rafmacalaba/datause-encoder-data huggingface dataset:
- Benayas, J., Newton, A., Diaz, A. and Bullock, J.: 2009, Enhancement of Biodiversity and Ecosystem Services by Ecological Restoration: a Meta-analysis, Science 325(5944), 11211124.

- Bouwman, A., Kram, T. and Goldewijk, K.: 2006, Integrated Modelling of Global Environmental Change: an Overview of Image 2.4, Technical report, Netherlands Environmental Assessment Agency, Bilthoven, The Netherlands.

- Brown, R., Rosenberg, N., Hays, C., Easterling, W. and Mearns, L.: 2000, Potential Production and Environmental Eects of Switchgrass and Traditional Crops under Current and Greenhouse-Altered Climate in the Central United States: A Simulation Study, Agriculture, Ecosystems & Environment 78(1), 3147.

- Bruner, A., Gullison, R., Rice, R. and Da Fonseca, G.: 2001, Eectiveness of Parks in Protecting Tropical Biodiversity, Science 291(5501), 125128.

- Bulte, E., van Soest, D., Van Kooten, G. and Schipper, R.: 2002, Forest Conservation in Costa Rica when Nonuse Benets are Uncertain but Rising, American Journal of Agricultural Economics 84(1), 150160.

- Burney, J., Davis, S. and Lobell, D.: 2010, Greenhouse Gas Mitigation by Agricultural Intensication, Proceedings of the National Academy of Sciences 107(26), 1205212057.

- Chakravorty, U., Hubert, M., Moreaux, M. and Nostbakken, L.: 2011, Will Biofuel Mandates Raise Food Prices?, Working Paper 2011-01, Department of Economics, University of Alberta.

- Chakravorty, U., Magné, B. and Moreaux, M.: 2008, A Dynamic Model of Food and Clean Energy, Journal of Economic Dynamics and Control 32(4), 1181 1203.

- Chazdon, R.: 2008, Beyond Deforestation: Restoring Forests and Ecosystem Services on Degraded Lands, Science 320(5882), 14581460.

- Chen, X., Huang, H., Khanna, M. and Önal, H.: 2011, Meeting the Mandate for Biofuels: Implications for Land Use, Food and Fuel Prices, Working Paper 16697, National Bureau of Economic Research.

39
Table A18: Gender, labor, and socio-emotional skills

||(1)<br>Work|(2)<br>ed in last 30|(3)<br>days|(4)|(5)<br>Labor Income|(6)|
|---|---|---|---|---|---|---|
|Woman|-0.08<sup>_∗∗_</sup>|-0.08<sup>_∗∗_</sup>|-0.08<sup>_∗∗_</sup>|-18.54<sup>_∗∗∗_</sup>|-18.73<sup>_∗∗∗_</sup>|-18.44<sup>_∗∗∗_</sup>|
||(0.03)|(0.03)|(0.03)|(4.80)|(4.82)|(4.78)|
|Agg. SES Index|0.05<br>(0.05)|||10.62<br>(8.80)|||
|Woman x Agg. SES|-0.06<br>(0.07)|||-8.45<br>(10.93)|||
|Intrapersonal SES||0.04<br>(0.05)|||7.69<br>(8.87)||
|Woman x Intra SES||-0.05|||-4.57||
|||(0.07)|||(11.06)||
|Interpersonal SES|||0.04<br>(0.05)|||10.92<br>(7.64)|
|Woman x Inter SES|||-0.05|||-10.00|
||||(0.06)|||(9.36)|
|Mean for men|0.45|0.45|0.45|47.59|47.59|47.59|
|Observations|895|895|895|895|895|895|
|p-value Women|0.87|0.94|0.82|0.74|0.64|0.87|

Notes: Gender gap is an indicator variable for women. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Robust standard errors reported.

