rafmacalaba/datause-encoder-data huggingface dataset:
increasing with time in some industries suggesting that developed economies might be responding to competition from China and other low-wage countries by raising the sophistication of their exports or dropping the least-sophisticated varieties from their export bundle.

# **Figure 1. Exports of Goods and Nonfactor Services as a Share of GDP, %**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>China<br>India<br>20<br>15<br>10<br>5<br>0<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: World Bank, World Development Indicators database.

# **Exports of Services**

A striking difference between China and India is in the importance of services relative to merchandise exports. India’s share of commercial services in total goods and services exports has been much higher than China’s, not just since the rapid expansion of exports of computing services around 2000, but for the entire period since 1992 during which comparable estimates are available (Figure 2). The share of services in India’s exports began, at around 20 percent, over twice as high as China’s. This share declined in India until the late 1990s, when it again began to rise sharply. Since 2000, services have accounted for over a quarter of India’s exports, while the share of services in China’s exports has declined to under 10 percent of total exports—although China’s exports of services have been growing rapidly in absolute terms.

5
**Figure 7. Density of log per equivalent adult expenditure Cashew Producers – Cashew Non-Producers**

<!-- Start of picture text --> 5 10 15 20<br>log per equivalent adult expenditure<br>cashew producer cashew non-producer<br>.6<br>.4<br>density<br>.2<br>0<br><!-- End of picture text --> Source: QUIBB 2010.

**Figure 8. Density of log per equivalent adult expenditure Rural Cashew Producer – Rural Cashew Non-Producer**

<!-- Start of picture text --> 5 10 15 20<br>log per equivalent adult expenditure<br>rural cashew producer rural cashew non-producer<br>.6<br>.4<br>density<br>.2<br>0<br><!-- End of picture text --> Source: QUIBB 2010.

The cashew shares, and thus the welfare effects from price changes, are likely to vary across the distribution of income. Poor farmers may have higher relative cashew production, and are thus more exposed to price changes. Maybe the beneficiaries of price increases are instead the middleincome households or the more well-off households. To explore this issue, descriptively for the 26
<!-- Start of picture text --> Building Fraction [%] | Building Height [m] Building Volume [m3]<br>Reference (mean of 20.78 7.77 11,798<br>19 reference cites)<br>CO<br>SME2.78 | 10.31MAE| RMSE14.09 | -230 | 356 | RMSE6.04 | -2,808 | 6,878 | 12,464RMSE |<br>Identical to v2.0 RMSE RMSE<br>ssGD OS (<br><!-- End of picture text -->
<!-- Start of picture text --> 55<br>= EU<br>50 a<br>= 45 al<br>| 2el<br>=t<br>oo<br>be 40a<br>2 China California<br>z 35 « Australia eee ail<br>2 a Canada p--* 7”<br>Pic} 30 ee ie . si<br>é—i<br>a2 eee _* if<br>us<br>20<br>2002 2004, 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

Notes: (1) dotted lines denote proposed standards (2) MPG = miles per gallon
period considered is of interest since it covers the pre- and post-Moroccan Arab Spring, and the subsequent political and economic changes set in motion by the protests.

A preliminary analysis of the two sets of variables already indicates that they do not go at par. Although Moroccan households experienced an improvement in their living standard between 2001 and 2013 and escaped from poverty and vulnerability to graduate into the middle classes – more than 70 percent of Moroccan households indicate that their well-being did not improve compared to the last 10 years. There is, however, a lot of variation depending on social status. While most of the poor, vulnerable and lower-middle-class households experienced few improvements compared to the last 10 years, middle-class households (but mostly those in the upper class) appear to have benefitted the most. When compared to the responses of the 2001 survey, the result is clearer. The upper classes appear to have experienced a significant increase in the share of those seeing their well-being improved; however, the share of those other classes experiencing an improvement did not vary much, with the only positive change being an increase in those declaring that conditions had not changed for them, as well as a reduction in the share of those seeing a decline.

