rafmacalaba/datause-encoder-data huggingface dataset:
Learning about their absolute values, however, is inhibited by the existence of a single equilibrium condition in the model to identify the two unknowns. Since Hsieh and Klenow [2009]’s approach to measuring idiosyncratic distortions does not require knowing the equilibrium prices and aggregates,<sup>21</sup> one can get an estimate of the distortion slope _γ_ directly from the data and use the model’s equilibrium condition to back out the entry barrier that matches the equilibrium’s average firm size with the average firm size in the data. We implement this strategy later in the paper.

Notice from the logic above that the identified entry tax rate is sensitive to three potential sources of bias: 1) measurement error in average size, 2) measurement error in the elasticity of TFPR _−_ TFPQ, and 3) model misspecification. The next section presents a series of adjustments to our firm-level databases that attenuate biases stemming from the first two sources. Furthermore, to minimize model misspecification, we conduct the quantitative analysis by appealing to a richer version of the model just presented that incorporates two key determinants of the size distribution: endogenous exit and endogenous firm dynamics.

Last, the theoretical results concerning the interaction between distortions will prove helpful when interpreting the magnitude of the TFP effects associated with altering the mix of distortions.

# **4 Data**

The identification of entry barriers hinges on two empirical inputs: the average firm size and the elasticity of idiosyncratic distortions with respect to firms’ physical productivity (i.e., the TFPR and TFPQ elasticity). For the purpose of extracting these inputs, we gathered a collection of firm-level databases from 19 countries, to which we have made adjustments so as to maximize the comprehensiveness and the comparability of the sample.

> 21Recall from Hsieh and Klenow [2009] that wedges are defined as deviations of marginal revenue products relative to industry averages. Thus, industry-wide variables related to the equilibrium, such as aggregate demand and wages, are subsumed in this average of revenue products across firms within an industry.

23
Teacher Incentives in the Kyrgyz Republic received a salary bonus as a result of this participation; these teacher-awardees are referred to as teachers in the rest of this paper.

# 5.1 Teacher Motivation

Overall, 91 percent of teachers thought the purpose of the incentive program was to increase professional motivation—that is, to encourage teachers to work harder; only 7.5 percent thought its purpose was to reward good teachers; thus, they did not view the teacher incentive program as a “merit pay” scheme. Teachers reported high levels of motivation from the program. For example, in response to the question “How would you rate the effectiveness of the pilot implementation of the teacher incentive/performance management program in increasing your motivation to take professional development courses”, 86 percent of the teachers answered “effective” or “extremely effective.” From the teacher survey, we created a _teacher motivation index_ , which was the sum of the ratings (from “not effective” to “extremely effective”) given by teachers to questions regarding their opinions regarding the extent to which the program increased their motivation to change their teaching practices, to take professional development courses and to continue working as a teacher. On average, there were no statistically significant differences among teachers receiving different performance ratings with respect to their opinions on the motivational aspects of the incentive program, with scores averaging around 9.2 points on a 12-point scale for all categories of teacher: Master Teacher (9.32), Professional-1 (9.30), Professional-2 (9.24), Specialist-1 (9.24) and Specialist-2 (9.19).

We next used OLS regression analysis to adjust for individual and location differences among respondents; the results are presented in columns 1 and 2 of table 3, once for performance rating as a scale and once for each category of performance rating. After controlling for district school location, teacher’s age, gender, experience and high qualification rank in 2007, we found that teachers who had received lower ratings in 2007 (Specialists 1 and 2) held less positive opinions in 2011 regarding the motivational aspects of the program, as compared with Master Teachers.

