rafmacalaba/datause-encoder-data huggingface dataset:
|X____|c=**200**|c**= 500**|c_=_1,**000**|
|---|---|---|---|
|Ap= 10|4%|10%|21%|
|Ap=40|1%|3%|5%|
|: p= 100|0.42%|1%<br>|*2%|
|AEp-=200|0.21%|1%|1%|

Table 1: Congestion Fraction for a 200 km line

|l-i|**c=200**|_c_<br>c=500<br>c|_c_<br>=1,000|
|---|---|---|---|
|Ap= 10|10%<br>|*26%|52%|
|APp=40|3%|6%|13%|
|Ap= 100|1%|3%|5%|
|Ap=200|1%<br>|I 1%<br>|13%|

Table 2: Congestion Fraction for a 500 km line We consider three values of average cost for transmission lines. The lower value is $200 per MW.km. This corresponds to the value reported by the Brazilian electricity company Electrobras for a 500 kV line. The intermediate value is $500 per MW.km. This is slightly above the average value reported by NGC, the transmission company in the United Kingdom ($400 per MW.km). Finally, the upper value is $1, 000 per MW.km, reported by New England Power Service in the United States.

We also consider four values of Ap. The lowest value is Ap = 10. The highest value Ap = 200 is larger than the difference between the highest bids in the United Kingdom Pool ($190 per MWh) and the average Pool price ($40 per MWh). The discount rate is 6 _=+r)_ where r is the interest, set at r = 10%. The results are robust to sensitivity on the interest rate.

Table 1 shows that congestion less than 5% of the time is sufficient for a short line (200 km) to be financially viable for Ap 2 40, for all cost scenarios. Congestion less than 6% of the time is sufficient for an intermediate line (500 _kim),_ except for the high cost (Table 2). Large

||c=**200**|c**=**500|**c=1,000**|
|---|---|---|---|
|Ap= 10|21%|52%|104%|
|ip = 40|5%|13%|26%|
|Ap= 100|2%|5%|10%|
|Ap=200|1%|3%|5%|

Table 3: Congestion Fraction for a 1,000 km line 14
73. So while Romania can certainly continue to improve on rules and regulations governing product market regulations, the pending challenge has to do with improving practices.

74. **Without effective implementation and enforcement, the effects of policy measures to increase competition in product markets will not be visible.** For instance, notwithstanding the enormous progress evident from the dramatic improvement since the 2002 PMR, BEEPS data indicate that competitive pressures on incumbents remain low compared to other countries in the region and have not substantially changed between 2002 and 2005 (Figure 20).

**Figure 20** . Intensity of price competition in product markets

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Czech Rep. Romania Bulgaria Slovak Rep. Poland Hungary<br>2002 2005<br><!-- End of picture text --> Source: WB-EBRD BEEPS 2002 and 2005.

Note: Percentage of firms responding ‘Many of our customers would buy from our competitors instead’ to the question ‘Now I would like to ask you a hypothetical question. If you were to raise your prices of your main product line or main line of services 10% above their current level in the domestic market (after allowing for any inflation) which of the following would best describe the result assuming that your competitors maintained their current prices?’ 75. A series of prerequisites is necessary to improve the effectiveness of the application of regulation and, ultimately, to increase the long run competitiveness of the Romanian economy. These belong to the institutional domain and include:

- (i) **Political commitment at the highest level.** Ensuring political support from the highest level of government is crucial to generate consensus for reform both within and outside public administration.  In particular, leadership and support from the Prime Minister’s Office and the Presidential Administration will help in clarifying the allocation of functions and responsibilities among ministries and relevant government agencies with respect to the ownership of the reforms.

- (ii) **Changes in the institutional architecture of the government to improve oversight and coordination of reforms pertaining to product market liberalization.** Political support should be reflected in the reorganization of the institutional architecture of government with the objective of improving

29
for information intervention in Table A2).

**Observational data.** In addition to the two experiments, we also have a non-experimental contrast between the control group from experiment 1 and the treatment group from experiment 2. By comparing these two groups, we can estimate the effect of receiving the standard grant amount associated with the parental involvement program. In other words, this non-experimental contrast reveals the effect of providing grants to parent associations at the extensive margin (i.e., no grant versus single grant) whereas the double grant experiment focuses on the effect of providing grants at the intensive margin (i.e., single grant versus double grant).

