rafmacalaba/datause-encoder-data huggingface dataset:
across countries will not be appropriate. If education quality differs dramatically between countries, appropriate matching would require a consistent measure of education quality across all of our sample countries. We have not been able to find such a measure for all of the countries in our sample and therefore offer the following results with the caveat that they may not be robust to differences in education quality across countries.

The results in column (1) of Table 8 are consistent with those presented in Tables 1-3. The effects of shocks are strong and positive and the lagged difference is negative and statistically significant. The coefficient magnitudes are not necessarily intuitive, however. Boyer and Hatton (1994) suggest that the speed of convergence can be estimated as (1-b)/b. This implies that the speed of convergence would imply that convergence would be very slow (taking more than 53 years). For comparison, columns (2) and (3) present the results from the same exercise for shocks within the United States (2) and Mexico (3). For the United States we use monthly data from the Monthly Outgoing Rotation Groups (MORG) of the Current Population Surveys. For Mexico we use the quarterly household surveys from the ENOE surveys.

The results in columns (2) and (3) suggest that within-country convergence is much more rapid than convergence across countries. For the United States, convergence back to an equilibrium differential would take about 4 months.<sup>6</sup> Convergence to the equilibrium differential within Mexico takes considerably longer – about 7.6 years. This result is consistent with Chiquiar (2001) who suggests that differentials between Mexico’s northern border and southern regions grow over time. Note, however, that the responsiveness of shocks is similar across the three columns, which may suggest that the three are subject to common external shocks.

> 6 For another comparison, Robertson (2000) finds that the speed of convergence to the equilibrium differential between the United States and the Mexican border city Tijuana is about 4 months.  Convergence to equilibrium differentials between the United States and the interior of Mexico takes longer.

16 | P a g e
<!-- Start of picture text --> 110 Relocate All Options<br>100 Rainwater (17%, 100cm) (32%, 100cm)<br>(10%, 100m) al * > *<br>90 NO - . ElevateGroundwater and Relocateand Rainwater with Adaptive<br>Groundwater. and Groundwater5 and. Rain- ((26%.fo, 100 cm)<br>_ Rainwater > water with Adaptive Ele-<br>£ (14%, 85cm) vate and Relocate Elevate|and Relocate<br>2 80 (17%%, 100cm) (21%%,100em)<br>1)<br>o<br>= 70<br>i<br>3<br>=a 60 Groundwater(7%, 55cm)<br>5 + +<br>2 30 (23%,Elevate 55cm)<br>” eo<br>c Baseline<br>> 40 (6%, 45cm)<br>n<br>i}<br>o<br>6 30<br>£ MONRE SLR<br>Estimate (30 CM)<br>20<br>10<br>IPCC SREX IPCC SREX<br>0 Mid Value (20%) High Value (35%)<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60%<br>Percent Increase in Rainfall Intensity<br><!-- End of picture text -->
left panel). The association also seems to be non-existent between total and average labor income and imports per worker (right panel).

**Figure 2: Exports, imports and average job outcomes, LFP and employment rate**

<!-- Start of picture text --> Exports Imports<br>Labor force participation Labor force participation<br>50 45.5 50 46.8<br>40.2<br>38.9<br>40 40<br>30 High exports 30 High imports<br>20 Low exports 20 Low imports<br>10 10<br>0 0<br>Employment rate Employment rate<br>50 43.3 50 44.5<br>40 38.1 40 36.8<br>30 High exports 30 High imports<br>20 Low exports 20 Low imports<br>10 10<br>0 0<br><!-- End of picture text --> Source: Own elaboration based on Population Census, Integrated Public Use Microdata Series and Customs data. Notes: (1) The sample is restricted to the urban and semi-urban municipalities with complete data in the whole period. (2) A municipality has relatively high exports (imports) if its exports (imports) per 2000 worker ratio in 2015 is above the median across municipalities and low exports (imports) if its ratio lies below the median.

Third, a municipality’s trade integration could strongly matter for migration patterns, both across domestic municipalities and with other countries, specifically the United States. More trade opportunities in a municipality may deter migration outflows, while attracting more migrants from other countries or pulling back Mexican migrants from the United States. In fact, the number of unauthorized migrants from Mexico to the United States declined from 6.9 million in 2007 to 5.6 million by 2015 (Gonzalez-Barrera and Krogstad 2019). Trade in a municipality may also have effects on remittances and the working age population. We therefore investigate the migration channel and again detect a clear difference between municipalities that are highly integrated in trade and those that are not. The average number of immigrants from other Mexican states in municipalities with high exports per worker is 142, while that in municipalities with low exports per worker is less than half this magnitude, namely 63 (Figure 4, upper left panel). Again, the relative difference is larger for imports (upper right panel). The analysis also shows a difference between the number of immigrants from abroad, which is less pronounced (bottom panel) and visually confirmed in the scatterplots across all municipalities (Appendix 3).