12
# **Table 2: SUMMARY STATISTICS**

|||**ALL CHIL**|**DREN**|**CO-RES**|**IDENT C**|**HILDREN**|
|---|---|---|---|---|---|---|
||**Mean**|**Median**|**N**|**Mean**<br>|**Median**|**N**|
||(1)|(2)||(4)|(5)||
|||||<br>**BANGLADESH**|||
|Years of Education of||**B**|**oth Son**|**s and Daughters S**|**ample**||
|Children|4.97|5.00|18587|5.52|5.00|5852|
|Father|3.39|2.00|14017|3.74|3.00|5599|
|Mother|1.46|0.00|14527|1.81|0.00|5523|
|Average of Parents|2.33|1.00|18505|2.78|2.00|5806|
|||||**Sons Sample**|||
|Children|5.84|5.00|9056|5.56|5.00|3873|
|Father|3.38|2.00|7126|3.53|2.00|3713|
|Mother|1.45|0.00|7261|1.64|0.00|3648|
|Average of Parents|2.34|1.00|9010|2.59|1.50|3844|
||||**D**|**aughters Sample**|||
|Children|4.14|4.00|9531|5.44|5.00|1979|
|Father|3.41|2.00|6891|4.16|3.00|1886|
|Mother|1.47|0.00|7266|2.14|0.00|1875|
|Average of Parents|2.33|0.50|9495|3.14|2.50|1962|
|||||**INDIA**|||
|Years of Education of<br>||**B**<br>|**oth Son**<br>|**s and Daughters S**<br>|**ample**<br>||
|Children|6.23|7.00|14877|6.97|8.00|9132|
|Father|4.37|2.50|14877|4.74|5.00|9132|
|Mother|1.83|0.00|14877|2.12|0.00|9132|
|Average of Parents|3.10|2.50|14877|3.43|2.50|9132|
|||||**Sons Sample**|||
|Sons|7.29|8.00|8341|7.54|8.00|6561|
|Father|4.31|2.50|8341|4.59|5.00|6561|
|Mother|1.82|0.00|8341|1.99|0.00|6561|
|Average of Parents|3.06|2.50|8341|3.29|2.50|6561|
||||**D**|**aughters Sample**|||
|Daughters|4.87|5.00|6536|5.54|6.00|2571|
|Father|4.46|2.50|6536|5.14|5.00|2571|
|Mother|1.84|0.00|6536|2.45|0.00|2571|
|Average of Parents|3.15|2.50|6536|3.79|3.25|2571|

Notes: Data Sources: India: Rural Economic and Demographic Survey (REDS) 1999; Bangladesh: Matlab Health and Socioeconomic Survey 1996.
population. The results show that these restrictions _are_ associated with less migration. Countries which restrict the rights of women to migrate have 5 to 6 percent less migrants per capita than countries with similar income, population, and governance levels which do not have these restrictions.

**Table 7: Do Restrictions on Women reduce Migration?**

Dependent Variable: Migrants per capita (%)

||(1)|(2)|
|---|---|---|
|Restrictions on Women Emigrating|-5.956<br>(3.30)***|-5.344<br>(3.49)***|
|Log of GNI per capita|3.001<br>(3.57)***|12.139<br>(3.80)***|
|Log of Population|-3.073<br>(5.34)***|-19.390<br>(3.18)***|
|Government Effectiveness|-3.421<br>(2.38)**|-1.456<br>(0.94)|
|Log of GNI per capita squared||-0.653<br>(2.81)***|
|Log of Population squared||0.535<br>(2.89)***|
|Constant|32.744<br>(3.27)***|125.053<br>(2.40)**|
|Observations|170|170|
|R-squared|0.37|0.47|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%, *** significant at 1% Table 8 details countries where two other forms of legal restrictions on movement are in force. The first is a set of countries which requires citizens to obtain government permission or an exit visa in order to be able to travel. While permission may be granted in most cases in some of these countries, the process of requiring this permission introduces additional costs and uncertainty into the migration decision. A second type of legal restriction occurs in several countries with compulsory national service, which restrict the rights of travel of citizens of national service age who have not completed

- 19 -
> - 44 -

# GRAPH 4a: ACTUAL COMMODITY PRICE CHANGES 4LICPF, FORECASTS AND ERROR BARS

<!-- Start of picture text --> &LICPF FORECAST=_<br>.290<br>.500 4_<br>* 400<br>-*300<br>.9 1980- !get Mt 1983 1984 1985 1986 1987 1988 1989-79<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988
7 price distribution is closer to a normal one."<sup>1</sup> Table 3 reports the statistics _fo'_ these log prices.