As described in Section 2 and noted in Figure 2, schools in experiment 1 were those already receiving the “standard” parental involvement package of grants and information, while schools in experiment 2 were those that had not yet received the parental involvement program. Historically, the government selected schools to implement the parental involvement program based on an increasing function of indigenous student population. This means that schools in experiment 1 historically had larger proportions of indigenous students than schools in experiment 2. While we do not know the precise selection formula, we confirm in Figure 3a that the probability of being assigned to experiment 1 is strongly increasing in the proportion of indigenous students. This implies that schools in experiments 1 and 2 have different proportions of indigenous students, and therefore, we cannot simply compare across the two experiments.

Given our knowledge of the selection process to experiments 1 and 2, we trim our data to exclude indigenous schools – focusing only on general schools – when comparing across the two experiments to estimate the effect of the single grant. As shown in Figure 3b, dropping the indigenous schools imposes a common support restriction on the proportion of indigenous students and brings the distribution of indigenous students in treatment schools (single grant & information from experiment 1) and comparison schools (no grants & information from experiment 2) close together. Moreover, we drop one treatment school that has an indigenous student population above the 99th percentile of that of comparison schools. Figure 3c shows the distribution of treatment and comparison schools up to the 99th percentile cutoff (.94). This additional trimming procedure follows guidance from the matching literature, which suggests dropping treatment group observations with propensity scores above the 99th percentile of the propensity score in the comparison group as a way to establish 12
**Figure 4 Unit Values of Cambodia's Apparel Exports to the EU-15 and the United States**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>EU-15 Unit Values (Euro/Kg) US Unit Values ($US/Dozen)<br>unit price<br><!-- End of picture text --> _Source:_ EU-15: Eurostat; United States: U.S. International Trade Commission (USITC). _Note:_ For the U.S. values, only products measured in dozens are included.

The declining trends in unit values were expected. After the MFA phase-out, the competitive pressure in apparel exports increased, which inevitably lead to a decrease in apparel prices. According to the simple model described in Section 3.a, the decline in apparel prices should have resulted in apparel manufacturing wages decreasing in the short run and infemale wages in all industries declining in the long run (in approximately three to five years). The next subsection provides empiricalevidencefor these theoretical results.

# _4.3. Wages_

In this section, the paper investigates the changes in wage levels, as well as the male-female wage differential and apparel sector premiums before and after the MFAphase-out. The analysis shows that, on average, wages were higher in textiles and garments than in agriculture in both countries (see table 2). In Cambodia, wages paid in apparel manufacturing were higher than the average wages in the economy. However, in Sri Lanka apparel wages were slightly lower than the economy-wide average.

12
have often been captured by country fixed effects. However, this cannot be applied to a crosssection analysis such as ours. Furthermore, many other characteristics of a country not necessarily related to sunk costs may be captured by the fixed effects.

In our study, we interpret sunk costs as the costs associated with entering the market and starting up a business. We include two separate measures of these costs. Our first measure controls for the minimum capital requirements banks need to comply with to begin operations in the countries in our sample. These data comes from a worldwide survey of bank regulators summarized by Barth, Caprio, and Levine (2001). We use data on the actual monetary amount of capital (expressed in U.S. dollars) banks have to put down and we also take into account the regulations in each country as to what may constitute bank capital. In particular, we build an index of initial capital stringency that can take values from 0 to 3, with higher number indicating greater stringency. This index comes from summing up responses to the questions below in the following way:

- (1) Can initial and subsequent infusions of regulatory capital include assets other than cash or government securities? 1 if no.

- (2) Can the initial infusion of capital be based on borrowed funds? 1 if no.

- (3) Are the sources of funds that count as regulatory capital verified by the

regulatory or supervisory authorities? 1 if yes.

