rafmacalaba/datause-encoder-data huggingface dataset:
# Export Volumes

Columns 6-8 of Tables S1a and S1b describe the WTF data. These descriptives focus on the intensive-margin estimations that require exporter-importer-industry observations maintain the minimum threshold of trade volume. Columns 6 and 7 provide comparable statistics about the mean export levels and growth rates for included routes. Germany and Japan have the highest average volumes, and Nicaragua and El Salvador have the lowest average volumes. Export growth rates are strongest in Nicaragua, Congo, and Costa Rica, and they are lowest in Guatemala and Zimbabwe. From an industry perspective, trade volumes have the highest average values in industries 382-384 (Machinery and Transportation equipment), and the lowest average volumes are observed in industry 361 (Pottery, china, earthenware). Industry 383 (Machinery, electric) has the highest growth rate, while industries 353 (Petroleum refineries) and 371 (Iron and steel) have the lowest.

Column 8 of Table S1a documents the share of total WTF exports for countries that are included in this sample. The main reasons why exports are not included are lack of corresponding UNIDO labor productivity estimates or that the exports are going to the United States. This sample accounts for 78% of exports to destinations other than United States from these countries (about 63% if exports to the United States are included in the denominator). Column 8 of Table S1b provides comparable data for industries. The sample accounts for 69% of exports in these industries to destinations other than United States. This share is lower than 78% due to the inclusion of exporters not captured in Table S1a. Much of the decline on the industry side comes through limited representation of major petroleum producers.

Price deflators are not available for this sample (exports or labor productivity data). To the extent that exporter-industry-year deflators are comprised of exporter-year, industry-year, and exporter-industry components, the fixed effects and first differencing strategy will control for them automatically. Residual exporter-industry-year trends could bias OLS estimations. The IV estimations will overcome any such OLS biases due to deflators.

As a final note, a break exists in WTF data collection procedures at 1984. This break does not have a significant impact on ISIC3-level export volumes, and the results are robust to dropping the initial period.

5
|self-employed|19.0|21.4|22.7|22.9|21.1|22.6|
|---|---|---|---|---|---|---|
|unemployed|2.5|1.8|5.4|2.3|2.0|4.2|
|retired|7.2|10.1|8.5|7.6|8.1|8.9|
|student|4.1|8.1|8.6|6.2|10.9|10.1|
|OLF|35.4|39.1|37.6|30.5|32.8|35.9|

Source: Authors’ calculations based on ECAPOV data.

**<u>Table A.4:  Profile of households  in the bottom 40 percent for selected years</u>**

|||Bottom 40|||Top 60||
|---|---|---|---|---|---|---|
||2003|2007|2009|2003|2007|2009|
|||**Number of ch**<br>|**ildren in HH**<br>||||
|0-1|12.5|17.9|14.1|26.0|30.6|32.7|
|2|18.7|19.8|18.6|21.9|22.3|26.7|
|3|20.1|23.4|21.2|20.7|21.5|17.6|
|4|19.5|19.0|21.3|14.3|13.0|11.9|
|5+|29.3|19.8|24.9|17.2|12.6|11.2|
|Dependency ratio|0.45|0.43|0.41|0.41|0.37|0.35|
|||**Size of the**|**household**||||
|1|0.2|0.1|0.0|0.8|0.6|0.6|
|2|0.4|0.6|0.2|2.2|1.6|1.7|
|3|1.2|1.8|0.6|4.3|4.2|4.2|
|4|3.2|4.6|2.2|9.1|8.4|7.4|
|5+|95.0|92.9|97.0|83.7|85.3|86.1|
|||**HH ed**<br>|**ucation**<br>||||
|incomplete 5-12|14.2|16.3|16.2|10.1|11.3|11.3|
|gen sec|48.9|42.6|40.0|37.0|33.9|34.6|
|spec sec|14.2|19.6|20.4|20.3|26.9|25.0|
|tertiary|11.6|13.8|12.6|22.1|20.4|22.5|
|none/<1-4|11.1|7.8|10.8|10.6|7.6|6.6|
|||**HH labor f**<br>|**orce status**<br>||||
|employee|44.0|29.3|28.2|45.3|37.9|29.6|
|self-employed|17.4|27.8|27.2|19.9|29.5|32.4|
|unemployed|2.7|1.7|3.1|2.3|2.0|4.5|
|retired|18.4|24.7|28.7|16.9|18.5|21.7|
|student|0.3|0.6|0.0|0.3|0.6|0.0|
|OLF<br>Source: Authors’ calculation|17.2<br>s based on ECAP|15.9<br>OV data.|12.9|15.3|11.6|11.9|

