rafmacalaba/datause-encoder-data huggingface dataset:
12 that we have a nested logit to express E(u1r |X) =θrpr. The search procedure consists in finding a set of θr and αi that simultaneously satisfy equations (6), (7) and (8).

To implement our estimation, the first stage model consists in estimating the probabilities to be in any part of the decision tree in 2000 and 2007, using a nested logit specification. To identify this part of the model, we need instrumental variables that enter the first stage model, but not the second stage. In our case, these instrumental variables are three: (1) the distance from kabupaten (district) to railroad station in 1930; (2) the level of rainfall in 1995-1999; and (3) unexpected rainfall in 2000.<sup>9</sup> Our rationale for using these three instrumental variables is as follows.

The first railroad line in Indonesia opened in 1867. A continuous railroad line between Jakarta and Surabaya, the two largest cities in Java, opened in 1894. Between 1900 and 1930 smaller railroad lines were constructed in Madura, Sumatra and South Celebes. In Indonesia distance to railroad lines in 1930 represents a good instrumental variable because it is related to migration costs in the past and to the need for sending migrants in the past, and therefore to the development of present day migrant social networks, but it is not correlated with the expenditure patterns of households at the time of the 2000 and 2007 IFLS Surveys. We calculated distance to railroad lines for each household using the distance from the kabupaten (district) to the nearest railroad station in 1930, using historical maps from the Indonesian Railway Authority, and then cross-checking this information with the IFLS Surveys. This type of instrument has been used before in the literature by Woodruff and Zenteno (2007) for the case of Mexico, and Adams and Cuecuecha (forthcoming) for the case of Guatemala.

Changes in rainfall have also been used before in the literature as an instrumental variable in the cases of Mexico, the Philippines, and Guatemala (Munshi, 2003; Yang and Choi, 2007;
national estimates only for the 32 high‐income OECD economies in our data and use modeled ILO estimates for the remaining 158 economies. While this introduces a source of heterogeneity into the measurement of our outcome variable, even if we were to use ILO estimates exclusively, there would be differences between countries in terms of the data, as ILO sources for labor force statistics vary between countries depending on data availability. Furthermore, the inclusion of country fixed effects in model estimation should address this issue. Data on the gender wage gap are also from two sources – the ILO and OECD; ILO gender wage gap data are the difference between the mean earnings of men and the mean earnings of women, expressed as a percentage of the earnings of men; OECD data are similarly calculated but use median as opposed to mean earnings. As the OECD figures should be less sensitive to outliers, we use the OECD data whenever possible, and only use ILO figures in countries and years where OECD data are not available. For the third outcome, occupational segregation, we use occupational data compiled by the ILO to compute an Occupational Segregation Index by sex.<sup>6</sup> We model the relationship between the WBL index and labor force outcomes as follows: Y is the outcome for country i at time _t_ ; WBL represents the WBL score in _t‐1_ ; and Z�,� represents a set of potential control variables, including fertility rate, female education, income and male population. The terms γ� and ∅� represent country and year fixed effects; u�,� is the error term. The WBL index is measured at a one‐year lag, as we would not expect to see an impact of legal reform on women’s labor market outcomes in the same year that reforms were implemented.

The coefficient β represents the relationship between gender equality and women’s labor force outcomes across 190 countries, accounting for unobservable differences between countries and time periods (captured by the fixed effects). However, within this framework we cannot attribute a causal effect of legal gender equality to female labor force participation, as there may be some time‐varying unobservable η�,�, which is correlated with both the WBL score and female labor supply. A likely candidate for this

> 6 As discussed by Blau, Brummund and Liu (2013), differences in the distribution of men and women across a range of occupations can be described in a simple index. The most commonly used measure of occupational segregation was developed by Duncan and Duncan (1955), and can be calculated as: OSI� ��0.5�∑|m� �� �f��| , where m�� and f�� are the proportion of males and females, respectively, that are employed in occupation _i_ at time _t._ ILO data on employment by occupation are available for 143 economies from 1991 to 2017; however, the series has several gaps, as coverage varies from year to year and between economies. The data are, in general, much more complete for high‐income OECD economies.