To further confirm the general perception that improvements in welfare had not been uniformly distributed—a trend captured in household perception – we looked at the perceived status (poorer or richer than neighbors) and compared the results from the 2006 and 2013 surveys, as this section of the questionnaire was not available in the 2001 survey data. Despite these improvements, in both rounds most households did not consider themselves richer than the households living in their neighborhood; however, as in the previous case, the perception of relative poverty is higher among the poorest and declines quasi-monotonically through to the top one. What is striking is finding how much this perception worsened among the lower and middle classes along with their absolute shares: in 2013 almost 50 percent of those in the lower middle class, and 30 percent of those in the middle class declared themselves either poorer or very poor compared to their peers, whereas in 2006 these percentages stood at 30 and 15 percent, respectively.

In the second part of the paper we linked these preliminary findings on perception to polarization. Our hypothesis is that the ongoing process of absolute polarization which was detected through the use of the relative distribution method conditions the perception of household’s well-being. Exploiting the chance to construct a dynamic indicator of polarization, i.e. by using the relative 26
_Table 10: Summary Statistics - green activities index and its components before and after imputation_

||**N.**<br>**Obs.**|**Mean**|**Std. dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|**_Before imputation_**||||||
|Monitor Energy Consumption|8,445|0.62|0.49|0.00|1.00|
|Strategic Objectives Mention Env. or Climate Change|7,289|0.22|0.41|0.00|1.00|
|Manager Responsible for Env. or Climate Issues|7,298|0.15|0.36|0.00|1.00|
|Monitor Water Usage|4,683|0.48|0.50|0.00|1.00|
|Targets on Energy Consumption|8,363|0.32|0.47|0.00|1.00|
|Targets for CO2 Emissions|8,330|0.07|0.26|0.00|1.00|
|**_After Imputation_**||||||
|Monitor Energy Consumption|8,445|0.62|0.49|0.00|1.00|
|Strategic Objectives Mention Env. or Climate Change|16,000|0.22|0.41|-1.07|1.71|
|Manager Responsible for Env. or Climate Issues|16,000|0.15|0.36|-1.50|1.39|
|Monitor Water Usage|16,000|0.46|0.50|-1.21|2.55|
|Targets on Energy Consumption|16,000|0.33|0.47|-1.51|2.00|
|Targets for CO2 Emissions|16,000|0.08|0.26|-0.84|1.08|
|**Latent measure ofgreen activities**|**8,445**|**0.00**|**0.85**|**-1.94**|**2.78**|

27
countries, omitting 15 mostly smaller island countries such as Palau, Samoa, and Vanuatu. The results are shown in column 5 of Table 4. The main results still hold: aid to the transportation, energy, banking, and business service sectors reveals a positive and significant relation with exports whereas aid to ICT services again is not statistically significant.

# _5.2. Estimates by Income and Regional Samples_

We estimate the differentiated impact of aid effectiveness by region and income level of recipient countries. A priori, aid effectiveness may differ in each region and income group, as each subgroup of countries produce and export a different bundle of goods given their initial endowments, quality of services, and production technology. Attempting to measure the effectiveness aid to services in each region and income group can provide a starting point for the analysis of individual country policy recommendations.

Table 5 displays the results of estimating equation (1) for each income group of countries, as defined by the World Bank.<sup>11</sup> Akin to baseline estimates, aid to transportation, ICT, energy, and banking in low-income countries has a positive and significant effect on manufacturing exports. Only aid to business services records a negative impact on exports in low-income countries. In lower-middle income countries, aid to ICT services is associated with lower levels of manufacturing exports whereas in upper-middle income countries aid to transportation services seems to have a negative impact.

The effect of aid to transportation on exports growth is lower the higher the income of the group of countries. As aid to transportation mainly consists in investments on physical infrastructure, this result is consistent with Portugal-Perez & Wilson’s (2010) finding that the

> 11 Economies are divided according to 2009 GNI per capita, calculated using the World Bank Atlas method. The groups are: low income, $995 or less; lower middle income, $996 - $3,945; upper middle income, $3,946 - $12,195; and high income, $12,196 or more.