Table A19: Results: Aggregate Soft Skills by English Ability

||(1)<br>Aggregate soft-skills<br>index|(2)<br>Intrapersonal<br>skills index|(3)<br>Interpersonal<br>skills index|
|---|---|---|---|
|Offered Training|0.01|0.03|-0.01|
||(0.05)|(0.05)|(0.06)|
|Offered * Advanced English|0.05<br>(0.06)|0.02<br>(0.06)|0.08<br>(0.07)|
|Aggregate SES Index|0.51<sup>_∗∗∗_</sup><br>(0.05)|||
|Intrapersonal||0.44<sup>_∗∗∗_</sup><br>(0.04)||
|Interpersonal|||0.50<sup>_∗∗∗_</sup><br>(0.05)|
|Control Mean|-0.02|-0.02|-0.02|
|Observations|849|849|849|

Notes: OLS regressions controlling for strata dummies used in the randomization and baseline value of the outcome. Soft skills were only measured at baseline and endline. Robust standard errors shown in parenthesis. The controls presented in the table are the baseline measures of each index.

39
monotonically with economic growth, nor should they decline as rapidly with per capita income as pedestrian fatalities per VKT.

Table 2 bears this out. The table presents fixed-effects models of ln(Pedestrian Fatalities/VKT) and ln(Occupant Fatalities/VKT) estimated using data for 1963-2002 for the countries in Table 3.<sup>11</sup> Model 1 contains only the log of per capita income (measured in 1996 International $) and a time trend. The log of income enters model 2 in a piecewise-linear fashion, with each spline segment containing an equal number of observations, while in model 3 ln(Pedestrian Fatalities/VKT) is a quadratic function of the log of per capita income.<sup>12</sup> The models for pedestrian fatalities per VKT suggest that fatality risk to pedestrians declines with per capita income, at least until a per capita income of $20,700 (1996 Intl $).<sup>13</sup> In contrast, the table suggests that there is no monotonic relationship between per capita income and occupant fatality risk, at least over the range of incomes in the data. For pedestrians, fatality risk declines with income for per capita incomes between $4,552 and $20,700 (1996 Intl $), with the elasticity of pedestrian fatality risk with respect to income ranging from -0.71 to -1.1. The only income range in which occupant fatality risk declines with income is $13,165 to $16,565 (1996 Intl $), and the

> 11 Data sources are described in Appendix A.

> 12 In all specifications shown in Table 2, Durbin-Watson statistics (calculated using a STATA program (xtdw) written for calculating DW statistics in fixed effects panel data models (Nunziata (2002), following Baltagi (1995), page 94) indicate the presence of positive within-panel serial correlation in the disturbances.  Therefore, the standard errors shown in parentheses under each coefficient have been adjusted to account for serial correlation in disturbances within countries over time, as well as for heteroskedasticity.  This procedure is theoretically justified when the number of panels is large (Liang and Zeger, 1986).  Recent studies of finite sample properties of robust variance matrix estimators find that this estimator works reasonably well in the context of fixed effects estimation and panel data even when the number of panels is not especially large relative to the length of each panel (Wooldridge, 2003).

> 13 This corresponds to the per capita income of Sweden in 1990 and the United States in 1980.

