rafmacalaba/datause-encoder-data huggingface dataset:
for _i_ = 0 _,_ 1 _, ..., N_ . Then the _N_ + 1 systems in (6) are combined to get the global model in levels: _′_ Here, _G_ 0 = ( _A_ 00 _W_ 0 _, A_ 10 _W_ 1 _, ..., AN_ 0 _WN_ ) is a known non singular _k × k_ matrix that _′_ depends on the trade weights and parameter estimates _Gi_ = ( _A_ 0 _iW_ 0 _, A_ 1 _iW_ 0 _, ..., ANiWN_ ) _′ ′_ for _i_ = 1 _,_ 2 _, .., p_ , _a_ 0 = ( _a_ 00 _, a_ 10 _, ..., aN_ 0) , _a_ 1 = ( _a_ 01 _, a_ 11 _, ..., aN_ 1) , _ut_ = ( _u_ 0 _t, u_ 1 _t, ..., uNt_ ) and _p_ = _max_ ( _pi_ ) across all _i_ . Premultiplying (7) by _G_<sup>_−_</sup> 0<sup>1,theGVAR(p)modelisobtainedas</sup> where, _b_ 0 = _G_<sup>_−_</sup> 0<sup>1</sup><sup>_a_0,</sup><sup>_b_1=</sup><sup>_G−_</sup> 0<sup>1</sup><sup>_a_1,</sup><sup>_Fi_=</sup><sup>_G−_</sup> 0<sup>1</sup><sup>_Gi_for</sup><sup>_i_= 1</sup><sup>_,_2</sup><sup>_, ..., p_and</sup><sup>_εt_=</sup><sup>_G−_</sup> 0<sup>1</sup><sup>_ut_.</sup> The dynamic properties of the GVAR model in (8) can then be examined using Structural Generalized Impulse Response Functions (SGIRFs).

# **3 Data and relevant tests**

This section describes the data, model specification, the results of the weak exogeneity test, time varying weights and the contemporaneous effects.

## **3.1 Data and model specification**

I use quarterly data from 1994Q1 to 2016Q4 for China, Japan and ten ASEAN countries (i.e. Brunei, Cambodia, Indonesia, the Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam). The model is estimated with data on total trade (TRADE), real GDP growth (GROWTH), real effective exchange rate (REER) and short term interest rate (SIRT). If the data were not available in seasonally adjusted form, they were adjusted using the census X-12 method. Though the main objective of this study is to investigate the effects of trade and growth shocks, the short term interest rate is 7
|(7)<br>2012<br>revenues<br>(IHST,<br>defen-<br>dant)<br>-0.735<br>(1.079)<br>-0.013<br>(0.023)<br>0.120*<br>(0.069)<br>0.685<br>Yes<br>Yes<br>Yes<br>19.850<br>6.943<br>0.017<br>1745<br>evels. Col 1:<br>g place after<br>dummy that<br>ecree. Cols<br>n), chamber<br> the hearing<br>el. Window<br>after decree<br>1.|
|---|
|(6)<br>2012<br>revenues<br>(IHST,<br>plaintif)<br>0.837<br>(0.811)<br>-0.039*<br>(0.020)<br>0.073<br>(0.055)<br>0.096<br>Yes<br>Yes<br>Yes<br>21.050<br>5.614<br>0.036<br>1692<br> (cols 2-4) l<br>ng is takin<br>cation is a<br>on of the d<br> (Interactio<br>lustered at <br> entry) lev<br>21 hearings<br> *** p_<_0.0|
|(5)<br>ial<br>2012<br>revenues<br>(IHS<br>transf.)<br>-0.150<br>(0.658)<br>-0.026*<br>(0.014)<br>0.115***<br>(0.042)<br>0.841<br>Yes<br>Yes<br>Yes<br>20.443<br>6.349<br>0.013<br>3437<br> 1) and case <br> if the heari<br>decree appli<br>he applicati<br> the decree <br>ard errors c<br> x hearing of<br>ween 4 and<br>, ** p_<_0.05,|
|(4)<br>Commerc<br>cases<br>0.027<br>(0.208)<br>-0.006<br>(0.004)<br>0.007<br>(0.013)<br>0.853<br>Yes<br>Yes<br>Yes<br>0.677<br>0.468<br>0.040<br>4795<br>earing (col <br>kes value 1 <br>tered after<br>ber after t<br>hange after<br>ol 1: stand<br> (chamber <br>ore and bet<br>: * p_<_0.10|
|(1)<br>(2)<br>(3)<br>Number<br>of<br>incoming<br>cases<br>Above<br>median<br>claim<br>More<br>parties<br>Hearing after decree<br>7.136<br>application<br>(4.691)<br>Trend<br>-0.086<br>(0.112)<br>Interaction<br>0.339<br>(0.288)<br>Entered after decree<br>-0.037<br>-0.011<br>application (_β_)<br>(0.047)<br>(0.035)<br>Trend<br>0.004***<br>-0.002*<br>(0.001)<br>(0.001)<br>Interaction<br>-0.006**<br>0.003<br>(0.003)<br>(0.002)<br>P-value 6pWCBoot<br>0.042<br>0.565<br>0.841<br>Chamber FEs<br>Yes<br>Yes<br>Yes<br>Calendar month FEs<br>Yes<br>Yes<br>Yes<br>Without adj. period<br>Yes<br>Yes<br>Yes<br>Pre-mean<br>14.283<br>0.506<br>0.253<br>Pre-sd<br>11.717<br>0.500<br>0.435<br>R-Squared<br>0.417<br>0.194<br>0.077<br>Observations<br>279<br>3286<br>4534<br>Notes<br>: Structural break diagnostic at the chamber-h<br>Hearing after decree application is a dummy that ta<br>the chamber’s application of the decree. Cols 2-6: En<br>takes value 1 when a case entered its assigned cham<br>1-6: All models include a linear trend - allowed to c<br>and calendar month fxed efects, and a constant. C<br>level. Cols 2-6: standard errors are clustered at the <br>includes cases entering between 38 and 4 hearings bef<br>application. Signifcance levels are denoted as follows|

