rafmacalaba/datause-encoder-data huggingface dataset:
[A1] – [A5] and the new population levels in each location. These new real wages in turn may affect migrants’ location choice according to (8) and (10), and so on. We repeat these steps iteratively until convergence is reached (i.e. real wages, and population shares no longer change between iterations). Upon convergence, we have obtained our counterfactual “NEN + no Hukou” spatial equilibrium.

Scenario 2 can be directly compared to Scenario 1 to assess whether, and if so how, China’s two main spatial policies differently affect its spatial economy. Also, it can be directly compared to the results shown in Bosker _et al._ (2012) that also consider the spatial effects of the abolition of the Hukou restrictions at the prefecture level, but without explicitly taking the large-scale investments in infrastructure into account. Note that the comparison to Bosker _et al._ (2012) is limited insofar as that paper focusses exclusively on the changes in the spatial distribution of people across prefectures, whereas we also consider the changes in, _inter alia_ , real wages and urbanization levels.

<u>Scenario 1b: The impact of the NEN under free labor mobility (i.e. no Hukou restrictions)</u> Scenarios 1 and 2 are our main scenarios of interest. This third scenario complements Scenario 1 (explaining why we call it Scenario 1b). It provides evidence on whether, and if so how, the construction of the NEN would have had different effects on China’s spatial economy had there been no Hukou restrictions on labor mobility. It allows us to further assess the postulated mitigating effect of free labor mobility on the increase in regional inequality associated with the construction of the NEN that was found in Roberts _et al._ (2012).

In this scenario, we compare the counterfactual “NEN + no Hukou”- spatial equilibrium that we already used in Scenario 2 to a counterfactual “no NEN + no Hukou”- spatial equilibrium. We simulate this last counterfactual using the no NEN travel time matrix to proxy transport costs between prefectures when solving equations [A1]-[A5], and combine it with (8) and (10) to iteratively solve for this “no NEN + no Hukou” spatial equilibrium.

# _5.2 Results – the impact of the NEN and/or the Hukou system on China’s spatial economy_

In this section, we discuss our main findings. Table 2 shows the changes in our main variables of interest in each of the three scenarios defined in Section 5.1. Table 3 complements these results by showing the correlation between these changes, as well as with the initial levels of urbanization, population, and welfare in each prefecture. Finally, Figures 1 – 4 and Appendix D further illustrate some of the most interesting correlations and provide detailed maps of the spatial distribution of these changes across China’s prefectures.

21
Policy ReseaRch WoRking PaPeR 4903

# **Abstract**

Services trade reform matters, but what is Doha doing about it? It has been hard to judge, because of the opaqueness of services policies and the opaqueness of the request-offer negotiating process. This paper attempts to assess what is on the table. It presents the results of the first survey of _applied_ trade policies in the major services sectors of 56 industrial and developing countries. These policies are then compared with these countries’ Uruguay Round commitments in services and the best offers that they have made in the current Doha negotiations. The paper finds that at this stage, Doha promises greater security of access to markets but not any additional liberalization. Uruguay Round commitments are on average 2.3 times more restrictive than current policies. The best offers submitted so far as part of the Doha negotiations improve on Uruguay Round commitments by about 13 percent but remain on average 1.9 times more restrictive than actual policies. The World Trade Organization’s Hong Kong Ministerial had set out ambitious goals for services but the analysis here shows that much remains to be done to achieve them.

This paper—a product of the Trade Team, Development Research Group—is part of a larger effort in the department to understand services trade, policy reform and international cooperation. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at bgootiiz@worldbank.org and amattoo@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
heteroscedasticity parameters. The intercept was between 2.6 and 2.8 in all cases, while the coefficients on predicted trade were between -0.09 and -0.12 in all cases.

# **V. Monte Carlo Analyses**

The Monte Carlo analyses were designed to be as simple as possible to focus on the question of interest and to avoid the plethora of cases that made inference challenging in studies, such as Martin and Pham (2020), using a wide range of models and degrees of heteroscedasticity. Because the key challenge for estimation appears to be limited-dependent variable bias, all coefficients should be affected equally (Greene 1981), allowing testing to focus on a single explanatory variable.