14
6 capital fo,mation, or provided additional balance-of-payments financing, or neither.

# **3** Does Foreign Direct Investment Pren'.ide **More** Balanceof-Payments Financing **or** Increase Capital Formation?

## 3.1 A Test for Additional Financing

As the global supply of capital has dwindlcd, FDi has been seen by some as a potent-al additional source of finance for the balance of payments. Using James Meade's (1951) distinction between autonomous and accommodating capital flows, Philip Turner (1991, pp. 91-95) addresses the question of whether or not net Fol flows are accommodating and hence constitute additional balance-of-payments financing by regressing components of the capital account on the current account financing requirement (both in first differences) for some OECD countries. The estimated equation takes the form:

## _AKFi = ao + a, AIJFR,_

where **_AKFi_** is the year-on-year change in the net capital flow item expressed as a percentage of **GNP** and _AKFR_ is the year-on-year change in the current account minus the official settlements balance also expressed as a percentage of GNP (both in current prices). Turner (1991, Table 33, p. 92) finds that short-term bank flows are significantly correlated with the current account financing requirement with coefficients ranging from 0.27 (United Kingdom) to 0.88 (Canada) for seven of the 10 OECD countries. In the case "t **FDI,** however, Turner concludes that this flow is much closer to being autonomous than accommodating; the coefficient is significant only in the case of France with a value of only 0.13. Turner ranks long-term banking lending as the most autonomous, **FDI** next, portfolio investmncat third, and short-term bank loans as the most accommodative type of capital flow; Turner's results are reproduced here in Table 2. Only coefficients with t statistics of 1.5 and over are reported.

Using Tufne:'s methodology with data on net capital flows from _International Financial Statistics_ **CD-ROM** for **a** sample of 16 developing countries,<sup>4</sup> I find a rather different response pattern, as shown in Table 3. In contrast to the **OECD** countries, other long-term capital flows appear to be just as sensitive to current account financing requirements as short-term flowF in this sample of developing countries. For these countries, therefore, one might rank portfolio

> **41 drop Bermuda, China,** Colombia, Hong Kong, Saudi Arabia, Singapore and **Taiwan from** the developing countries **listed in** Tabie 1 on thie **basis** of their small size **or** data deficiencies, but add Indi.-, Pakistan, **and** Sri Lanka.
the specific bilateral border where the new tariffs could be in place, i.e. the GDP potentially affected by trade barriers, can be computed using the following syntax ``` .*ReplicatedataofWDR2020Figure4.9 .icio_load,iciot(eora)year(2015) Loadingtableeora2015...loaded .icio,exp(chn)imp(usa)persp(bilat)output(va)origin(all)save(wdr_4_9a.xls) Decompositionofgrossexports: Perspective:bilateral Origin:ALL Exporter:CHN Importer:USA Output:Value-Added ```