66
## Fig 1. Perception costs, optimal subsidy and emigration rates

<!-- Start of picture text --> mss, σ ss<br>Subsidy rate<br>Emigration rate<br>γ c γ<br><!-- End of picture text -->

# 3 Empirical analysis

A central prediction of our theoretical model is that, whether an ine¢ cient government can control emigration or not, there should exist a negative relationship between skilled emigration rates and public education subsidies. In this section, we use a recent data set on emigration to address two empirical issues. Firstly, do human capital accumulation and public subsidies e¤ectively respond to skilled migration rates? Secondly, does the endogeneity of public subsidies modify the gains and losses of skilled migration compared to previous studies? We address these questions in a sample of 108 countries. We eliminate high-income countries (we select nations for which migration is likely to induce a clear incentive e¤ect), countries for which data on public expenditures in education are not available and countries that experienced civil war and political turmoil during the 1990s.<sup>4</sup>

> 4Among countries for which data is available, we exclude Afghanistan, Armenia, Bosnia and Herzegovina, Dem. Rep. of Congo, Croatia, Eritrea, Georgia, Guinea-Bissau, Iraq, Liberia, Macedonia, the Federated States of Micronesia, Rwanda, Serbia and Montenegro, Sierra Leone and Somalia.

11
in the same 3-digit industry varied in terms of their level of pre-reform productivity which can be taken as proxy of technological capability. On dimensions such as labor regulation, the institutional environment in which firms function also varied across Indian states as we studied above.

In Table 4 we report how interactions between the liberalization reform (captured by a dummy variable which switches on in 1991), pre-reform technological capability (captured by the ratio of labor productivity in a 3-digit state-industry relative to the most productive state industry in India) and pre-reform state-level investment climate (captured by the labor regulation measure) affected performance in the registered manufacturing sector in India. We look at these relationships using a panel of 3-digit state-industries for the period 1980-1997. In the first row of Table 4 we see that state-industries with higher pre-reform technological capability (ie, closer to the Indian productivity frontier for that industry) saw greater increases in output, employment, labor productivity and total factor productivity post-reform. This shows that state-industries with greater technological capability benefited more from liberalization. In contrast, laggard state industries which were below median productivity for India experienced below trend rates of increase in output, employment, labor productivity and total factor productivity following liberalization in 1991. Manufacturing performance is therefore a function of the interplay between a 1<sup>st</sup> advantage factor (market access) and a 2<sup>nd</sup> advantage factor (technological capability).

In the second row of Table 4 we observe, in line with the results presented in Table 2, that state-industries located in states with more pro-worker labor regulation saw less growth in output, employment, labor productivity and total factor productivity for the 1980-1997 period. This makes it clear that institutional environment (as captured by 1<sup>st</sup> advantage factors such as labor regulation) in which 3-digit industries function affects how well they perform.

