rafmacalaba/datause-encoder-data huggingface dataset:
others’ portfolios, possibly generating crowd effects as managers minimize the risk from behaving differently from others. Of course managers might also herd into (or out of) the same securities over some period of time for other reasons not related to underperformance penalties. First, managers may receive correlated private information, perhaps from analyzing the same indicator (Hirshleifer et al. (1994)). Second, a manager might infer private information from the prior trades of better-informed managers and trade in the same direction (Bikhchandani et al. (1992), Sias (2004)). Third, managers might disregard private information and trade with the crowd due to the reputational risk of acting differently from other managers (Scharfstein and Stein (1990)). Finally, managers might simply have correlated specific preferences over certain types of securities. An underperformance penalty, such as the MRG, resembles a reputational risk, in that the manager might be penalized for having lower returns than her peers. With the MRG, the risk is explicit as the manager will be penalized financially if returns are below the maximum allowed shortfall relative to the peer benchmark.

When the number of competing money managers is small, relative performance concerns might lead to strategic behavior. In this environment, strategic interactions imply that a manager’s optimal portfolio choice needs to take into account the impact of his trades on other managers’ decisions. In such setting, the effects from a peer performance penalty on portfolio strategies and trading dynamics might be more pronounced.

The DC pension industries in several Latin American and Eastern European countries are natural candidates to display strategic interaction, because they consist of a small number of competing Pension Fund Administrators (PFA), who act as asset managers and make portfolio choices on behalf of the working population.

I use detailed data on the security allocations chosen by Colombian PFAs between 2004 and 2010 to study the strategic interaction between managers under relative performance concerns. The Colombian institutional set up satisfies several key conditions necessary for testing for strategic behavior of managers. PFAs manage the savings of a captive market, namely individual retirement accounts, so their set of competitors is restricted to other PFAs, and excludes other asset managers. Each PFA must comply with a MRG that is calculated based on peer performance, creating an explicit incentive to care about the portfolio choice and performance of other managers. Since the number of PFAs is small (six), strategic behavior might be more pronounced than with a large number of competitors. Previous empirical work on strategic behavior has examined data on managers’ broad asset allocation or overall portfolio returns. By using monthly detailed portfolio holdings, I am able to test richer implications of models with strategic 3
the other hand, smaller SMEs have had to resort to loopholes such as an increasing reliance on apprenticeship. In either case the adjustment appears to have been effective enough to downgrade labor costs and relations as an obstacle to grc wth.

# Obstacles to Gnowth

Overall For the entire sample, lack of access to finance, problems of taxes, and political and economic uncertainty weighed most heavily on respondents (see Figure 1). It is interesting to note that high interest rates were rated a considerably smaller constraint than access to finance, perhaps reflecting credit rationing under controlled interest rates. A number of the most interesting conclusions lie in the details of these aggregated results, broken down by firm category. For example, finance poses an obstacle for all groups of enterprises, although corporations are clearly less hindered by it than other firms (see Figure 2). Not surprisingly, the smallest firms, which have the greatest trouble obtaining finance, thus have the least complaint about the high cost of finance -- unregistered firms and sole proprietorships regard this as a lesser obstacle than ULLEs and corporations.

Taxes, as discussed below, were difficult both in their magnitude, as the government tried to maintain its revenue from a shrinking tax base, and in terms of their bureaucratic requirements. Not surprisingly, microenterprises rated taxes a much smaller constraints than other firms. Uncertainty about the country's economic and political future made investment decisions difficult, underscoring a pressing need for credible government commitments to political and economic reform. A number of the most interesting conclusions lie in the details of these aggregated results, broken down by firm category.

TABLE 6: Propertv Rights Problems of Larger Firms

|Corporate<br>Status|(Memo item:<br>Average<br>Employment)|Political/<br>Economic<br>Uncertainty|Failure<br>of<br>Justice|Fraudulent<br>Competition|Bureaucratic<br>Procedures|
|---|---|---|---|---|---|
|Unregistered<br>(n-9)__|(6)|L<br>_<br>_<br>_<br>_|L<br>__<br>_<br>_<br>__|n.a.|L<br>_<br>_<br>_<br>_<br>_|
|Sole<br>Proprietorship<br>(n-14)<br>|<br>(12)<br>l|H|M|L|L|
|ULLE<br>(n-10)<br>|(21)<br>l|H|H<br>.|H|M|
|Corporation<br>(n-4)<br>l|(77)|H|H|H|H|

Key: H - High obstacle; M - Moderate obstacle; L - Low obstacle Source: CECPS survey 15
poverty map with the revised poverty map based on the new consumption aggregate, we obtain estimates at a disaggregated geographical level of the impact on poverty of the shock.

