rafmacalaba/datause-encoder-data huggingface dataset:
Figure 6: Domestic value‐added of services in comparative advantage sectors (2011) <!-- Start of picture text --> Agribusiness Metals<br>BRA MEX CAN ZAF TUR COL IDN ZAF BRA CAN MEX TUR COL IDN<br>Distribution Transport Finance R&D / Business Distribution Transport Finance R&D / Business<br>Automotives Chemicals<br>ZAF BRA CAN TUR MEX COL IDN BRA MEX ZAF TUR CAN COL IDN<br>Distribution Transport Finance R&D / Business Distribution Transport Finance R&D / Business<br>15 8<br>6<br>10<br>4<br>5<br>2<br>0 0<br>10<br>10<br>8<br>8<br>6<br>6<br>4 4<br>2 2<br>0 0<br><!-- End of picture text --> Source: Source: Authors’ calculations using TiVA.

Position of Brazilian companies in GVCs Brazil’s lower international engagement concerning GVCs is reflected when looking at the entire “chain” of the production lines in each of the selected sectors. The so‐called length of the GVC takes stock of the fact that multiple countries take part in the production chain. For analyzing this length of the chain, a measure called the average (upstream) number of production stages is used. It is constructed as a recursive measure of importing on the sourcing side of exports (I2E), as developed by Fally (2011) and applied to a large set of countries by Backer and Miroudot (2012).

To illustrate the concept, consider a simple hypothetical value chain where the electronics industry in the United States requires 40 cents of parts and components from Taiwan, China, for each dollar worth of electronic devices produced (the other 60 cents being value‐added brought in by the US‐based electronics industry). Assuming that Taiwanese parts are 100 percent Taiwanese value‐added, the length of the chain is 1+0.4=1.4 (1 for the United States and 0.4 for Taiwan, China). The concept becomes recursive in more complex examples. For instance, if the 40 percent of value‐added sourced from Taiwan, China, instead included parts made in Japan, the chain would be longer. Assuming that Japanese parts are 100 percent Japanese value‐added, and make up 30 percent of the value of the Taiwanese parts exported to the United States, Taiwan, China’s electronic industry chain length would be 1+0.3=1.3 (1 for Taiwan, China, and 0.3 for Japan). Thus, the United States’ electronic industry chain length would be 1+0.4(1.3)=1.52.

11
O.A.M.D.G.

and section 4 tests the findings of the model with Colombian data. Finally, section 5 summarizes the main conclusions.

# **CONCEPTUAL FRAMEWORK**

As laid out in the introduction, we want to explore two household decisions in the context of violence, which we assume are made jointly: savings and migration. In the presence of violence, the amount people save depends on their envisaging migration or not; but the decision to migrate may also depend on the amount of assets held.<sup>1</sup> Our conceptual framework attempts to capture both decisions in a joint savings and migration model. The representative individual is risk averse and lives over two periods. She receives a constant exogenous income _w_ in the first period and can split it between consumption and savings, so that _c = w – a._ Our model is ‘autarkic’ in the sense of Besley (1995b), i.e. we abstract from financial intermediators. Savings from period 1 earn a return _R_ in a peaceful environment or _P_ in a violent environment, R> _P_ . Violence not only lowers the prospective return, it also can eat away part of the savings with a probability _p_ , so that from savings _a_ in period 1 only _a (1-pv)_ earns a return in period 2, with violence _v_  1,0  . We do not assume any violence to impact _w_ . A salaried employment that contributes to household income, such as admin work in the public or private sector is not immediately exposed to violence. Violence with a displacement threat will eventually threaten salaried income. Yet, the knowledge to carry out the salaried job will not be lost and can possibly be applied elsewhere.

