rafmacalaba/datause-encoder-data huggingface dataset:
high-skilled factor abundance of country i. We interact them with factor intensities for each sector k calculated using data for the USA from EU-KLEMS, averaged over the years 2003-2004; this approach is in line with previous work on comparative advantage. Specifically, skill-intensity is calculated as the ratio of the hours worked by high-skilled labor to the total number of hours worked by all types of skills in a sector. To get a measure of intensity, this ratio is multiplied by the total share of high-skilled labor in value added. The country-specific factors for capital are calculated by using the Conference Board and Groningen Center for Growth databases. Specifically, we take non-ICT capital, measured as the share of the non-ICT capital stock in the total capital stock. In addition, we use the World Development Indicators to source data on internet users per 100 people as an indicator of ICT capital. The interaction term is again formed by multiplying the factor abundance terms by factor intensities calculated using data from EU-KLEMS. Non-ICT capital intensity is first measured as the ratio of non-ICT capital compensation to total capital compensation. This measure is multiplied by the total share of non-ICT capital in value added. We perform similar calculations for ICT capital intensity, which is measured as the share of total ICT capital compensation in total value added for each sector. All three factors correct for material input use, and closely follow Romalis (2004).

The final set of variables represents institutional quality in each country interacted with the intensity of institution use in each sector. We use two measures, as follows: The first variable in each interaction term measures the quality of the legal system and security of property rights in each exporting country, and is taken from Gwartney and Lawson (2004) and the Fraser Institute. The industry characteristic in the first interaction term represents a measure that assesses the complexity of the services sector using the methodology due to Costinot (2009). It is computed based on PSID survey data, which ask how long it takes for each employee to be fully educated and qualified for 16
time activities.<sup>11</sup> Therefore, total income earnings for the reference year are divided by the months worked full-time.<sup>12</sup> Earnings distributions and deciles are defined for each of the groups of workers (full time, employers, regular employees and casual employees). Similar to the inflation adjustments applied to household disposable income, nominal earnings are converted into real values using decile-specific inflations rates computed using HBS data.

The sample for the analysis of real minimum wages is restricted to employees who work full time. For each year, earnings in cash are compared to the value of the official minimum wage to identify workers who are paid the minimum wage. However, this approach comes with a caveat. The identification of minimum wage earners is measured with error due to the self-reporting nature of the income data in SILC, in particular the rounding of earnings and months of work in addition to the likely reluctance of some individuals to report their full income to avoid losing social benefits that are tied to the value of the minimum wage. In fact, the empirical distribution of earnings amongst formal workers shows bunching right below the minimum wage threshold. In contrast, the distribution of earnings for informal workers does not display any discontinuities around the same threshold.

# **5. Results**

## **5.1** **_The incidence of inflation_**

As expected, there is significant variation in the composition of the consumption baskets of Turkish households across the income distribution. Expenditure data for 2019 classified by the 12 COICOP groups presented in Figure 2 shows that food, non-alcoholic beverages and housing make up a larger share of overall consumption for the bottom of the distribution compared to the upper deciles. While food and non-alcoholic beverages account for 41.1% of the total expenditures for households from the bottom of the distribution, this share is a third (13.1%) for the top decile –and also smaller for the median household (26.2%). Similarly, poor families allocate 28% of the expenditures to housing whereas a typical household from the richest decile spends 21%. Although the gap is smaller, a comparison of the budget shares between the Bottom 40 and the Top 60 reveal

> 11 Since there might be individuals within the same household who are in and out of employment or the work force, doing the analysis at the individual level rather than across households provides a more accurate picture of workers’ wages.

> 12 Lack of data on the number of hours worked for part-time workers makes it impossible to calculate monthly or hourly wages for these workers.