||`Millions of $`|`% of export`|
|---|---|---|
|`AUS`|`2115.55`|_`0.57`_|
|`BEL`|`610.65`|_`0.17`_|
|`BRA`|`1046.77`|_`0.28`_|
|`CAN`|`955.66`|_`0.26`_|
|`CHE`|`977.48`|_`0.27`_|
|`DEU`|`4058.78`|_`1.10`_|
|`FRA`|`1919.28`|_`0.52`_|
|`GBR`|`1604.30`|_`0.44`_|
|`HKG`|`1489.18`|_`0.40`_|
|`IDN`|`1692.15`|_`0.46`_|
|`IND`|`1123.87`|_`0.31`_|
|`ITA`|`1564.26`|_`0.42`_|
|`JPN`|`7535.08`|_`2.05`_|
|`KOR`|`6554.82`|_`1.78`_|
|`MYS`|`1604.49`|_`0.44`_|
|`NLD`|`748.18`|_`0.20`_|
|`RUS`|`2208.83`|_`0.60`_|
|`SGP`|`1005.21`|_`0.27`_|
|`THA`|`964.69`|_`0.26`_|
|`USA`|`5126.99`|_`1.39`_|

``` Outputsavedas:wdr_4_9a.xlsintothecurrentworkingdirectory ``` ``` .icio,exp(usa)imp(chn)persp(bilat)output(va)origin(all)save(wdr_4_9b.xls) Decompositionofgrossexports: Perspective:bilateral Origin:ALL Exporter:USA Importer:CHN Output:Value-Added ```

||`Millions of $`|`% of export`|
|---|---|---|
|`AUS`|`235.58`|_`0.18`_|
|`BRA`|`361.65`|_`0.28`_|
|`CAN`|`3326.80`|_`2.56`_|
|`CHE`|`295.33`|_`0.23`_|
|`CHN`|`2322.90`|_`1.79`_|
|`DEU`|`1255.74`|_`0.97`_|
|`FRA`|`559.10`|_`0.43`_|
|`GBR`|`649.05`|_`0.50`_|

23
<u>Table D.7: Treatment Efects on Labor Market Outcomes Without Covariates</u>

||(1)<br>Employed|(2)<br>Hours<sup>c</sup>|(3)<br>Earnings<sup>c</sup>|(4)<br>Hourly wage<sup>c</sup>|(5)<br>Written contract|
|---|---|---|---|---|---|
|Treatment|0.046|0.175|0.336|0.206|0.018|
||(0.013)|(0.058)|(0.076)|(0.041)|(0.010)|
|Mean outcome|0.309|8.848|159.291|9.840|0.120|
|Mean outcome for employed||28.847|518.291|32.283|0.392|
|# observations|6607|6598|6589|6574|6575|
|# clusters|84|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments and randomization block fixed effects without any other covariates. Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. All outcomes use a 7-day recall period. Outcomes marked with<sup>c</sup> use the inverse hyperbolic sine transformation. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t know the answer.

Table D.8: Treatment Effects on Self-Beliefs through Time

||(1)<br>Perceived numer|(2)<br>acy tercile correct|(3)<br>Above-medi|(4)<br>an self-esteem|
|---|---|---|---|---|
|Public|0.233|0.315|0.001|-0.001|
||(0.013)|(0.015)|(0.013)|(0.015)|
|Private|0.200|0.333|-0.002|0.016|
||(0.015)|(0.016)|(0.014)|(0.015)|
|p: public = private|0.010|0.240|0.806|0.239|
|Mean outcome|0.396|0.399|0.553|0.479|
|# observations|6601|5297|6609|5027|
|# clusters|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. Above-median self-esteem is an indicator equal to one if the candidate’s response on a shortened version of the Rosenberg (1965) self-esteem scale is above the sample median. Columns (1) and (3) report results from the main phone follow-up survey. Columns (2) and (4) report results from the text message survey conducted 2-3 days after treatment. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t know the answer.

55
Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

|**Table 5: Production Functions – In**<br>Dependent variable:Log ofannual re|**strumental Va**<br>venue<br>(1)|**riable Estim**<br>(2)|**ation, Constr**<br>(3)|**uctors only**<br>(4)|(5)|
|---|---|---|---|---|---|
|**_Factors_**||||||
|Capital-per-worker (log)|0.035|0.049|0.090|0.035|0.042|
||(0.04)|(0.04)|(0.07)|(0.04)|(0.09)|
|Inputs-per-worker (log)|0.73***|0.596***|0.467***|0.708***|0.962***|
||(0.17)|(0.05)|(0.11)|(0.18)|(0.31)|
|**_The Impact of the program_**||||||
|Program-Dummie||-0.489|-2.017|-0.396|5.215|
|||(0.55)|(1.88)|(0.59)|(3.52)|
|Program-capital-per-worker (log)|||-0.178||0.008|
||||(0.26)||(0.34)|
|Program-Inputs-per-Worker (log)|||0.305||-0.674|
||||(020)||(049)|