Finally, we combine the dollar measure with the index of initial capital stringency by obtaining the principal component of the two series. We refer to the first principal component of the two series as _Minimum capital requirements._<sup>_8_</sup>

> 8 It should be noted that cross-border lending also requires capital for the head office and that it will generally be higher for more vulnerable host countries, for the same loan size. While the two needs (the minimum capital requirement to operate locally and the capital needed for cross-border lending) are not easily comparable, minimum capital requirements should generally involve larger costs when a bank starts to operate locally.

12
# Markups of foreign firms vis-à-vis domestic firms

Despite foreign-owned firms increasing competition and decreasing markups at the sector level, we also find evidence that individual foreign-owned firms generally possess higher ability to price above marginal cost relative to their domestic-owned counterparts. Table 4 displays estimates of (3). Across specifications, the coefficient on MNEi,s,t is consistently positive and statistically significant, reflecting how foreign-owned firms are able to charge higher markups than their domestic-owned counterparts. This finding is robust to controls for (lagged) firm productivity, suggesting that the higher markups are driven by higher market power and not just improved efficiency.

In addition, our results indicate that foreign-owned firms are less able to charge markup premiums in sectors already characterized by high competition from global trade or other foreign firms. In Table 4, the coefficient on the interaction term MNEi,s,t * EXPi,t−1 is negative across specifications and statistically significant in our preferred specification with markup estimates assuming a translog production function. A similar pattern exists for MNEi,s,t * FDIi,t−1 . This finding suggests that foreign-owned firms are less able to charge markup premiums in the face of increased competition from other firms or with respect to global markets.

_Table 4. Comparison of markups of_ _<u>foreign-owned vs. domestic-owned firms</u>_

|VARIABLES|(1)<br>Markup<br>(CD)|(2)<br>Markup<br>(TL)|(3)<br>Markup<br>(CD)|(4)<br>Markup<br>(TL)|(5)<br>Markup<br>(CD)|(6)<br>Markup<br>(TL)|
|---|---|---|---|---|---|---|
|MNEi,s,t|0.193***|0.0954***|0.227***|0.227***|0.230***|0.202***|
||(0.0287)|(0.0192)|(0.0529)|(0.0297)|(0.0513)|(0.0425)|
|MNEi,s,t*EXPs,t−1|||-0.106<br>(0.0981)|-0.404***<br>(0.0989)|||
|MNEi,s,t*FDIs,t−1|||||-0.0753<br>(0.0705)|-0.212**<br>(0.0870)|
|log (Output)i,s,t−1|-0.166***|-0.0297***|-0.166***|-0.0293***|-0.166***|-0.0294***|
||(0.00750)|(0.00544)|(0.00756)|(0.00526)|(0.00755)|(0.00546)|
|log (Capital)i,s,t−1|0.0727***|0.00954*|0.0723***|0.00889*|0.0725***|0.00906*|
||(0.00631)|(0.00470)|(0.00665)|(0.00474)|(0.00633)|(0.00454)|
|TFPR (CD)i,s,t−1|-0.239||-0.259||-0.247||
||(0.275)||(0.286)||(0.274)||
|TFPR (TL)i,s,t−1||0.133***||0.115***||0.118***|
|||(0.0359)||(0.0348)||(0.0343)|
|Constant|1.619***|1.235***|1.627***|1.238***|1.622***|1.238***|
||(0.106)|(0.0406)|(0.111)|(0.0387)|(0.106)|(0.0395)|
|Firm fixed effects|No|No|No|No|No|No|
|Industry-year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|78,596|75,673|78,596|75,673|78,596|75,673|
|R-squared|0.280|0.415|0.280|0.423|0.280|0.418|

Robust standard errors clustered at the industry level in parentheses. Dependent variable displayed at the top of each column: CD denotes markups derived assuming a Cobb-Douglas production function, while TL denotes markups derived assuming a translog production function.

*** p<0.01, ** p<0.05, * p<0.1

13
Dryden‐Peterson, Sarah and Lucy Hovil. 2003. Local integration as a durable solution: refugees, host populations and education in Uganda. UNHCR EPAU Research Paper no. 93, Policy Development and Evaluation Service, United Nations High Commissioner for Refugees, Geneva.