29
helps to define their value by clarifying whether the occupation is subject to uncertainty and whether it is short or long term.

# **6. Conclusions**

The paper addresses the theme of the inclusion of use rights held in the form of usufruct in household wealth sample estimates. After recalling the theoretical appropriateness of correcting traditional estimates for this component, the paper presents an application to the Italian case. Using SHIW data shows that, although the phenomenon of usufruct is relatively uncommon (around 3 percent of households), the distribution of wealth is significantly affected by such an adjustment. Younger households appear to be relatively slightly wealthier than in unadjusted estimates. Overall, adjusted estimates exhibit levels of the Gini concentration index that are lower than unadjusted ones by approximately 1 percentage point.

The estimates do not take account of a different form of use right, namely the right to inhabit, which typically arises when one spouse passes away and leaves behind a residential home that was jointly owned or owned solely by the deceased spouse. On the one hand, SHIW data do not provide information about this condition; on the other, the right to inhabit is personal and cannot be transferred or assigned to another individual; in the absence of a market, the monetary evaluation of this right can be more complex. In any case, this additional element should also be considered in the measurement of household wealth.

We also conducted a brief analysis of residential homes occupied free of charge by households in European countries whose data are included in the HFCS. In several countries, residential homes occupied for free are relatively widespread, with shares similar to that found in Italy. However, the lack of data does not allow us to identify those cases in which the occupation takes place on the basis of a right of use which justifies its inclusion in households' wealth. An initial tentative evaluation shows that the effects of such an adjustment could lead to a significant reduction in wealth inequality.

These results also suggest that the replication of the analysis in countries where the phenomenon of use rights separated from bare ownership is more widespread than in Italy could produce a much greater impact on wealth estimates.
**Table 2.1 Morocco: Labor Force Participation Rate by gender, 1999‐2016**

|||National|||Urban|||Rural||
|---|---|---|---|---|---|---|---|---|---|
||1999|2016|1999‐<br>2016|1999|2016|1999‐<br>2016|1999|2016|1999‐<br>2016|
|Labor Force Participation|54.5|46.4|50.3|48.1|40.5|44.3|63.1|55.7|59.1|
|(15+)||||||||||
|Labor Force Participation|79.3|70.8|75.5|74.6|66.3|70.8|85.5|77.9|82.0|
|(15+), male||||||||||
|Labor Force Participation|30.4|23.6|26.4|22.8|16.6|19.2|40.9|34.9|36.9|
|(15+), female||||||||||
|Ratio of male to female|2.6|3.0|2.9|3.3|4.0|3.7|2.1|2.2|2.2|
|Labor Force Participation||||||||||
|(15+),%||||||||||

**Figure 2.13 Morocco: Distribution of the Labor Force by education level and gender, Average 1999‐2013 (in percent)**

<!-- Start of picture text --> 60%<br><!-- End of picture text --> <!-- Start of picture text --> 60% 56.3% National<br>50%<br>Female Male<br>40%<br>30.0%<br>30% 28.4%<br>20% 17.1%<br>13.9%<br>8.7% 9.3%10.6% 10.2%<br>10% 6.5% 7.2%<br>0.3%<br>0%<br>No schooling Preschool, Basic (1st Basic (2nd Secondary Superior<br>M"Sid, Koranic cycle) cycle)<br><!-- End of picture text --> _Source_ : Ragbi et al. (2017).