|**_Activities_**|||.||.|
|PCB|0.032|0.323|0.451|0.229|0.551|
||(0.22)|(0.33)|(0.58)|(0.38)|(0.60)|
|HCB|-0.184|-0.037|-0.032|-0.147|-0.105|
||(0.24)|(0.19)|(0.20)|(0.25)|(0.29)|
|Gravel|0.978*|0.827*|1.29**|1.04*|0.364|
||(0.56)|(0.47)|(0.65)|(0.56)|(1.05)|
|Wood|0.068|0.094|0.059|0.116|0.172|
||(0.19)|(0.20)|(0.22)|(0.20)|(0.24)|
|Wall|0.018|0.043|0.275|0.087|0.013|
||(0.34)|(0.34)|(0.51)|(0.35)|(0.63)|
|Structural|-0.013|0.130|0.315|0.001|0.056|
||(1.17)|(1.13)|(1.25)|(1.16)|(1.37)|
|Electrical|<br>-0.303|<br>-0.272|<br>-0.265|<br>-0.283|<br>-0.383|
||(0.43)|(0.43)|(0.47)|(0.43)|(0.51)|
|Sanitary|0.563|0.460|0.308|0.564|0.700|
||(0.39)|(0.35)|(0.40)|(0.39)|(0.50)|
|Finishing|<br>0.041|<br>-0.048|<br>-0.138|<br>-0.015|<br>-0.041|
||(0.40)|(0.39)|(0.46)|(0.40)|(0.50)|
|Production-inputs|0.094|0.106|0.117|-0.030|-0.430|
||(0.36)|(0.35)|(0.54)|(0.39)|(0.62)|
|Other|0.291|0.260|0.090|0.224|0.327|
||(0.30)|(0.31)|(0.41)|(0.32)|(0.51)|
|Constant|2.695***|3.912***|4.859***|3.038**|0.833**|
||(1.32)|(0.54)|(0.94)|(1.46)|(2.60)|
|**_Endogenous_**||||||
|Inputs per worker|Yes|||Yes|Yes|
|Program Dummy||Yes|Yes|Yes|Yes|
|Program*inputs per worker|||Yes||Yes|
|Program*capital per worker|||Yes||Yes|
|**_Instruments Included_**||||||
|Input Price|Yes|||Yes|Yes|
|Labor force at startup||Yes|Yes|Yes|Yes|
|Lab force at start*inputspw|||Yes||Yes|
|Lab force at start*capitalpw|||Yes||Yes|
|<br>Lab force at start*Input Price|||||Yes|
|<br>Anderson LR|13.95|13.03|4.55|8.67|3.35|
|Cragg-Donald F|13.24|12.23|1.36|4.01|0.749|
|<br>N|148|148|148|148|148|
|Hausman Chi(2) (df )|0.77(13)|0.39 (14)|4.50 (16)|0.96(14)|3.96(14)|
|<br>Prob Chi(2)|<br>1.00|<br>1.00|<br>0.99|<br>1.00|<br>0.99|

Note: *, **, *** indicate significance at the 10%, 5% and 1% level respectively

-15-
**Table 1: OLS estimations of labor productivity and exports**

||Between estimation<br>|FD estimation<br>|
|---|---|---|
||(1)|(2)|
||<br>Panel A: Weighting bilateral route<br>importer trade in industry (sum|<br>s by the interaction of exporter and<br>med across all bilateral routes)|
||DV: Log bilateral exports|DV: Δ Log bilateral exports|
|Log country-industry labor|0.640***||
|productivity|(0.242)||
|Δ Log country-industry labor<br>productivity||0.573***<br>(0.185)|
|Observations|149,547|103,839|
|Importer-Industry-Yr FE|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|
||Panel B: Excludi|ng sample weights|
||DV: Log bilateral exports|DV: Δ Log bilateral exports|
|Log country-industry labor|0.361***||
|productivity|(0.091)||
|Δ Log country-industry labor<br>productivity||0.210***<br>(0.041)|
|Observations|149,547|103,839|
|Importer-Industry-Yr FE|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: Panel estimations consider manufacturing exports taken from the WTF database. Data are organized by exporter-importer-industry-year. Industries are defined at the three-digit level of the ISIC Revision 2 system. Annual data are collapsed into five-year groupings beginning with 1980-1984 and extending to 1995-1999. The dependent variable in Column 1 is the log mean nominal value (US$) of bilateral exports for the five years; the dependent variable in Column 2 is the change in log exports from the prior period. The intensive margin sample is restricted to exporter-importer-industry groupings with exports exceeding $100k in every year. The $100k threshold is chosen due to WTF data collection procedures discussed in the text. Labor productivity from the UNIDO database measures comparative advantages. Column 1 estimates Ricardian elasticities using both within-panel variation and variation between industries of a country. Column 2 estimates Ricardian elasticities using only variation within panels. Estimations in Panel A weight bilateral routes by the interaction of total exporter and importer trade in industry; estimations in Panel B are unweighted. Estimations cluster standard errors by exporter-industry. Importer-Industry-Yr FE are defined at the two-digit level of the ISIC system. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.