12
**Table 2: Poverty and self-reliance among hosts and refugees (percent)**

|||**Hos**|**ts**|||**Refugees**|||
|---|---|---|---|---|---|---|---|---|
|**IPL**||**Poverty**<br>**incidence**|**Self-**<br>**reliance**|**Poverty**<br>**incidence**|**Own**<br>**income**<br>**poverty****|**Own income**<br>**poverty gap**|**Aid**<br>*******|**Self-**<br>**reliance**|
|2.15|Chad (*)|57.8|42.2|67.7|82.0|42.3|15.4|18.0|
||Ethiopia|24.9|75.1|74.8|85.5|65.0|32.2|14.5|
||Addis Ababa|17.8|82.2|7.3|9.1|2.1|0.8|90.9|
||in camps (*)|31.7|68.3|83.7|95.6|73.3|36.3|4.4|
||Niger|45.8|54.2|62.1|69.3|32.2|8.9|30.7|
||Uganda|37.5|62.5|69.5|85.9|56.3|28.0|14.1|
||Kampala|3.1|96.9|2.7|8.8|2.9|2.8|91.2|
||Outside Kampala (*)|41.3|58.7|74.2|91.3|60.1|29.8|8.7|
|3.65|Kenya|39.8|60.2|57.0|70.7|38.9|17.7|29.3|
||Kakuma (*)|87.1|12.9|69.9|83.7|49.5|22.9|16.3|
||Kalobeyei (*)|87.1|12.9|72.2|92.8|63.5|33.2|7.2|
||Dadaab (*)|61.0|39.0|44.5|61.4|27.9|12.1|38.6|
||Nairobi|22.5|77.5|33.1|33.9|9.8|0.5|66.1|
||Other urban|19.7|80.4|30.7|32.2|11.9|5.7|67.8|
|6.85|Bangladesh (*)|NA|NA|NA|99.8|89.7|NA|0.2|
||Colombia|39.2|60.8|49.8|51.9|21.7|1.9|48.1|
||Costa Rica|13.4|86.6|23.6|27.2|10.1|2.0|72.8|
||Jordan|NA|NA|32.3|59.1|23.7|17.2|40.9|
||Amman|NA|NA|28.0|47.4|16.5|11.6|52.6|
||Outside Amman|NA|NA|41.8|61.3|26.2|16.9|38.7|
||Azraq (*)|NA|NA|19.5|77.5|34.6|32.2|22.5|
||Zaatari (*)|NA|NA|13.3|68.6|25.5|23.8|31.4|
||Lebanon (UMIC) (****)|2.3|97.7|16.4|30.2|7.2|4.3|69.8|
||Lebanon (NPL)|34.2|65.8|88.9|90.7|45.8|6.0|9.3|
||West Bank & Gaza|15.2|84.8|31.4|38.4|15.6|6.3|61.6|
||West Bank|5.1|94.9|5.7|8.4|1.7|0.8|91.6|
||Gaza<br>|47.6<br>|52.4<br>|46.2<br>|55.6<br>|23.5<br>|9.3<br>|44.4<br>|

_Note: (*) Indicates a refugee camp or settlement; (**) Refers to pre-assistance income poverty (***) Aid is expressed in percent of the poverty line (****) Survey data for Lebanon is available from the 2022 Vulnerability Assessment Panel. There are large variations in refugee poverty estimates for Lebanon ranging from 1 percent using the LMIC poverty line (PPP$ 3.65) and 16 percent using the UMIC poverty line (PPP$ 6.85) to 40 percent using the national poverty line. As Lebanon became a LMIC in July 2022, the first estimate should apply, even though, since Lebanon only recently became a LMIC a case could be made for using the UMIC poverty line._<sup>_4_</sup> 4 Yet even the poverty estimate derived for the UMIC line is on the low side, certainly in comparison to other countries in the region. Lebanon’s low PPP conversion factor, which leads to low poverty lines, is behind this. For instance, the UMIC poverty line is only 38 percent of the national poverty line, whereas it is about 70 percent of the 9
Table 2: Summary statistics - aggregate statistics at the grade level

|||Perc|entage of|immigran|ts in the gr|ade|
|---|---|---|---|---|---|---|
||All|No immigrant|0-10|10-20|20-50|50+|
|**Grade-level characteristics**|||||||
|Number of students in the grade|26.34|22.03|29.79|23.11|21.96|29.24|
||(13.02)|(12.36)|(14.11)|(11.23)|(11.49)|(15.82)|
|Fraction of immigrant students|0.063|-|0.053|0.137|0.279|0.820|
||(0.133)|-|(0.021)|(0.027)|(0.072)|(0.180)|
|Share of natives with low parental education|0.446|0.406|0.441|0.560|0.598|0.582|
||(0.497)|(0.491)|(0.497)|(0.496)|(0.490)|(0.493)|
|**Average test score in Dutch language**|||||||
|All students|49.89|51.13|49.77|47.37|45.72|45.93|
||(5.37)|(5.01)|(4.91)|(5.46)|(5.41)|(5.65)|
|Immigrant students|44.93|-|45.73|44.10|43.24|45.57|
||(9.13)|-|(10.12)|(8.21)|(6.86)|(6.09)|
|Native students|50.14|51.22|50.81|47.89|46.63|44.87|
||(5.42)|(5.04)|(4.97)|(5.71)|(5.79)|(8.15)|
|**Average test score in mathematics**|||||||
|All students|49.88|50.76|49.76|48.08|46.99|47.41|
||(4.94)|(4.82)|(4.58)|(4.95)|(5.12)|(4.76)|
|Immigrant students|46.86|-|47.30|46.37|45.94|47.12|
||(9.03)|-|(10.05)|(8.10)|(7.10)|(5.07)|
|Natives|50.00|50.81|49.89|48.35|47.34|46.99|
||(5.04)|(4.87)|(4.68)|(5.16)|(5.33)|(6.75)|
|Number ofgrade-level observations|12,053|6,522|3,403|1,322|686|120|