16
# **<u>Table B3. SCM Detailed Results for All IHC Firms – Inspection rates</u>**

|IHC firm #<br>6-month<br>average<br>treatment<br>effect:<br>change in<br>inspection rate<br>(�_�������<br>�<br>�<sup>)</sup>|Number of donor<br>firms chosen by<br>synthetic control<br>procedure|Rank of IHC firm<br>relative to 25 placebo<br>firms|Percentile rank<br>statistic<br>(rank/26)|
|---|---|---|---|
|1<br>0.037|10|22|0.846|
|2<br>-0.058|14|6|0.231|
|3<br>0.000|10|11|0.423|
|4<br>0.002|11|17|0.653|
|5<br>-0.008|14|13|0.500|
|6<br>0.009|8|20|0.769|
|7<br>0.001|12|14|0.538|
|8<br>-0.079|57|3|0.115|
|9<br>-0.023|14|7|0.269|
|10<br>-0.008|50|15|0.577|
|11<br>-0.014|12|11|0.423|
|12<br>0.008|10|16|0.615|
|13<br>-0.021|11|7|0.269|
|14<br>-0.026|9|8|0.307|
|15<br>-0.022|6|9|0.346|
|16<br>0.023|10|18|0.692|
|17<br>-0.014|9|9|0.346|
|18<br>0.033|5|21|0.808|
|19<br>-0.005|7|14|0.538|
|20<br>0.001|4|12|0.462|
|21<br>-0.007|7|11|0.423|
|Mean % change in<br>inspection rate<br>-0.008||Mean percentile rank<br>statistic (�)|0.483|
|Median %<br>-0.007||||
|changein inspection rate||||

Notes: Synthetic control firms were constructed with the benchmark specification that includes three pre-treatment values of the outcome variable as part of the set of conditioning variables. The summary statistics at the bottom left of the table are obtained as the mean and the median of the treatment effect across the 21 firms. The statistic at the bottom right of the table �̅ given 21 firms has 5% critical values of 0.377 and 0.623 (based on Appendix table A1 in Dube and Zipperer (2015)).

50
**Table 4.A. Model comparison: Bias of oil price forecasts (U.S. dollars per barrel)**

||||**Bivariate**||||
|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**<br>**correlations**|**BVAR**|**OEM Machi**|**ne learning**|
|Consensus|3|-3.1|...<br>...|...|...|...|
|Futures|3||-1.5<br>...|...|...|...|
|Bivariate correlations|3||1.7|...|...|...|
|<br>BVAR|3|||2.2|...|...|
|OEM|3||||3.6|...|
|Machine learning|3|||||2.2|
|Consensus|6|-2|...<br>...|...|...|...|
|Futures|6||-1.6<br>...|...|...|...|
|Bivariate correlations|6||4.6|...|...|...|
|BVAR|6|||3.8|...|...|
|OEM|6||||2.5|...|
|Machine learning|6|||||3.8|
|Consensus|9|-1|...<br>...|...|...|...|
|Futures|9||-2<br>...|...|...|...|
|Bivariate correlations|9|*|*<br>5.8|...|...|...|
|BVAR|9|||5.4|...|...|
|OEM|9||||1.5|...|
|Machine learning|9|||||3.6|
|Consensus|12|-0.1|...<br>...|...|...|...|
|Futures|12||-2.3<br>...|...|...|...|
|Bivariate correlations|12|*|*<br>7.6|...|...|...|
|BVAR|12|||8|...|...|
|OEM|12||*||1.3|...|
|Machine learning|12|||||3.2|
|Consensus|15|0.7|...<br>...|...|...|...|
|Futures|15|...|...<br>...|...|...|...|
|Bivariate correlations|15|...|...<br>...|...|...|...|
|BVAR|15||...<br>...|10.7||...|
|OEM|15||...<br>...||1.3|...|
|Machine learning|15||...<br>...|||2.6|
|Consensus|18|1.3|...<br>...|...|...|...|
|Futures|18|...|...<br>...|...|...|...|
|Bivariate correlations|18|...|...<br>...|...|...|...|
|BVAR|18|*|...<br>...|14.6|...|...|
|OEM|18||...<br>...|*|1.3|...|
|Machine learning|18||...<br>...|||2.1|
|Consensus|21|1.8|...<br>...|...|...|...|
|Futures|21|...|...<br>...|...|...|...|
|Bivariate correlations|21|...|...<br>...|...|...|...|
|BVAR|21|*|...<br>...|17.3|...|...|
|OEM|21||...<br>...|*|1.5|...|
|Machine learning|21||...<br>...|||2.3|
|Consensus|24|2.3|...<br>...|...|...|...|
|Futures|24|...|...<br>...|...|...|...|
|Bivariate correlations|24|...|...<br>...|...|...|...|
|BVAR|24|*|...<br>...|25.4|...|...|
|OEM|24||...<br>...|*|0|...|
|Machine learning|24||...<br>...|*||2.4|

_Source_ : World Bank.

