rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: SSA Region Regressions: Manufacturing value added as percent of GDP (constant prices)**

||We|st|Ea|st|Cent|ral|Sout|hern|
|---|---|---|---|---|---|---|---|---|
||Coefcients|Marginal<br>Efects|Coefcients|Marginal<br>Efects|Coefcients|Marginal<br>Efects|Coefcients|Marginal<br>Efects|
|Population (logarithm)|0.777|0.100*|1.749|0.083|9.353***|-0.146|4.178***|-0.148***|
||(1.491)|(0.057)|(3.170)|(0.058)|(2.458)|(0.108)|(0.779)|(0.053)|
|Population squared (logarithm)|0.013||-0.022||-0.340***||-0.177***||
|20|(0.061)||(0.089)||(0.054)||(0.034)||
|GDP per capita (logarithm)<br>|0.762|-0.009|-0.177|0.036***|-0.084|0.055***|3.960***|0.029*|
||(1.914)|(0.016)|(1.823)|(0.012)|(1.395)|(0.009)|(1.088)|(0.017)|
|GDP per capita squared (logarithm)|-0.058||0.040||0.045||-0.229***||
||(0.122)||(0.118)||(0.092)||(0.071)||
|Intercept|-17.989||-22.421||-68.173**||-45.201***||
||(15.324)||(28.636)||(26.858)||(6.886)||
|Observations|464|464|398|398|284|284|394|394|

Standard errors in parentheses. Results based on fixed effects fractional logistic regressions in equation (2). Sub-regions based on the African Union geographic classification of SSA countries. See Table A.3 for additional details. _∗p <_ 0 _._ 10 _, ∗∗ p <_ 0 _._ 05 _, ∗∗∗p <_ 0 _._ 010
17

|Country/|**Table 6**<br>Years of<br>(15+ ye|**.Comparin**<br>schooling<br>ars old)|**g Korea an**<br>Per capi<br>(1990 co|**d Pakistan **<br>ta income<br>nstant $)|Economic<br>growth rate|
|---|---|---|---|---|---|
|Year|1950|2010|1950|2010|(%)|
|Pakistan|1.0|5.6|643|2,239|1.8|
|S. Korea|4.5|11.8|854|19,614|5.2|

Source: Years of schooling from Barro-Lee (2010); per capita income from Maddison (2010) It may take a decade for a country to increase the mean level of education attainment of the population by one year, as shown in Figure 7. It also depicts the _S_ -shaped path countries follow in their educational development history. That is, slow progress is made in the beginning, then accelerating and eventually slowing down.

**Figure 7.  Mean years of schooling of the adult population, Korea and Pakistan**

<!-- Start of picture text --> S<br><!-- End of picture text --> <!-- Start of picture text --> 14.0<br>12.0<br>Korea<br>10.0<br>8.0<br>6.0<br>4.0 Pakistan<br>2.0<br>0.0<br>1940 1950 1960 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> In order to establish what Pakistan‘s per capita income would have been in the period 1900 to 2050 had it followed a different education policy, we must first establish a relationship between education and income. In this illustrative example such a relationship is obtained by fitting a Mincerian function using per capita income ( _Y_ ) and years of schooling ( _S_ ) circa 2009 in 128 countries:
7 mix of sub-products within any product heading of the trade bundle. In particular, a change in the mix of differently priced sub-products will result in a change in the product-level unit value index even if there is no change in any price. In our study of Korean footwear exports, one might expect to observe price increases when the VERs on Korean footwear exports became binding. If, however, there were also a simultaneous change toward the shipment of more (less) expensive shoes to the restricted market, the unit value index would overstate (understate) the true price change. It is generally agreed that unit values which are constructed at the industry level, and which consequently treat all commodities as perfect substitutes, lose reliability with aggregation.

These drawbacks or unit-value indices used in most previous studies of VERs [e.g. Yoffie (1983)] can be remedied with the use of index numbers. Relying on the theory of index numbers developed by Caves, Christensen and Diewert (1982), and applied by Aw and Roberts (1988) to the study of US footwear imports, we construct multilateral price indices to compare price and product indices for Korean footwear exports to different countries.

