rafmacalaba/datause-encoder-data huggingface dataset:
Peruvian AFPs are higher than those charged in Chile and about the same as those charged in Argentina. In October 1996, the average fixed commission in Peru amounted to US$ 1.40, compared to US$ 0.40 in Chile and US$ 1.35 in Argentina. The average variable commission of 2.02 percent and the average premium payable for disability and survivors' insurance added up to 3.25 percent, however subject to a ceiling, compared to 2.97 percent in Chile and 3.3 percent in Argentina.

**Table 3 AFP Commissions (October 1996/January 1997)**

||**Horizonte**|**Profuturo**|**Integra**|**Union**|**Nueva**<br>**Vida**|
|---|---|---|---|---|---|
|**Fixed (soles)**|2.54|2.95|2.54|2.54|_5.51_|
|**Variable (%)**|2.03|2.03|2.03|2.03|1.99|
|**(October 1996)**||||||
|**Variable (%)**<br>**(January 1997)**|2.30|2.50|2.35|2.28|2.29<br>**J**|

Source: SAFP Since January 1997, the law allows only the variable commission as a percentage of contributions. Fixed fees on monthly contributions as well as variable fees charged on the balance of the affiliates have been disallowed. None of the AFPs had been charging the commission on the balance administered. After the fixed fee had been abolished, AFPs increased their variable fees, as shown in Table 3. They now range from a low of 2.29 percent of salaries to 2.5 percent. The amendment also allows for discrimination of commission between affiliates in order to discourage them from switching AFPs. For benefit payments, both fixed and variable commissions are still allowed.

In 1994, the commissions collected by the AFPs covered only 45 percent of the operating costs. Although income from commissions more than doubled during 1995, it was sufficient to cover only 80 percent of operating costs due to the AFPs expansion of their salesforces. As a percentage of covered salaries, the operating costs have fallen from 2.6 percent in 1994 to 2.2 percent in 1995 and declined further to 1 percent in 1996. This represents a decrease from 26 to 13 to 4.5 percent of the total assets accumulated in the funds at the end of each year.

Out of the five AFPs only one, **Integra,** has been able to break even. All other companies have been running high operating losses. The accumulated losses of the system amount to more than S. 200 million (approx.US$82 million) corresponding to two thirds of the AFPs' total capital .

# **_Fund Accumulation and Investment_**

The total funds under management by the AFP system amounted to S.2.3 billion or about US$ 1 billion at the end of 1996, corresponding to about 1.5 percent of GDP. Out of the total contribution rate of currently 11 percent, 8 percent is used for long-term capital accumulation. The monthly inflow is about US$25 million. It was originally planned to increase the contribution rate for capital accumulation to 10 percent starting January 1997, but in the recent amendment to the law, this increase has now been delayed until after December 1998. There is no income ceiling for the 15
economy, leading to aggregate consequences on output and productivity. Fifth, the inclusion of the home production sector is indeed relevant for assessing the efficiency gains of a rise or fall in the premium paid in the public sector.

# **4 Calibration strategy**

We calibrate the parameters in our model to match key moments of the allocation of talent in Egypt. Our data were extracted from ILOSTAT databases.<sup>10</sup> We select _α_ such that _ηeα_ (the share of capital) equals a capital income share of 30%, as is standard in the literature (e.g., Gollin, 2002). We assume that the capital income share in the public firm is the same as in the private sector. Hence, we determine _κ_ such that _ηgκ_ equals 0.3. The fractions of women and men in the population, _φf_ and _φm_ , are directly taken from the data: _φf_ = _φm_ = 0 _._ 5. For the parameters of the Euler equation used to set the location of the infinitely elastic supply of capital ( _β_ and _δ_ ), we take the standard value of 0.95 for the discount factor _β_ , as reported in the literature, and we follow Cagetti and De Nardi (2006) and Buera and Shin (2013) to choose _δ_ = 0 _._ 06.<sup>11</sup> In line with Guner et al. (2008), we assume that log-managerial ability follows a (truncated) normal distribution with a mean of _m_ and a standard deviation of _sd_ . Additionally, we impose that this distribution represents the majority of production units, constituting a total mass of 1 − _p_ ˆ. To address the remaining portion of the establishment distribution, we set a top ˆ ˆ ˆ value for managerial skill, _s_ , along with its corresponding fraction, _p_ . We normalize _p_ = 0 _._ 1% and let _s_ ˆ to match the employment share in firms with 20 or more employees. Note that _m_ , _sd_ , and the span of control parameter, _ηe_ , directly determine the distribution of firms and the composition of female and male entrepreneurs. Consequently, we calibrate these parameters to match the employment share in firms with 20 or more employees, firms with 1-9 employees, the proportion of female entrepreneurs, and the proportion of male entrepreneurs.