This is also the procedure used here. In the case of a food price shock, the key is to assess impacts on both the consumer side (higher food prices reduce welfare) and the producer side (higher food prices increase incomes for producers), while making sure than when a food item is produced and auto-consumed, neither effects are taken into account since prices are irrelevant. On the literature on how the short term impact of higher prices on poverty is typically measured, see among others Deaton (1989), as well as the applications of Deaton’s framework by Barrett and Dorosh (1996) to Madagascar, Budd (1993) to Cote d’Ivoire, and Loening and Oseni (2007) to Ethiopia.

We simulate the impact of an increase in the price of rice of 50%, since rice is the basic staple food in the country. Rice represents 45 percent of the caloric intake of a typical Guinean household, and even more in some urban areas. According to the EIBEP data, urban poor households spent 16 percent of their total consumption on rice, versus 9 percent for nonpoor urban households. In order to construct the revised consumption aggregate in the EIBEP after the price shock, a number of assumptions are used. First, we assume that the cost of an increase in rice prices for a household translates into an equivalent reduction of its consumption in real terms. This means that we do not take into account the price elasticity of demand which for non-marginal changes in prices may lead to substitution effects and thereby help offset part of the negative effect of higher prices for rice. Similarly, an increase for producers in the value of their net sales of rice translates into an increase of their consumption of equivalent size, and we again do not take into account the role that the price elasticity of supply may play here.

As for rice auto-consumed by producers (which represents a substantial share of total rice consumption), it is not taken into account in the simulations since changes in prices do 11
Several other studies have considered the impact of BDH transfers: Paxson and Schady (2008) show that transfers improved the health and development of preschool-aged children, and Schady and Rosero (2008) show that a higher fraction of transfer income is used on food than is the case with other sources of income. Most directly related to this paper, Schady and Araujo (2008) show that the program had large effects on child schooling. Though the transfers are small - 8.9 percent of expenditures in median household – the impact they have on children seems to be large.

Time allocation is available in the BDH evaluation data for children 6 to 17. Table 2 summarizes their time allocation in the baseline data by the child's treatment status. There are no statistically significant differences in activities at baseline between the treatment and control samples. Three-fourths of children attend school, and three fourths work in domestic work such as cooking, cleaning, and other household chores. Less than half participate in market work. Most that do are engaged in unpaid family work. Less than one in ten children participate in market work for pay.

# **3. The effect of the BDH on child labor supply**

# **3.1 Set-up**

In this section, we examine the response of child labor supply to the BDH transfer in a simple version of the model of child labor supply developed in Basu and Van (1998, hereafter BV). We consider the case of a two person household, one adult and one child. The BV model is built on two explicit assumptions. First, child and adult labor are perfect substitutes subject to a productivity shifter, _a_ . One child worker is equivalent to α adult workers, α < 1. This is known as the substitution axiom. _wc_ is the child's labor income, _wA_ is adult labor income (adult labor supply is inelastic). Equilibrium between the child and adult labor markets and the substitution axiom, together, imply _wc_ = α _wA_ .