Let k be an index over footwear categories, c an index over countries, and i and j refer to footwear bundles, where i and j may refer to a country c, or a time period t. Assume that the price per unit of service of bundle i is equal to the average price per unit of quantity (unit value), Pi, divided by the level of quality, Ai. As shown by Caves, Christensen and Diewert (1982) the product-mix-adjusted Tornqvi&2 multilateral price index defined bys (1) ln P 1 E(S + §k)(ln Pi - ln Pk) 2 (S J + k)(ln PJ In k
<!-- Start of picture text --> Duties as % Trade [l Average Nominal Tariff, %<br>Vf1 Imports as % GDP<br>50 -<br>D-Cd!- .<br>40- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ t 2 - - _ _ _ _ _ _ _ __,_ _ _<br>40 -1J3 s t _ 11 _ '_s3 >* J _<br>30 -<br>20 - *; f3_3vi'<_ ~ s 5 ]<br>~D 21 "><br>-41~~~~~~~~~~~~~~<br>tJ3~~~~ 1 3",, s __ (3< _ N<br>Figure~~~ 1: Trd olic by GS Satu, ConryYa Avergs 1976200<br>Dropped from GSP On GSP<br><!-- End of picture text --> Figure 1: Trade Policy by GSP Status, Country-Year Averages, 1976-2000 The figure includes 141 (29) countries with at least one year on GSP (dropped from GSP), for a total of 2466 GSP country-years and 176 ex-GSP country-years. T-tests (with unequal variances) of equal means across GSP and ex-GSP country-years for _Duties, Taiff,_ and _Closure,_ with two-tailed p = 0.00, p = 0.00, and p = 0.01, respectively, reject the null hypothesis in each case.

33
prices was based on the raw (level 0) intraday submissions, subset by internal identifier (ID) records that differentiated volunteer crowds from trained enumerators in the data pool. While the full pre-processed crowdsourced datasets in the public database are presented as daily averages for ease of use and understanding by public users (EC-JRC, 2022), detailed intraday datasets for the second major wave of data collection (in 2021) are accessible through another repository on EC-JRC data-m portal (Solano-Hermosilla, et al., 2021). The full monthly AI-estimated prices are presented as open, high, low, and close prices for each country in the aforementioned repository.

# **Code Availability**

Data analysis was conducted with R-software within R-studio environment (R Core Team, 2013). The entire code is saved in GitHub to reproduce the results and charts, and can be accessed through https://github.com/PJNation/FoodPriceAnalytics).

# **References**

Adewopo, J., Solano-Hermosilla, G., Micale, F., & Colen, L. (2022). Crowdsourced data reveal threats to household food security in near real-time during COVID-19 pandemic. In J. McDermott, & J. Swinnen (Eds.), COVID-19 and global food security: Two years later (pp. 4045). International Food Policy Research Institute (IFPRI). doi:10.2499/9780896294226_05 Adewopo, J., Solano-Hermosilla, G., Colen, L., & Micale, F. (2021). Using crowd-sourced data for real-time monitoring of food prices during the COVID-19 pandemic: Insights from a pilot project in northern Nigeria. (Jun:29:100523). doi:10.1016/j.gfs.2021.100523 Aldoseri, A., Al-Khalifa, N., & Hamouda, A. (2023). Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges. Applied Science, 13(12), 7082. doi:10.3390/app13127082 Andrée, B.P.J. (2021a). Estimating food price inflationfrom partial surveys. World Bank, Development Data Group. The World Bank Group. Washington D.C. Retrieved 01 05, 2024, from https://openknowledge.worldbank.org/server/api/core/bitstreams/7bb07f51-6a93- <u>5207-a43f-eb563b24d87f/content</u> Andrée, B.P.J. (2021b). Monthly food price inflation estimates by country (Version 2023-1211). Washington, D.C., USA. Retrieved 10 11, 2023, from World Bank Microdata Library: <u>https://microdata.worldbank.org/index.php/catalog/4509</u> Andrée, B.P.J. (2022). Machine Learning Guided Outlook of Global Food Insecurity Consistent with Macroeconomic Forecasts. Policy Research Working Papers. Washington, D.C., Washington, USA. doi:10.1596/1813-9450-10202 Andrée, B.P.J., & Pape, U. (2023). Machine Learning Imputation of High Frequency Price Surveys in Papua New Guinea. 10559. Washington D.C., Washington D.C.: World Bank. Retrieved 02 01, 2024, from http://hdl.handle.net/10986/40410 the main text and all data sets can be retrieved by querying RTP (Real-Time Prices): <u>https://microdata.worldbank.org/index.php/catalog/?page=1&sk=RTP&ps=15</u> 16
related to the decrease or a full loss of remittance income.<sup>1</sup> Similarly, 61 percent responded income from “garment factory work” remained the same, while 34 percent reported an increase and 4 percent reported a decrease. This data is clearly not a comprehensive assessment as it is difficult to say what income from these sources would have been in the absence of the economic crisis. However, the fact that so few households appear to have been adversely affected appears consistent with ex-post macro-economic data. Remittances increased by about 13 percent between 2008/09 and 2009/10 (from $9.7 billion to $11 billion). Export earnings from garments in 2009/10 also increased compared to the previous year ($12.3 billion in 2008/09 and $12.5 billion in 2009/10) though less so than the change in remittances. Overall, „economic shocks‟ appear to be mostly from agriculture-related events rather than loss of employment or business.