> 10International Labour Organization (ILO), accessed on April 2024. The share of employment in the public sector in the data includes workers in State-Owned Enterprises.

> 11This value for the depreciation rate is common in the business cycle literature (Cooley, 1995; Cagetti and De Nardi, 2006; Heathcote et al., 2010). Furthermore, Stokey and Rebelo (1995) show that calculations based on the capital consumption allowance and estimates of the aggregate capital stock yield an average depreciation rate of around 6%.

21
Table A.8: Eect of a direct SLM training intervention on contact farmers' adoption and knowledge, includes administrative post xed eects

|Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|
|CFs' Knowledge and Adoption, unweighted||||
|Knowledge score<br>0.633<br>[0.173]|0.046<br>[0.036]|347|0.103|
|Number of techniques known by name<br>4.131<br>[1.626]|0.859**<br>[0.367]|347|0.105|
|Number of techniques adopted on own plot<br>1.786<br>[1.309]|0.726***<br>[0.216]|347|0.256|
|Number of techniques adopted on any plot<br>3.738<br>[1.889]|0.658**<br>[0.258]|347|0.244|
|Sources: Contact farmer survey, 2010, 2012, 2013; Hous|ehold survey,|2012|, 2013.|
|Notes: Regressions include the same explanatory variabl<br>except replacing district indicators with administrative p<br>***, **, and * indicate signicance at the 1, 5, and 10 p|es as models <br>ost indicator<br>ercent critical|in Ta<br>s.<br> level.|ble 4,<br>|
|CF=contact farmer; ITT=intent-to-treat; SLM=sustaina|ble land man|agem|ent.|

51
example, if a farmer has crop failures for a year or two as he learns about a new crop, this transition cost is not reflected in the analysis. Similarly, if the farmer makes the decision to move to a new crop suddenly, the model does not capture the cost of decommissioning capital equipment prematurely. Transition costs are clearly very important in sectors where there is extensive capital that cannot easily be changed. For example, studies of timber (Sohngen et al. 2002) show that modeling the transition is absolutely necessary in order to reflect how difficult it is to change the forest stock. Although agriculture adapts quickly to changes in prices, many intertemporal agricultural studies argue that farms will have more difficulty adapting quickly to climate change (Kaiser et al. 1993a,b; Kelly et al. 2005). Given how slowly some innovations in modern agriculture have spread in Africa in particular, transition costs may be very important.

Another drawback of the Ricardian approach is that it cannot measure the effect of variables that do not vary across space. Specifically, this approach cannot detect the effect of different levels of carbon dioxide since carbon dioxide levels are generally the same across the world. Changes in carbon dioxide levels have occurred over recent decades. In principle, one might be able to detect the effect of these increases in CO2 by looking at productivity over time. However, it is impossible to distinguish the effect of the carbon dioxide changes from the much larger effect of technical changes that have occurred across the same time period (Mendelsohn 2005). The best evidence about the magnitude of the fertilization effects of carbon dioxide comes from controlled experiments. These studies report an almost universal fertilization effect for all crops, although the magnitude of this effect varies from crop to crop (Reilly et al. 1996). Reilly reports an average improvement in productivity of 30% associated with a doubling in CO2. However, these results must be interpreted cautiously because the conditions in the controlled experiments may not be representative of farms across the world. In most cases, the laboratory experiments have been done in near ideal conditions where other nutrients are freely available. In practice, if nutrients are scarce, the fertilization benefits from increased carbon dioxide levels may be lower. Thus in many developing countries, where fertilizers are not fully applied, the actual carbon fertilization benefits may be less than 30%.

Another potential drawback is that the variation in climate that one could observe across space may not resemble the change in climate that will happen over time. For example, the temperature range across space could be small relative to the change in temperature over the 7
The multicountry inter-sectoral input-output relationships are obtained from the OECD Inter-Country Input-Output Database (ICIO). The ICIO tables are available for the years 1995, 2000, 2005, 2008, 2009, 2010 and 2011. As our sample spans the period from 1991 to 2012, we use the 1995 edition of ICIO table for samples in 1991-1995, the 2000 edition for 1996-2000, the 2005 edition for 2001 to 2005, and the 2011 edition for 2011 and 2012. While both the IEA and the ICIO tables rely on ISIC Rev 3.1 to classify industries, ICIO sectors are slightly more disaggregated than IEA sectors because some IEA sectors are aggregated from multiple ISIC two-digit sectors. For the main analysis, we aggregate ICIO sectors to match the IEA data. We also apply different levels of sector disaggregation for robustness checks. Table 3 shows the correspondence between IEA and ICIO sectors.

