are no available high-frequency spatial data for the Dominican Republic in this regard.

# _II.B Best Track Data_

The source for hurricane tracks is the HURDAT Best Track Data, which provides six hourly data values on all tropical cyclones in the North Atlantic Basin, including the position of the eye of the storm and the maximum wind speed. We linearly interpolate these to 1 hourly positions. We also restrict the set of storms to those that came within 500 km of the Dominican Republic and that achieved hurricane strength (at least 119 km/hr) at some stage in their life-time, since these are those likely to have caused any damage due to wind exposure.<sup>12</sup>

> 12 We are here not modeling the impact of excess rainfall due to tropical storms, since data are only available at a spatially much more aggregate level (roughly 25km) and over a shorter time period (since 1998).  One may want to note that, however, that for hurricanes (storms classified above 119 km/hr), the extent of local rainfall and wind tends to be highly correlated; see Jiang et al (2008).

8
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