First, it assumes that past the world avoids major crisis with impacts beyond past experience.<sup>17</sup> Second, if the increase in global inequality that we have seen recently (2005-2008) continues then this trajectory would be unattainable without a further acceleration in growth. (Later we will see how much inequality matters to attaining any given target.) Third, a constant annual percentage point decline cannot, of course, continue indefinitely—it must slow down, becoming nonlinear in time.<sup>18</sup> But when? It is worth considering this issue further.

In looking for clues as to when the nonlinearity might emerge, one can study the experiences of places that have seen substantial reductions in poverty to under 10% or so. Figure 5 gives the poverty rate over time in urban and rural China.<sup>19</sup> The nonlinearity only becomes evident below about 5% for urban China, but is not found yet for rural China. By contrast, Figure 6 gives the graphs for Argentina (after the 2001-2 financial crisis) and Brazil. In Argentina, we see the expected nonlinearity emerging around a poverty rate of 4%, while in Brazil a marked nonlinearity emerges below about 8%, but in the opposite direction, with lower poverty rates than one would have expected.

So there is no clear pattern, although these are just a few countries, each with their own special features. To look at this issue more systematically I used the data set described above of the changes in poverty across countries from <u>PovcalNet.</u><sup>20</sup> Here the focus is on those countries that started out with more than “negligible” poverty, which I define as more than 1% of the population living below $1.25 a day. This gave 82 countries.

Figure 7 plots the annualized change in the $1.25 a day poverty rate against the initial poverty rate across countries. It is striking that no country starting out with a poverty rate under 10% has attained a rate of poverty reduction at the “global” average rate of 1% per annum. A downward “stickiness” at poverty rates under 10% is evident.

Another observation from Figure 7 is that benchmarking progress against poverty in percentage points per annum is unlikely to have universal relevance across countries with different initial levels of poverty. As is plain from Figure 7, the variance in rates of poverty reduction tends to be higher at higher initial levels of poverty. While it might be reasonable to

> 17 World Bank (2012b) discusses alternative scenarios for a global crisis emanating from sovereign debt defaults in Europe, which would entail appreciable short-term impacts on the growth rates for the developing world. 18 There are two types of nonlinearity relevant here, nonlinearity of progress in reducing poverty over time and nonlinearity with respect to the poverty line relative to the mean. The text will make clear which is being discussed.

> 19 For rural China I use a lower poverty line, at $0.75 a day, since the $1.25 a day poverty rate does not fall below 20%. Also, I only use consumption-based estimates for rural China.

> 20 On the use of such country level data for benchmarking performance against poverty see Newman et al. (2010).

8