Moreover in the third row of Table 4, where we report results for the interaction 30
# **Table 4.1. OLS results**

_Dependent variable: the rate of land lease transactions in the total number of land transactions by region (%)_

||Model 4.1|Model 4.2|Model 4.3|Model 4.4|
|---|---|---|---|---|
|Constant|55.96<br>(204.81)|-37.234<br>(-0.867)|-89.87*<br>(48.81)|203.28<br>(173.48)|
|Ln duration of land plot privatization<br>procedure No. 3.a. byARCS companies|18.37**<br>(8.41)|15.21*<br>(8.37)|26.16**<br>(8.65)|31.71***<br>(8.49)|
|Ln regional GDP per capita|-8.40<br>(18.02)|||-28.72<br>(16.43)|
|Land pricing factor in the regional capital||1.57<br>(1.44)|||
|Rate of refusals by government authorities<br>under the Procedure No.3A|||1.03*<br>(0.57)|1.53**<br>(0.60)|
|Adjusted R squared|0.190|0.260|0.375|0.482|
|N|13|13|13|13|
|Mean of the dependent variable|57.61|57.61|57.61|57.61|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

The importance of the procedure duration can be illustrated using Model 3.3 results. If the procedure duration is reduced from its mean length of approximately 200 days, to 150 days, (i.e. by 25%) other things being equal, then the rate of land lease transactions would go down by about 15 percentage points. Therefore, the rate of land privatization would, presumably, rise by the same number of percentage points.

It is also worth noting the level of influence of the variable for the rate of refusals for applications to privatize land on the rate of land lease (see models 3.3 and 3.4 in Table 4.1). Although this variable is significant only in the settings controlled for the procedure duration and is less significant than the latter one, its absolute influence on the dependent variable is rather striking. For example, from model 3.3, one can see that each percentage point increase in refusals to privatize land, other things being equal, causes slightly more than one percentage point increase in the rate of land lease (presumably at expense of land privatization). In other words, refusals not only discourage existing applicants, but also negatively influence potential applicants. The latter relation is even more pronounced in the Model 3.4 setting.

Thus, lengthy land privatization procedures create a strong incentive for businesses to apply to lease the land, rather than to apply for privatization. At the same time, refusals to approve of land privatization procedures by government authorities, have a similar effect, i.e., to encourage businesses to apply for a lease rather than ownership of land (see Chart 4.1 below). Table 4.2 below shows results of the paired t-test analysis of the mean aimed to establish whether the latter statements can be supported by the survey results.

18
to work in tandem with their own economies and political systems. The notion of free and fair elections also became doubly important, especially due to the Cold War.

In adopting multi-party electoral systems, most developing countries had none of the required deep-seated social checks and balances. Colonialism had often wiped entire social landscapes clean, redrawn borders and boundaries, fragmented communities, diminished previous institutions, community structures and values. In applying multi-party electoral systems on quasiblank social pages, developing countries were to become, for many decades, centers of systematic clientelism. Furthermore, as the emerging elite tried to accumulate their own capital in the absence of full-fledged capitalism, they had little choice but to plunder their own state and public resources in a clientelist setting. Clientelism thus became an unnamed historical epoch, especially for lowincome countries, defining the post-colonial era up to today.

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Page **23** of **25**
rule that is generally viewed as appropriate by village residents. The deaths‐election‐timing interaction thus picks up variation across villages, driven by differences in demographic change, in how residents interpret the relationship between election timing and a future reallocation.

The lineage group share – election‐timing interaction terms exploit the institutional complementarity between elections in more and less homogenous villages and land reallocation to identify differences across villages in how the time to the next election forms expectations over the possibility of a future land reallocation. For these interaction terms to be valid instruments their joint effect must be independent of ∆���� in equation (11), which requires that the interaction terms are not systematically related to other time‐varying factors associated with the productivity of farming, employment in the local economy or the net returns to migrating. Given the possibility that proximity to an election may be systematically related to public goods investments, over which the village leader may have control, and that such investments may be compliments to returns to farming or local activities, we thus examine directly whether our four interaction terms are associated with the timing of investments in local public goods. In Table 5, we show results from estimating regression models in which the four interaction instruments for change in significant reallocation, the village level exogenous regressors and province‐year dummy variables conditional on village fixed effects from model 11 are regressed on changes in a range of annual public goods investments, including: total public investment (log); total number of projects; roads and bridges; drainage; irrigation; land infrastructure; protective forests; and environment and sanitation improvements.<sup>24</sup> Lack of correlation between interactions with election‐ timing dummy variables and changes in other public goods investments suggests that the relationship between our instruments and future land reallocation risk is not contaminated by other decisions of village leaders that may affect individual decisions over productive investments or migration.