The experiments were performed by first generating observations using the model presented graphically in Figure 1: where all variables are in natural logarithms and β =1. The pseudo-random samples used _x_ ~ _N_ ( _4.2_ , _4.85_ ) for the EK-H-based data generating process (DGP) and _N_ ( _8.2_ , _2.8_ ) for the PPMLbased DGP. The error terms for the EK-H model were distributed with variance 2 ) (aa+bbyyii ii where _a_ = 2.7 and _b_ = –0.115, while simulations based on PPML parameters used σσ _a_ = 2.75 and = ee _b_ = –0.095. Each simulation included 10,000 observations for comparability with typical samples of bilateral trade flows, repeated 1000 times to obtain an indication of the standard error likely associated with real-world analyses. The required random numbers were generated using the pseudo-random number generator in STATA with seed value 10101.

For the OLS estimator, the lowest 41 percent of outcomes were mapped to missing, and eliminated from the sample. For the PPML, the values of the dependent variable were first 20
Table 6: Heterogeneous effects of SPS and TBT provisions across sectors depending on trade concerns Panel A: SPS <u>provisions</u>

||Ex|port value of ag|ricultural p|roducts||Export value|of any prod|uct|
|---|---|---|---|---|---|---|---|---|
||All SPS|Essential SPS|All SPS|Essential SPS|All TBT|Essential TBT|All TBT|Essential TBT|
||No S|PS STCs|At least|one SPS STC|No T|BT STCs|At least|one TBT STC|
|1<sup>_st_ </sup>tercile _×_ Prov|0.658|0.758|0.772<sup>_∗∗∗_</sup>|1.190<sup>_∗∗∗_</sup>|-0.0646|-0.240|0.567<sup>_∗_</sup>|1.022<sup>_∗∗_</sup>|
||(0.818)|(1.276)|(0.238)|(0.262)|(0.758)|(1.056)|(0.319)|(0.505)|
|2<sup>_nd_ </sup>tercile _×_ Prov|-0.191|-0.410|0.261|0.377|1.112|1.942|0.259|0.362|
||(0.735)|(0.834)|(0.215)|(0.232)|(0.888)|(1.481)|(0.296)|(0.499)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.442|-0.670|-0.203|-0.282|-0.940|-2.824<sup>_∗∗_</sup>|-0.0443|-0.320|
||(0.527)|(0.513)|(0.209)|(0.218)|(0.868)|(1.209)|(0.290)|(0.498)|
|fxed-efects|fhj fht jt|fhj fht jt|fhj fht jt|fhj fht jt|fhj fht jt|fhj fht jt|fhj fht jt|fhj fht jt|
|cluster|ij|ij|ij|ij|ij|ij|ij|ij|
|N|41678|41678|1109606|1109606|209274|209274|4025557|4025557|

Panel B: TBT <u>provisions</u>

||||Export value of|agricultural p|roducts|||Export value|of any product||
|---|---|---|---|---|---|---|---|---|---|---|
||MR SPS|Harmonization SPS<br>|Transparency SP|S<br>MR SPS|Harmonization SPS|Transparency SPS|Harmonization TBT|Transparency TBT|Harmonization TBT|Transparency TBT|
|||No SPS STCs|||At least one SPS|STC|No TBT|STCs|At least on|e TBT STC|
|1<sup>_st_ </sup>tercile _×_ Prov|0.712|-0.318|0.573|0.577<sup>_∗∗∗_</sup>|0.745<sup>_∗∗∗_</sup>|0.251<sup>_∗_</sup>|0.0800|0.321|1.221|0.482<sup>_∗∗∗_</sup>|
||(0.742)|(0.374)|(0.465)|(0.101)|(0.182)|(0.133)|(1.448)|(0.313)|(0.924)|(0.162)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.0684|0.0913|-0.371|0.325<sup>_∗∗∗_</sup>|0.207|-0.00749|-0.458|0.554<sup>_∗_</sup>|-1.361|0.331<sup>_∗∗_</sup>|
||(0.458)|(0.493)|(0.420)|(0.0842)|(0.137)|(0.118)|(1.283)|(0.309)|(1.155)|(0.151)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.255|-0.254|-0.339|-0.0249|-0.143|-0.292<sup>_∗∗∗_</sup>|-0.728|0.0789|0.359|0.238|
||(0.223)|(0.227)|(0.251)|(0.0732)|(0.120)|(0.0991)|(1.993)|(0.273)|(0.259)|(0.148)|
|fxed-efects|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|fkj fkt jt|
|cluster|ij|ij|ij|ij|ij|ij|ij|ij|ij|ij|
|N|41678|41678|41678|1109606|1109606|1109606|209274|209274|4025557|4025557|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