18
**Table 4. Summary of types of costs included in previous costing studies**

|**Cost item, age-group to which item applies and summary of calculation method**|**Example**|
|---|---|
|**Undernutrition**||
|**Direct costs: treatment costs of acute malnutrition:**calculat_e from unit costs per child from existing_<br>_studies, multiplied by numbers of under-five children treated._|Isanaka et al., 2017|
|**Indirect costs: Present value of future productivity losses due to premature mortality:**_calculate_<br>_projected annual deaths in 0-59 months group from prevalence of stunting multiplied by_<br>_corresponding mortality risk. Project lifetime losses of productivity over the working life age 15-64,_<br>_proxied by current wage of those 15-64, adjusted by real growth of per capita GDP and discounted by_<br>_3%_|ECLAC and WFP, 2017; see<br>Bertram et al. 2017 for<br>discussion of discounting|
|**Indirect cost: Present value of future productivity losses due to cognitive losses which lead to**<br>**reduced educational attainment**:_calculate from prevalence of stunting in children 0-59 months,_<br>_multiplied by corresponding projected loss of wages associated with the associated underachievement_<br>_of education, adjusted by real growth of per capita GDPand discounted by 3%*_|ECLAC and WFP, 2017.|
|**Overweight and obesity**||
|**Direct costs: treatment costs for obesity-related illnesses:**_both medical treatment costs and patient_<br>_out-of-pocket costs e.g. travel and time for treatment_;|Okunogbe et al.,2022|
|**Indirect costs: costs of premature mortality****_:_**_lost years of life expectancy for those currently 20 and_<br>_older in the population_|ECLAC and WFP, 2017|
|**Indirect costs: costs of productivity losses due to morbidity**:_for those 20-64 due to absenteeism_<br>_(either of individual who is sick, or caregiver)**_|ECLAC and WFP, 2017|

***** ECLAC and WFP (2017) also include additional costs of grade repetition to the education system: these costs are relatively small compared to future wage losses and may not be worth the effort to calculate

**Note that some, e.g. Okunogbe et al. (2022) included losses due to presenteeism, i.e. lower work effort associated with overweight/obesity; others include lower employment rates or decreased full-time work by those with overweight/obesity; however it is less clear what the direction of causality is, hence whether or not this should be included.

31
_Table 6: Regression estimates of the probability of seeking ante-natal care_

||**IV-PROBIT**|**PROBIT**|
|---|---|---|
|Years of schooling|0.2025|0.0754|
||(0.0822)**|(0.0078)**|
|Log of household consumption per capita (Rp.'000000)|0.0007<br>|0.0026<br>|
||(0.0016)|(0.0007)**|
|Age at childbirth|-0.0028<br>|0.0038<br>|
||(0.0109)|(0.0104)|
|**_Parity (reference group: first birth)_**<br>|||
|Low parity: 2<sup>nd</sup>or 3<sup>rd</sup>birth|0.1206|0.0649|
||(0.1042)|(0.1020)|
|Medium parity: 4<sup>th</sup>or 5<sup>th</sup>birth|0.1609|-0.1709|
||(0.2665)|(0.1135)|
|High parity: 6<sup>th</sup>birth or higher|-0.1029|-0.5539|
||(0.3732)|(0.1305)**|
|Outcome of previous pregnancy: alive|0.1186<br>|-0.074|
||(0.1619)|(0.0903)|
|Urban area of residence|0.0348|0.2738|
||(0.1913)|(0.0542)**|
|Time to nearest health centre or midwife - hrs|-0.0575<br>(0.1064)|-0.1792<br>(0.0622)**|
|**_Year of childbirth (reference group: 1985-89)_**|||
|1990-1994|0.1364|0.2162|
||(0.0942)|(0.0637)**|
|1995-1997|0.4164|0.6118|
||(0.2034)*|(0.1050)**|
|1998-2000|0.4517|0.6333|
||(0.2161)*|(0.1419)**|
|**_Region fixed effects_**|||
|Sumatera|0.4830|0.6259|
||(0.1840)**|(0.1229)**|
|Java|0.6783|0.7342|
||(0.14104)**|(0.1119)**|
|Jakarta|0.6122|0.7022|
||(0.1880)**|(0.1607)**|
|Bali|1.3109|1.3481|
||(0.2170)**|(0.1849)**|
|Nusa|0.4532|0.4374|
||(0.1369)**|(0.1397)**|
|Kalimantan|0.3172|0.2871|
||(0.1557)*|(0.1602)|
|**_Cohort of birth fixed effects_**|||
|Cohort 1: 1-6yrs in 1974|0.4651|0.321|
||(0.7507)|(0.7825)|