Second, child labor occurs only if family income is very low. Denote<sup>_s_</sup> _i_ as the perceived subsistence level of family _i_ .<sup>_c_</sup> _i_ is the family's consumption. This second assumption, the luxury axiom, is written by BV as: 5
25 The information on export financing, the final category, is equally inconclusive. As shown in Table 1, the three export-oriented programs, Foreign Military Sales, Export-Import Bank, and P.L. 480 long term export credits, contribute a substantial share to 'he direct loans by the U.S. government. They comprise 29.5% of the direct loans and 13.9% of the guaranteed loans. The loan guarantee programs include the loans by the Commodity Credit Corporation in addition to the programs mentioned above. Periodically, the U.S. Congress holds hearings on financing for the Export-Import Bank. Various advocates hail the importance of the Export-Import Bank in generating exports, but no hard analysis exists of whether in fact ExIm financing generates more exports. Cursory evidence sheds some doubt on this position. Throughout the 1980's U.S. exports have been rising. The value of exports under ExIm programs has been falling, resulting in a fall in the percentage of U.S. exports under ExIm programs from 8% in 1980 to around 2% in 1990.2 Furthermore, a survey by the National Association of Manufacturers in 1989 indicates that lack of export financing ranks ninth in importance among factors limiting export growth (out of 12 factors).' Similar lack of evidence exists for the other export programs, such as the P.L. 480 Program. One explicit aim of the program is to expand markets for exports of agricultural commodities. A recent study cites data showing that 7 of the top 10 importers of U.S farm products and 34 of the top 50 importers received P.L. 480 commodities. However, economists from the Department of Agriculture have been unable to prove a causality between the two." Z Hearing on Export-Import Bank Before the Subcommittee on International Development, Finance, Trade and Monetary Policy of the Comnittee on Banking, Finance and Urban Affairs, House of Representatives, 102 Congress, 1st Session, April 11, 1991, p. 157.

24Hearing on ExIm Bank, p. 219.

25 United States General Accounting Office, Report to the Chairman, Committee on Agriculture, House of Representatives, Food Aid: Improving Economic and Market Development Impact in African Countries (Washington: GAO/NSIAD-88-55, December 1987).
# **_1. Introduction_**

Developing East Asia – here defined as the following set of countries: Cambodia, China, Indonesia, Lao People’s Democratic Republic, Malaysia, Mongolia, Myanmar, the Philippines, Thailand and Vietnam – has been experiencing a consistent and steady pattern of economic growth in the past two decades. This successful performance can be attributed to a broad set of policies that can be grouped in three key pillars: outward orientation, investment in basic human capital and sound economic governance (Mason and Shetty, 2018). From a GDP growth accounting perspective, data for this set of countries show that physical and human capital accumulation (particularly labor quantity) were the main drivers of growth, while (total factor) productivity growth played a less important role.

**Figure 1. Contribution to GDP growth in developing East Asia* (percent, 2000-2017)**

<!-- Start of picture text --> 10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>-2.0<br>Cambodia China Indonesia Malaysia Myanmar Philippines Thailand Vietnam<br>(Official)<br>Labor Quantity Contribution Labor Quality Contribution Total Capital Contribution<br>Total Factor Productivity Growth of GDP<br><!-- End of picture text --> Source: Data from Total Economy Database. Note: *there was no data available for Mongolia and Lao PDR.

Looking forward, developing East Asian countries face a common challenge: the need to increase productivity which will depend on their ability to innovate. In this context, as these countries attempt to climb up the income ladder while being exposed to increasing competitive pressure, economic growth will depend on innovation, and specifically on their ability to build the resources and capabilities to innovate. This will be the ultimate driver to support diversification into new (and higher value added) areas of production, going beyond technology assimilation and production of standardized commodities.

In view of this, the current paper aims at shedding light on innovation performance and its determinants across developing East Asia. It follows a comprehensive approach about national innovation systems while highlighting the supply and demand dimensions of innovation as well as the markets where firms make accumulation decisions of different forms of capital (including knowledge). The overarching approach applied for the current analysis draws from the framework 2
Table 11: First Order Marginal Dominance of Food Stamp Transfers for the Poor by Various Behavioral Assumptions, 1989-1991

