rafmacalaba/datause-encoder-data huggingface dataset:
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other. Third, a local firm can also make a consortium with foreign firm(s). Fourth, it is possible that more than one foreign bidder submit a joint bid together. Finally, a foreign bidder may choose to participate in an auction alone.

<!-- Start of picture text --> Local<br>solo<br><!-- End of picture text --> <!-- Start of picture text --> Local-<br>Local foreign Foreign Foreign<br>joint JV joint solo<br>Joint bidding<br><!-- End of picture text --> <!-- Start of picture text --> Foreign<br>solo<br><!-- End of picture text --> Our data on procurement auctions are collected from ODA-related infrastructure projects assisted by the Japanese Government and the World Bank. It contains 221 public contracts for road, water and sewage, and electricity projects in 29 developing countries. In 221 auctions, 862 firms and bidding consortia are identified ( see Estache and Iimi (2008) for details).

These firms and consortia were composed by 1,656 individual firms.<sup>4</sup> Local firms who participated—either solely or jointly—in the procurement process amount to 60 percent in the road sector (Table 1). In the water and sewage sector the share of local firms reaches as much as 75 percent. By contrast, foreign firms are much dominant in the electricity sector; about 70 percent of bidders come from abroad.

The probability of a firm making a bidding coalition is highest in electricity projects and lowest in road procurements. Half of firms choose to jointly bid in the former sector. The

> 4 If a firm participates in more than one auction, they are double counted. We accounted for different names of a single firm in our sample data, but only at the primary level, meaning that it is accounted for if they are clearly the same company (e.g., misspelling and abbreviation) or if a local firm clearly represents its parent company. However, we have _not_ taken into account the potential capital relationships between firms because of technical difficulties. It is also ignored the possibility that some firms are intended to be subcontracted performance of part of the work by other firms.
Figure 1 shows estimates of the impact of winning the lottery on the probability of attending daycare for at least 0, 1, 2, 3, and 4 years.<sup>11</sup> Treatment children were 20 percentage points more likely to ever attend daycare than control children. Treatment assignment increased the probability of attending daycare for 3 years or more, for example, by 10 percentage points. Appendix Table 3 shows the corresponding regression estimates.

**Figure 1: First Stage**

_Notes_ : The figure shows that treatment children were more likely to attend daycare than the control. The bars show intent-to-treat estimates of the effects of daycare attendance on the fractions of study participants attending daycare for at least 0, 1, 2, 3, and 4 years. The brackets show 95% confidence intervals. _N_ = 1,857 .

Children selected in the lottery were guaranteed a slot in 2008 in the daycare center that they had applied to, but enrollment was optional. Being placed on the waiting list also did not prevent control children from attending childcare, since they could apply for multiple daycare centers, be eventually called from the waiting list, attend a private daycare center, or enter the lottery in the following years. Therefore, a non-negligible fraction of the control children eventually attended daycare in 2008 or in subsequent years.

> 11 The surveys conducted by Attanasio et al. (2021) in 2008, 2012, and 2015 collected detailed data on daycare attendance in each semester. The variable _years of daycare attendance_ is 0 if a child never attended daycare, 1 if a child attended for 1 or 2 semesters, 2 if she attended for 3 or 4 semesters, 3 if she attended for 5 or 6 semesters, and 4 if she attended for more than 6 semesters. The variable is available for only 1,857 participants because some of the children who participated in the incentivized experiments were not interviewed in 2008, 2012, or 2015.