36
Both Zou and Hastie [2005] and Tibshirani [1996] point out that the lasso may not perform well empirically in the cases where the number of variables is higher than the number of observations<sup>9</sup> , there are groups of variables with high pairwise correlation, or there are high correlations between all predictors. These are all possible concerns for our keywords from Google Trends. To account for these issues, we employ the elastic net estimator of Zou and Hastie [2005].

The elastic net estimator is a linear combination of the _L_ 1 _−_ penalty of the lasso and the _L_ 2 _−_ penalty of ridge regression [Hoerl and Kennard, 1970]. The objective function of the elastic net is where _α_ and _ρ_ are chosen via K-folds cross-validation with _K_ = 5. Using both the lasso and the elastic net, we compute the index in the same way as the linear OLS index except that the _β_ coefficients are obtained from the two new estimators. Figure 9 contains an example of an index created using the expanding elastic net model. The fit is somewhere between the high variance OLS model and the conservative lasso model. In the following section we describe the empirical results of using these indices.

# **5 Forecasting Results**

Our hypothesis is that there is additional information in our transformations of the Google Trends data that allows improved nowcasts of the series of interest before their respective data releases have been made versus an ARMA model and our respective benchmarks. To test this hypothesis we compute one-step ahead forecasts using (6) and (9) and compare them to the chosen models from (5). Just like for the benchmarks, we start with two years of monthly data and then estimating expanding window models until time _T −_ 1 where _T_ is the last period for which we have data for the series we wish to forecast and for which we have _T_ Google Trends index values. At each time _t_ in _t_ = 24 _. . . T_ + 1 we recompute the order of the seasonal ARIMA(X) model as described above. This is to emulate what a practitioner would do in any given period. For each forecast (and nowcast) we compute the one-step ahead forecast error

> 9This is not the case in the current analysis, though we do have the case where the number of variables is only slightly smaller than the number of observations in the early periods of our index construction

22
_“... The first piece is located in Bagua Industry Park. It is a three-floor factory with a construction area of 1,593 square meters. The tenure is from 1985 to 2015. Currently it is leased out with an annual rent of 600,000 RMB... The second piece is controlled by our subsidiary Sege Baohua. It is in Huaqiangbei Industry Park. The total area is 2,213 square meters with a construction area of 10,509 square meters. The tenure is from 1982 to 2012. Currently it is leased out with an annual rent of 25 million RMB...”_ The assumption that titling for firms should always be associated with a sharp fluctuation in their share prices need not be satisfied: the market might lack relevant landholding information of these firms, or the impact of titling could be viewed as trivial. To ensure that we fully capture titling firms, we also used two other methods. First, we checked all post-2009 annual reports of listed companies that either were headquartered in Shenzhen or had branches located in Shenzhen, in search of any mentioning of constructions related to the land titling program. We further explore the name lists of listed firms whose construction applications on land titling were approved by the government. We find another 26 firms that mentioned their participation in the land titling program. The total number of treatment firms is thus 43. As a comparison, the number of listed firms headquartered in Shenzhen when the titling program was officially carried out in 2012 was 70.