|_Alternative Poverty_||_1989_|||_1990_|||_1991_||_1989-_|<br>_1989_|_1990-_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_Lines_||||||||||_90_|_-91_|_91_|
|_(as multiples of the_<br>_poverty lineJ$6198)_|_Before-FS_<br>_Expenditure_|_After-FS_<br>_Expenditure_|_Food_<br>_Stamps_|_Before-FS_<br>_Expenditure_|_After-FS_<br>_Expenditure_|_Food_<br>_Stamps_|_Before-FS_<br>_Expenditure_|_After-FS_<br>_Expenditure_|_Food_<br>_Stamps_|_T-test_|_s for food_<br>_means_|_stamp_|
||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|_(9)_|_(10)_|_(11)_|_(12)_|
|Food**Stamps**<br>**Assumed Exogenous**|||||||||||||
|0.50|2345|2362|102|2345|2351|35|2362|2382|126|3.66|-1.11|-4.51|
|0.75|3896|3913|104|3883|3889|36|3904|3926|137|4.33|-1.39|-4.97|
|1.00|5430|5446|92|5442|5446|25|5350|5368|105|4.18|-0.58|-4.49|
|1.25|6978|6989|70|6950|6956|33|6964|6981|103|2.50|_-1.15_|-1.98|
|1.50|8480|8488|53|8473|8477|23|8462|8477|92|2.25|-1.40|-1.95|
|No Behavioral<br>**Assumptions**|Before-FS<br>Expenditure|After-FS<br>Expenditure|Food<br>Stamps|Before-FS<br>Expenditure|After-FS<br>Expenditure|Food<br>Stamps|Before-FS<br>Expenditure|After-FS<br>Expenditure|Food<br>Stamps|T-test|s for food<br>means|stamp|
|0.50|2284|2421|137|2344|2396|52|2299|2511|212|3.70|-1.89|-3.31|
|0.75|3876|3971|102|3885|3914|29|3894|4062|168|4.92|-2.09|-4.06|
|1.00|5417|_5505_|88|5441|5474|33|5342|5433|90|3.98|-0.09|-2.85|
|1.25|6971|7033|62|6955|6989|34|6953|7031|79|2.09|-0.98|-2.25|
|1.50|8480|8543|63|8470|8477|8|8461|8497|35|3.49|1.62|-2.47|

27
<!-- Start of picture text --> ~<br>*<br>+, Pad<br>+ EStoHnay —"¢<br>co *<br>5 tw sant gare SBMS<br>@ $5,+*<br>5 . hd * o *zech Republic<br>Foa_o. .* ** 6 *-mipRulgaedial+"+<br>8° ® Aorgania<br>**<br>=&a * +. 6+*<br>~<br>f 3 g 10 14<br>Log (Value of GDP per Capita PPP}<br><!-- End of picture text --> <!-- Start of picture text --> —_ *<br>*<br>hd *<br>° * 4%, +<br>= t «<br>oO *<br>renee) * *<br>a * ;<br>aa * +<br>S ol Estemta ©<br>ai . . It la e<br>Ss2a—a oO * ** * * +°* *@Ramania®*. rank"? « *sisHungar geeapligeetcgary*Sloveni& |.<br>= Py # Poland<br>Oo * + *<br>a Turkty<br>&<br>*<br>wT<br>o *<br>f 8 9 10 11<br>Log (Value of GDP per Capita PPP)<br><!-- End of picture text -->
Most of the analysis presented in the paper is confined to the period between 1980 and 1992. Ukraine performed reasonably well until 1991 and then the decline in the living standard began. Since January 1993, the real wages have been falling at a phenomenal rate. Table 26 supplied to us by the Statistical Department of Ulcraine is a clear demonstration of rapidly falling standard of living in Ukraine between 1992 and 1993. The average per capita consumption of meat, fish and dairy products have fallen. Per capita consumption of these items among low income families is much lower than the norms of consumption worked out by the Institute of Hygiene.<sup>13</sup> How long will the standard of living in Ukraine continue to fall? Nobody knows the answers. On the one hand, to control the hyperinflation, the government has to follow rigorous demand management policies and on the other hand it has to protect the poorest and most vulnerable groups in society. The task is gigantic, particularly when there exists no reliable information on which to formulate policies. The present paper provides a basis for determining the magnitude, causes and characteristics of inequality and poverty. This should help in designing appropriate policies but a lot more work needs to be done. And it needs to be done quickly.

3It might seem puzzihng from Table 26 that consumption of vegetables and frits increased by about 30% in the 1992-93 period while the real income collapsed substantially during the same period. This may be explained by the fact that many households grow vegetables and frits in their plots which they can afford to eat even if they do not have large money incomes. In the paper, we nodced that the snallholding did increase in the 1991-92 perod and this trend might have continued in 1993.

