rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6.2 Rates of return on investments and domestic and foreign savings, 2003–2011**

|_EOCK input values_||||
|---|---|---|---|
||Domestic investment|Domestic savings rate|Foreign savings marginal|
||rates of return|(net)|cost|
||_π_|_rs_|_MCFS_|
|2003|10.5%|7.1%|10.7%|
|2004|11.3%|7.7%|10.5%|
|2005|11.3%|7.3%|11.2%|
|2006|12.2%|7.9%|10.4%|
|2007|12.4%|8.0%|9.5%|
|2008|12.5%|7.5%|8.0%|
|2009|10.4%|6.5%|9.0%|
|2010|11.5%|7.1%|8.7%|
|2011|12.1%|8.4%|8.9%|
|Average|**11.6%**|**7.5%**|**9.7%**|

_Source:_ Authors’ calculations using SHCP, INEGI, BAMEX, IFS (IMF), Global Finance Statistics (IMF), and WDI data. _Note:_ For domestic and foreign savings, effective tax rates of 10 percent have been assumed.

# **_3.3.1 Results of the estimation_**

**The estimation of the EOCK for Mexico, which comes out to 10.4 percent (Table 7), is obtained by applying the weights to the relevant returns** . By applying the average weights to domestic investment (f1), domestic savings (f2), and foreign savings (f3) that result from the calculation presented in the table, as well as the rates of return reported earlier for gross of tax returns to domestic investment (π), net of tax returns to domestic savings (rs), and marginal cost of foreign savings (MCFS), we can estimate the EOCK for the 2003–2011 period.

**Table 7 Top-down approach for the EOCK, 2003–2011**

||Domestic investment|Domestic savings|Foreign savings|
|---|---|---|---|
||_f1_|_f2_|_f3_|
|Average weights|65.8%|24.7%|9.6%|
||_π_|_rs_|_MCFS_|
|Rates of return|11.6%|7.5%|9.7%|
|**EOCK**|||**10.4%**|

# **_3.3.2 Discussion and sensitivity analysis of the results_**

**The sensitivity analysis confirms that taking into account the price elasticity of domestic savings is critical.** As capital markets develop in Mexico, different financial savings instruments are becoming available to savers (households, corporations, pension funds). This will necessarily

21
2 areas may offer students just a fraction of the time governments and donors put at their disposal. The time wastage constitutes failure to deliver a basic service to the poor .<sup>2</sup> A striking symptom of this failure in some countries is students’ inability to read fluently (if at all) until the later primary grades.<sup>3</sup> Lacking parental support, poorer students tend to fall behind early and repeat grades or drop out illiterate. In middle-income countries like Brazil, poor time use means that some students are surrounded by piles of books that they cannot read. Performance is often worse in multilingual countries that must teach in official languages, particularly languages with complex spelling systems like English, French, and Portuguese; there is simply not enough time to teach the basics.

When and how is time lost? International studies have mainly focused on teacher absenteeism (Chapter 2). But absenteeism constitutes only one source of loss in the delivery of basic educational services. Students’ opportunity to receive information and to process it is reduced in multiple stages. Sometimes the loss happens in large blocks, due to strikes or late teacher assignments. But time is frequently lost in small increments; a day here, an hour there. It is lost minute by minute when teachers fail to start work promptly in class, distribute textbooks, or leave the room to chat with others (Figure 1.1). Kids are often happy to have extra vacations or to get off school early, so no one may complain. However, they are the ones who ultimately lose the most; they may sacrifice current earnings to spend years nominally attending school and lose future earnings as well as social status if they lack the skills needed for the labor market.

**Figure 1-1. Instructional Time Loss Model**

Intended class time as allotted by a government (e.g., 200 days, 1000 teaching hours) Remaining after school closures (strikes, weather, inservice training, extra holidays, weather) Remaining after teacher absenteeism and tardiness Remaining after student absenteeism Class time devoted to any learning task Learning time relevant to curriculum The wastage of instructional time should be an important concern for those who finance education. Governments, donors, and parents pay for educational inputs, buildings, salaries, teacher training, textbooks, and materials in order to provide quality education. The financing is expected to translate into time and opportunities for students to learn, and it is commonly expected that 100% of the investments will be used for student learning. Thus, a class hour in a particular school corresponds to a budgetary fraction given the amount of time schools officially operate (for about 4-5 hours in about 200 days – see Chapter 2). The degree of budgetary wastage resulting from time loss has probably not yet been calculated.