> 1 Note that Disney et al (2003) discuss another possible link between assets and short distance migration: people move in order to sell assets, especially real estate, in order to realize capital gains. In other words, asset value changes can prompt moves. This connection may obviously be present in the Colombian context as well. It will, however, likely be dominated by violence considerations, see Moser and McIlwaine (2000).
Table 8 shows the income decomposition for households participating in BFP. A considerable share of income comes from self-employment. Furthermore, many working conditions categorized as income from employment may be informal without official contract or provide so little income that they are tax exempted and hence do not appear in the official social insurance registers. For these reasons, it is relatively easy for households (at this low income level) to under report their official income. This situation poses a challenge for our identification strategy which relies on the assumption that individuals do not have complete control over the forcing variable. We argue that, at best, households only have partial control over the forcing variable. Many households are unable to cheat due to their formal modes of employment and the monitoring of eligibility by the public authorities. Furthermore, it is unlikely that they manage to cheat on the second part of the rule: the number of children. Nevertheless, to rule out lingering doubts about the identification strategy, we carry out extensive robustness tests, including tests on whether predetermined characteristics are locally randomized around the thresholds which would indicate systematic cheating.

|Variable|Mean|Share|
|---|---|---|
|Total hh income|811.1|1|
|Income from labor|579.6|0.71|
|Income from employment|373.7|0.46|
|Income as employer|16.01|0.02|
|Self employed|189.9|0.23|
|Income from transfers|216.8|0.27|
|Income transfer from INSS|78.61|0.1|
|Public Pensions|10.84|0.01|
|Private Pensions|1.56|0|
|Federal Social Programs|96.69|0.12|
|Allowances|18.06|0.02|
|Other|11.07|0.01|
|Rental income|3.54|0|

Table 8: Income decomposition for BFP participants 40
investment regime over time, particularly regarding variable-income instruments and foreign assets.<sup>37</sup>

# **_4.2. In Which Asset Classes Do Pension Funds Invest?_**

One striking feature of pension fund asset holdings is the proportion they invest in assets that can be easily liquidated, namely, bank deposits, government bonds, and more generally short-term instruments among fixed-term securities. For example, Figure 6 shows that PFAs hold a significant fraction of their portfolios in assets issued by financial institutions (mostly bank deposits) and government paper. Table 1 shows details by fund type. On average, for the entire period, Fund A (the riskiest one) holds almost 18 percent of its assets in bank deposits and government paper. As a benchmark, US equity mutual funds that invest internationally hold on average only 3.5 percent in “cash,” typically money management instruments.<sup>38</sup> In the case of Fund E, this ratio jumps to 58 percent.

Figure 7 and Table 2 show that pension fund holdings are also tilted towards the short term. (See Opazo et al., 2008, for more on Chilean pension funds and shorttermism.) Figure 7 shows the maturity schedule for all fixed-term instruments held by PFAs, averaged across the entire sample period and at the end of December 2005. When considering the whole period, 44.8 (24.2) percent of investments in fixed- term securities is held in instruments maturing within three (one) years. Table 2 shows the breakdown by fund type. Fund A holds 76 percent of its fixed-income securities in instruments with a term to maturity of up to three years, 60 percent up to one year, and 12 percent up to 30 days. At the other extreme, Fund E holds 59 percent of its fixed-income instruments in assets that have a term to maturity of up to three years and 24 percent up to one year.

Consistently with the bias towards fixed-term instruments documented above, asset allocation in the domestic market has been done mostly (about 75 percent) through investment in fixed-income instruments (Figures 8 and 9). The participation of corporate bonds in the portfolio of pension funds more than doubled between 2000 and 2005, coinciding with the tenfold increase in issuance of Chilean companies during this period, probably responding to falling domestic interest rates and regulatory changes (Braun and

> 37 Closely held shares are the shares held by insiders of a firm, which are unlikely to be floated on the market and are thus unavailable to outside investors. For details, see Dahlquist et al. (2003). The source of the closely held estimate is the World Bank Financial Sector Development Indicators. 38 See Didier et al. (2008).

14
|**Country**|**Absolute QFD**|**———**|**—— Relative**|**QFD—————**|
|---|---|---|---|---|
||**(current US$**<br>**million)**|**(% current**<br>**GDP)**|**(% revenue**|**s)**<br>**(% cash collected)**|
|Nigeria|$2,928|0.5|174|264|
|Rwanda|$78|1.0|82|86|
|São Tomé and Príncipe|$21|6.1|174|293|
|Senegal|$325|2.2|55|59|
|Seychelles|-$4|-0.3|-4|-4|
|Sierra Leone|$33|0.9|97|123|
|South Africa|$11,329|3.4|90|92|
|Sudan|$1,024|1.4|233|233|
|Swaziland|$52|1.2|40|40|
|Tanzania|$193|0.3|17|19|
|Togo|$70|1.6|32|36|
|Uganda|-$19|-0.1|-5|-5|
|Zambia|$317|1.2|47|49|
|Zimbabwe|$643|5.2|82|94|
|Maximum|$11,329|6.08|353|408|
|Minimum|-$19|-0.26|-5|-5|
|Median|$78|1.04|62|63|

_Source_ : World Bank staff calculations based on utility financial statements, annual reports, and other documents.