Our empirical analysis starts with all domestically listed companies except those in the financial industry in light of their difference in business nature. We obtain financial data, including stock prices and information from financial statements, from the China Securities Market and Accounting Research (CSMAR) database, the most comprehensive database of its type that contains all trading prices and financial statement data for listed firms trading on both the Shanghai and the Shenzhen exchanges. We also rely on land price in Shenzhen to estimate the value of titling when Allocated land was converted.<sup>20</sup>

> 20 These land prices are found on the website of the “Urban Planning Land and Resources Commission of Shenzhen Municipality”, the government land agency, and “Soufan.com”, the largest online source of land

19
# Figure 5

# Figure 6

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This paper contributes to the growing literature of migration's impacts of non-income based outcomes and, in particular, this is among the rst papers to investigate the impact of international migration on time allocation and leisure consumption by gender in Nepal. To my knowledge, Lokshin and Glinskaya (2009) is the only other study that explores this topic in the Nepalese setting. However, they limit their analysis to women's labor supply for wage-employment and exclusively on the extensive margin. Using the 2003/04 round of Nepal Living Standard Survey (NLSS II) data, Lokshin and Glinskaya (2009) nd that men's migration from Nepal has discouraging eect on women's labor force participation. In contrast, this paper uses the 2010/11 round of NLSS, when work-related international migration was at record high, and incorporates both extensive and intensive margins by gender. This is especially important given the traditional roles of rearing children and household chores women assume within the household and society, the left-behind women are expected to be aected dierently than men. One of the most salient features of the employment related emigration in Nepal is that it is predominantly a male phenomenon. With males migrating abroad, the authority over household decision making may shift to women. However, the shift may be accompanied by extra responsibilities requiring extra hours of work or it might compel women to give up their jobs to assume the new roles. An additional important distinction between the papers is the timing of the surveys: while the NLSS II was implemented during a critical phase of the Maoist insurgency in the country, the NLSS III data were collected well after the end of the civil conict.

The primary identication strategy of the paper relies on an instrumental variable (IV) approach using a popular instrument in migration literature, that is, historical migration networks. Specifically, using the 2001 Nepal Census, I compute the percentage of international migrants from a village as an instrument for migration in 2010/11. A decade lagged migration shares are unlikely to aect local economic conditions and hence, labor supply decisions. Furthermore, I use GDP growth between 2001 and 2011 of the most popular destination countries interacted with the local level decade lagged migration shares as an additional instrument and the results are robust.

The analysis has four major ndings. First, solely on the extensive margin, having international migrants in the family discourages the left-behind members from participating in wage-employment. This is true for both male and female members. However, female members signicantly increase participation in self-employment, almost entirely through subsistence farming. Whereas, having 2
**Table A4** Impact of Treatment on Intimate Partner Violence (IPV), Separating Goal into High and Low Strategies

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**Boys**<br>**Treatment**|**2-3 strategy**<br>**Treatment**|**1 strategy**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|IPV often index||||||
|treatment x post|-0.229**<br>(0.100)|-0.404***<br>(0.130)|-0.134<br>(0.112)|0.000|5,182|
|treatment x post x Goal|0.121<br>(0.170)|||||
|IPV in last year index<br>treatment x post|-0.150*|-0.242**|-0.129|-0.000|5,182|
||(0.078)|(0.122)|(0.120)|||
|treatment x post x Goal|0.118<br>(0.140)|||||

_Notes._ This table presents estimates of a modified specification of equation 1 that splits the goal treatment indicator into two mutually exclusive categories for females assigned to the _Goal_ arm: set 2 or 3 strategies or set 0 or 1 strategy. For each outcome defined at the start of the rows, the coefficients from a single regression are presented in two sub-rows, with estimates of _β_ 1 in column 1 and the coefficients on the two _Goal_ arm indicators in columns 2 and 3 in the first of the two rows, labeled treatment x post, and the estimate of _γ_ 1 in the second of the two rows, labeled treatment x post x Goal. Column 4 presents the outcome mean at endline among females in ELA only communities who were not assigned to the _Goal_ treatment, and column 5 shows the number of observations in the model. See notes for Table 2 for detail on outcome definitions and control variables. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Female baseline and endline data, balanced panel.