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electricity, natural gas, telecommunications, railroads, and water supply. Common problems included low productivity, high costs, bad quality, insufficient revenue, and shortfalls in maintenance spending (see World Bank (1994) and Kessides (2004)). The low productivity of public investment is apparent in recent growth accounting exercises showing that contribution of physical capital accumulation to growth in MENA countries has declined over time, despite the fact that there has been no attempt in the existing literature to separate explicitly the growth in public and private capital.<sup>2</sup> The extent to which public investment especially in infrastructure complements or crowds out private investment and the role of quantity versus quality in the productivity of public investment in MENA remain largely unknown. To date, there have been few empirical studies focusing on these issues in the region (see Appendix A for a review of studies linking public investment to private capital formation and growth in developing countries). In an early paper on Egypt, for instance, Shafik (1992) found that public investment tends to crowd out private investment through its effect on credit markets, and to crowd it in through investment in infrastructure. Everhart and Sumlinski (2001), using panel regression techniques and a proxy for the quality of public investment, find no significant effect of public investment on private investment in MENA. Dhumale (2000), using a model that accounts for credit to the private sector and the accelerator effect, finds that public investment in infrastructure appeared to have a crowding out effect in oil-exporting countries, and a crowding-in effect in the non oil-exporting countries. Mansouri (2004) finds that public capital had a positive effect on private investment in Morocco.

However, existing studies are lacking in at least three respects. First, they seldom make a clear distinction between the _flow_ effect of public investment, and the _stock_ effect of public capital. But this is crucial, given that the transmission channels

> 2 The same observation applies to the growth in total factor productivity, which has been either negative or below international levels since the 1980s (Nabli and Keller (2002)). The efficiency of public spending on education has also been low, limiting the contribution of advances in educational attainment in the region to growth (see Pritchett  (1999) and World Bank (2004)).

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poverty line only 11 percent higher in urban than<sup>rural areas. As a result, the poverty</sup> incidence in urban areas, which is 16.3 percent,<sup>is less than a half of the poverty rate</sup> in rural areas, which is 34.1 percent.

The BPS method, meanwhile, uses references<sup>groups that are chosen reflecting</sup> an _assumption_ of higher costs of living in<sup>urban than rural areas. They choose a</sup> reference group range that is non-overlapping<sup>(the lower limit of urban is Rp. 80,000,</sup> which is the same as the upper limit of rural) and<sup>which is between 25 and 33 percent</sup> higher for urban areas. The result is a poverty line<sup>that is 27 percent higher in urban</sup> than in rural areas. Not surprisingly, the poverty rate in<sup>urban areas by this method,</sup> which is around 20 percent, is 77 percent of that in<sup>rural areas, which is 25.9 percent.</sup> So, in spite of much lower nominal expenditures, the cost of attaining<sup>the poverty</sup> basket is _assumed_ to be much lower in rural<sup>areas. This implies that the differences in</sup> poverty rate between urban and rural areas<sup>are possibly as much an artifact of method</sup> and assumptions as they are a finding of "fact"- the<sup>poverty line is higher because it</sup> is assumed to be higher. However, there is no double<sup>check within the BPS method</sup> on the initial assumptions about the appropriate reference<sup>groups</sup>

# _E) Changes in regionalpoverty during the crisis_

During the crisis, there has been a significant<sup>deterioration in household</sup> welfare (Skoufias _et al,_ 2000). If this is true, we<sup>can expect that this will be reflected</sup> in poverty incidence. We examine this by comparing Susenas<sup>February 1999 and</sup> Susenas February 1996 -one and a half years before<sup>the crisis started. Specifically,</sup> the question we want to answer is that given the level<sup>of welfare implied by the</sup> 16
duration of outages each time. This result suggests that firms on average would prefer shorter, more frequent shortages than fewer, long-lasting outages.

# **5.3 Heterogeneity by Sector**

Figures 5 and 6 show the impacts of the duration of outages on firm’s total output value and value-added by sector, respectively. Figures 7 and 8 show impact of the frequency of outages on firm’s output value and value-added, respectively. All results are based on sector-by-sector regressions.

The results indicate significant heterogeneity in impacts by sector. In the duration regressions, sector 16 (i.e. the manufacture of wood and products of wood and cork, except furniture) has the largest effect in absolute value, with an elasticity of roughly - 0.24 in Figure 5, but it is imprecisely estimated, due to a small sample of observations. In the frequency regressions, sector 18 (printing and reproduction of recorded media) has the largest effect, with an elasticity of -0.4, again imprecisely estimated.