The quasi-fiscal deficit relative to cash collected is correlated using a 1-percent significance test with capital cost per kWh billed, with a positive sign, suggesting that the higher the capital cost, the higher the deficit relative to cash collected.

# The case of Ethiopia

With its QFD more than 350 percent of utility revenues, Ethiopia merits special attention. The large deficit is driven by high capex combined with average tariffs that are among the lowest on the continent. Since 2005, the government of Ethiopia has embarked on an aggressive power sector expansion program, including major investments in hydropower (including Gibe 3 and the Grand Renaissance Dam) and network expansion through the Universal Electricity Access Program providing grid-based electrification in rural areas. With more than 2,000 staff members, a large investment envelope (approximately US$245 million in 2015), and thousands of kilometers of MV and LV distribution lines constructed every year, the program is one of the largest electrification programs in Africa. In Ethiopia, it accounts for more than 95percent of the financial resources allocated to the expansion of electric service on an annual basis.

The government’s ambitious power sector objectives were reflected in the country’s development agenda. The first Growth and Transformation Plan covering the period 2010–2015 included the target of doubling the number of electricity customers from two to four million. The second plan has set an even more ambitious target: to reach seven million customers by 2020.

The massive power sector investments and notably those associated to the Universal Electricity Access Program translate in the short term to high costs per kWh, which are expected to fall over time as electricity access increases. However, access to electricity services remains far below expectations. Against the target of 4 million, 2.4 million new customers were connected by 2015. Initially, as generation capacity started growing significantly, the backlog of investments in T&D infrastructure 37
**Table 2. Political-Economy Typologies**

||**Credibility of interte**<br>|**mporal commitment**<br>|
|---|---|---|
|**Political inclusiveness**|Less credible / weaker enforcement|More credible / stronger enforcement|
|Less inclusive /<br>less collectively oriented|**Patrimonial Rule**<br>Individualized political authority, built on a<br>hierarchy of cronyism; emphasis on private<br>(elite) goods; exploitation of public resources<br>for private gain|**Hegemonic Government**<br>Institutionalized one-party regime; either<br>predatory or benevolent; emphasis on<br>private (elite) goods with some particularist<br>and public goods|
|More inclusive /<br>more collectively<br>oriented|**Clientelist Pluralism**<br>Political competition based on extensive use<br>of clientelism; provision of particularist<br>goods; low horizontal accountability|**Programmatic Pluralism**<br>Electoral competition based on programs<br>geared toward collective welfare<br>enhancement; provision of public goods;<br>democratic accountability|

_Source:_ Barma, Kaiser, Le and Vinuela (2011) 21. We draw on proxy indicators to broadly situate the MENA countries at the on-set of the Arab Spring by this typology in Figure 4. This raises questions about what kind of shifts one might want to look out for, and in turn what implications these will have for intertemporal credibility and inclusiveness. Ideally political transitions would see movements over time to programmatic pluralistic democracies (i.e., to the North East quadrant), and the policies associated with them. But as democratic transitions from other regions demonstrate (see Box 1), major regional political transition episodes can yield a significant diversity of associated policies and institutional evolutions. These may also have implications for future violent conflict resilience, and well as PSM/PSD reform prospects and priorities. As with any governance indicators, these are subject to significant measurement error and issues of interpretation. However, we argue they provide a useful benchmark to assess potential forward looking trajectories. The highly oil endowed and institutionalized MENA countries of Bahrain, Oman, and Saudi Arabia are based squarely in the hegemonic quadrant. This in part is also a reflection of their monarchical institutions. By these measures, Kuwait is actually just placed under programmatic pluralism, suggesting that potential ―beacons‖ do exist in the region. The fragility of Yemen, particularly around inter-temporal credibility, places it in the patrimonial government setting, although it rates relatively highly on inclusiveness.