44
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21 on the change in total factor productiv:ty A _TFP:_

# _ATFP = f(FDlY, P, D, FDIY-P, FD1Y-D, FDIY-P-D)._

This is the expression illustrated in Figure 3 for a fixed value of _FDIY._ The empirical evidence suggests that some distortions can reduce investment efficiency quite considerably. Whether or not package size exerts effects of similar magnitudes for any given level of distortion remains to be tested. Casual observation of differing policies towards FDI in the Pacific Basin developing market economies might suggest that the distortion slope is considerably steeper than the package size slope. However, as Hill (1990, p. 43) concludes: " ... the central issue of whether what may conveniently be termed the 'Singapore' or the 'Korea' strategy is preferable, from either host or home country perspective, has not been addressed carefully in the literature." In the absence of suitable indicators of incentive-disincentive packages,"<sup>4</sup> I illustrate the approach with indicators of financial and trade distortions, the real deposit rate of interest .and the black market foreign exchange premium. Both these variables were extracted from the _World Development Report 1991: Supplementary Data_ diskettes. As the World Bank (1989, pp. 29-31) points out: "Historically, the quality of investment has been at least as important for growth as the quantity. Although the fastest-growing countries had higher rates of investment than the others, empirical stud ios generally find that less than half the growth in output is attributable to increases in labor ani -apital. Higher productivity explains the rest. ... Faster growth, more investment, and greater financial depth all come partly from higher saving. In its own right, however, greater financial depth also contributes to growth by improving the productivity of investment." One way of analyzing the efficiency-improving role of financial intermediation starts by recognizing the fact that when real interest rates are negative there is no incentive to use capital efficiently. Excess capacity is costless, so plants are built with far more capacity than required for immediate production plans. Overtime, shift work, and other measures that increase the effective utilization of plant and machinery are not worthwhile when keeping the capital stock idle is costless. Under such circumstances, the measured capital stock exceeds the effective capital stock. For example, the effective capital stock might be equivalent to 66 per cent of the measured capital stock **wheii** the real interest rate is -15 per cent. However, the effective capital stock might equal the measured capital stock at a real interest rate of 5 "in a fuller study, one might be able to use the seven indicators compiled by the United Nations Centre on Transnational Corporations (1991) or the index constructed by Agarwal, Gubitz, and Nunnenkamp (1991, pp. 35-36).
suitable for growing sugarcane as an instrument for income inequality in a cross-section of 104 countries. He finds that inequality has a significant negative average effect on GDP per capita, as we do. Easterly does not explore how the impact of income inequality on GDP per capita varies depending on countries' initial level of development. In contrast to our panel analysis, the results in Easterly are driven exclusively by cross-country variation.

There exist a number of empirical studies using panel data which instrument inequality using internal instruments (i.e. lagged values of inequality). Examples are Forbes (2000), Panizza (2002), Banerjee and Duflo (2003), Voitchovsky (2005), and Halter et al. (2014). Unfortunately, none of these papers address the important issue of whether the instruments for inequality are relevant. As noted in Bound et al. (1995), IV regressions based on weak instruments lead to inconsistent estimates. It should, furthermore, be pointed out that none of the papers that use an IV approach in a panel context explores how the impact of income inequality on GDP per capita depends on countries' initial level of economic development.