The fact that the non-poor report a larger incidence of shocks relative to the poor (56% compared with 51%) may at first glance appear counter-intuitive (table 5). This is consistent with the results found in Quisimbing (2007) where she explains the possible presence of respondent bias when reporting on shocks of certain types. For example, it is possible that only better-off households will actually be able to pay for certain types of “economic shocks” such as dowries and wedding expenses and thus only wealthier households will report having these types of shocks. Non-poor households are likely to have more assets compared to poor households and thus report higher incidence of asset shocks. Heltberg and Lund (2010) also report that the nonpoor face more shocks than the poor in Pakistan, and that shocks are predominantly healthrelated.

# Insert Table 5

According to Table 5, the type of shocks reported varies significantly depending on poverty status and the location of the household. Conditional on experiencing a shock, the incidence of climatic shocks is larger among poor households (defined as the bottom 40 percent of the population along the expenditure distribution) while asset and health related shocks appear to be more common among non-poor households. Conditional on experiencing a shock, only the incidence of climatic shocks varies significantly across urban and rural households, with the 7
<!-- Start of picture text --> FCS Portfolio by Region<br>Number of Active Projects per FY<br>RAR FY01-06<br>FY07-13<br>EAP FY0 71 - 1306<br>ECA FY01-06<br>FY07-13<br>LCR FY01-06<br>FY07-13<br>MNA FY01-06<br> Fy07-13<br>SAR FY01-06<br>FY07-13<br>0 50 100 150 200 250<br><!-- End of picture text --> <!-- Start of picture text --> FCS Portfolio by Network<br>Number of Active Projects per FY<br>FPD FY01-06<br>FY07-13<br>HDN FY01-06<br>FY07-13<br>PREM FY01-06<br>FY07-13<br>SDN FY01-06<br>FY07-13<br>0 50 100 150 200 250<br><!-- End of picture text --> <!-- Start of picture text --> FCS Portfolio by Region<br>Net Commitment of Active Projects per FY ($ m)<br>FY01-06<br>RAR FY07-13<br>EAP FF Y0 71 - 1306<br>FY01-06<br>ECA FY07-13<br>FY01-06<br>LCR FY07-13<br>FY01-06<br>MNA Fy07-13<br>FY01-06<br>SAR FY07-13<br>0 2,000 4,000 6,000 8,000<br><!-- End of picture text --> <!-- Start of picture text --> FCS Portfolio by Network<br>Net Commitment of Active Projects per FY ($ m)<br>FY01-06<br>FPD FY07-13<br>HDN FY01-06<br>FY07-13<br>FY01-06<br>PREM FY07-13<br>SDN FY01-06<br>FY07-13<br>0 2,000 4,000 6,000 8,000<br><!-- End of picture text -->
**<u>Table 6. Log earnings returns to schooling</u>**