12
**Table 5: Maximum Likelihood Estimates for High Technology Exportsa**

|Model<br>Equation|Probit|(I)<br>Gravity|Probit|(II)|Gravity|
|---|---|---|---|---|---|
|_Intercept_|-8.300|-14.272|-8.334||-14.225|
||(-32.67)|(-28.75)|(-31.67)||(-28.02)|
|_GDPi_|0.987|1.804|0.987||1.803|
||(47.17)|(44.86)|(47.15)||(44.85)|
|_GDPj_|0.270|0.927|0.265||0.936|
||(18.71)|(36.09)|(15.10)||(29.05)|
|_Populationi_|-0.305|-0.658|-0.305||-0.658|
||(-17.00)|(-18.43)|(-17.01)||(-18.41)|
|_Populationj_|-0.086|-0.097|-0.081||-0.105|
||(-4.28)|(-2.67)|(-3.60)||(-2.59)|
|_Distance_|-0.596|-1.062|-0.595||-1.064|
||(-17.27)|(-18.78)|(17.21)||(-18.76)|
|_Border_|-0.121|0.129|-0.116||0.124|
||(-0.84)|(0.58)|(-0.810)||(0.565)|
|_Language_|0.706|1.225|0.707||1.226|
||(11.01)|(10.66)|(11.03)||(10.67)|
|_EC_||0.326|||0.332|
|||(1.14)|||(1.15)|
|_EFTA_||0.086|||0.089|
|||(0.17)|||(0.18)|
|_LAIA/LAFTA_||0.720|||0.702|
|||(2.96)|||(2.86)|
|_ASEAN_||3.467|||3.455|
|||(6.97)|||(6.93)|
|_CACM_||2.661|||2.640|
|||(5.20)|||(5.15)|
|_,pRSb_|||0.0132||-0.022|
||||(0.50)||(-0.45)|
|||2.121|||2.121|
|_obs._|7309|3342|7309||3342|
|||-0.027||-0.027||
|-2 InA_(p= O)c_||0.451||0.442||
|-2 InA_({r,,f}_=**Q)O**||8725.684||8726.12|7|

**a** t-statistics in parentheses

b Park and Ginarte index of the destination country of the trade flow, that is countryj in the case of exports and country i in the case of imnports.

**c** For a definition of the likelihood ratio test statistics, see Appendix A.

17
The small coefficient and significant distance coefficient for the product groups 86 to 89 probably stems from the fact that several of these products are dominantly provided by a small number of suppliers. For example in the case of the automobile industry, we know that European car firms made repeatedly use of price discrimination when selling into the European market.

**Table 6:** **_Quality and Trade Cost Effect by Product Group_**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|HS Chapters|01 -05|06- 15|16- 24|25- 27|28-38|
|Wei. Dist.|0.106***|0.166***|0.157***|0.366***|0.246***|
||(0.023)|(0.029)|(0.034)|(0.038)|(0.036)|
|Observations|39432|43246|54892|13484|89891|
|R-squared|0.384|0.461|0.360|0.472|0.530|
||(6)|(7)|(8)|(9)|(10)|
|HS Chapters|39- 40|41 - 43|44 - 49|50-63|64 -67|
|Wei. Dist.|0.267***|0.185***|0.246***|-0.101**|-0.454***|
||(0.020)|(0.052)|(0.030)|(0.051)|(0.145)|
|Observations|42535|11166|38586|120374|14464|
|R-squared|0.375|0.345|0.512|0.351|0.420|
||(11)|(12)|(13)|(14)|(15)|
|HS Chapters|68-71|72 -83|84 -85|86-89|90-97|
|Wei. Dist.|0.255***|0.238***|0.147***|0.045|0.071**|
||(0.025)|(0.023)|(0.028)|(0.035)|(0.035)|
|Observations|29915|95229|170448|21902|70889|
|R-squared|0.621|0.519|0.325|0.340|0.388|

Notes: All regressions are run with importing and exporting country fixed effects as well as product fixed effects, but for which the coefficients are not reported; Wei. Dist. stands for the log of weighted distance; ***,**,* denotes significance at the 1 %, 5 %, and 10 % level, respectively; robust standard errors, which are in parentheses, are corrected for clustering by country pair.