Of the significant impacts we estimate, sector 15 (manufacture of leather and related products) has an elasticity of roughly -0.15, and sector 17 (manufacture of paper and paper products) has a precisely estimated effect close to the overall average, with an elasticity of -0.06. Sectors 24 and 25, both of which deal with the manufacturing of metal products, have larger than average elasticities in the range of -0.15 to -0.20.

# **6. Discussion**

Pakistan faces severe power shortages, but the question remains as to what impact shortages have on the national economy. Using a survey of 4,500 manufacturing firms in Punjab for 2011, we provide new empirical evidence on the impact power shortages have on firm productivity in Pakistan. Though the data in this study are limited to a cross-section, results in the paper suggest that there is indeed a strong negative correlation between electricity shortages and manufacturing revenues and value-added in Pakistan.

We use two measures of the reliability of power supply: the duration and frequency of power outages. Our baseline estimates suggest that the elasticity of the value of output to total duration of outages is roughly -0.014. That is, a 10 percent increase in the duration of outages on average leads to a 0.14 percent decrease in a firm’s total value of output. On the value-added side, the elasticity is more than twice as large; a 10 percent 10
_Tax Reform in Transition_ _5_

**Table 1: Tax Revenue and Fiscal Deficit, 1991-1997**

# **(in percent of GDP)**

|Tax|1991|1992|1993|1994|1995|1996|Est.<br>1997|
|---|---|---|---|---|---|---|---|
|Income tax|3.8|_5.4_|_5.0_|4.4|4.2|4.7|4.7|
|Profits tax|17.3|6.8|2.2|3.7|3.8|4.5|4.5|
|Social security<br>contributions|7.8|10.7|10.1|8.9|8.0|7.3|7.3|
|Excises and customs|4.8|4.6|6.8|6.2|5.2|3.7|3.7|
|Turnover/ VAT|3.8|3.6|3.5|7.3|6.8|7.0|7.0|
|Other taxes|0.0|2.0|1.3|1.3|1.7|1.0|1.0|
|Totaltaxrevenue|37.5|33.1|28.9|31.7|29.7|27.9|27.9|
|Fiscal Deficit|8.2|7.2|10.9|6.4|5.7|13.4|4.4|
|memo. item||||||||
|GDP growth|-11.7|-7.3|-1.5|1.8|2.1|-10.9|-7.4|

_Source: Hassan and Bogetic (1996); World Bank (1997)._ Before discussing the revenue potential, it is necessary to outline the actual tax structure and the associated actual revenue performance. The 1992 personal income tax exemplified a steeply graduated tax-rate schedule (see Table 2). There were seven tax brackets including the lowest tax-free bracket. The exemption level for the poor was low, comprising individuals earning up to 9,000 Lev, or about 50 percent of average household per capita income as reported in the 1992 household budget survey (nearly 88 percent of the minimum annual wage). There was a progressive scale, with marginal tax rates ranging from 20 percent to a maximum of 40 percent at annual income over 240,000 Lev.
program has continued throughout the 1990s. Under the Ministry, the program has enlarged considerably in terms of human and financial resources. A typical MVR in this period is based on information gathered through monthly government inspections on about 10,000 air and water polluting facilities, using a total of approximately 15,000 man-days from local governments and Regional Environmental Offices. Since November 1990, the Reports are also publicly disclosed through the Ministry of Environment official website, thus widening considerably its public outreach.<sup>7</sup> While similar in spirit to the U.S. EPA’s Toxics Release Inventory (TRI), the Korean MVR differs significantly from the TRI in that it reports the names of companies that are effectively deemed to be _in violation_ of Korean environmental laws, as well as the nature of enforcement actions undertaken by the Ministry. The TRI is limited to reporting quantities of toxic wastes produced by a set of facilities, without attempting to assess the compliance status of these facilities. To this extent, the Korean MVR is conceptually similar to the lists by the Ministry of Environment, Lands and Parks of British Columbia (Canada) which aims to publicly disclose firms that either do not comply with the existing regulation or whose environmental performance is of concern to the Ministry.<sup>8</sup> Over the period 1993-2002,<sup>9</sup> a total of 7,073 violation events appeared on a total of 113 violation lists published on a monthly basis. Over this period of observation, a

> 7 See: www.me.go.kr

> 8 See Foulon et al. (2002) for more details.