||||**Men**|**W**|**omen**|
|---|---|---|---|---|---|
||**Completed**<br>**schooling years**|||||
||**and education**||**Selectivity-**||**Selectivity-**|
|**Countries**|**splines**|**OLS**|**corrected**|**OLS**|**corrected**|
|**All countries**|_Years_|_0.065***_|_0.064***_|_0.069***_|_0.081***_|
||<10 years|0.037***|0.036***|0.020**|0.014|
||10-13 years|0.045***|0.045***|0.056***|0.068***|
||>13 years|0.121***|0.118***|0.143***|0.177***|
|**Armenia**|_Years_|_0.028**_|_0.001_|_0.056***_|_0.044***_|
||<10 years|-0.014|0.045|0.064**|0.082***|
||10-13 years|0.022|0.005|-0.027|-0.040|
||>13 years|0.036|-0.003|0.101***|0.086***|
|**Bolivia**|_Years_|_0.045***_|_0.045***_|_0.067***_|_0.073***_|
||<10 years|0.037|0.036|0.068***|0.072***|
||10-13 years|-0.038|-0.038|-0.035|-0.034|
||<br>>13 years|0.121***|0.121***|0.161***|0.172***|
|**Colombia**|_Years_|_0.071***_|_0.070***_|_0.061***_|_0.070***_|
||<10 years|0.015|0.017|0.017|0.011|
||10-13 years|0.069**|0.066**|0.050|0.080**|
||<br>>13 years|0.182***|0.182***|0.194***|0.210***|
|**Georgia**|<br>_Years_|_0.045**_|_0.029_|_0.094***_|_0.127***_|
||<10 years|--|--|--|--|
||10-13 years|-0.072|-0.103|-0.004|-0.003|
||>13 years|0.076***|0.065**|0.118***|0.160***|
|**Ghana**|<br>_Years_|_0.089***_|_0.090***_|_0.098***_|_0.094***_|
||<10 years|0.105***|0.102***|-0.055|-0.056|
||10-13 years|-0.001|0.003|0.073*|0.070*|
||>13 years|0.172***|0.173***|0.256***|0.257***|
|**Kenya**|<br>_Years_|_0.085***_|_0.086***_|_0.067***_|_0.065***_|
||<10 years|-0.005|-0.005|0.024|0.016|
||10-13 years|0.113***|0.112***|0.022|0.032|
||<br>>13 years|0.289***|0.286***|0.379***|0.382***|
|**Serbia**|_Years_|_0.074**_|_0.085**_|_0.123***_|_0.099**_|
||<10 years|0.083|0.161|-0.015|0.241|
||10-13 years|0.044|0.041|0.155***|-0.003|
||>13 years|0.105*|0.106*|0.132***|0.148**|
|**Ukraine**|<br>_Years_|_0.012_|_0.015_|_0.084***_|_0.087***_|
||<10 years|0.069***|0.106***|--|--|
||<br>10-13 years|0.005|0.002|0.003|0.004|
||<br>>13 years|0.007|0.011|0.120***|0.120***|
|**Vietnam**|<br>_Years_|_0.050***_|_0.050***_|_0.068***_|_0.078***_|
||<10 years|0.009|0.009|-0.007|-0.015|
||<br>10-13 years|0.054**|0.056**|0.092***|0.106***|
||<br> >13 years|0.086***|0.082***|0.129***|0.161***|

Note: Results are from the full model estimates that include also measures of cognitive and noncognitive skills. All-countries pooled results are estimated with country fixed effects. See Table 4 and Appendix Table 4 for complete results. Statistical significance is denoted as * p < 0.10, ** p < 0.05, *** p < 0.01. Data sources: STEP data for 9 countries, 2012-13.

44
# **1. INTRODUCTION**

The literature on innovation and international trade has, until recently, focused almost exclusively on the manufacturing sector. One reason for this is that the bulk of international trade has actually taken place in manufactured products. Another is that technological developments have been traditionally associated with new or improved _physical_ products. The services sector has been relatively neglected as an object of analysis because it was seen as largely untouched by both trade and innovation. This neglect has persisted despite the growing importance of services in the trade of most economies, and the growing importance of productivity growth in services for overall economic growth. For example, according to IMF balance of payments data, 70 percent of mid- and high-income economies have seen the share of service exports in total exports expand over the last 30 years. As a consequence, today many developing economies, from Malaysia to Mexico, see services exports and innovation as a potential source of dynamism for their economies and as a way of breaking out of the so-called middle income trap.<sup>1</sup> Is this faith justified?