One might argue that the results only hold for product groups with relatively low unit values. The argument is that for products with higher unit values the transport costs decline. In order to test this hypothesis we first calculate the average unit value in logarithm of each product group and sort the whole sample accordingly. We then form ten sub-samples at 10 percent steps and run the basic regression again. The results are depicted in Table 7. Our result of a positive relationship between unit value and trade costs holds true for nearly all groups. Only the last tenth, containing products with extremely high unit values, yields the contrary result. For these products distance seems to even enhance trade. We further notice that, overall, the distance coefficient is not declining with a higher average unit values. We conclude that the dynamics described in our model seem to play an important role for shaping trade flows in nearly all product groups, irrespective of their average unit value.

29
represent more than a third of exporters on average across all countries. They represent an even higher share over 40 percent in most African countries as well as in Albania and Mexico. The percentages shown in Table 8 are quite close to the 40 percent reported by Bernard, Jensen, Redding, and Schott (2009) for the U.S.<sup>45</sup> However, these single-product single-destination firms account for a minimal fraction of total exports, on average less than 3 percent across countries. That fraction is particularly low in Botswana, Costa Rica, and Niger at 0.5 percent or less.

Table 8 also shows that firms exporting more than four products to more than four destinations represent a relatively small percentage of exporters, 12 percent on average across all countries. Those percentages do exhibit a substantial degree of heterogeneity across countries ranging from about 3.5 percent in Albania and Botswana to 21.5 percent in South Africa, 22.4 percent in Bangladesh, and 29.4 percent in Cambodia. These multi-product multi-destination firms account for a large share of total exports in all countries, more than 60 percent on average. In Cambodia, Costa Rica, and South Africa that share is actually close to 80 percent whereas in Albania and Niger it is less than 30 percent.<sup>46</sup> This high degree of concentration of total exports in the hands of a small number of multi-product multi-destination exporters can be rationalized by the model of multi-product multi-destination exporters developed by Bernard, Redding, and Schott (2011), where firms face fixed costs to export each product and serve each market. Only higher ability firms are able to generate variable profits to cover those fixed costs and thus supply a wider range of products to each market.

> 45 Note, however, that the U.S. percentage is based on products defined at the HS 10-digit level.

> 46 Focusing on three country examples, Appendix 4 shows further that only 1.2% of exporters in Tanzania, 2.3% in Colombia, and 2.2% in Mexico export more than 11 products to more than 11 destinations and these very rare exporters account for about 32% of total exports in Tanzania, 26% in Colombia, and 45% in Mexico.