> 2.World Development Report 2004. 3. IEG 2006.
only notable change in point estimates, when unemployment is introduced (in equation 3), the coefficient on the linear counter variable increases in absolute value, i.e. becomes even more negative. Unemployment is strongly correlated with per capita income, however, and when we test unemployment without income as a regression (equation 4), the coefficient falls back to its usual range of a 4 to 5 percent annual decline and loses significance.<sup>11</sup> Most of these economic variables are strongly significant predictors of aid, although not always in the expected direction. Perhaps surprisingly, the share of government expenditures in GDP is not significant (equation 1). The lagged _budget surplus_ is negatively associated with aid disbursements (equation 2). Despite lagging budget surplus by a year, we cannot rule out the possibility that surpluses are achieved by cutting aid along with many other spending categories. This would be consistent with Faini’s (2006) finding that a larger public-sector debt is associated with lower aid, if debt necessitates budget surpluses<sup>12</sup> . In any event, the effect quantitatively is quite small.

_Unemployment_ is positively correlated with disbursements (equation 3) when income is also included in the regression but this counterintuitive result disappears when income is omitted (equation 4). Higher _inflation_ is associated with lower aid disbursements in equation 5.<sup>13</sup> A stronger _domestic currency_ is associated with higher aid (equation 6). Contrary to the evidence from bilateral trade relationships (Boschini and Olofsgard 2007), greater _openness_ as measured by trade share/GDP (equation 7) is not a significant predictor of aid flows.

> 11 Unemployment data are unavailable for many countries in the earlier part of our sample period, so we lose one-sixth of our observations when unemployment is included in our regressions.

> 12 We tried testing directly the hypothesis that debt stocks could explain aid disbursements, by including a measure of debt stock/GDP from the OECD.  There are a lot of missing observations on debt stocks from our 24 donors, however, and so including that variable cost us over one-fifth of our observations (although we still ended up with substantially more than Faini’s analysis did).  The coefficient did not prove significant, nor did it significantly affect the estimated coefficients on the banking crisis, per-capita income, or budget surplus variables.

> 13 Mendoza, Jones and Vergara (2009) show that U.S. aid over time is inversely related to a “misery index” equal to the inflation rate plus the unemployment rate.  They do not test them separately.  Nor do they offer a theoretical justification for their assumption that a percentage point increase in unemployment has the same impact on aid as a percentage point increase in the inflation rate.

16
**Table 7: Regressions for the female share of employment in MGNREGS**

||Full<br>sample<br>|Samp<br>headcou<br>avai<br>|le with<br>nt index<br>lable<br>|Sample wit<br>female wag<br>|h male and<br>es available<br>|
|---|---|---|---|---|---|
|Constant|0.676|0.829|0.193|0.131|0.697|
||(8.671)|(5.341)|(0.887)|(0.246)|(3.922)|
|Rationing rate|-0.505|-0.419|-0.307|-0.456|-0.469|
||(-3.758)|(-2.898)|(-2.904)|(-5.108)|(-5.972)|
|Headcount index of rural|n.a.|-0.515|0.241|n.a.|n.a.|
|poverty||(-1.538)|(1.518)|||
|Female casual non-PW|n.a.|n.a.|-0.549|-0.744|n.a.|
|wage (log)|||(-2.056)|(-3.475)||
|Male casual non-PW wage|n.a.|n.a.|0.675|0.806|n.a.|
|(log)|||(5.156)|(3.556)||
|Female wage relative to|n.a.|n.a.|n.a.|n.a.|-0.836|
|malewage(log)<br>|||||(-3.301)|
|R<sup>2</sup>|0.336|0.467|0.811|0.692|0.687|
|SEE|0.164|0.145|0.093|0.111|0.108|
|N|20|18|18|19|19|

_Notes_ : The dependent variable is the share of total person days of employment on MGNREGS going to women. The rationing rate is the share of those who wanted work who did not get it. The headcount index is the percent of population below the poverty line. The t-ratios in parentheses are based on White standard errors.

30
- Gautam, M., 2000. Agricultural extension: The Kenya experience: An impact evaluation. World Bank Publications.