_Note_ : Bias is defined as the difference between the actual price and the predicted price. The forecast period is 2015Q12022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate estimates of average bias for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to a t-test (bias).

38
# Ghana Real Producer Prices Deflated by rural CPI

<!-- Start of picture text --> 1977 cedis/MT (Thousands)<br>6<br>5<br>4<br>3<br>2<br>0<br>1953/54 1958/59 1963/64 1968/69 1973/74 197B/79 1983/84<br>crop year<br>7 cocoa i maize X rice °<br>Figure yam<br><!-- End of picture text -->
allocation of extra school resources to grades with more migrants is unlikely. We can show that, even if our estimates were biased by the non-random allocation of resources to grades with more migrants, the magnitude of the upward bias would be small. Over our period our analysis, the average expenditure per student in primary schools in the Netherlands in the median year of our sample (1999) was about $4,000 (OECD (2002)). According to the Dutch weighting system, an increase in the share of recent immigrants by one standard deviation (5 percentage points) would lead to an increase of $180 per student in the grade. As estimates from Krueger and Whitmore (2001) suggest that an additional $1,000 per student increases test scores by about 0.06 of a standard deviation, the additional amount of resources brought by additional immigrants in the grade would increase test scores by about 0.06*180/1000=0.011 standard deviation. Accounting for this upward bias would increase the magnitude of our estimate for a one standard deviation increase in the share of recent migrants from -0.031 (Table 4) to -0.042.

# **6.6 Potential Mechanisms at Play**

Previous contributions that report a negative and significant effect of immigrant concentration in the classroom on natives’ achievement have not attempted to uncover the mechanisms underlying those effects. The broader literature on peer effects in the classroom, however, suggests at least three main mechanisms through which classroom composition may affect students’ achievement. These mechanisms can be adapted to the context of immigrant peer effects to formulate hypotheses about mechanisms at play. First, immigrant students may be more prone to disruption and misbehavior in the classroom relative to native students, therefore reducing teaching time to the entire classroom (disruption hypothesis; Lazear (2001); Lavy and Schlosser (2011)). Second, if achieve32
treasury biU rates from the bill which expires closest to the time the option expires, as provided by Data Resources, Inc. The options price data for corn and wheat is daily closing prices, obtained from the Chicago Board of Trade, while the rest of the options data is daily closing prices, obtained from Data Resources, Inc. Information regarding the features of various options and futures contracts (such as the last trading day, contract months, etc.) is taken from the descriptions published by the different commodity exchanges. Table 1 provides a brief overview of the options data by commodity and serves to illustrate the magnitude of the data sets and the breadth of contracts traded per day.

# TABLE 1

The GARCHt,T and HISTt,T forecasts require spot price data, which was obtained from Data Resources, Inc. In order to evaluate the forecasts, a measure of the 'true" 225-day volatility is needed. One measure used in the literature and the one which we use here, is the realized standard deviation of returns over the forecast horizon. This is computed by calculating the square root of the average daily squared return over the forecast horizon. This is called ACTUAL to represent the actual volatility of returns over the period of interest. Clearly, comparing the various long-run forecasts to ACTUAL requires spot data which extends 225 days beyond the last option data, so the time span covered by our spot data is from about January 1987 - July _1SJl._

# V. Estimation and Results.

The three ISD-based 225-day volatility forecasts are computed from equations (5), (6) and (7) above. In order to construct the GARCH forecast and the COMB forecast, we need to estimate the GARCH model (equation 8) and the COMB model (equation 11). The relevant maximum likelihood estimates are presented in Table 2, with asymptotic t-statistics in parentheses<sup>7</sup> . The Q<sup>2</sup> statistic, which tests for remaining serial correlation in the standardized squared residuals and is distributed X22 under the null of no remaining serial co.:elation, indicates that the estimated models adequately capture the dynamics in the second moments. One result of interest is that for many of the commodities (cotton and wheat are the exceptions), the variance equation coefficients sum to approximately one (i.e. a + d t 1). This means that shccks to the variance are persistent, i.e., shocks remain important determinants of the variance forecasts long after the shocks occur. This is easily seen by setting a _+3 =_ 1 in the second line of the GARCH forecasting equation (9).

**The observation period used in estimating these models excluded the final eight weeks of our sample (40 observations)** in order to facilitate out.of-sample forecasting later in this paper.

