rafmacalaba/datause-encoder-data huggingface dataset:
labor ratio). This is consistent with anecdotes that global retail chains tend to be leaders in adopting advanced retail technologies, from large sales rooms and warehouses to computerized inventory tracking systems. In terms of sales per worker, real wage per worker, and value added per worker, global retail chains exhibit a premium in terms of all three variables. They have higher sales per worker, higher labor productivity and tend to pay higher wages. Moreover, we find that global retail chains enjoy larger market shares. However, we do not find any differences in terms of profitability measured by return on assets and return on sales.

To summarize, although their entry into Romania lagged behind their expansion in other more advanced transition countries in CEE, global retail chains expanded rapidly in Romania since 2000. Their expansion was uneven across regions with Bucharest area receiving the first and the most entries. Overall, global retail chains have played an increasingly important role in the sector and accounted for over one fifth of the total retail sales in 2005. Our simple econometric analysis finds that global retail chains differ significantly from other retailers in the country. They are larger in scale and more capital intensive. They enjoy higher labor productivity and larger market share. Their rapid expansion and larger size suggest that they may have greater bargaining power vis a vis suppliers while at the same time offering them access to a larger market and lower costs. In short, the presence of global retail chains has brought significant changes to the landscape of the retail sector in Romania. In the next section, we explore the implications of their presence on the performance of the supplying industries, which is the main objective of this study.

# **6. Impact on the Total Factor Productivity in the Supplying Industries**

# **6.1 Identifying Assumptions**

In our analysis of the relationship between the presence of global retail chains and the performance of the supplying industries, we take advantage of regional variation in foreign chains’ expansion. We rely on the Nomenclature of Territorial Units for Statistics (NUTS) and divide Romania into eight NUTS regions with an average territory of 29,800 square kilometers.<sup>10</sup> We focus on the changes in suppliers’ performance following the entry of foreign chains into their region. Our presumption is that the impact of global chains’ entry tends to be limited to the regional level.

> 10 The 8 regions are Bucharest-Ilfov, North East, South East, North West, South West, South, West and Center.

11
us to see which of these potential problems from such exclusions appear to be empirically important.

In addition, we test for the effects of corporate governance on bank portfolio allocations of funds between loans and other assets, across types of loans, across industries, and across regions. The portfolio reallocations help us to analyze the sources of change in bank performance associated with governance changes, e.g., whether profits increased because of a shift into higher-return types of loans.

Our data set on Argentina in the 1990s provides an excellent laboratory for examining these research and policy issues. Argentine banks underwent significant dynamic changes of all types during this period, including the migration of more than one-third of the banking assets to foreign control and the movement of more than half of the credit in some provinces from provincial government control to private control. The data set includes quarterly information on virtually all Argentine banks from 1993:Q2 to 1999:Q4, avoiding the potential for significant sample selection biases. The data set allows us to employ multiple measures of bank performance and portfolio allocations to guard against the findings being driven by the choice of a single performance or portfolio measure.

Section 2 reviews some of the research literature on the performance effects of corporate governance in banking. Section 3 gives background information on the Argentine banking system in the 1990s. Section 4 shows our empirical models and variables, and Section 5 displays our empirical results. Section 6 concludes.

# **2.  Literature on bank governance and performance**

In this section, we briefly review some of the research literature on the performance effects of corporate governance in banking, including domestic ownership and M&As, foreign ownership and acquisitions, and state ownership and privatization.