5
# **Discussion**

Migration out of U.S. counties hit by average hurricanes did not increase between 1992 and 2017. The migration that did occur after hurricanes, even some of the most damaging ones, generally did not involve movement to counties that are at substantially lower risk of future hurricanes than the sending county. Rather, we find that the population of impacted counties may actually increase after storms, consistent with prior evidence that demand for homes in impacted counties exceeds supply in the years after hurricanes.<sup>35</sup> We also find evidence that exposure to hurricane risk has increased between 1992 and 2017 at the population level due to changes in the geographic distribution of the U.S. population. Taken together, these results suggest that migration has not been occurring in adaptive ways.

There are many reasons why migration may not be adaptive. It is well documented that individuals move for many reasons and there may be a number of reasons why adaptation to climate change would not be a primary motivation for migration in the U.S.

We find evidence that a trade-off exists in the U.S. between moving to locations with more economic opportunity and moving to those that are at lower risk from future hurricanes. Many of the most productive economic counties in our sample (e.g. New York County, Harris County, and Miami-Dade County) are also among the most exposed to storms. It appears that the agglomeration benefits offered by locating in these cities may currently outweigh the incentives to adapt to future storms by relocating. This is a consequence of the particular economic geography of the U.S. and of particular policies. Migration to adaptation in the U.S. might thus still happen in the future, but our results suggest that it will not happen without significant changes in the economic, social, and policy landscape.

One response to our results could be that the gravity-type models that urban and trade economists have used for years have long recognized the importance of agglomeration and economic opportunity as dominant forces driving migration. We acknowledge those models, but point out that this same class of models is often used to show that migration will occur in ways that substantially reduce the risks of climate change, implying that individuals move away from at-risk areas.<sup>9,33</sup> In showing that those at-risk areas are often centers of economic opportunity – at least in the U.S. – and that this opportunity appears to dominate the adaptive motivation for migration in the data, our paper complicates these existing models. Specifically, our empirical results suggest that the relative weights given to economic opportunity and climate change in gravity model calibration may need to be revised. Consistent with this conclusion, recent work utilizing these models calibrated to U.S. county data finds that migration may have a negligible impact on aggregate losses from climate change.<sup>36</sup> Our results are also subject to several significant caveats. First, it is difficult to prove a null 13
# **1. Introduction**

No complete valuation of an ecological public good of the magnitude and complexity of the Amazon rainforest has yet been undertaken. Such a project faces several daunting challenges, which include choice of time frame and the fact that many impacts are likely to require very long time horizons; the intangibility of many value components (passive-use and carbon values; also biodiversity and other ecosystem services); and the likely changing nature of many relevant value components.

This paper focuses on the global public-good benefits of the Amazon forest, to populations in other parts of the world than the South American region where the Amazon is located. We can distinguish between two main aspects of global value or benefits of preserving the Amazon rainforest, namely

- A. The rainforest as a carbon sink, and

- B. All other global values, which include rainforest-derived products (including pharmaceutical ones), various ecosystem externality effects, recreation and ecotourism, and passive use (or existence) values attached to preserving the Amazon by the worldwide population.

Aspect A should most reasonably rely on assessed “social cost of (SCC) carbon” values, which correspond to (present discounted) losses to the global economy from additional carbon emissions (Tol 2005, 2008), multiplied by net amounts of carbon emitted when forest is lost. Aspect B is more complex. Much or most of it is likely to consist of passive-use or existence value, which can be captured only with the use of stated preference (SP) studies.

Surprisingly little is known about willingness to pay (WTP) to protect the Amazon rainforest, in countries both inside and outside South America. Two previous SP studies, Kramer and Mercer (1997) and Horton et al. (2003), might throw some light on the latter issue. Kramer and Mercer conducted a random population survey among the U.S. population in 1995, to elicit WTP for protecting 5% of _global_ rainforests (thus not specifically in the Amazon), as a _one-time_ payment per U.S. household, finding an average expressed WTP per household in the range $212
economic interdependence between them, we construct a series of indicators to measure the economic environment that a province faces.

Using panel provincial data from 1978 to 1999, this paper studies the spillover effects in the case of China, and came out with three findings: (1) at national and regional levels, the rapid growth of a province encourages that of the neighbors – the regional development is in a virtuous circle;<sup>4</sup> (2) spillover effects differ in size and in significance among different province groups – some coastal provinces serve as growth locomotives; and (3) the rapid take-off of some coastal provinces in the last two decades did maximize aggregate growth of the entire economy, while encouraging growth in the inland hubs that have strong forward/backward linkages with other provinces is the most effective way of reducing the regional development gap.

The paper is organized as follows: section 1 describes the growth trajectories and the center-peripheral regional growth pattern between coastal and inland provinces; section 2 focuses on the construction of a series of indicators of “neighbor performance”; section 3 examines the differentiated spillover effects on local growth issued from and received by different provinces; section 4 simulates the impact of a hypothetical performance enhancement of various provinces on regional growth, examines the effectiveness of the regional growth pattern, and identifies the target locations of development aid. Finally, the paper concludes.