63
pooled treatment on grade-specific enrollment by school location. Depending on the grade, differential mean impacts for the urban subsample turn from negative to positive or are positive and grow in size over the three years. The third-year differential mean impact in the urban subsample is 12 students in first grade, and declines monotonically by grade to 1.9 students in fifth grade. As a _percentage_ of baseline mean grade-specific enrollment in urban schools, the differential mean impacts across grades are similar in size. The third-year differential mean impacts are significant for all but fifth grade.

# **V. Conclusion**

Most evaluations of teacher incentives in low- and middle-income countries can be seen as efficacy trials. This study reports findings from an effectiveness trial in a low-income setting, where, in contrast to the efficacy trials, the government was responsible for the design and implementation of a teacher performance pay program in public primary schools, including using their own administrative data for measuring performance. Monetary bonuses provided to teachers were linked to gains in school enrollment; to gains in school mean scores in a fifthgrade standardized exam; and to the level of participation in the exam by fifth-grade students. To test whether leveraging head teachers either suffices or further augments program impacts, the receipt and size of the bonus were also randomly assigned across schools according to whether or not the teacher was the school’s head teacher.

Looking at the first three years of implementation, and using the government’s program administrative data for the evaluation, we find mixed evidence of significant mean impacts on school enrollment, exam participation rates, and student exam scores. Significant positive mean impacts are concentrated in the third year. The program raises school enrollment and exam participation rates in the third year. The trends in impacts over the three years however suggest possible learning by teachers on how to respond to the program or possible growing confidence among teachers in the administration and continuation of the program. We do not find that the program raises student exam scores in any year. Inference results for the program as a whole are mirrored by each of the treatment variants in the case of exam participation rates and mean student exam scores and by the treatment variant that offers bonuses solely to head teachers in the case of school enrollment. Post-hoc subgroup analysis reveals that the third-year program mean impacts on school enrollment and exam participation rates in the total sample are driven by 21
The ES data we use was collected in different years for the various countries. Thus, we account for the annual global shocks to R&D activity by using dummy variables indicating the year the ES was conducted in the country (year fixed effects).

Next, we control for overall level of development using (log of) GDP per capita (PPP adjusted and at constant 2011 international dollars). We use lagged values (by two years) of the variable and the data source is World Development Indicators, World Bank. Following the study by Lederman and Maloney (2003), GDP per capita is used a broad proxy measure for financial depth, protection of intellectual property rights, government capacity to mobilize resources, and the quality of research institutions that may impact R&D efforts.

Our next set of baseline controls include several firm characteristics. These are firm-size proxied by the (log of) number of permanent full-time workers at the firm (three fiscal years ago, lagged), a dummy variable equal to 1 if the firm is part of a larger organization and 0 otherwise ( _Multi-firm_ ), age of the firm (logs), managerial experience proxied by the (log of) the number of years of experience the top manager has working in the industry, and a dummy variable equal to 1 if the top manager of the firm is a female and 0 otherwise. For access to finance, we use two separate variables. The first is a dummy variable equal to 1 if the firm has overdraft facility and 0 otherwise ( _Overdraft_ ). The second is how severe is (poor) access to finance as an obstacle to firms’ current operations ( _Finance Obstacle_ ). The severity level is as reported by the firms in the ES and recorded on a 0-4 scale with higher values implying a more severe obstacle.<sup>9</sup> We now aim to capture various dimensions of the business environment. Some of the controls here are defined at the firm-level and others at the country level. The firm-level variables

> 9 Our main results are unaffected if we use other access to finance variables available in the ES. These include: (i) if the firm has an outstanding loan; (ii) a measure of financial constraint based on if the firm applied for a loan during the last year, the reasons for not doing so if it did not apply for a loan, and the outcome of loan application.

19