- Gemo, H., Eicher, C. K., Teclemariam, S., 2005. Mozambique's experience in building a national extension system. Michigan State University Press.

- Glewwe, P., Kremer, M., Moulin, S., 2009. Many children left behind? textbooks and test scores in kenya. American Economic Journal: Applied Economics 1 (1), 112135.

- Glewwe, P., Kremer, M., Moulin, S., Zitzewitz, E., 2004. Retrospective vs. prospective analyses of school inputs: the case of ip charts in kenya. Journal of development Economics 74 (1), 251268.

- Gollin, D., Morris, M., Byerlee, D., 2005. Technology adoption in intensive post-green revolution systems. American Journal of Agricultural Economics 87 (5), 13101316.

- Haggblade, S., Tembo, G., 2003. Conservation farming in zambia. International Food Policy Research Institute EPTD Discussion Paper #108.

- Hanna, R., Mullainathan, S., Schwartzstein, J., 2014. Learning through noticing: Theory and evidence from a eld experiment. The Quarterly Journal of Economics 129 (3), 13111353.

- Hazell, P., 2013. What makes african agriculture grow?. 2012 global food policy report. Washington, DC: International Food Policy Research Institute (IFPRI).

- Jayachandran, S., 2006. Selling labor low: Wage rresponse to productivity shocks in developing countries. Journal of Political Economy 114 (3), 538575.

- Jones, M., Kondylis, F., Mobarak, A. M., Stein, D., 2015. Evaluating the integrated agriculture productivity project in bangladesh.

- Karlan, D., Ratan, A. L., Zinman, J., 2014. Savings by and for the poor: A research review and agenda. The Review of Income and Wealth 60 (1), 3678.

- Kondylis, F., Mueller, V., Zhu, S. J., 2015. Measuring agricultural knowledge and adoption. Agricultural Economics 46 (3).

- Laajaj, R., Macours, K., January 2015. The reliability and validity of skills measurement in rural household surveys.

- Lee, D. S., 2009. Training, wages, and sample selection: Estimating sharp bounds on treatment eects. The Review of Economic Studies 76 (3), 10711102.

- Levitt, S., List, J., Syverson, C., 2012. Toward an understanding of learning by doing: Evidence from an automobile assembly plant. Journal of Political Economy 121 (4), 643681.

- Linden, L. L., 2008. Complement Or Substitute?: The Eect of Technology on Student Achievement in India. InfoDev.

- Liniger, H., Studer, R. M., Hauert, C., Gurtner, M., 2011. Sustainable land management in practice guidelines and best practices for sub-saharan africa. TerrAfrica, World overview of conservation approaches and technologies (WOCAT) and food and agriculture organization of the United Nations (FAO).

26
substitutability between the various layers, when it comes to information flows, complementary dominates. In fact, there might be a high degree of complementarity of the information flows that contribute to growth. Therefore, policies to promote balanced connectivity in many dimensions – one that focuses on trade, migration and FDI – is more beneficial than focusing on a policy to enhance only one. Indeed, reducing connectivity in one dimension may have adverse impacts on growth derived from other dimensions. Proposals to reduce migration flows, for example, may have adverse consequences for the growth‐enhancing benefits of trade and FDI flows.