32
<!-- Start of picture text --> CDF of endline cons pc, by current location<br>2 Pe ‘dl ra<br>= A a rs oa<br><!-- End of picture text --> <!-- Start of picture text --> fT<br>0 ; 500000 ; 1000000 1500000<br>annualized HH total consumption per capita in 2010 TZS<br>—— c.d.f. of Wr ——= cdf of ¥Wos<br>essesss=*'6.0.1.0F VOC ——— cdf. of Ws<br>—— c.d.f. of We<br>The 10% highest cons pc values were dropped for a better rendition ofthe graph<br><!-- End of picture text -->
Table 1: Housekeeper Analysis <u>(y:</u> Log Profile Clicks)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|South African|0.0755|0.0306|0.0195|0.0353|0.0327|
||(0.0476)|(0.0395)|(0.0398)|(0.0410)|(0.0410)|
|Zimbabwian|-0.0337|-0.0413<sup>_∗_</sup>|-0.0463<sup>_∗∗_</sup>|-0.0409<sup>_∗_</sup>||
||(0.0237)|(0.0214)|(0.0212)|(0.0225)||
|Malawian|-0.109<sup>_∗∗∗_</sup>|-0.105<sup>_∗∗∗_</sup>|-0.104<sup>_∗∗∗_</sup>|-0.0846<sup>_∗∗∗_</sup>||
||(0.0201)|(0.0185)|(0.0191)|(0.0199)||
|Foreign|||||-0.0689<sup>_∗∗∗_</sup>|
||||||(0.0180)|
|Male||||-0.0572<sup>_∗∗∗_</sup>|-0.0639<sup>_∗∗∗_</sup>|
|||||(0.0216)|(0.0213)|
|Female||||0.0176|0.0220|
|||||(0.0174)|(0.0174)|
|Age: <25||||0.0332|0.0386|
|||||(0.0332)|(0.0331)|
|Age: 26-30||||-0.0217|-0.0161|
|||||(0.0226)|(0.0226)|
|Age: 31-35||||-0.0222|-0.0125|
|||||(0.0345)|(0.0342)|
|Age: 35-60||||-0.0557|-0.0455|
|||||(0.0384)|(0.0378)|
|Experience (yr)||||0.0129<sup>_∗_</sup>|0.0132<sup>_∗_</sup>|
|||||(0.00715)|(0.00715)|
|Reference||||-0.0327<sup>_∗_</sup>|-0.0325<sup>_∗_</sup>|
|||||(0.0171)|(0.0171)|
|Employer posts||||0.00163|0.00187|
|||||(0.0467)|(0.0467)|
|Refer. phone||||0.0993<sup>_∗∗∗_</sup>|0.100<sup>_∗∗∗_</sup>|
|||||(0.0320)|(0.0321)|
|Picture||0.446<sup>_∗∗∗_</sup>|0.455<sup>_∗∗∗_</sup>|0.438<sup>_∗∗∗_</sup>|0.442<sup>_∗∗∗_</sup>|
|||(0.0449)|(0.0453)|(0.0448)|(0.0445)|
|Punctuation||-0.0282|-0.0241|-0.0238|-0.0203|
|||(0.0217)|(0.0219)|(0.0219)|(0.0218)|
|Wrong grammer||-0.0663<sup>_∗∗_</sup>|-0.0676<sup>_∗∗_</sup>|-0.0583<sup>_∗_</sup>|-0.0586<sup>_∗_</sup>|
|||(0.0337)|(0.0338)|(0.0341)|(0.0341)|
|Nr Mistakes||-0.0137|-0.0140|-0.0182|-0.0150|
|||(0.0247)|(0.0246)|(0.0252)|(0.0252)|
|Profle Syntax|N|Y|Y|Y|Y|
|Suburb|N|N|Y|Y|Y|
|Control Var|N|N|N|Y|Y|
|_R_<sup>2</sup>|0.011|0.198|0.204|0.216|0.217|
|N|3218|3218|3217|3217|3217|
|p-value: Zim=SA|0.028|0.083|0.115|0.050||
|p-value: Mal=SA|0.000|0.001|0.002|0.003||
|p-value: For=SA|||||0.012|

_Notes:_ standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 1 ,<sup>_∗∗_</sup> _p <_ 0 _._ 05 ,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 Base group for nationality are job seekers who do not reveal their nationality. _Employer posts_ refers to whether a former employer posts on their behalf. _Reference phone_ captures whether a phone nr of a reference is provided. _Reference_ captures any other forms of references mentioned.

11
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42
# 2.4 Analysis of decarbonization technology indices

We now turn to the question of whether the quantification of countries’ opportunities in key value chains is associated with greater competitive strengths in that value chain in future periods. Here we estimate the relationship between the DTO index and future changes in the DTS index. Our estimation approach takes the following form: ΔDTSc,t−(t−1) = α + βDTOc,t−1 + Xc,t−1′ γ + θt + εc,t (2)

> where ΔDTSc,t−(t−1) represents the 1-year change of country c’s decarbonization technology strength (DTS) index

> in each value chain VC; DTOc,t−1 is country c’s decarbonization technology opportunity (DTO) index in each value chain 1-year earlier. Xc,t−1′ represents a vector of lagged control variables: country c’s baseline DTS index, GDP per

> capita, exports of goods and services to GDP ratio, C02 emissions and population. θt represents year fixed effect. Because we observe limited within-country variation in the DTS and DTS indices for the relatively short period covered by our data, we do not use country fixed effects in our baseline specification. εct captures the random error term. We estimate equation (2) with Ordinary Least Squares (OLS), using robust standard errors.

Table 3 shows the resulting analysis. For each specific value chain, higher DTO scores (representing greater decarbonization opportunities) are positively associated with greater decarbonization strengths (see columns 2-4). We note a small effect size. For example, a one standard deviation increase in a country's EV decarbonization opportunities is associated with a 1.3% increase in its decarbonization strengths (column 2). This result is robust to the inclusion of relevant control variables. Interestingly, aggregating opportunities across value chains remains significantly associated with overall changes in the aggregate DTS index, though this association is weaker (column 1).