Bilateral trade data are obtained from the United Nations COMTRADE Database. Trade data are reported at the six-digit Harmonized Commodity Description and Coding System (HS) level. Using the concordance provided by Pierce and Schott (2012), we convert the data from the HS level to the ISIC Rev. 3.1. We always use the trade values reported by the exporters.

Manufacturing value added, employment costs, export value and IEA and ERRA energy prices are reported in current prices denominated in US dollars. Energy prices from government reports are reported in current prices denominated in local currencies. We convert local currencies into U.S. dollars using the exchange rates reported by the World Bank. We convert values for different years into 2010 prices using country-specific GDP deflators obtained from the International Monetary Fund.

# **3.4 Descriptive Statistics**

Table 4 provides summary statistics for the key variables. Table 5 shows how intensively each row sector uses inputs from each column sector. The numbers are the weighted average shares of factor inputs across all countries in the sample in 2010. While the production of most sectors relies largely on inputs from subsectors within their own two-digit classification, there are strong interdependencies among the machinery, transport equipment and metals sectors.<sup>7</sup> For example, for the manufacture of transport equipment, 12 percent of

> 7In addition, because the “non-specified’" sector includes rubber and plastic products, a large share of its inputs are from the chemicals sector.

15
is also proxied by its book value. Assets are taker, to be assets reported on the balance sheet to the exclusion of off-balance sheet assets such the bank's contingent claim on the Federal Deposit Insurance Corporation. Some of the limitations of this omission are discussed in more detail below.

Letting 0. and _°2_ stand for the market values of one dollar of LDC loans and of one dollar of non-LDC assets, we can now write the balance sheet identity as follows MVe + BVp + BV1 - OlAide + 2<sup>A.</sup>

# where

MV0 - market value of common equity

- BVp book value of preferred equity

BV1 - book value of liabilities Aldc - book value of LDC loans A0 - book value of other assets Both sides of the above expression can be divided by a bank's total assets, denoted by At,t,0 which gives the following equation to be estimated.

MV0 + BVp + BV1 Alde Atatal _ a + 0 p Atotalitt- **tot&L** where a _- °2_ _°1_ e - _02_ In the above equation, a is again the market value of one dollar of non-LDC assets, and p is the discount of LDC loans vis-a-vis non-LDC asets implicit in stock prices. The above equation is estimated using bank stock 12
KLEMS labor productivity increase substantially between 1997 and 2004 in all EU-8 countries except the Slovak Republic (for the unweighted average), as in Figure 6.

**_Figure 7:  Average KLEMS Labor Productivity in the Services Sector Excluding Real Estate Activities_**

<!-- Start of picture text --> Panel A. Unweighted Average Labor Productivity Panel B. Weighted Average Labor Productivity<br>EU-15 EU-15<br>Hungary Hungary<br>Estonia Poland<br>Poland Czech Rep.<br>Czech Rep. Slovenia<br>Slovenia Estonia<br>Latvia Slovak Rep.<br>Slovak Rep. Latvia<br>Lithuania Lithuania<br>0 20 40 60 80 100 120 0 20 40 60 80 100<br>Thousands of PPP Euros per Employee Thousands of PPP Euros per Employee<br>1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: KLEMS database 2007.

Note: The averages of labor productivity are taken across 19 NACE 2-digit sub-sectors listed in Appendix Table A.1 excluding real estate activities (NACE 70).

Figure 7 also shows that the efficiency of services in EU-8 countries lags clearly behind that in EU-15 countries. During 1997-2004, the best EU-8 performer Hungary exhibits unweighted (weighted) average labor productivity that is 70.7% (59.7%) of the EU-15 average labor productivity, while the worst EU-8 performer Lithuania exhibits unweighted (weighted) average labor productivity that is only 25.5% (26.1%) of the EU15 average labor productivity.<sup>13</sup> However, note that between 1997 and 2004 the labor productivity gap between EU-8 countries and EU-15 countries is reduced. Considering weighted average KLEMS labor productivity, Hungary’s level is 59% of the EU-15 level in 1997-2000 but increases to 60.3% in 2001-2004, while Lithuania’s level is 23.2% of the EU-15 level in 1997-2000 but increases to 28.9% in 2001-2004.