> 24The history of specific public goods investments is enumerated Table F4 of the Supplemental 2004 Village Governance Survey.

_Land Tenure Insecurity and Migration, Page 25_
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clined in India over the past two decades, not even during the rapid growth period since the early 1990s. In the DHS, where 55.3 percent of Indian households defecated openly in 200506, 63.7 did in the earlier 1998 survey round, and 69.7 did in 1992. This is particularly true for poor people: the joint UNICEF and WHO report concludes that “the poorest 40 percent of the population in Southern Asia have barely benefited from improvements in sanitation.” In 2010, 86 percent of the poorest quintile of South Asians defecated openly.

Therefore, it is already well-known that open defecation is bad for children’s health; that early-life disease leads to lasting stunting; and that open defecation is exceptionally widespread in India. The contribution of this paper is to quantitatively assess the importance of the link among these facts. The results indicate that sanitation is a statistically important predictor of differences in the height of children in developing countries and can explain differences of interest to economists and of significance to human development.

# **2 Evidence from country means: 140 DHS surveys**

Across countries, observed in different years, how much of the variation in child height is explained by variation in open defecation? This section uses 140 DHS surveys, each collapsed into a single observation, to show that sanitation alone explains more than half of the variation across country-years.

The analysis proceeds in several steps. First, section 2.2 documents that, across country means, height is associated with open defecation, with little change after controlling for GDP. Next, section 2.2.1 uses country fixed effects and replication on sub-samples of world regions to show that no geographic or genetic differences are responsible for the result. Then, section 2.2.2 verifies that other dimensions of infrastructure or well-being do not similarly predict child height. Section 2.2.3 observes that children would be more exposed to fecal pathogens where population is more dense, and finds that open defecation interacts with population density. Section 2.2.4 documents that the association between height and open defecation is 6
# **Why Don’t Remittances Appear to Affect Growth?**

Michael A. Clemens David McKenzie Center for Global World Bank, Development & NYU FAI BREAD, CREAM, and IZA _Keywords:_ Remittances, Growth, Migration, Measurement _JEL codes:_ F24 _,_ F22, E01, O15.

We thank the William and Flora Hewlett Foundation and Good Ventures for generous financial support, and we thank Yuvraj Pathak and Nabil Hashmi for excellent research assistance. We thank Adrian Mander for his Stata code, and Evis Rucaj for her insights on remittance measurement. We received helpful ideas from Ralph Chami, Peter Montiel, Dean Yang, and from seminar participants at the International Monetary Fund, but we are responsible for any errors. The views expressed here are those of the authors only; they do not necessarily represent the views of the Center for Global Development, the World Bank, or any of their boards and funders.
**The Arab world has yet to reap the full benefits of trade integration** . Integration has been a goal for the Arab World since the establishment of the Arab League in 1945. However, for a variety of (mainly political) reasons, that goal has remained elusive for a long time (Aluwaisheg 2004). For many Arab countries, regional trade accounts for less than 10 percent of total trade. At less than 5 percent, the Maghreb countries had the lowest share of intra-regional non-oil merchandise trade (Figure 2). And this share only marginally increased since 2000. Intraregional trade in the Mashreq and Gulf Cooperation Council (GCC) represents a somewhat larger share of trade.  In the case of Syria and Yemen, regional markets account for more than half of all non-oil exports; for Bahrain, Lebanon, Oman, and the UAE it is in the 35-40 percent range; while it is more than 25 percent for Egypt, Jordan, Kuwait, and Saudi Arabia. Recent political developments in the region toward more open and participative societies may create the conditions for improved political collaboration in support of regionalism.

**_Figure 2.  Export share by destinations (excluding Oil) for Maghreb Countries_**

<!-- Start of picture text --> 2% 1% 1% 2000 3% 1% 1% 2007<br>4% 3%<br>US US<br>16% EU 19% EU<br>0% China China<br>ROW 1% ROW<br>Maghreb Maghreb<br>Mashreq Mashreq<br>76% GCC 72% GCC<br>Export share by destinations (excluding Oil) for Maghreb Countries<br>2000 2007<br>10% 11% US 10% 9% US<br>2% 7% EU EU<br>9%<br>China China<br>3%<br>ROW 26% ROW<br>Maghreb Magh<br>37%<br>30% Mashreq Mashr<br>GCC<br>GCC<br>37% 6%<br>3%<br>Source: Comtrade<br><!-- End of picture text --> 6