The literature studying the internationalization of services companies is still in its infancy, with only a limited number of micro-level studies focusing on technological change and trade. The most notable exception is a set of studies from the innovation and marketing literature, which has focused on the analysis of the services sector in its own merit and comparing the innovative behavior of services firms with manufacturing ones. However their focus and methodologies are relatively distant from the current theoretical and empirical economic literature. Erramilli and Rao (1993) is an example.<sup>2</sup> Moreover, the few firm-level analyses of services companies have so far focused exclusively on developed economies. This paper presents a firm-level empirical analysis comparing the manufacturing and ‘tradable’ services sector from a joint trade and innovation perspective.<sup>3</sup> We focus on Chile, an upper-middle economy that 2
<!-- Start of picture text --> fo}<br>co<br>=<br>a]<br>5>o©<br>oN<br>ES<br>QiB3Sln} |<br>a2<br>85 |<br>5&<br>2~ | M\,<br>YAM cent<br>y,<br>o<br>1960 1970 1980 1990 2000 2010 2020<br>—— Oil Naturalgas ———— Gold<br><!-- End of picture text -->
# **Table 5: Impacts of RCEP with the Krugman and Armington Structures**

(results are percentage change from initial equilibrium, unless otherwise indicated)

||||**Krug**|**man Struc**|**ture (FDI**|**and IRTS**|**Goods)**|||**Armington**|
|---|---|---|---|---|---|---|---|---|---|---|
||Benchmark<br>**RCEP**|**FDI Services**<br>**Barriers Only**|**Cross-border**<br>**Services**<br>**Barriers Only**|**Import Trade**<br>**Facilitation**<br>**Only**|**Export Trade**<br>**Facilitation**<br>**Only**|<br>**Import Tariffs**<br>**Only**|<br>**RCEP**<br>**Partner**<br>**Tariffs Only**|**Import**<br>**NTMs Only**|**RCEP Partner**<br>**NTMs Only**|**RCEP**|
|**Scenario definition**|**1**|**2**|**3**|**4**|**5**|**6**|**7**|**8**|**9**|**10**|
|RCEP FDI barriers (% reduction)|50|50||||||||50|
|Cross-border services barriers (% reduction)|50||50|||||||50|
|Import Trade Facilitation (% reduction for RCEP countries)|20|||20||||||20|
|Import Trade Facilitation (% reduction for non-RCEP countries)|5|||5||||||5|
|Export Trade Facilitation (% reduction)|20||||20|||||20|
|Export Trade Facilitation (% reduction for non-RCEP countries)|5||||5|||||5|
|Tariffs on Filipino Imports from RCEP (% reduction)|100|||||100||||100|
|Tariffs on Filipino Exports to RCEP (% reduction)|100||||||100|||100|
|NTMs on Filipino Imports from RCEP (% reduction)|20|||||||20||20|
|NTMs on Filipino Exports by RCEP partners (% reduction)|20||||||||20|20|
|Elasticity of Substitution Multiplier|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.24|
|FDI Elasticity Multiplier|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|30.00|
|**Aggregate welfare**|||||||||||
|Welfare (EV as % of consumption)|3.1|0.9|0.0|0.5|0.3|0.0|0.4|0.3|0.5|1.5|
|Welfare (EV as % of GDP)|2.5|0.7|0.0|0.4|0.2|0.0|0.3|0.3|0.4|1.2|
|**Government budget**|||||||||||
|Tariff revenue (% of GDP)|0.3|0.7|0.7|0.7|0.7|0.3|0.7|0.7|0.7|0.4|
|Tariff revenue|-48.9|1.0|0.0|0.7|1.2|-50.3|1.2|0.2|1.6|-47.5|
|**Aggregate trade**|||||||||||
|Real exchange rate|0.4|0.8|0.0|0.3|-0.5|0.6|-0.5|0.2|-0.5|-0.2|
|Aggregate exports|11.6|1.6|0.0|1.5|1.0|2.4|1.3|**1.1**|1.9|10.8|
|**FDI Output**|30.0|**26.8**|0.0|0.7|0.3|0.4|0.3|0.4|0.3|25.9|
|**Factor Earnings**|||||||||||
|<br>Skilled labor|2.2|0.9|0.0|0.5|0.1|0.2|0.0|0.3|0.2|1.0|
|Unskilled labor|3.9|0.7|0.0|0.2|0.5|-0.1|1.3|0.0|0.7|2.6|
|Capital|3.3|1.1|0.0|0.4|0.3|0.4|0.3|0.2|0.5|2.0|
|**Diagnostics***|||||||||||
|Feenstra Ratio Summary|100.84%|100.26%|100.00%|100.09%|100.08%|100.10%|100.09%|100.05%|100.14%|100.00%|
|Terms of trade|100.01%|99.96%|100.00%|99.61%|100.31%|99.52%|100.38%|99.69%|100.47%|100.36%|