10
Pooling multiple years of data allows us to follow each cohort as they age and measure the true rate of net worth accumulation. As an example, the three triangles in Figure 1 identify household heads born in 1960, whose net worth we observe at ages 41, 50 and 58. It appears clearly that when the 1960 cohort reaches age 58 (brown triangle), its average net worth is much higher than that of a 58 year-old in 2001. Therefore, the line that goes through the three data points corresponding to the 1960 cohort is much steeper than the crosssectional profiles: over 20 years, the average net worth for that cohort increases by nearly 40 months of consumption. In other words, considering household net worth by age in a specific year significantly understates the rate at which households accumulate net worth over time. In the example from Figure 1, the rate would look two times slower.

_Figure 1 Household net worth by age in 2001, 2010 and 2018_ <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65<br>2001 2010 2018<br> Note: triangles denote the average household net worth for the cohort born in 1960<br>months of household consumption<br><!-- End of picture text --> To measure the rate of wealth accumulation experienced by households over their life cycle, net of differences across cohorts and years, we apply the decomposition methodology by Deaton (1997) after pooling 8 rounds of the PSLM spanning 18 years of data (2001-2018). For each of the variables of interest (measures of wealth, consumption, and income), we run a decomposition into age, cohort, and year effects. We estimate the following model using simple OLS regression, after applying cross-sectional sampling weights: + γ (1) where and are matrices of age and cohort dummies, respectively, and yy= ββ+ α + δδYY<sup>∗</sup> + uu, is a matrix of year dummies with<sup>∗</sup> 11
# **REFERENCES**

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- and aerosol forcing: Projected climate for the 21st century. _Climate Dynamics_ 16: 427 450.

- Cline WR, 1996. The impact of global warming on agriculture: Comment. _American Economic Review_ 86: 1309–1312.

- Dinar A, Hassan R, Kurukulasuriya P, Benhin J & Mendelsohn R, 2006.  The policy nexus between agriculture and climate change in Africa.  A synthesis of the investigation under the GEF / WB Project, _Regional Climate, Water and Agriculture:  Impacts on and Adaptation of Agro-ecological Systems in Africa._ CEEPA Discussion Paper No 39, Centre for Environmental Economics and Policy in Africa, University of Pretoria.

- Emori S, Nozawa T, Abe-Ouchi A, Namaguti A & Kimoto M, 1999. Coupled ocean-atmosphere model experiments of future climate change with an explicit representation of sulfate aerosol scattering. _Journal of the Meteorological Society of Japan_ 77: 1299–1307.

- Fafchamps M, Udry C & Czukas K, 1998. Drought and saving in West Africa: Are livestock a buffer stock? _Journal of Development Economics_ 55: 273–305.

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- Hahn GL & Mader TL, 1997. Heat waves in relation to thermoregulation, feeding behavior, and mortality of feedlot cattle. Proceedings of the 5th International Livestock Environment Symposium, Minneapolis, MN, USA, pp. 563–571.

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ILRI (International Livestock Research Institute), 2005. http://www.ilri.org

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Table 9: Intensification in the Crop Sector Annual Trend Rates of Growth.

|Inputs|1970-791|1980-89|
|---|---|---|
|Cropping Intensity (%)|1.1|1.1|
|Fertilizer (Kg/Ha)|12.4|6.6|
|Water (000 cubic feet/ha)|1.9|1.9|
|Tractor (number/l000 ha)|15.1|10.1|
|Labor (labor days/ha)|1.3|1.|

Source: Ali and Velasco (1993).

Table 10: Input Production Elasticities in the Crop Production sector

|in P<br>|akistan, 1970-<br> <br>|79 and 1980<br> <br>|-89.<br> <br>||
|---|---|---|---|---|
|Input||<br>Lab|or<br>|<br>Po|wer|
|Cropping System|1970-79|1980-89|1970-79|1980-89|
|Wheat-Cotton|-0.24|1.06|0.55|0.39|
|Wheat-Rice|1.17|1.40|-0.53|-0.24|
|Wheat-Mixed|0.18|1.41|0.78|-0.39|
|Punjab and Sind|0.15|0.83|0.64|-0.06|

Source: Ali and Velasco, Table 8. Generalized Least Squares estimates. Input production elasticities are calculated at mean values.