> 13 Labor productivity in the services sector for EU-15 countries is obtained as the average across all EU-15 countries of labor productivity in all sub-sectors, where KLEMS labor productivity for each sub-sector and country is calculated in similar way to KLEMS labor productivity for each sub-sector and ECA country. The consideration of a simple average of labor productivity across the more and less advanced EU-15 countries may actually underestimate its value. Hence, EU-8 countries may be significantly farther away from the labor productivity levels of the best performers among EU-15 countries.

16
# **Bibliography**

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Arnold, J., B. Javorcik, M. Lipscomb and A. Mattoo (2015) “Services Reform and Manufacturing Performance: Evidence from India”, _The Economic Journal_ , Vol. 126, Issue 590, pages 1‐39.

Bauer, M., F. Erixon, H. Lee‐Makiyama, M. Krol (2013) “The Economic Importance of Getting Data Protection Right: Protecting Privacy, Transmitting Data, Moving Commerce”, Washington DC: US Chamber of Commerce.

Bourlès, R., G. Cette, J. Lopez, J. Mairesse and N. Nicoletti (2013) “Do Product Market Regulations in Upstream Sectors Curb Productivity Growth? Panel Data Evidence for OECD Countries”, _The Review of Economics and Statistics_ , Vol. 95, No. 5, pages 1750‐1768.

Christensen, L., A. Colciago, F. Etro and G. Rafert (2013) “The Impact of the Data Protection Regulation in the EU”. Intertic Policy Paper, Intertic.

Ferracane, M.F. (2017), “Restrictions on Cross‐Border data flows: a taxonomy”, ECIPE Working Paper No. 1/2018, European Center for International Political Economy, Brussels: ECIPE.

Ferracane, M.F. and E. van der Marel (2018) “Do Data Flows Restrictions Inhibit Trade in Services?”, ECIPE DTE Working Paper Series No. 2, Brussels: ECIPE.

Ferracane, M.F., H. Lee‐Makiyama and E. van der Marel (2018b) “Digital Trade Restrictiveness Index”, European Centre for International Political Economy, Brussels: ECIPE.

Ferracane, M.F., J. Kren and E. van der Marel (2018a) “Do Data Policy Restrictions Impact the Productivity Performance of Firms and Industries?”, ECIPE DTE Working Paper Series No. 1, Brussels: ECIPE.

Goldfarb, A. and C. Tucker (2012) “Privacy and Innovation,” in Innovation Policy and the Economy (eds.) Josh Lerner and Scott Stern, / University of Chicago Press, pages 65–89. See also NBER Working Paper Series No. 17124, National Bureau of Economic Research, Cambridge MA: NBER.

Guellec, D. and C. Paunov (2018) "Innovation Policies in the Digital Age", OECD Science, Technology and Industry Policy Papers, No. 59, OECD Publishing, Paris.

Horrace, W. and R. Oaxaca (2006) “Results on the Bias and Inconsistency of Ordinary Least Squares for the Linear Probability Model," Economics Letters, Vol. 90, No. 3, pages 321‐327.

Manyika, J., S. Lund, J. Bughin, J. Woetzel, K. and D. Dhingra (2016) “Digital Globalization: The New Era of Global Flows”, McKinsey Global Institute, Washington DC: McKinsey and Company.

‐ OECD (2019) “East Asia Going Digital: Connecting SMEs”, OECD, Paris, www.oecd.org/going <u>digital/East‐asia‐connecting‐SMEs.pdf.</u> <mark>van der Marel, E., H. Lee‐Makiyama, M. Bauer and B. Verschelde (2016) "A Methodology to Estimate the Costs of Data Regulation",</mark> _<mark>International Economics</mark>_ <mark>, Vol. 146, Issue 2, pages 12‐39.</mark> 16
**Table 1: Summary statistics 2009/10**