_Note._ Reported statistics were aggregated at the grade level within a school. Standard deviations at the grade level are reported in parentheses. Native students with low parental education are defined as students whose father did not complete upper secondary education.
# Table C2. Distribution of Manufacturing Establishments by Source of Electricity and Employment Size, Indonesia

ELECTRIC (SOURCE OF ELECTRICITY) FIENSIZE (EMPLOYEES per FIRM) <!-- Start of picture text --> Percent<br>now Pet<br>Cot Pet 1-19 120-49 1S0-f 1100-199 1200-499 1500-999 11000 5 I Total<br>I I I I I I lovr I<br>-... +. ................. ....... ........<br>Public only 2 49 1a 16 6 6 100<br>0.69 16.90 6.21 6.21 2.07 2.07 0.34 34.48<br>2.00 49.00 18.00 18.00 6.00 6.00 I1.00<br>40.00 52.13 32.14 29.03 13.64 31.58 10.00<br>....... ......... ........ 4------------ .....-------- +'''' ................. ....... .'<br>Public mInl 21 361 281 35 301 91 147<br>0.69 12.41 9.66 12.07 10.34 3.10 2.41 50.69<br>1.36 24.49 19.05 23.81 20.41 6.12 4.76<br>40.00 I 38.30 1 50.00 S6.45 I 68.18 1 47.37 I 70.00<br>............ ........ ........ ..... ------- . ---.----- ........ ........<br>own 1 1 I 1 9 1 5 | 5 S 21 2t 31<br>Genmrators 1 0.34 I 2.41 I 3.10 9 1.72 1. 72 0.691 0.69 10.69<br>mIn I 3.23 22.5s 29.03 16.13 16.13 6.45 6.45<br>- 20.00 7.45 16.07 8.06 11.36 10.53 20.00<br>.... .......- --- ....... .................... ................<br>Own I 01 21 1 1 4 31 2 1 O 12<br>enwratora 0.00 0.69 0.34 1 1.381 1.03 1 0.69 0.00 4.14<br>only 0.00 16.67 8.33 i 33.33 1 25.00 I 16.67 0.00<br>0.00 2.13 1.79 1 6.45 1 6.82 10.53 1 0.00 I<br>..... ............ . .- ........<br>Total S 94 56 62 44 19 10 290<br>1.72 32.41 19.31 21.38 15.17 6.55 3.45 100.00<br><!-- End of picture text -->
Table 3: Balance on Student Characteristics

|||Mean|(SD)|||P-Value||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Overall|Control|GS|GS + R|GS vs. Control|GS + R vs. Control|GS vs. GS + R|
|Male|0.448<br>(0.004)|0.457<br>(0.006)|0.412<br>(0.006)|0.476<br>(0.007)|0.035|0.326|0.024|
|Age|16.603<br>(0.010)|16.612<br>(0.015)|16.555<br>(0.017)|16.643<br>(0.017)|0.48|0.68|0.599|
|Is the student repeating the current grade?|0.064<br>(0.002)|0.071<br>(0.003)|0.053<br>(0.003)|0.068<br>(0.004)|0.062|0.811|0.139|
|Whether father can read and write in English|0.679<br>(0.003)|0.683<br>(0.006)|0.706<br>(0.006)|0.641<br>(0.007)|0.364|0.16|0.101|
|Whether mother can read and write in English|0.53<br>(0.004)|0.532<br>(0.006)|0.548<br>(0.006)|0.507<br>(0.007)|0.592|0.485|0.525|
|Household asset index|0<br>(0.007)|-0.007<br>(0.011)|0.065<br>(0.010)|-0.065<br>(0.013)|0.428|0.543|0.383|
|Baseline English test z-score|0.043<br>(0.008)|0<br>(0.012)|0.121<br>(0.014)|0.011<br>(0.015)|0.182|0.911|0.377|
|Baseline Math test z-score|0.047<br>(0.008)|0<br>(0.012)|0.096<br>(0.013)|0.054<br>(0.015)|0.374|0.631|0.672|
|Spend more than 30 minutes in Math (baseline)|0.661<br>(0.004)|0.66<br>(0.006)|0.674<br>(0.006)|0.648<br>(0.007)|0.501|0.605|0.468|
|Absent at the endline exam|0.266<br>(0.003)|0.282<br>(0.005)|0.267<br>(0.006)|0.241<br>(0.006)|0.614|0.169|0.3203|
|Observations|18,281|7,105|5,962|5,214||||