# References

<mark>Alfaro, Laura, et al. "FDI and economic growth: the role of local financial markets." Journal of international economics 64.1 (2004): 89‐112.</mark> <mark>Beck, Thorsten. “The econometrics of finance and growth.” Vol. 4608. World Bank Publications, 2008.</mark> <mark>Ben‐David, Dan. "Equalizing exchange: Trade liberalization and income convergence." The Quarterly Journal of Economics 108.3 (1993): 653‐679.</mark> <mark>Bookstaber, R., & Kenett, D. Y. (2016). Looking deeper, seeing more: a multilayer map of the financial system.</mark> _<mark>Office of Financial Research Brief</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(06).</mark> <mark>Borensztein, Eduardo, Jose De Gregorio, and Jong‐Wha Lee. "How does foreign direct investment affect economic growth?" Journal of international Economics 45.1 (1998): 115‐135.</mark> <mark>Czernich, Nina, et al. "Broadband infrastructure and economic growth." The Economic Journal 121.552 (2011): 505‐532.</mark> <mark>De Domenico, M., Sole‐Ribalta, A., Omodei, E., Gomez, S., & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Comm. 6, 6868.</mark> <mark>Duernecker, Georg, Moritz Meyer, and Fernando Vega‐Redondo.</mark> “ <mark>The Network Origins of Economic Growth.” No. 14‐06. Working Paper Series, Department of Economics, University of Mannheim, 2014.</mark> <mark>Dollar, David. "Outward‐oriented developing economies really do grow more rapidly: evidence from 95 LDCs, 1976‐1985." Economic development and cultural change 40.3 (1992): 523‐544.</mark> <mark>Edwards, Sebastian. "Openness, productivity and growth: what do we really know?" The economic journal 108.447 (1998): 383‐398.</mark> 37
ronment for the years 1950, 1960, 1970, 1980 and 1990. Where the associated future environments are concerned, we assume that agents possessed perfect foresight, except for the timing of the outbreak of the epidemic and its immediate consequences. In this regard, the data in Table 1 and the demographic data up to 1990 suffice for 1950 and 1960. Thereafter, we need progressively more. In view of (11), we require _q_ 70+20 for 1970, which involves the population pyramid for 2010; for 1980, we require the entire population pyramid for 2000 and _q_ 80+20, whereby the latter involves _N_ 120<sup>3;andwere-</sup> quire the corresponding items for 1990. All these elements are estimated in Bell _et al._ (2004), and _qt_ is reported in Table 1. As for the question of whether the outbreak of the epidemic featured in expectations about the future at that time, it is surely safe – to rule out this possibility except, possibly, for 1990. The cumulative number of deaths due to AIDS was still rather small in relation to the population in 1990, and the prevalence rate at that time was also low. It seems very plausible, therefore, that the overwhelming majority of Kenyans had not, at that time, come to recognise the dimensions of the wave of mortality that was about to befall them. In any case, that is the position we adopt for the purposes of calibration.

It still remains to estimate the series _{σt_<sup>1</sup><sup>_}t_</sup> _t_<sup>=90</sup> =50<sup>.Accordingtoasurveyofsome6000</sup> farm households, representatively drawn from districts in six of Kenya’s eight provinces in 1981-82, no less than 20 percent of total expenditure on average was devoted to education (Evenson and Mwabu, 1995: 12-15). For the purposes of calibration, we assume that this proportion stayed constant from 1950 until the mid 1980s, though it seems a bit questionable in the early years, when school-places were rather rationed. By the mid 1990s, there is reliable evidence that this budget proportion had fallen substantially (Kenya, 1996). In calibrating the model, therefore, no use will be made of the year 1990.

The number of equivalent, full-time schoolchildren in period _t_ is _e_<sup>1</sup> _t_<sup>_N_1</sup> _t_<sup>.Usingthe</sup> budget share 0.20 and recalling that, by assumption, the whole of GDP accrues to 21
<!-- Start of picture text --> a Grand Total<br>Upper middle income<br>° + ° * +<br>a *Lower middle income<br>a ALow income<br>HF BHigh income: OECD<br>+<br>+ + High income: nonOECD<br>1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> Figure 2.2: Average primary math test score by region (1965-2010) <!-- Start of picture text --> 6b<br>60 u<br>55<br>50<br>45<br>a<br>3%“ £ >Ux<br>30 ra<br>25<br>1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> <!-- Start of picture text --> © Australia<br>© East Asia & Pacific<br>& Europe & Central Asia<br>* Latin America & Caribbean<br>* Middle East & North Africa<br>® Norh America<br>+* S outhub-Saharan Asia Africa<br>© Grand Total<br>—— Expor.(Grand Total)<br><!-- End of picture text -->
Sánchez-Martín, Escribano and de Arce, 2014. “How regional integration and transnational energy networks have boosted FDI in Turkey” How has the beginning of accession negotiations between Turkey and the EU affected the geographical origin of FDI flows? Even when it would very difficult to isolate the effects of regional integration on capital flowing to Turkey, stylized facts lead to the intuition that it may have been in fact a relevant factor. As shown in the next figure, both intra and extra regional flows increased significantly since 2005, when the EU gave green light to the accession process. At the same time, it is worth mentioning the caveat that these developments took place in the context of expansion in global capital flows; in fact, FDI flows declined sharply in 2009 and 2010, in the context of global economic slowdown, and with cooling down EU accession perspectives for Turkey.