# **2.1. Domestic ownership and M&As**

Studies of domestic bank governance generally focus on the performance effects of bank scale or domestic M&As, and typically do not account for static differences in performance between domesticallyowned banks and their foreign-owned or state-owned rivals, if any. Much of the research uses U.S. data. Research on bank cost scale efficiency using data on U.S. banks from the 1980s generally finds very little scale economies or diseconomies (e.g., Berger, Hanweck, and Humphrey 1987), although data from the 1990s suggests that there may be more cost scale economies than in the 1980s due to technological progress (e.g., Berger and Mester 1997). Some also find possible revenue benefits for large banks in the 1990s (e.g., Demsetz and Strahan 1997). Other research finds that large and small banks may serve 3
Each household is endowed with three types of labor: skilled, semi-skilled and unskilled. These are distinguished by educational attainment of the worker<sup>7</sup> with semi-skilled workers having a middle- or high school education, and skilled workers having an educational attainment beyond high school<sup>8</sup> . Households are also endowed with profits from family-owned agriculture and non-agriculture enterprises, property income and transfers. Agricultural profits represent returns to family labor, land and capital. However, as noted above, the off-farm labor supply decision is a function of the combined return to labor and land in agriculture, owing to the absence of an effectively functioning land market in many rural areas.

Specification of the value of the off-farm labor supply elasticity draws on the econometric work of Sicular and Zhao (2004). Those authors report results from a household labor supply model estimated using labor survey data from the 1997 CHNS data set for nine central provinces. This survey measures the labor supply of individuals within each household to farm and non-farm activities. Sicular and Zhao estimate the implicit wage for each individual in the sample if they were to work in agriculture or non-agricultural self-employment, and they also estimate the non-agriculture wage that this person could obtain. They then estimate labor supply equations for self-employed agricultural labor, self-employed non-agricultural labor, and wage labor. From these equations, it is possible to calculate elasticities of labor transfer from farm to non-farm activities. They report a variety of elasticities in their paper.9 We adopt their estimate

> 7 We would prefer to base this split on occupation – what they actually do – versus their potential as determined by education. However, the rural household survey does not support this type of  labor split. 8 Since the rural survey only reports the highest educational attainment of the household we do not have endowment by worker. This biases the skill level of rural households upwards. However, since the vast majority of rural households are unskilled, this is less of a problem in practice. 9 Due to the variety of labor supply elasticities in response to the three different wages in their model, the authors obtain a variety of labor transfer elasticities, depending on the “thought experiment” being conducted. These are asymmetric, with the response to a change in shadow wages differing from the response of labor supply to a change in the market wage. However, this response is treated as symmetric in our model. This makes it difficult to choose the correct parameter for our analysis. We focus on the transfer of labor from agriculture to market wage employment in response to a change in returns to agriculture, since this transfer accounts for the bulk of the labor flow in our analysis. In a companion paper to this study which focuses exclusively on

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Table 1: Proportions in the population

||1999|2000|2001|2002|2005|2006|2007|
|---|---|---|---|---|---|---|---|
|Male|51.2|50.8|50.6|50.9|53.6|52.4|50.6|
|_Female_|_48.8_|_49.2_|_49.4_|_49.1_|_46.4_|_47.6_|_49.4_|
|Indigenous|63.8|53.5|58.5|54.8|54.7|50.9|51.3|
|_Non-indigenous_|_36.2_|_46.5_|_41.5_|_45.2_|_45.3_|_49.1_|_48.7_|
|Urban|43.1|46.5|43.3|49.2|45.3|56.7|57.4|
|_Rural_|_56.9_|_53.5_|_56.7_|_50.8_|_54.7_|_43.3_|_42.6_|
|Spanish|99.1|97.0|93.7|91.2|98.7|96.8|95.0|
|_Non-Spanish_|_0.9_|_3.0_|_6.3_|_8.8_|_1.3_|_3.2_|_5.0_|
|Ed M Head|-|-|27.8|20.0|26.9|32.2|29.9|
|_Non-ed M Head_|-|-|_72.2_|_80.0_|_73.1_|_67.8_|_70.1_|
|Ed F Head|-|-|4.1|10.0|5.5|5.3|6.6|
|_Non-ed F Head_|-|-|_95.9_|_90.0_|_94.5_|_94.7_|_93.4_|
|Extr. poor|78.4|54.4|55.6|54.3|52.4|48.5|53.4|
|_Non-extr. poor_|_21.6_|_45.6_|_44.4_|_45.7_|_47.6_|_51.5_|_46.6_|
|Observations|2912|4622|5848|5614|1187|3102|3165|

Source: MECOVI.