_Notes_ : This table reports the balance test for various student level variables captured in baseline survey. Means, standard deviations and p-value of differences is reported by GS (Goal-Setting), GS + R (Goal-Setting + Recognition) and Control group. Standard errors are clustered at the level of school.
full-package capabilities. Vietnam also emphasizes CMT assembly, but its apparel export dependence ratio is relatively low (14%) because of the importance of its agricultural exports.

The main apparel exporting countries can be placed into the following categories:

- **Steady Growth Suppliers** (overall increasing market share since the early 1990s) **:** China, Bangladesh, India, Vietnam, and Cambodia; Pakistan and Egypt as well, but with smaller market shares.

- **Split Market Suppliers:** Indonesia is increasing its market share in the United States and Japan, and decreasing in the EU-15; conversely, Sri Lanka is increasing market share in the EU-15 and decreasing in the United States.

- **Pre-MFA Suppliers** (sharp declines after MFA quota phase-out that have accelerated during the crisis): Canada, Mexico, CAFTA, EU-12, Tunisia, Morocco, and Thailand.

- **Past-Prime Suppliers:** (decreasing since early 1990s): Hong Kong, South Korea, Taiwan, Malaysia; also countries with smaller market shares: Philippines, Singapore, and Macau.

The last two years have reinforced many of the trends occurring after the phase-out of quotas. China, Bangladesh, Vietnam, and Indonesia are increasing their market shares in North America and the European Union, primarily at the expense of near-sourcing options such as Mexico and the Central American and Caribbean suppliers to the United States, as well as apparel exporters from North Africa and Eastern Europe to the EU-15 (see Figures A-1 and A-2 in the Appendix).

Leading apparel suppliers like China, India and Turkey, concerned about a slowdown in global exports, have also begun to focus more on sales to their domestic markets. This trend not only taps into the added purchasing power of those emerging economies, but it also allows them to accelerate the upgrading process associated with moving beyond assembly and full-package supply to original design manufacturing (ODM) and original brand manufacturing (OBM).

# **ii. Regional Trends**

From a regional perspective, how have different apparel exporters managed to cope with the MFA phase-out and the economic recession? Since our export data for 2008 only captures the initial year of the economic recession, these findings are provisional yet they reveal some interesting trends.

The growth of regional suppliers for finished apparel to the European Union and the United States has decreased markedly since 2005, largely due to the expansion of China’s exports to these markets (see Tables 2 and 3). Regional and bilateral trade agreements in Asia are also increasing, such as those in the South Asian region (SAFTA) and those involving the Association for South East Asian Nations (ASEAN), including the new China link that went into full effect starting Jan. 1, 2010 (see Table A-1 in the Appendix).

9
This paper presents the first evidence of whether capital incentives distort recent technology diffusion - using the cloud versus own IT investment as an example. We employ novel firm-level panel data which captures the adoption of cloud, big-data analytics and AI, along with traditional investments in tangible capital, including total IT investment, as well as hardware and software.<sup>4</sup> In addition, we use matched employer-employee panel data to measure the demand for data analytics workers - as an alternative proxy for technology adoption following Corrado et al. (2022).<sup>5</sup> If the AIA policy affects firm adoption of data analytics technologies, such as cloud, big data analytics or AI, then one would expect that this would also materialise in the demand for workers performing data analytics tasks.