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Internal Displacement Monitoring center. 2014. Global Overview 2014: People Internally Displaced by ‐ Conflict and Violence (May 2014) Accessed at http://www.internal <u>displacement.org/publications/2014/global‐overview‐2014‐people‐internally‐displaced‐by‐conflict‐and‐ violence/</u> Internal Displacement Monitoring center. 2013. Global Overview 2012: People Internally Displaced by ‐ Conflict and Violence (April 2013) Accessed at http://www.internal <u>displacement.org/publications/2013/global‐overview‐2012‐people‐internally‐displaced‐by‐conflict‐and‐ violence</u> Internal Displacement Monitoring center. 2012. Global Overview 2011: People Internally Displaced by ‐ Conflict and Violence (April 2012), accessed at <u>http://www.internal displacement.org/publications/2012/internal‐displacement‐global‐overview‐2011‐people‐internally‐ displaced‐by‐conflict‐and‐violence;</u> Internal Displacement Monitoring center. 2011. Internal Displacement: Global Overview of Trends and ‐ Developments in 2010 (March 2011), accessed at <u>http://www.internal</u> <u>displacement.org/publications/2011/internal‐displacement‐global‐overview‐of‐trends‐and‐</u> <u>developments‐in‐2;</u> 34
**Table 7. Access to Credit: Probit Marginal Effects, OLS and Fixed Effects Estimates**

|Dependent Variable||**Cr**|**edit**||**Obstacle to**<br>**Finance**|
|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Estimation|**DPROBIT**|**OLS**|**FE**|**XTPROBIT**|**FE**|
|Formality|0.144*|0.120*|0.051*|0.183*|-0.107*|
||(2.923)|(2.936)|(3.767)|(3.885)|(-2.717)|
|Fowned|-0.090*|-0.086*|-0.029**|-0.105**|-0.295*|
||(-3.085)|(-3.137)|(-2.295)|(-2.451)|(-8.155)|
|Age|0.002*|0.002*|0.000***|0.002**|-0.003*|
||(3.100)|(3.190)|(1.856)|(2.049)|(-4.146)|
|Exporter|0.107*|0.098*|0.025**|0.080**|0.013|
||(3.353)|(3.258)|(2.435)|(2.285)|(0.424)|
|Manager’s education|0.106*|0.093*|0.059*|0.201*|0.050***|
||(3.452)|(3.273)|(6.312)|(6.378)|(1.845)|
|Size dummies: Small|0.207*|0.159*|0.105*|0.374*|-0.087**|
||(6.975)|(6.406)|(8.485)|(8.562)|(-2.433)|
|Medium|0.314*|0.264*|0.207*|0.692*|-0.120*|
||(7.593)|(7.282)|(12.892)|(12.545)|(-2.593)|
|Large|0.325*|0.276*|0.247*|0.832*|-0.178*|
||(6.344)|(6.061)|(14.890)|(14.500)|(-3.691)|
|Very Large|0.307*|0.253*|0.274*|0.939*|-0.303*|
||(5.389)|(4.775)|(14.927)|(14.710)|(-5.676)|
|Location dummies: City pop> 1mil|0.093|0.089|0.006|0.038|-0.030|
||(0.948)|(1.006)|(0.419)|(0.817)|(-0.734)|
|250,000<Pop<1,000,000|0.000|0.004|0.027**|0.105**|0.046|
||(0.000)|(0.113)|(2.051)|(2.381)|(1.199)|
|50,000<Pop<250,000|0.022|0.021|0.025***|0.092**|-0.058|
||(0.465)|(0.528)|(1.916)|(2.108)|(-1.532)|
|Pop<250,000|0.080**|0.073**|0.011|0.060|-0.052|
||(2.108)|(2.156)|(0.635)|(1.098)|(-1.068)|
|Cons||0.045|0.227*|-0.999*|2.036*|
|||(0.713)|(10.696)|(-10.712)|(33.048)|
|Number of observations|11923|11923|11923|11923|11396|
|Number of countries|49|49|49|49|48|
|Adjusted R2|0.1354|0.1673|0.1089||0.0444|