|State|Headcount<br>index of<br>poverty<br>(% below<br>povertyline)|Participation<br>rate (share of<br>rural h‘holds<br>working on<br>MGNREGS)|Demand rate<br>(share of rural<br>h‘holds who<br>want work on<br>MGNREGS)|Rationing rate<br>(share of rural<br>h‘holds who<br>wanted work but<br>did notget it)|Female share<br>of employment<br>on MGNREGS<br>(% of total<br>person days)|
|---|---|---|---|---|---|
|Andhra Pradesh|20.64|0.354|0.472|0.249|58.1|
|Assam|42.28|0.182|0.413|0.559|27.7|
|Bihar|56.47|0.099|0.461|0.785|30.0|
|Chhattisgarh|56.39|0.479|0.690|0.306|49.2|
|Gujarat|32.54|0.215|0.382|0.438|47.5|
|Haryana|24.18|0.051|0.195|0.738|35.6|
|Himachal Pradesh|11.90|0.334|0.418|0.202|46.0|
|Jammu & Kashmir|n.a.|0.097|0.334|0.709|7.0|
|Jharkhand|43.50|0.192|0.517|0.628|34.3|
|Karnataka|31.34|0.080|0.228|0.648|36.8|
|Kerala|11.74|0.112|0.232|0.517|88.2|
|Madhya Pradesh|45.85|0.406|0.646|0.371|44.3|
|Maharashtra|33.90|0.044|0.277|0.840|39.8|
|Orissa|49.93|0.220|0.507|0.567|36.3|
|Punjab|19.44|0.052|0.312|0.833|26.0|
|Rajasthan|31.2|0.618|0.732|0.155|66.9|
|Tamil Nadu|22.81|0.335|0.414|0.190|82.9|
|Uttar Pradesh|40.75|0.162|0.350|0.536|21.7|
|Uttarakhand|n.a.|0.292|0.406|0.280|40.1|
|West Bengal|35.05|0.432|0.658|0.344|33.4|
|All India|36.43|0.249|0.447|0.444|48.1|

_Notes and sources_ : Poverty rates are based on Tendulkar poverty lines updated from 2004/05 to 2009/10 using statespecific consumer price indices for agricultural laborers (CPIAL) and per capita consumption expenditures in Schedule 1.0. Poverty rates for J&K and Uttarakhand not reported because data not available on state-level CPIAL. Female share of person days from MGNREGS administrative data (http:\\nrega.nic.in). Remaining columns from authors‘ calculations from unit record data of 2009/10 National Sample Survey Schedule 1 (for headcount rate) and Schedule 10.

25
characteristics, such as their household size and demographic composition, which can influence welfare in ways that may not be evident in consumer demand behavior. More precisely, if demands _q_ ( _pi_ , _yi_ , _xi_ ) can be integrated back to a utility function _u_ ( _qi_ ,<sup>_x_</sup> _i_ ) then the same demands also maximize the broader welfare function _w_ [ _u_ ( _qi_ , _xi_ ), _xi_ ]. Thus there is no unique utility function that can be inferred from market demands alone.<sup>13</sup> We need stronger identifying assumptions, or more data. The rest of this paper will consider the scope for using data on selfassessed welfare for addressing these problems.

# **3. Anchoring poverty measures to subjective welfare**

There are essentially two approaches to collecting subjective data on welfare. The first uses qualitative categories in the welfare space. An example is the ―economic ladder question‖ (ELQ): _“Imagine six steps, where on the bottom, the first step, stand the poorest people, and on the highest step, the sixth, stand the rich (show a picture of the steps). On which step are you today?”_ A popular indicator of overall welfare has been the ―satisfaction with life‖ (SWL) question: _“Overall, how satisfied (content, happy) are you with your life? Are you (1) very unsatisfied; (2) unsatisfied; (3) neither unsatisfied nor satisfied; (4) satisfied; (5) very satisfied?”_ The ELQ is arguably better suited to poverty measurement as it is tied to a concept of economic welfare, while ideas such as SWL or ―happiness‖ are broader and more nebulous in scope.

The second approach asks for a money-metric of subjective welfare. An example is the ―minimum income question‖ (MIQ): _“What income level do you personally consider to be absolutely minimal? That is to say that with less you could not make ends meet.”_ This can be thought of as a special case of Van Praag’s (1968) ―income evaluation question,‖ which asks what income is considered ―very bad,‖ ―bad,‖ ―not good,‖ ―not bad,‖ ―good,‖ ―very good.‖ There are three main ways such data have been used to inform poverty measurement. The first is as a means of testing objective poverty lines, by regressing self-rated welfare on income normalized by the poverty line _plus_ the variables that went into the construction of the poverty line, which should be jointly insignificant if those lines accord with subjective welfare.<sup>14</sup> The second is to calibrate a composite welfare index using the subjective welfare regression

> 13 This point has long been recognized; see Pollak and Wales (1979), Pollak (1991) and Browning (1992).

> 14 This approach is outlined in Ravallion and Lokshin (2002) and illustrated using Russia’s poverty lines.

7