There is a high correlation between the (logs of the) RM/PSE prices on one hand and the voucher prices (Table 3). Here the correlations are calculated for the 235 stocks which are traded on both the RM and the PSE at the end of November/early December and for which there also is a voucher price. The correlations between the (log of the) three prices are between 0.72 and 0.90. As expected, the highest correlation is between the RM and PSE prices as these prices are only 10 days apart.-<sup>2</sup> Firm Data In addition to the ownership variables mentioned above, for almost all the firms which were part of the voucher scheme, I have the following firm-specific variables: output, profit, credit, employment (the first three for the years 1989, 1990 and 1991), book value of equity, total book value of the firm, total liabilities (these for the year 1991 only), region, city, and branch of industry. I use these variables or combinations of them to control for the influence of non-ownership factors on prices. I do not have all independent variables for all firms for which I have one or more prices. This is particularly so for the secondary market prices. There are, for example, firms which are newly listed on the Prague exchange for which I do not have data on profits, ownership, etc. The number of useable observations for the PSE and RM prices is therefore less than the total available. In the basic regression, there are only 590 usable observations for the RM-system and 287 for the PSE, compared with 1204 for the voucher prices.

# 2. Corporate Governance, Firm Performance and Equity Prices

Ideally, one would like to have measures of actual individual firm performance (including changes in management) to investigate the influence of corporate governance and distribution of ownership on the firm. Megginson et al. (1994), Pinto et al. (1994), Pinto and van Wijnbergen (1994), and Galal et al. (1994) pursue this approach for state and/or newly privatized firns.'<sup>3</sup> This has drawbacks, however. First, performance data will only be available ex-post, often with a considerable lag time. Also, frnm data in Central and EasternEurope are of poor quality and international accv-unting standards are still being introduced. Second, exogenous shocks can affect firm performance, making it difficult to separate the effect of ownership.

An alternative approach which deals with some of these drawbacks is to use (publicly 'This was verified using the Jarque-Bera normality test.

1<sup>2</sup> The correlations between the first prices on the RM-system (6118/93) and the PSE (7/13/93) and the 5th round prices are even higher: 0.90 and 0.97 respectively.

'Note that these studies concem themselves only with state or privately-owned firms, and do not study the effects of ownership distribution of privately-held firms on firm performance.

l
**Table 4: Kaufman and Kray Controls**

||1<br>[OLS]|2<br>[OLS]|3<br>[OLS]|4<br>[OLS]|5<br>[OLS]|6<br>[OLS]|
|---|---|---|---|---|---|---|
|Total Outstanding Debt/GDP (initial)|-0.771|-0.771|-0.839|-0.59|-0.581|-0.792|
||[1.82]*|[1.82]*|[2.02]*|[1.55]|[1.43]|[2.02]*|
|Initial GDP per Capita|-0.407|-0.526|-0.648|-0.967|-0.911|-0.923|
||[1.63]|[1.93]*|[2.53]*|[3.96]**|[3.65]**|[3.69]**|
|Population Growth|-62.015|-53.03|-48.783|-24.355|-32.314|-39.386|
||[2.19]*|[1.78]*|[1.73]*|[0.95]|[1.14]|[1.55]|
|Secondary Schooling|1.027|1.095|1.224|1.034|1.064|1.059|
||[3.01]**|[3.14]**|[3.49]**|[3.17]**|[3.15]**|[3.28]**|
|Trade Openness|0.925|0.929|0.422|0.487|0.261|0.429|
||[1.51]|[1.51]|[0.65]|[0.85]|[0.44]|[0.74]|
|Voice and accountability||0.3<br>[0.94]|||||
|Political Statbility|||0.724<br>[2.92]**||||
|Government Effectiveness||||2.383<br>[6.55]**|||
|Rule of Law|||||1.896<br>[4.85]**||
|Corruption||||||2.111<br>[4.88]**|
|Observations|250|250|250|250|250|250|
|Robust t statistics in brackets|||||||
|* significant at 10%; * significant at 5%; ** sign|ificant at 1%||||||