***** The percentage change is the value indicated minus 100. Source: Authors’ estimates

52
# _The Punto Final Program in Mexico_

For three years, the FOBAPROA program initiated in 1995 had failed to reduce the amount of past due loans and to provide debtor relief (see case study in section 2.5). In a final attempt to offer debt relief, the government initiated in December 1998 the Punto Final program, which was a government-led debt-relief program targeted to mortgage holders, agribusinesses, and small and medium-sized enterprises (SMEs). The program offered large subsidies (up to 60% of the book value of the loan) to bank debtors to pay back their loans.

The Punto Final program consisted of three debtor-relief programs that were directed toward mortgage holders, the rural and fishing sectors, and enterprises. The discounts depended on the program, the amount of the loan, and on whether the bank restarted lending to the sector. Large loans only received discounts up to a certain amount. The government would increase its share of the discount offered according to the following rule: for every three pesos of new loans extended by the bank, the government would assume an additional one peso of discount. Only new loans to individuals and small and medium-sized enterprises would qualify for counting toward receiving this additional government subsidy. The program thus combined loss sharing between the government and the banking sector with an incentive to restart lending (World Bank 2003).

Although both the Punto Final program and the FOBAPROA program combined an element of loss sharing between the government and the financial institutions, there were four key improvements of the Punto Final program vis-à-vis its predecessor. First, loss sharing was geared toward small loans, as the discount offered was higher for smaller loans. Small borrowers are a desirable group to target, since assisting them improves competition in the economy, and since assistance channeled to these borrowers is less likely to result from their political or 18
two working papers at most.<sup>9</sup> This resulted in a database of 107 research papers linked to on one of 8 blogs: Aid Watch, Baseline Scenario, Chris Blattman, Economix, Freakonomics, Marginal Revolution, Overcoming Bias, and Paul Krugman. Among the other blogs, the majority were either finance blogs or blogs based on discussion of macroeconomic issues typically without reference to working papers. We use this database to formally test for whether blogging about a paper increases its abstract views and downloads through event study analysis.

The RePEc statistics are available at a monthly frequency, and so for each paper _i_ which is blogged about, we define _t=0_ in the month in which the blog entry occurred, _t=-1_ in the month before, _t=+1_ in the month after, etc. Then we estimate the impact of blog _s_ blogging about a paper using the following regression: _Blogi,t_ is a dummy variable which takes value one if the paper is blogged about in time period _t,_ βs is our coefficient of interest, measuring the increase in abstract views in the month of blog _s_ blogging compared to the paper-specific average, and γs allows for a one-month lagged effect which may arise particularly for blog posts towards the end of the month. We include paper-specific fixed effects (αi) and cluster the standard errors at the paper level. The corresponding equation is likewise estimated for paper downloads.

Equation (1) is known as the constant mean model in event study analysis (Campbell et al. 1997). A first threat to this assumption is if abstract views or downloads are trending over time. Paper view statistics appear to trend downwards over the first couple of months of release of the typical paper, but

> 9 There also seem to be spikes for these other blogs. For example, the blog Angry Bear linked to a paper which had abstract views jump from 3 to 150, and downloads from 0 to 40 comparing the month before to month of blogging.

8