# **3. Data**

**Income Inequality.** The main indicators of income inequality -- the Gini coefficient and the share of income held by the 1st, 2nd, 3rd, 4th and 5th quintiles -- are taken from Brueckner et al. (2015). Brueckner et al.'s (2015) primary data source is the UN-WIDER World Income Inequality Database, supplemented with data from the World Bank’s POVCALNET database for developing countries. According to Brueckner et al. (2015), comparability between the two data sources is ensured by making adjustments to the data sets for individual countries so that the income (or consumption) shares consistently correspond to those of a consumption (or income) survey. The authors drop duplicates as well as survey-years with inferior data quality from the WIID, survey-years for which no extra information (consumption/income) is available; and survey-years for which the income shares add up to less than 99 or more than 101. As robustness checks on our main source of income

5
regions could be due to a welfare criterion in which poorer regions or poorer people receive more transfers. However, Columns 3 and 4 show that overrepresented areas are neither poorer nor more unequal than underrepresented areas. We thus interpret the results in Table 8 as providing evidence that stronger legislative representation translates into more political influence for overrepresented regions. Moreover, since overrepresented regions are more likely to vote for parties associated with pre-democratic regimes (as shown in Section 4.1), voters living in these regions may associate the higher transfers with these parties and may continue voting for them, thus re-enforcing and extending the political influence of pre-democratic elites.

# **5. Concluding Remarks**

In this paper, we argue that pre-democratic elites can strategically create malapportionment in the electoral system during the transition to democracy in order to safeguard their economic interests in a newly established democracy. Our results show that higher levels of malapportionment foster transition to democracy, presumably because it makes pre-democratic elites feel less threatened by the policies that might be implemented during the new democratic regimes.

Using within-country data, we find – in line with the argument we propose – that overrepresented electoral districts are more likely to vote for parties that are close to former nondemocratic regimes. Moreover, we highlight two political and economic policy features of malapportionment. First, malapportionment is associated with an increase in political concentration particularly for parties close to former non-democratic regimes. Second, overrepresented districts receive larger transfers per capita from the central government, despite the fact that they are not poorer or more unequal. This contrasts with traditional models of redistributive political economy and highlights that larger legislative representation induces greater political influence.

In future research, we plan to investigate the effects of malapportionment on economic development. The decrease in political representation or miss-allocations of public transfers we document in this paper may have negative effects on regional development. The big challenge with identifying these effects though is to find a source of exogenous variation in malapportionment at the state level. Finally, we believe that looking at specific features of 20
# **1   Introduction**

Sectoral linkages – the extent to which goods from different sectors are utilized as intermediates in the production of sectoral goods, bear a systematic relationship with GDP per capita. The share of services in intermediate expenditure is higher in the developed countries. While this holds in the production of all sectoral goods, the gap in the services share across countries is starker in the production of services itself (Sposi, 2018). An interpretation that helps reconcile the cross-country variation is that distortions exist in the intermediate markets.

Nevertheless, such analyses typically employ a production technology with a CobbDouglas (CD) association across sectoral inputs. Hence, linkages do not respond to changes in relative prices. On the other hand, a well-documented fact in the development literature is that the relative price of services rises with GDP per capita.<sup>2</sup> If intermediates from different sectors enter production as complements, then the higher relative price of services in developed countries will lead to the sector commanding a larger share of the aggregate intermediate expenditure seen in the data.<sup>3</sup> The objective of this note is to examine if the elasticity of substitution across sectoral inputs differs from unity, making the price channel a factor in accounting for the cross-country variation in linkages. Using panel data on input-output intensities and expenditure prices from 28 countries, I find the elasticity to be less than one in each of the three sectors. Intermediates are most complementary in the production of services, while it is easiest to substitute across intermediates in the production of agricultural goods.

**2   Theoretical Framework and Data** The gross output of a sector is produced using the intermediate bundle and a composite aggregated from all other factors. The production technology is CD in and . In jj jj QQ jj XX addition to price-effects, I also allow for scale-effects in production technology, i.e., the jj jj jj ZZ XX ZZ intermediate expenditure shares react to changes in . The scale-effect establishes an additional link between incomes and linkages as the size of the intermediate bundle rises with income. jj XX Borrowing from Comin et al. (2018), is a scaled CES aggregate of intermediates from all sectors implicitly defined as jj XX

> 2 Balassa (1964) is an early example that documents this relationship. More recently, Duarte & Restuccia (2010) provide the within-country evidence.

> 3 The idea of complimentary in sectoral linkages goes way back (Leontief (1936), Hirschman (1958)) and is more recently investigated in Jones (2011).

2