26
- 34 -

Table 8: THE WORLD MERCHANT FLEET Newbuilding Requirements for Principal Tonnage Categories in million dwt

|Category|1992 - 1995|1996 - 2000|
|---|---|---|
|Oil Tankers|22|65|
|Specialized Tankers<br>|||
|(Gas, Chemical, and<br>Product categories)|7|23|
|Dry Bulk Carriers|||
|(incl. Combination<br>Carriers )|44|86|
|Container Vessels|8|7|
|General Cargo Ships|||
|( inct. RoRo tonnage)|_5_|24|
|Reefer Ships|2|3|

Source: Author's estimate 67. In the meantime, however, several countries in the former socialist block of Eastern Europe have been able to establish firm market niches. Low wages and comparative advantages in other factor inputs have enabled yards in these countries to offer newbuilding tonnage at attractive prices. China has been another country that has been able to penetrate the shipbuilding markets. Chinese-built vessels have a consistent record of high quality and reliability, and are thus in demand. But many Chinese yards are still antiquated and are in need of major improvements. Notwithstanding the subsidy issue, for many of these developing nations, engaging in shipbuilding activities has been beneficial. Since shipbuilding is an assembly industry, there are many forward and backward linkages with other parts of the national economies. A recent Bank survey' which was addressing options for the shipbuilding industries in the FSU countries concluded that for each one million currency units of shipbuilding contracts, 40 percent would accrue to a yard and that the balance of 60 percent would benefit national steel manufacturers, equipment suppliers, and others.

68. During the remainder of the 1990s, several changes in shipbuilding and individual country shares are likely to materialize. The number of large and medium-size shipyards will increase. There are presently over 80 countries engaged in shipbuilding, and yards in more than 60 percent of these countries are capable of building vessels with carrying capacities above 5,000 dwt. The Far Eastern yards will maintain their market dominance, with Japan continuing in the lead, if not with market shares sustained during the 1970s and 1980s. China can be expected to expand its shares. South Ko-ea's shipbuilding industry will enter a stage of stabilization, but is likely to defend a market share of around 20 percent. The West and Northern European shipyards with their superiority in the development and building of sophisticated and specialized tonnage will remain a market force. Recent initiatives by major shipbuilders

> 4 Peters, Hans **J.** 1993. _"Waterborne Transport in the Russian Federation - Trends, Issues, and Remedial Options"._ Mimneo. World Bank, Washington, D.C.
probability of privatization (increases the privatization rate) - for example, the positive sign on percent of loans to the public sector indicates that a greater public portfolio increases the probability of privatization. A negative sign would indicate the opposite. The baseline hazard function, a (non-parametric) function of time, allows the probability that the bank will be privatized to change over time. This will help control for changes in national laws or institutions and exogenous shocks that affected the entire banking sector which (proportionally) affect the privatization decisions of all provinces (e.g. the Tequila Crisis, changes in reserve requirements, and the new Charter of the Central Bank of Argentina enacted in September, 1992).

Table 3 describes the variables included in the analysis. In the estimation, bank quality variables are lagged one period. After the decision to privatize was made, performance indicators, such as net worth and portfolio quality variables, sharply declined. Rather than being the result of actual changes in performance, this decline appears to have been caused by a more careful auditing of bank assets (perhaps intended to help the province separate the bank into a privatized provincial bank and a residual entity).<sup>20</sup> Since this auditing occurred only if the bank was privatized, coefficients on contemporaneous bank quality variables might be negative even if bank performance did not affect privatization. To avoid this potential problem, we include lagged, rather than contemporaneous, bank performance variables. Similarly, since the proceeds from privatization might affect provincial deficits, we also lag the fiscal deficit variable.

Results from the estimation are shown in Table 5.21 Coefficients on the lagged bank quality variables are statistically significant (at least at a 5% level).<sup>22</sup> All variables 20 See Clarke and Cull (1997).