Table 2: Enrollment, Attendance and Working

||1999|2000|2001|2002|2005|2006|2007|
|---|---|---|---|---|---|---|---|
|Enrollment (%)|90.8|91.1|90.9|91.1|94.6|94.2|95.6|
|Attendance (%)|53.2|71.2|49.5|55.0|62.1|75.4|50.6|
|Working (%)|26.5|19.1|22.2|20.3|17.04|19.5|21.9|

Source: MECOVI.

20
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of which have complete data on all 12 variables. We also point to a data pitfall that should be avoided before doing the factor analysis. Section 4 contains the empirical results and the main contribution of the paper. We analyze principal components separately from the true factor analysis results. There are three main components to this section. The first is the use of factor loadings in order to name the factors. We show that with the KAM data we are able to achieve a fairly simple structure. The second is a set of formal tests for the dimensionality. The third is another methodological pitfall, whose resolution confronts us with a tradeoff. We indicate how and why we choose to resolve this in the manner we do. The choice is obvious from our overriding objective of computing factor scores as precisely as possible. Section 5 discusses the output from this factor analysis. We use graphs to show how countries have changed their rankings on the underlying dimensions over this ten year period. Section 6 concludes.

# **2. Factor Analysis Models**

The notation and material in this section borrows from Reyment and Joreskog (1993, Sections 2, 4). The general factor analysis model is where **X** is the data matrix of _p_ variables, **F** is the matrix of _k < p_ factors, and _N_ is the sample size. The _k × p_ “factor loadings” matrix **A**<sup>_′_</sup> is used to linearly sum the factors to predict each column of **X** . What cannot be predicted is collected in the error matrix **E** .

In the context of the KAM data, each column of X is a measure (i.e. variable) containing “scores” for a set of _N_ countries. There are _p_ such measures on which country scores have 4
Herfindahl index (H) of exports for each country using the HS 4-digit, HS 6-digit, and SITC 3- digit export data, and estimate the following equation<sup>6</sup> : We use GDP per capita rather than the log of GDP per capita to remain consistent with the approach of Imbs and Wacziarg (2003). The results, summarized in Table 3, indicate that, similar to total production, a country’s export basket becomes more diversified as income rises until a relatively high level, at which point the process reverses itself and specialization occurs. This result is also found when employing a fixed-effects OLS regression on the SITC data from 1972-2002.

**<u>Table 3: Stages of Export Diversification</u>**

|**Variable**|**HS 4-Digit**<br>**1995**|**HS 6-Digit**<br>**1995**|**SITC 3-Digit**<br>**1972-2002 (FE)**|
|---|---|---|---|
|Coefficient on GDP per capita|-.0000333<br>(-4.55)|-.0000183<br>(-3.07)|-.0000173<br>(-7.01)|
|Coefficient on GDP per capita squared|9.53e-10<br>(3.30)|5.25e-10<br>(2.44)|4.98e-10<br>(6.71)|
|Minimum Point(highest levelofdiversification)|$17,471|$17,429|$17,369|
|AdjustedR-Squared (OLS) /F-Statistic (FE)|.2683|.2047|60.79|
|NumberofObservations / Groups|100|53|146|

Note: parentheses indicate t-statistics. Source: Author’s calculations All three data types yield a level of GDP per capita at which economies switch from diversification to specialization between $17,350 and $17,500. Using domestic production data, Imbs and Wacziarg (2003) find a switching point in the $13,150 to $14,600 range (in equivalent 1996 dollars), which is slightly lower than our findings using export data. These results support

> 6 Measures of GDP per capita are 1996 PPP values from PWT 6.1 (Aten, Heston and Summers 2002). See (3) in section 2 for the definition of the Herfindahl index. The value of the Herfindahl index ranges from 0 to 1, with lower values indicating greater diversification of export earnings.

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3 finding is consistent with the view that free riders who punish want to increase the payoff difference between themselves and the cooperators.