**Figure 4. Evolution of FDI flows to Turkey over the past decade**

<!-- Start of picture text --> 25000<br>20000<br>Africa and LAC<br>15000<br>North America<br>Asia<br>10000<br>MENA<br>Rest of Europe<br>5000 EU 25/27<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: authors' elaboration from OECD stats, WDI<br>US$ million<br><!-- End of picture text --> Interestingly, even when FDI flows from EU members increased in absolute terms in 2005-2008, in relative terms, Middle Eastern and Other European investors were more avid in bringing capital to Turkey (flows from these regions increased by 29 and 23 times respectively in 2005). This seems to conform to the theory, which predicts that FDI with third countries outside the region (in this case the EU) are more likely to increase than intraregional FDI when a regional integration treaty is signed. (te Velde, D. and Bezemer, D. , 2006; Blomstrom, M. and Kokko, A., 1997; Stein et al., 2003). Indeed, European Union accession negotiations are accompanied by a series of improvements in the business 16
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that there remains substantial scope for improving the distribution of social benefits.

**Table 10: Distribution of Main Social Benefits, 1992** (%)

||Low Income|Middle Income|**High**Income|
|---|---|---|---|
|Population|20.0|60.0|20.0|
|Income|9.7|54.2|36.1|
|**Distribution of Social Benefits:**||||
|Pensions|22.2|57.6|20.2|
|Unemployment|28.8|59.2|12.0|
|Children's|18.8|68.5|12.7|
|Allowances||||
|Social Assistance|33.4|56.6|10.0|

Source: World Bank staff estimates from 1992 Individual Bud2ets of Households, NSI.

Pensions are virtually evenly distributed across all income deciles (see Table 10). Low-income households receive a slightly higher share (22 percent) than their population share; high-income households also receive 20 percent of total pensions. That is, the rich as well as the poor benefit equally from the current pension scheme. This is not surprising as all workers (virtually everyone worked) are entitled to a pension. The dilemma for the poor is that they are almost totally dependent on pensions for their livelihood.

Furthermore, Bulgaria's aging population, the surge of retirements during the transition, and the recent generous provisions for early retirement have pushed the number of pensioners up
# **2 Data and empirical strategy**

## **2.1 Data**

The database consists of sectoral and aggregate labor productivity statistics for 91 countries, and 8 sectors covering the period up to 2018.<sup>4</sup> Compared with the literature using sectoral datasets, it employs a large and diverse sample of countries.<sup>5</sup> The database combines data from the APO Productivity Database, the OECD STAN database, the OECD National Accounts, and the GGDC/UNU-WIDER Economic Transformation Database (ETD, de Vries et al. (2021)) for value-added data and employment. In addition, this database is extrapolated backwards using annual growth rates from KLEMS, the GGDC 10-Sector database (GGDC, de Vries et al. (2015)) or the Expanded Africa Sector Database (EASD, Mensah and Szirmai (2018)) to construct a long time series dataset. Also, if there is no available employment data, ILO modelled estimates are supplementary employed. See the Appendix for more details.

## **2.2 Within sector and between sector effects**

Following de Vries et al. (2012), McMillan et al. (2014) and Diao et al. (2017), we start by employing a shift-share-analysis which decomposes aggregate labor productivity into the within sector and between sector effects: where _y_ is aggregate labor productivity, _y j_ is labor productivity of sector _j_ , _Yj_ is initial value-added of sector _j_ , _s j_ is the employment share of sector _j_ . Between sector effects are driven by the change in employment share. They are further decomposed into those which are due to the reallocation of sources to sectors with higher productivity levels (static sectoral effect), and those due to reallocation toward sectors with higher productivity growth (dynamic sectoral effect). Within-sector productivity growth may reflect the effects of improvements in

> 4The eight sectors distinguished in the dataset are agriculture, mining, manufacturing, utilities, construction, trade services, transport and financial services, and government and personal services. According to Economic Transformation Database, Business services include “Information and communication” whereas those in other databases are included in transport service.” Hence, we combined transport and financial services to construct a long time series database.

> 5McMillan et al. (2014) and Diao et al. (2017) employ 38 and 39 countries; Martins (2019) use 7 sectors and 169 countries, IMF (2018) use 10 sectors and 62 countries.

4