Notes: t-statistics in parenthesis. In Probit and OLS estimations errors are clustered at the country level. Significance: 01 - ***; .05 - **; .1 - *; Size dummies: reference category: micro firms; location dummies: reference category: capital city. Industry dummies included 28
**Table 3:  Weights Used to Aggregate Individual Data Sources in 2006 WGI**

|**VA**<br>**Commercial Business Information**|**PV**<br>**Providers**|**GE**|**RQ**|**RL**|**CC**|**Average**|
|---|---|---|---|---|---|---|
|**bri**<br>..|0.093|0.087|..|0.070|0.008|0.064|
|**dri**<br>..|0.115|0.036|0.028|0.025|0.027|0.046|
|**eiu**<br>0.103|0.168|0.079|0.059|0.113|0.048|0.095|
|**ijt**<br>..|0.087|..|..|..|..|0.087|
|**mig**<br>..|0.073|0.036|0.037|0.044|0.036|0.045|
|**prs**<br>0.047|0.054|0.053|0.067|0.023|0.031|0.046|
|**qlm**<br>..|..|..|..|0.077|0.082|0.080|
|**wmo**<br>0.051|0.169|0.148|0.169|0.089|0.067|0.116|
|**Surveys of Firms or Households**|||||||
|**afr**<br>0.029|..|0.103|..|0.013|0.022|0.042|
|**bps**<br>..|..|0.001|0.000|0.001|0.022|0.006|
|**gcb**<br>..|..|..|..|..|0.019|0.019|
|**gcs**<br>0.016|0.035|0.082|0.064|0.093|0.084|0.062|
|**gwp**<br>0.003|..|0.005|..|0.004|0.006|0.005|
|**lbo**<br>0.010|..|0.019|..|0.008|0.000|0.009|
|**prc**<br>..|..|..|..|..|0.083|0.083|
|**wcy**<br>0.008|0.053|0.034|0.065|0.066|0.108|0.056|
|**Non-Governmental Organization D**|**ata Provider**|**s**|||||
|**bti**<br>0.121|..|0.067|0.089|0.031|..|0.077|
|**ccr**<br>0.168|..|..|..|0.006|0.002|0.059|
|**egv**<br>..|..|0.007|..|..|..|0.007|
|**frh**<br>0.108|..|..|..|0.115|0.235|0.153|
|**gii**<br>0.091|..|..|..|0.020|0.002|0.038|
|**her**<br>..|..|..|0.032|0.050|..|0.041|
|**msi**<br>0.120|..|..|..|..|..|0.120|
|**obi**<br>0.036|..|..|..|..|..|0.036|
|**rsf**<br>0.023|..|..|..|..|..|0.023|
|**Public Sector Data Providers**|||||||
|**adb**<br>..|..|0.064|0.137|0.046|0.029|0.069|
|**aeo**<br>0.010|0.029|..|..|..|..|0.020|
|**asd**<br>..|..|0.091|0.031|0.017|0.010|0.037|
|**ebr**<br>..|..|..|0.105|..|..|0.105|
|**hum**<br>0.033|0.083|..|..|0.009|..|0.042|
|**ifd**<br>0.008|..|0.030|0.029|0.014|0.022|0.021|
|**pia**<br>..|..|0.041|0.066|0.050|0.045|0.051|
|**tpr**<br>..|..|..|..|0.004|..|0.004|
|**Commercial Business Information**|**Providers**||||||
|Share of Sources<br>0.17|0.64|0.33|0.33|0.29|0.32|0.35|
|Share of Weights<br>0.20|0.79|0.45|0.37|0.45|0.30|0.43|
|**Surveys of Firms or Households**|||||||
|<br>Share of Sources<br>0.28|0.18|0.33|0.20|0.25|0.36|0.27|
|Share of Weights<br>0.07|0.09|0.25|0.13|0.19|0.35|0.18|
|<br>**Non-Governmental Organization D**|**ata Provider**|**s**|||||
|<br>Share of Sources<br>0.39|<br>0.00|<br>0.11|0.13|0.21|0.14|0.16|
|Share of Weights<br>0.68|0.00|0.08|0.12|0.23|0.24|0.22|
|**Public Sector Data Providers**|||||||
|Share of Sources<br>0.17|0.18|0.22|0.33|0.25|0.18|0.22|
|Share of Weights<br>0.05|0.12|0.23|0.38|0.14|0.11|0.17|

29
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