> 9  The Koran regulation pertaining to the length of time that information must be stored is such that all records previous to 1993 are unfortunately no longer available. While in this paper we shall refer to the

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foreign bank participation in Turkey between 1980 and 1997. The study shows that foreign bank entry had a positive impact on competition in the sector as witnessed by the decline in profits and overhead costs. Looking at the performance of 219 banks, between 1995-2001, from a sample of ten countries in Central and Eastern Europe (Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovenia, and Slovakia), Uiboupin (2004) offers evidence consistent with the notion that foreign bank entry increased competition in those countries. In particular, the study finds that foreign entry is associated with lower profits, non-interest income, and loan interest rates.

In the case of Latin America, however, the evidence on the impact of foreign bank entry on bank competition and efficiency is mixed. Studies such as Barajas, Steiner, and Salazar (2000) and Martinez Peria and Mody (2004) offer evidence in favor of the hypothesis that foreign bank participation improves efficiency and competition. On the other hand, others such as Haber and Musacchio (2005), Schulz (2006) and Levy-Yeyati and Micco (2007) provide evidence to the contrary.

Barajas, Steiner, and Salazar (2000) study the competitive effect of foreign entry in Colombia, using panel estimation and controlling for other aspects of financial liberalization (including the number and relative size of new domestic entrants and overall increases in capital flows). They find that foreign entry improved the efficiency of the domestic banking system by reducing nonfinancial costs. Similarly, Martinez Peria and Mody (2004) offer evidence consistent with the notion that foreign bank participation had positive implications for the Latin American region enhancing efficiency and competition. Focusing on Argentina, Chile, Colombia, and Mexico, Martinez Peria and Mody (2004) find that foreign bank participation did not affect spreads directly, but caused a drop in administrative costs. This result is consistent with the notion that foreign participation improved efficiency and fostered competition.

In the case of Mexico, foreign bank participation has not been found to increase competition and efficiency. Haber and Musacchio (2005) show that the entry of foreign banks led, instead, to a retrenchment in lending and no improvements in efficiency and competition. They argue that this is related to the fact that Mexico had an extremely concentrated banking system both before and after foreign bank entry. Focusing also on the case of Mexico, Schulz (2006) shows that foreign bank entry from 1997 to 2004 had no effect on administrative costs and employment levels. He too argues that this lack of impact on the overall efficiency of the 11
commodities (e.g. fossil fuels) quickly enough to adapt to the new market conditions when the mitigation policies are started to be implemented.

<!-- Start of picture text --> 25<br>90<br>20<br>76<br>80 20<br>70<br>60 53 15 12<br>50<br>10<br>40<br>5<br>30 24 5<br>20 12<br>0<br>10 4<br>0<br>0<br>-5 -3<br>-10 -2 -4 -5 -5<br>-7 -6<br>-20 -10<br>2025 2030 2035 2040 2025 2030 2035 2040<br>Net Zero No lab. mar. frictions Net Zero No lab. mar. frictions<br>% difference from baseline % DIFFERENCE from baseline<br><!-- End of picture text -->

**Figure 6:  Electricity demand (left) and price (right)**

The impact of higher electricity price is significant in all scenarios. The annual growth rate falls back to the baseline average of 4.5 percent or even below after 2037 when the demand for additional electrification increases (see figure 7). Thus, although GDP is higher in level compared to the reference case, the growth rate is lower as early as 2035 in some scenarios. Supply also contributes to fluctuations in electricity prices as the switch from fossil fuels to renewables occurs, filling the gap left by stranded fossil assets. Increasing the supply of low-carbon electricity by removing barriers to system expansion would mitigate inflationary pressure on prices. Our simulations show that labor market frictions and subsidies for renewable energy production support more robust economic growth, in particular through more affordable electricity. This result highlights the importance of well-designed electricity tariffs to protect the rest of the economy and facilitate its electrification.