50
if importing both capital and intermediate goods improves firms’ total factor productivity (TFP) by estimating a production function relying on the methodology developed by Levinsohn and Petrin (2003) (henceforth LP). The LP approach controls for simultaneity bias in the estimation of firms’ production function.<sup>6</sup> LP is based on Olley and Pakes (1996) methodology that develop a two-stage method to control for unobserved firm productivity.<sup>7</sup> We modify the LP-OP estimation by incorporating the importer status of capital goods and intermediate inputs in the production function estimation. We also control for the volume of imports of capital goods and/or intermediates to avoid that importer status pick up the fact that firms that import both goods capital and intermediates will tend to have a larger volume of imports that can affect total efficiency.<sup>8</sup> Table A1 in the appendix reports the results. In column (2) we include the dummy variables indicating whether the firm imports only intermediates or both inputs and capital equipment goods. Firms producing with foreign inputs and domestic capital goods have greater TFP relative to firms using only domestic inputs (18 percent). The estimates also show that firms producing with both foreign inputs and imported capital goods are 22 percent more productive than non-importers.<sup>9</sup> _Empirical fact 4: Producing with both imported inputs and foreign capital equipment goods improves firms’ global efficiency in the production process._ Given such complementarity, input-trade liberalization should affect firms’ decision to upgrade foreign technology in imported capital goods. The average tariff on variable imported intermediate goods fell 27 percentage points between 1989 and 1997.<sup>10</sup> At the same time that input tariffs drop, the share

> 6Simultaneity arises because firms’ variable input demands and unobserved productivity are positively correlated: the firm-specific productivity is known by the firm but not by the econometrician and firms respond to productivity shocks by modifying their purchases of variable inputs.

> 7Levinsohn and Petrin (2003) build upon the idea of Olley and Pakes using primary input demand (electricity) instead of the investment decision to control for unobserved productivity shocks. Their rationale lies in the idea that investment data are often missing or lumpy, whereas data on raw inputs are of better quality thus guaranteeing strict monotonicity without efficiency loss. The Prowess dataset reports information on electricity inputs so we rely on the LP methodology.

> 8We thank an anonymous referee for suggesting this control variable.

> 9Note that this evidence gives just an empirical motivation of the model assumption of complementarity between imported capital goods and intermediate inputs. Although the production function is estimated pooling industries, the estimation includes 3-digit industry fixed effects.

> 10Input tariffs are computed as tariff on variable intermediate goods other than capital equipment goods at the 3-digit industry level as described in section 5.1.

10
up all standardized variables, constructing a skill index which varies at the worker-level. For instance, for the non-routine manual category, we construct a skill index that is the sum of the two standardized components (“Operating Vehicles” and “Control/Feel Objects; Manual dexterity”). These skill indexes are standardized over the entire distribution, using the sampling weights. Finally, the indexes are collapsed at the occupational level (1-digit), using again the sampling weights. These occupation-specific indexes are calculated both for the pooled STEP sample and for each specific STEP country. The final skill indexes vary at the level of occupations, with a scale that depends on the underlying distribution. For the sake of concreteness, a 1-unit differential across occupations in a given skill is interpreted as 1 standard deviation of the whole distribution of that skill among the employed workforce of all STEP countries. When applying these indexes to other developing countries that do not have a STEP survey, we use those calculated for the pooled sample (i.e. not the country-specific ones).

Using the STEP surveys to measure skills, rather than relying on O*NET, has the obvious advantage that it allows us to investigate whether the skill content of jobs differs across countries. Given that we can independently estimate occupation-specific skill indexes, we do not need to assume that different countries use the same technology or have the same labor force. Nonetheless, a couple of caveats need to be made: first, the mapping between tasks in the STEP variables and skills is not trivial; second, we need to assume that workers do not differ in their way of reporting the tasks performed at work (which may be problematic in the case of subjective opinions); and third, by excluding rural areas, the sample under-represents the agricultural sector, which represents a significant fraction of employment in the developing world.

Our analysis is based on the ISCO-08 occupational classification at the 1-digit level. We do not use a higher level of disaggregation for two reasons. First, because for most of the countries covered in STEP surveys, the sample size is not large enough to make reliable inferences using more detailed occupations, as many of the cells would contain very few observations or be empty. Second, since the second goal of this paper is to make comparisons across countries and across time, it is not feasible to harmonize the occupational classifications for all the household surveys (which are around 600 in this study). This is because in addition to changes in the ISCO over time, many countries use their own-specific occupational categories that are difficult to map to ISCO at finer disaggregation levels. Appendix 3 shows that while the level of aggregation may affect the 9