Social psychologists also have found evidence for spiteful preferences (Paul A. M. van Lange 1999). Van Lange examined the social preferences of more than 2000 subjects in the Netherlands with the ring test (Wim B. G. Liebrand and Charles G. Mcclintock 1988) and found that roughly 12-13% of them are willing to pay for increasing inequality. These subjects prefer, for example, the allocation (480 for self, 80 for other) relative to the allocation (540 for self, 280 for other), thus sacrificing total surplus and equality for the sake of a larger payoff difference between “self” and “other.” In a wide variety of settings, spiteful preferences would constitute an obstacle to trade, cooperation and, thus, development. A spiteful individual is willing to forgo material gains from trade unless the terms of trade give him a large share of the pie. A spiteful individual is also more likely to violate contracts – either by providing low effort or low quality or by a lower willingness to pay the bill – because such contract violations increase the shirker’s payoff at the expense of the trading partner. If spiteful shirking is anticipated, it will further decrease the willingness to trade. A spiteful individual is harder to motivate to cooperate because he has a higher marginal cost of contributing to public goods or joint activities: First, he bears the pecuniary costs like all other individuals and, second, he has a nonpecuniary cost because any contribution may reduce the difference in material payoffs between himself and others. Finally, spiteful punishment also has a detrimental impact on cooperation because it diminishes the _net_ incentive for cooperation that emerges from the punishment of defectors. If defection _and_ cooperation are punished, the potential free-riders have less incentive to cooperate. In fact, Gächter and Benedikt Hermann (2007) provide evidence from experiments in Russia indicating that punishment opportunities may fail to reduce free-riding because of the punishment of cooperators.

# **II. Spiteful Punishment in India**

We conducted a sequential, one-shot, exchange game with third party punishment in Uttar Pradesh in order to study the potential impact of an extreme social hierarchy on the willingness of disinterested third parties to punish violations of informal agreements. In our experiment, three players – A, B and C – are involved and have an endowment of 50, 50 and 100 rupees, respectively. Fifty rupees are roughly equal to one day’s unskilled wage. Players A and B
DKK 307 billion (€ 41 billion) in 2004. It is one of the largest financial institutions in Denmark.

The second supplementary scheme, introduced in 1977, is **the LD scheme** . Contributions to this scheme were paid into individual accounts as a trade off for wage increases. The LD scheme has not received any contributions or new members since 1980.

The third supplementary scheme, the **SP (Special Savings Pension) Scheme,** was introduced in 1998 with a view to dampening economic activity and increasing savings. Contributions to the SP were suspended for 2004 and 2005 and the suspension was later extended to 2007.

Pension institutions used to invest heavily in Danish bonds, especially Danish mortgage bonds that enjoyed a higher yield over government paper while being considered as highly safe. However, in the 1990s, following a liberalization of investment rules, pension institutions expanded their investments in equities, especially foreign equities. In 2000 the average asset allocation included nearly 40 percent in equities, while pension institutions with a high solvency ratio were allowed to increase their allocation to equities to up to 70 percent.

**Figure 3:  Asset Allocation**

<!-- Start of picture text --> Asset allocation 1999-2004 Asset class 2004<br>100,0%<br>Real property<br>80,0%<br>Other investments<br>Shares incl. subsidiaries<br>60,0% Bonds<br>40,0% Shares incl. subsidiaries Bonds<br>Real property<br>20,0%<br>Other investments<br>0,0%<br>1999 2000 2001 2002 2003 2004<br>year<br><!-- End of picture text --> Source: Danish Financial Supervisory Authority Following the collapse of equity prices between 2000 and 2002, the equity allocation was substantially reduced, both as a result of changes in relative prices (equity prices fell while bond prices rose in response to the substantial decline of interest rates) and as a result of portfolio reallocation strategies. Pension institutions were required by the regulators to hedge their long-term risks and many proceeded to invest in derivatives, especially interest rate swaps, in order to hedge their liabilities against further declines in interest rates. At the end of 2004, 68 percent of total assets were invested in bonds, 20 percent in equities, 7.5 percent in real estate and 4.5 percent in other assets.