10
Figure 1: Distribution of responses per year <!-- Start of picture text --> 2005 2010 2015<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Responses on the 10−step ladder<br>Graphs by year<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> Comparing the joint distribution of the survey’s responses to the Cantril ladder and the deciles calculated from actual consumption levels, as in table 1, show clearly the discrepancies between subjective and objective rankings. If there were a complete equivalence between the consumption-based and the subjective rankings, then the number of respondents in the non-diagonal cells of table 2.2 would be zero. Table 1 shows the percentage of the population in the whole survey that, for each decile of consumption in their specific country, has given a certain response to the 10-step ladder, and vice versa.

The matching of household consumption and subjective ranking rankings is clearly weak. We observe that the percentage of respondents on the diagonal is relatively low. The bold numbers show the percentage of respondents for each decile of consumption that choose a response on the 10-step ladder that is equal to their decile of consumption. 24% of respondents place themselves in the middle of the economic ladder by answering 5 to the question and 57% of the respondents choose either 4, 5 or 6 as a response on the 10-step economic ladder. This proportion varies little with the decile of consumption as the percentage of respondents across all consumption levels is between 21% and 27%. This means that the individual decile of consumption can, at best, only partially explain the perceived decile of economic standing. There is a clear bunching in the middle for all categories of consumption levels. Differences appear when we look at the distribution of consumption deciles for each response (first row). Only 17% of those choosing step 1 in the ladder, i.e. those reporting that they feel at the bottom of the scale are actually in the first, poorest, decile of consumption. This means that 83% of people who are actually 6
# **Children Left Behind in China: The Role of School Fees**<sup></sup>

Hai-Anh Dang,<sup>±</sup> Yang Huang,<sup></sup> Harris Selod<sup></sup>

**Key Words:** child migration, school fees, public education spending, urbanization, China **JEL Codes:** I22, J61, O15

> * We are grateful to Marc Gurgand, John Giles, Carine Milcent, Steven Brakman, Hillary Caruthers, Yi Chen, Elena Glinskaya, Tiago Freire, Laurent Gobillon, Thierry Magnac, David McKenzie, Scott Rozelle, Ruixin Wang, Jeffrey Zax and participants at the Conference on Urban Development in China (Suzhou) and the 7<sup>th</sup> International Symposium on Human Capital and Labor Markets (Beijing) for comments on earlier drafts.

> ± Hai-Anh Dang, Survey Unit, Development Data Group, World Bank and Vietnam’s Academy of Social Sciences. Email: <u>hdang@worldbank.org.</u>

>  Yang Huang, Human Development and Public Services Team, Development Research Group, World Bank and Paris School of Economics. Email: <u>yhuang5@worldbank.org.</u>

>  Harris Selod, Energy and Environment Team, Development Research Group, World Bank. Email: <u>hselod@worldbank.org.</u>
<!-- Start of picture text --> MAP 2<br>IBRD 23903<br>77 8 7. 89 81o '2 e3 8?W<br>MAHARASHTRA ,i * <v-^s ,,POTENTIALLY AFFECTED AREA DUE TO<br>) HELIOTHIS MIGRATION FROM GUNTUR<br>MADHYA S*<br>161 <<br>u~~~~~~~~~~~~~~~~~~~~~~~~~~fr<br>1A4 ~~ ~Av-1<br>is,<br><!-- End of picture text -->
with central government transfers accounting for the rest (Solans 2000).<sup>1</sup> The national government transfers are conditional funds that can only be used for specific purposes, such as health, education, or housing.

Cali and the Valle de Cauca were one of the hardest hit regions in the economic recession in Colombia in the second part of the 1990s. The recession in Cali was partly linked to a national economic downturn and also attributable to the crack-down on drug trafficking within the city, which reduced income and wealth considerably – money which used to fuel the local economy, especially the construction sector and local merchandise. Agriculture, construction, manufacturing and the public utilities sectors recorded negative real growth rates from 1995 onward (Urrea and Ortiz 1999, Annex 8).

While the city was in deep recession at the end of the 1990s, the hands of the municipal government to counter the national economic trend were tied. The city hit its indebtedness limits at the beginning of 1997 and faced the tough situation of having to reduce real expenditures due both to high debt payments and lower tax revenues. The possibility of starting an expansionary, counter-cyclical fiscal policy were hence limited.