The results are robust to alternative lag structures (Appendix Table SI4) and more demanding country fixed effect panel regression (Appendix Table SI5), which increases the explanatory power by an order of magnitude.

|**Table 3: OLS regression re**<br> ΔDD IIIc,t−(t−1)|**sults of equati**<br>(1)<br>All VCs|**on (2)**<br>(2)<br>EV narrow|(3)<br>Solar|(4)<br>Wind|
|---|---|---|---|---|
|<br>DTO Indexc, t-1|0.004*|0.013***|0.008***|0.008***|
||(0.002)|(0.004)|(0.003)|(0.003)|
|DTS Indexc, t-1|-0.003|-0.016***|-0.007*|-0.007**|
||(0.004)|(0.005)|(0.004)|(0.004)|
|GDP per capitac, t-1, log|-0.007***|-0.004|-0.008***|-0.006**|
||(0.002)|(0.004)|(0.003)|(0.003)|
|Exports/GDPc, t-1|0.000**|0.000|0.000|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|
|CO2 emissionc, t-1, log|0.003**|0.000|0.005***|0.003|
||(0.002)|(0.003)|(0.002)|(0.002)|
|Populationc, t-1, log|-0.001|0.002|-0.003|-0.001|
||(0.002)|(0.004)|(0.002)|(0.002)|
|Observations|3,579|3,425|3,545|3,568|
|R-squared|0.015|0.013|0.016|0.013|
|Year Fixed Effects|YES|YES|YES|YES|

**Table 3: OLS regression results of equation (2)**

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 9
10 losses.<sup>5</sup> Perhaps the key point to note from the table is that no other country group has experienced any general loss of competitive position which come close to matching that for Africa.

Table 3 provides another perspective on Africa's trade performance by showing how the demand and competitive factors (i.e., equations 3 and 4) affected the regions total exports, and how Africa's experience compared with that of other developing countries. The table shows average 1962-64 and 1991-93 exports from Africa as well as from other groups of countries.<sup>6</sup> In addition, compound annual growth rates for those products each country group exported in the base period are given (see column 4 - demand growth) as well as the actual export growth rates (column 5) that occurred. Finally, the table gives the percentage change in each groups exports caused by the demand, competition and diversification factors.' The key point reflected in Table 3 is that Sub-Saharan Africa's export performance differed markedly from that of every other country group. With the exception of North Africa, all other developing country groups increased their levels of international competitiveness with the result that actual exports were above (often substantially) levels that would have occurred as a result of demand alone.

5For some products the statistics in Table 2 require clarification. The 10 percentage point increase in OECD countries' market share for coffee is largely due to increased roasting of coffee beans in industrial countries and exporting the processed beans to other OECD members. The four-digit SITC group does not distinguish between green and roasted beans and industrial countries have been shifting from Africa to other suppliers (mainly in Latin America) for the former. For example, in 1992 Germany (the largest OECD exporter) imported $1.3 billion of what appears to be largely green beans ($500 million came from Colombia alone) and exported $378 million of what appear to be largely roasted beans.

> 6 Many World Bank studies utilize a classification scheme that groups countries by geographic region and by income level. The groupings have been used in preparing the statistics reported in Table 2. For more information concerning this approach see World Bank (1995, Tables I and 2).

7A specific example may help explain these numbers. Table 3 indicates the demand factor, which assumes that a group merely retained its 1962-64 market shares, would have increased low income Asia's exports by 779 percent or $18. 1 billion. However, this country group substantially increased its market shares for its major export products -- this further increased the gains from the demand factor by 383 percent to approximately $87.4 billion. As a result, low income Asia's 1991-93 exports totaled $89.5 billion -- i.e., the $2.1 billion trade base plus the $87.4 billion contribution of the demand and competitive factor. Note that in the case of Africa the competitive factor reduced the regions exports 41.7 percent below the level that would have occurred if only the demand factor were operative.