21 Several experts suggested that we should omit the Banco de Provincia de Buenos Aires from our sample. Experts at the Central Bank noted that the 1853 _Pacto San Jose de Flores,_ a pact between the province of Buenos Aires and the Federal government, which predates the Central Bank, means that the Central Bank cannot effectively control this bank. They suggested that this gives this public provincial bank a special status which makes its privatization unlikely. Further, several other experts noted that political rivalry between the Governor of Buenos Aires (Eduardo Duhalde, a member of the PJ) and President Menem made the Governor less willing to compromise with the President. However, our results are similar 12
to get a sense of where in the province the stipend program was active. Figure 2 condenses this information and distinguishes between treated and comparison districts directly.

# **4. Data**

This paper utilizes data from provincial school censuses made available by the Project Management Implementation Unit (PMIU) of the Punjab Education Sector Reform Program (PESRP). School censuses have been conducted annually since 2003 (the baseline year). This paper uses all the rounds from 2003 to 2008. Each school is assigned a unique eight-digit code in the Educational Management Information System (EMIS) which allows schools to be tracked from year to year.

Each census covers approximately 60,000 government schools in the province of Punjab. More than 40 percent of these schools are primary schools. While they are, strictly speaking, only meant to enroll students in primary school grades (classes 1 to 5), some schools do report students in typically middle school grades (classes 6 to 8). Schools report whether or not they are functional and what their designated level is – whether they are a primary or middle school and whether they are a high school or a higher secondary school.<sup>4</sup> In addition information is provided on whether the school is urban or rural and whether the school is for boys or for girls or in some instances for both. The survey instrument asks about the type of material used in construction of the school building, its state of repair, its ownership status, the year the school was established and how much covered and uncovered area is occupied by the school.

Each year the census form requires schools to declare the number of boys and girls enrolled in each class as of the start of the school year in September/October. These enrollment

> 4 A functional school is one that is open and operating. Schools may not be open or may not operate if there is a lack of teachers – hence the distinction between functional and non-functional schools.

7
throughout the period of study. The reductions from the 630 baseline occur due to the following. First, the ASI surveys only record data for about 400 districts due to the lack of organized manufacturing (or its extremely limited presence) in many districts.<sup>8</sup> Second, we drop states that have a small share of organized manufacturing.<sup>9</sup> Last, we make an additional restriction for our regression sample that manufacturing activity in terms of plants, employment, and output in districts be observed at all points from 1994 to 2007.

The requirements with respect to continuous measurement of districts are motivated by a desire to have a consistent sample before and after the GQ upgrades. The requirements with respect to minimum share of states in organized manufacturing are motivated by a desire to have reasonably measured plant traits, especially with respect to labor productivity and plant TFP. With respect to the latter, we also exclude plants that have negative value added, which accounts for 6%-7% of employment. These restrictions are again not very significant in terms of economic activity, with our final sample retaining more than 90% of Indian manufacturing activity.

Our next step is to measure the distance of districts to various highway networks. We calculate these distances using official highway maps and ArcMap GIS software. Our reported results use the shortest straight-line distance of a district to a given highway network. We find very similar results when using the distance to a given highway network measured from the district centroid. The empirical appendix provides additional details on our data sources and preparation, with the most attention given to how we map GQ traits that we ascertain at the project level to district-level conditions for pairing with ASI data.

Our empirical specifications use a non-parametric approach with respect to distance to estimate treatment effects from the highway upgrades. We define indicator variables that take a value of one if the shortest distance of a district to the indicated highway network is within the specified range; a value of zero is assigned otherwise. We report most of our results using four distance bands: nodal districts, districts located 0-10 km from a highway, districts located 10-50 km from a highway, and districts over 50 km from a highway. In an alternative setup, the last distance band is further broken down into three bands:

> 8 For instance, the ASI surveys the entire country except the states of Arunachal Pradesh, Mizoram, and Sikkim and Union Territory of Lakshadweep, so these states are naturally excluded.

> 9 These states are Andaman and Nicobar Islands, Dadra and Nagar Haveli, Daman and Diu, Jammu and Kashmir, Tripura, Manipur, Meghalaya, Nagaland and Assam. The average share of organized manufacturing from these states varies from 0.2% to 0.5% in terms of establishment counts, employment or output levels.

10