Social indicators show how the city recession impacted the lives of the population. Table 1 contains unemployment, inequality and child labor statistics for the five largest Colombian cities in 1994 and 1998. Cali was the only large city in Colombia that recorded an increase in income inequality over this time period of economic decline. Open unemployment in Cali grew by 8.4 points and affected almost one out of every five Caleños in the labor force in 1998, significantly higher than in other large cities in Colombia. With the economic recession came also a rise in informality (and hence reduced access to formal social security and health care; Santamaria 1999, p.21) and child labor. In 1998, urban crime rates were at a high in Cali with a homicide rate of 80 per 100,000 inhabitants.<sup>2</sup>

> 1 During the 1990s, tax revenues in Cali increased rapidly, outpacing the inflation rate.  In 2000, personnel expenses made up about 80 percent of total expenses (excluding debt service; Solans 2000).

> 2 Different geographic areas in the city showed varying patterns of crime.  Youth gangs, some of them operating under guerilla influence or organized crime, assumed a prominent role in conducting the crimes (Martin 2000).

4
**Table 7. The effect of electricity access on medium-sized manufacturing firms’ exports (% total sales): Sensitivity tests to natural resources.**

|Dependentvariable:**Exports % total sales**||
|---|---|
|Resource intensive countries|Non-resource intensive countries|
|(1)|(2)|
|**Treatment**<br>5.81|8.55|
|(1.91)**|(4.65)*|
|<br>Firms’ age<br>0.15|<br>-0.23|
|(0.10)|(0.13)|
|<br>Firms’ status<br>1.98|<br>-0.73|
|(1.46)|(1.32)|
|<br>Gender<br>-3.72|<br>17.28|
|(6.00)|(11.48)|
|<br>Log (Labor(t-3)+ 1) <br>-0.15|<br>3.30|
|(1.89)|(4.21)|
|<br>Log(Output(t-3)+1)<br>-2.40|<br>1.41|
|(0.51)***|(1.12)|
|<br>Transport<br>0.31|<br>0.58|
|(2.03)|(1.09)|
|<br>Exchange rate growth(t-1)<br>2.42|<br>-0.24|
|(0.49)***|(0.06)***|
|<br>Trade openness growth(t-1)<br>6.41|<br>1.47|
|<br>(0.88)***|(0.36)***|
|<br>Generator use<br>1.56|<br>5.69|
|(2.70)|(4.33)|
|<br>Electricity losses(t-1)<br>-4.03|<br>-0.80|
|<br>(0.97)***|(0.26)**|
|<br>GDP per capita(t-1)<br>-0.11|<br>-0.01|
|<br>(0.02)***|(0.00)***|
|<br>GDP growth(t-1)<br>-0.97|<br>-0.41|
|(0.38)**|(0.41)|
|<br>Rule of law growth(t-1)<br>0.02|<br>-0.35|
|<br>(0.27)|(0.10)***|
|<br>R2<br>0.32|0.35|
|_N_<br>623|372|
|# Countries<br>39|39|
|- FixedEffects:CountryYear<br>Yes|Yes|
|, <br><br>Weighted ordinary least squares regression is the estimation approach, with standard errors clustered at country-level in parent|heses. The treatment variable is a dummy variable taking 1 if the company|
|declares not suffering from power outages and 0 otherwise. The dependent variable is the companies' share of exports in to|tal sales. We include control variables (firms' age, firms' status, manager|
|<br>gender, labor and sales three years ago, the quality of transport service, the real exchange rate, trade openness index) and|<br>covariates (firms' use of generators, firms' size, electricity losses during|
|transmission and transport, GDP per capita, GDP growth, rule of law growth). Country-level covariates are lagged by one year t<br>access in the first stage of the entropy balancing method. Country and year fixed effects are introduced in all full estimates. T|o avoid reverse causality between them and the occurrence of electricity<br>his table considers only medium-sized companies employing between 20|
|<br>and 99 workers and the dependent variable is the exports in total sales. Resources intensity’s classification is from the IMF’s<br>represent more than 25% of its total exports. *_p_<0.1; **_p_<0.05; ***_p_<0.01.|<br>REO. A country is considered as resource intensive if it natural resources|

22
<!-- Start of picture text --> x<br>i<br>go<br>><br>2<br>as)<br>ON<br>=<br>2<br>5<br>ae<br>oO .<br>aa) seh<br>oO<br>1990 1995 2000 2005 2010 2015<br>Year<br>—— China ——— Indonesia<br>——— Malaysia ———. Thailand<br>——— Philippines ——— Vietnam<br><!-- End of picture text -->