Power includes bullock and mechanical power but mechanical power is the dominant component in this category. Note how the input elasticities for labor rise between the 1970s and 1980s, while those of power fall. This indicates an increasing marginal productivity of labor over time, brought about by capital deepening in agriculture, to the point where fiurter increments of mechanical power cease to be productive. Some of this can be explained by the tightening of rural labor markets and indeed agricultural wages have increased dramaticaUy. However, given the incentives to mechanize resulting from subsidies and the threat of land reforms, it is a safe conjecture that the capital labor ratio in agriculture is higher ffian it would have been in the presence of undistorted factor prices.

The increase in labor's elasticity came against a background of declining elasticities for the other inputs (fertilizer, seed, pesticide, and water). As noted earlier, Ali and Velasco attribute the general decline in the productivity of inputs to resource degradation. Total factor productivity also stagnated or declined in the major cropping systems in the 1970s and 1980s. It 24
**Figure 1 – Household Income**

<!-- Start of picture text --> Non-poor<br> Poor<br>Very Poor<br>0 .2 .4 .6 .8 1<br>Income shares<br>Autoconsumption Agricultural sales<br>Agricultural wage Unskilled Wage<br>Skilled Wage Other income<br><!-- End of picture text -->

# **Figure 2a and 2b – Household Consumption**

<!-- Start of picture text --> Non-poor<br>Poor<br>Very Poor<br>0 .2 .4 .6 .8 1<br>Consumption shares<br>Autoconsumption Food<br>Goods Services/Other<br><!-- End of picture text --> <!-- Start of picture text --> Non-poor<br>Poor<br>Very poor<br>0 .2 .4 .6 .8 1<br>Expenditure shares<br>Cereal Vegetables<br>Animal Based Other Food<br><!-- End of picture text -->
percent). As a result, its GDP per capita over this period, relative to that of Brazil, increased from 43 to 49 percent. In 1999, RN’s GDP was R$7.6 billion, or 6.0 percent of the Northeast’s GDP.

<!-- Start of picture text --> Figure 4.1: Per Capita GDP for Rio Grande do Norte<br>–<br>Relative to the Northeast and Brazil (1947 98)<br>2<br>1 .8<br>1 .6<br>1 .4<br>1 .2<br>1<br>G D P P C R N / G D P P C N o rth e a st<br>0 .8<br>G D P P C R N / G D P P C B ra z il<br>0 .6<br>0 .4<br>0 .2<br>0<br>1 9 4 7 1 9 5 0 1 9 5 3 1 9 5 6 1 9 5 9 1 9 6 2 1 9 6 5 1 9 6 8 1 9 7 2 1 9 7 5 1 9 7 8 1 9 8 1 1 9 8 4 1 9 8 7 1 9 9 0 1 9 9 3 1 9 9 6<br>ye a r<br>Relative per capita GDP<br><!-- End of picture text --> _Source:_ Calculations based on data from Azzoni (1999) and _Contas Regionais_ (1985–1998).

Figure 4.1 shows the recent performance of RN’s per capita income relative to the Northeast’s. The data show strong growth compared to the rest of the states in the Northeast from 1947 to the end of the 1970s. During this period, RN’s per capita income ranged from 90 to 180 percent of the per capita income of the Northeast. After the collapse of cotton production due to the boll weevil pest in the late 1970s, RN’s GDP per capita as a share of the Northeast’s GDP reached a historic low of about 90 percent. The economy recovered slightly after 1984 and is currently on a positive path, but the ratio of its per capita GDP to the Northeast per capita GDP of 105 percent is well behind its ratio five decades ago (Figure 4.1).

**Table 4.1:  Real Per-Capita GDP Growth in the Northeast, 1985–98**

||**1985**–**90**|**1990**–**98**|**1985**–**98**|
|---|---|---|---|
||**(%)**|**(%)**|**(%)**|
|Brazil|1.72|2.49|2.34|
|Northeast|0.17|2.65|1.77|

7