7
# Table **5** Average Per Capita GDP Growth by Regiona

||**Per Capita GDP**|**Growth in**%b|Observations|
|---|---|---|---|
||**S1985**<br>**nt'l prices**|**1987 US dollars**||
|All Countries||||
|1964-73|3.1|3.0|112|
|1974-83|0.9|1.0|126|
|1984-93|_0.5_|0.7|137|
|High Income OECD||||
|1964-73|4.0|3.9|22|
|1974-83|1.4|1.6|22|
|1984-93|2.1|1.8|22|
|All Developing Countries||||
|1964-73|2.8|2.7|87|
|1974-83|0.9|0.8|97|
|1984-93|0.2|0.4|108|
|Sub-Saharan Africa||||
|1964-73|2.0|1.7|35|
|1974-83|0.1|0.0|38|
|1984-93|-0.6|-0.3|43|
|Latin America and Carribean||||
|1964-73|2.8|2.8|25|
|1974-83|0.1|0.2|27|
|1984-93|0.7|1.2|29|
|East Asia and Pacific||||
|1964-73|4.7|4.9|11|
|1974-83|3.3|3.8|12|
|1984-93|2.9|3.0|15|
|South Asia||||
|1964-73|-0.4|_0.5_|_5_|
|1974-83|2.8|2.2|5|
|1984-93|2.4|2.5|_5_|
|Middle East and North Africa||||
|1964-73|5.2|4.5|9|
|1974-83|-0.8|-0.0|13|
|1984-93|-2.5|-2.4|14|
|Europe and Central Asia||||
|1964-73|5.4|_5.5_|_5_|
|1974-83|2.8|2.7|8|
|1984-93|0.6%|0.3%|8|

'Source: PWT5.6 and World Bank

**b** Per capita GDP growth in 1985 international prices only available up to 1992; unless otherwise noted, all regional breakdowns exclude high income countries; East Asia and Pacific includes high income countries that are not OECD members; Middle East and North Africa includes one high income country.

28
3 examine this possibility by testing for equality in baseline characteristics for the sample interviewed in the follow-up survey. In Table 2 we see the means for the follow-up sample look similar in magnitude to those of the full sample answering the baseline, and we only reject equality of coefficients in 5 out of 48 cases at the 10 percent level, which is consistent with random chance. Table 3 looks at balance on the listing variables and the small number of follow-up survey variables which are likely to be time invariant. We find only 3 out of 22 coefficients significant at the 10 percent level, and 1 at the 5 percent level, which is again consistent with random chance.

The presence of administrative data (detailed below) allows us to test further whether there is differential attrition in the follow-up survey by the key outcome of interest: whether or not a firm has formalized during our intervention window. We estimate the following regression for firms in the baseline survey assigned to the control, communication, or free cost groups: InterviewedatFollowupi,s = α+ λ1Formalizedi,s + λ2Formalizedi,s ∗Communicationi,s + λ3Formalizedi,s ∗FreeCosti,s + βCommunicationi,s + γFreeCosti,s + ∑δsds + εi,s (4) Where _InterviewedatFollowup_ is an indicator of whether the firm was interviewed in the follow-up survey, and _Formalized_ is an indicator of whether they probably formalized during the intervention period according to our administrative data. Then λ1 measures whether firms in the control group which formalize are more or less likely to answer the follow-up survey than firms which did not formalize, and λ2 and λ3 measure whether any differential survey response varies with treatment status. We estimate the analogous equation similarly for the inspector versus control block groups.

Table 5 reports the results. For the baseline sample (who had already been interviewed once before), we cannot reject that whether or not the control group answered the follow-up survey is unrelated to whether or not they formalized during their intervention window. Moreover, likewise we cannot reject that there is no differential survey response with formalization according to treatment status. In the sample used for testing the effects of inspections, we do find that firms in the control group are more likely to answer the follow-up survey if they formalized during the intervention period. Nonetheless, they are not differentially likely to do so if they are assigned to either of the inspector treatments relative to being assigned to the control.

Taken together these results suggest that both the level of attrition and which firms attrit are not affected by treatment status, and so in what follows we estimate treatment effects ignoring attrition, as well as using administrative data to measure the key formalization outcomes free of attrition.

16