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country i with respect to country j was lower in 2003 than in 1990. The corresponding calculations are reported in Table 3.

The first column of the table reports that aggregate stocks in China have grown significantly faster than in Latin America during this period. Between 1990 and 2003 stocks in China grew two times faster than in Argentina, Chile and Colombia, three times faster than in Brazil and Costa Rica and four times faster than in Guatemala. OECD stocks in China also grew faster than in Latin American countries.

Column 3 shows that the fall in the relative stocks is more dramatic in the U.S. data. For most countries, relative stocks in 2003 were less than 20 percent of its 1990 level. In Brazil, Colombia, and Costa Rica the ratios are below 0.1. This reveals that U.S. stocks in China caught up with those in Latin American countries. The relative decline was particularly remarkable in the manufacturing sector. Relative stocks in this sector in the major countries declined the most: stocks in Brazil, Mexico, Argentina, and Colombia were at 5 percent or less than its 1990 levels. Again, this reflects that China is relative more attractive to capital in the manufacturing sector.

The second panel in the table displays the changes in the stocks relative to those in China and Hong Kong (China). Column 5 shows that aggregate relative stocks in China and Hong Kong (China) grew faster than in most Latin American countries. However, this pattern is reversed when we focus on the stocks from OECD. Most countries from Latin America accumulated more stocks from the OECD than China and Hong Kong (China) during this period. In fact, only in Brazil and Guatemala the relative stocks from the OECD turn out to be smaller in 2003 than in 1990.

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Evidence from Mexico indicates that non-wage factors are more important than wages in determining job transitions, and 86 percent of voluntary job exits are in fact driven by changes in family circumstances such as marriage or family care (Kaplan, Lederman and Robertson 2013).

When frictions to the labor mobility of workers are large, workers delay or altogether avoid adjustment, reducing the potential gains from trade. _Labor mobility costs_ are the costs perceived by a worker to move to a different firm or industry independent of the reason for the move. When, however, workers are unable to move in response to an exogenous shock because of labor mobility costs, the total costs incurred by workers – and the economy – are defined as _labor adjustment costs_ . Mobility costs not only slow the pace of adjustment to new jobs, but also reduce the extent of labor reallocation, which in turn affects equilibrium wages. As a result, if the costs of labor reallocation across industries and employers are high, the gains from trade might be reduced. Idle labor or labor stuck in lower productivity firms and industries implies lost incomes and diminished gains to growth (see Hollweg, Lederman, Rojas and Ruppert Bulmer (2014) for a comprehensive analysis of labor mobility and adjustment costs).

Preliminary evidence suggests these costs may be borne disproportionately by poorer households. Workers face high mobility costs, especially workers in developing countries. For many developing countries, limited data means that estimates of labor mobility costs and their correlates are possible only at the aggregated country level. Using widely available data on manufacturing employment levels and average wages by sector for 47 countries (using the UNIDO Industrial Statistics Database), Artuç, Lederman and Porto (2013) estimate that mobility costs for workers in developing economies average 4.93 times the annual wage, compared to 2.41 times the annual wage in developed countries. This result suggests that workers in developing countries will be more vulnerable to a trade-related shock due to the higher cost of switching jobs. The higher the mobility costs, the slower the transition to the new steady state in the labor market, which in turn reduces the potential gains from the trade liberalization. In other words, workers’ inertia results in foregone welfare gains from trade in terms of employment and earnings, reducing the positive welfare effects. Figure 7 illustrates the positive correlation between mobility costs and foregone gains.

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