We study capital investment and technology diffusion in the setting of a quasi-natural experiment that exploits the introduction and adjustments to the eligibility threshold for a capital incentive policy in the UK, the Annual Investment Allowance (AIA). This scheme allowed firms to deduct the cost of investment in capital (including IT capital) against profits up to a threshold value of profits, where this threshold was adjusted over time.<sup>6</sup> The AIA impacted the marginal investment cost of some firms and not others, allowing us to use a difference-in-differences approach comparing treated versus control firms.<sup>7</sup> As firms can adjust their investment in response to the AIA, this poses a potential selection problem - firms may limit their investment in a given year to stay below the AIA incentive thresholds. To address this issue, we follow the empirical approach of Bjuggren (2018), Saez et al. (2019) and Bøler et al. (2015). These papers examine the effects of employment protection legislation or R&D tax credits in settings that also feature changes

> 4We note that software is considered an intangible asset, but was nonetheless eligible for the AIA policy in addition to conventional tangible assets, as discussed in section 2

> 5Corrado et al. (2022) use changes in employment of occupations that perform data tasks to construct economy-level time series of data investments. In our context we use changes in demand for occupations that perform data tasks as a measure of diffusion of firm-level data technologies.

> 6As noted earlier, cloud computing expenditures are not eligible for the incentive.

> 7The introduction of the AIA will of course have affected the average tax rate for both treatment and control firms. However, as Fullerton (1984) writes, “average effective tax rate are appropriate for measuring cash flows and distributional burdens, while marginal effective tax rates are designed to encourage the use of new capital” (p. 30), indicating that it is the marginal rate that is relevant here. That the change in the marginal tax rate is zero for firms above the capital investment threshold explains why we do not use a regression kink design (Card et al., 2015).

3
**Figure 4: Evolution of inter-province and inter-district inequality**<sup>**18**</sup>

<!-- Start of picture text --> 0.500<br>0.450<br>0.400<br>0.350<br>0.300<br>0.250<br>0.200<br>Provincial GDP PC Kabupaten/Kota GDP PC<br>Gini Coefficient<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> The pattern of increasing inequality across districts varies by province and island group. Table 1 shows the Theil and Gini inequality measures for each of the major island groups in the country. The general pattern of an increase in inequality across districts at the national level is confirmed by the large increase in both inequality measures between 1993 and 1999. However, the national increase is driven by increasing inequality across districts in Sumatra, Java and Bali, Sulawesi, and Papua, with inequality across districts in Kalimantan, Maluku and Nusa Tenggara falling during this period.

**Table 1: Inequality of GRDP per capita across districts by island group**

||Theil Ind|ex|||GiniCoef|ficient|||
|---|---|---|---|---|---|---|---|---|
||1993|1999|2001|2005|1993|1999|2001|2005|
|Sumatra|0.14|0.20|0.13|0.15|0.26|0.31|0.27|0.27|
|Kalimantan|0.28|0.27|0.28|0.38|0.38|0.37|0.39|0.42|
|Java & Bali|0.26|0.33|0.38|0.35|0.35|0.38|0.40|0.39|
|Sulawesi|0.04|0.07|0.06|0.09|0.16|0.20|0.19|0.23|
|Maluku + Nusa Tenggara|0.10|0.07|0.20|0.17|0.23|0.20|0.32|0.29|
|Papua|0.75|1.15|0.80|0.76|0.57|0.68|0.57|0.57|
|**Total**|**0.31**|**0.43**|**0.35**|**0.35**|**0.37**|**0.41**|**0.39**|**0.39**|
|Eastern Indonesia|0.56|0.92|0.52|0.45|0.41|0.49|0.41|0.40|
|Western Indonesia|0.24|0.29|0.30|0.31|0.34|0.37|0.38|0.38|
|Java|0.28|0.35|0.41|0.38|0.36|0.38|0.41|0.40|
|Off Java|0.33|0.47|0.32|0.33|0.38|0.42|0.38|0.38|

Source: Authors’ calculation based on GRDP data from BPS. Gini coefficient of real GRDP per capita across districts.

> 18 Provincial GDP data based on 1993 prices are only available from 1993 to 2002, while _kabupaten/kota_ GDP data based on 1993 prices are available from 1993 to 2003.

9