# **Section 1: Center-peripheral unbalanced regional growth pattern**

The success of the Chinese economic reforms, which were launched in the late 1970s, is well documented in the literature.<sup>5</sup> Through incrementally reforming the centrally planned system to improve incentives and increase the scope of the market in resource allocation, and through building new institutions to support a market system before old institutions were destroyed, China’s transition has achieved a remarkable success (Qian, 2000; 2003). The GDP per capita of the Chinese provinces increased more than 5 times in average from 1978 to 1999 (table 1).

> 4 Baumont et al. (2001) give a thorough discussion on the spillover effects and shows the positive geographic spillover in a study of the European regions.

> 5 See, for example, Naughton (1995) and Renard (2002).

4
by municipal and county governments. The hierarchy of Chinese government consists of, from top to bottom of the ladder, the central, provincial, municipal, and county governments.

The enterprise reforms in the 1980s and 1990s in China aimed to decentralize the oversight of SOEs and to grant SOEs more autonomy in production, investment and financing (Li, 1997). Some industries were decentralized earlier than others, such as machinery, which started decentralization as early as 1985. According to the _China Industrial Census Statistics Book 1985 and 1995_ , the share of industrial output accounted for by county SOEs increased from 12.3% in 1985 to 25.8% in 1995.

A large wave of SOE restructuring began from 1997 (Xu, Zhu and Lin 2005). In 1998, the State Council decreed to decentralize all 94 central SOEs (and their subsidiaries) in the coal industry. As a result, 3.2 million workers and 1.3 million retirees were under the oversight of provincial governments (Nie and Jiang 2011). Similarly, the decentralization of provincial SOEs to municipality and county government SOEs was also widespread. Figure 1 shows the 1998 distribution of SOEs with oversight government at different levels, in terms of number of firms, employment, and output, respectively. Differing from earlier waves of decentralization, many SOEs, after being decentralized, were also privatized by lower governments. Indeed, many decrees of provincial governments mentioned that the purposes of decentralizing SOEs include facilitating firm restructuring, and even “first decentralize, then restructure ownership.”<sup>4</sup> What determines which SOEs become decentralized? To understand this, consider a simplified economy with just two layers of government, upper and lower governments, under which an SOE can be governed. What benefits do the oversight rights entail for the oversight government? Even though all SOEs are state-owned,

> 4 See: http://gsrb.gansudaily.com.cn/system/2004/06/04/000120947.shtml, and <u>http://jxrd.jxnews.com.cn/system/2009/09/30/011217616.shtml</u>

8
supplies at below-market prices also helps to attract new members and establish a discipline of meeting attendance, saving, and (re)payment.

A final element of the project strategy is to support the building of capacity and equity. As weak capacity had been a key reason for the failure of earlier credit and saving programs, the program placed strong emphasis on forming training institutions in each mandal to train leaders and accountants from new SHGs in basic management and accounting, making available trained “master book keepers” to check the accounts of SHGs, and supporting community assistants to provide technical assistance to the start-up and conduct of entrepreneurial activities. The main instrument to assist in rapidly building up group equity was the community investment fund (CIF) which was made available to SHGs for on-lending to their members and to provide collateral for borrowing from banks. This allowed to jump-start lending and provided the group with a reliable source of income and profit in the form of interest payments.<sup>2</sup> Spreads earned in this way provide a source of revenue that may be used for economic activities such as adding value to local agricultural production through marketing or processing, or social mobilization.

Despite considerable interest in promoting SHGs that spawned many tools to help with implementation, evidence on economic impacts has been very limited. A survey in one of the first SHG-districts finds that, at a qualitative level, SHGs helped reduce vulnerability to drought, encouraged entrepreneurial behavior and livelihood diversification, and improved social capital (Garikipati 2008). Positive empowerment impacts on SHG participants are also found from a larger five-state sample (Swain and Wallentin 2008).

Studies of the AP project considered here support the general conclusion of a significant and positive impact on social empowerment but are less clear on economic impacts. One study concludes that, while the project had a positive impact on risk coping, some aspects of female empowerment, and non-food expenditure, a lasting impact on livelihood activities is unlikely (Lastarria-Cornhiel and Shimamura 2008). Similarly, after three years of implementation, SHG participants had improved their nutrition and social empowerment but there were no significant impact on economic outcomes such as income or asset accumulation (Deininger and Liu 2009). Below, we test whether economic effects materialized at subsequent stages of group development.

# **2.2 Data description and household level evidence**

We rely on two survey rounds at household and group levels conducted from February to June of 2004 and August to October of 2006. The survey covers 51 mandals from three districts (Anantapur, Adilabad and Srikakulam) which were chosen to represent the state’s three macro-regions (Rayalaseema, Telangana, and Coastal AP). Out of the 51 mandals, 41 were assigned to be treatment where the program 6
<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Agriculture Industry Services Unknown<br>Nonag (macro, ra w) Nonag (macro, adj)<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-1<br>Agriculture Industry Services Unknown<br>Sector output ratio (output per person per yr)<br>Sector output ratio (output per hour)<br><!-- End of picture text --> Figure 6: Productivity gaps by sector. Figure 6a (top) shows the ratio between productivity in each sector and agriculture based on per-person-per-year productivity measures. The fourth column depicts the raw productivity gaps between agriculture and non-agriculture as constructed using national accounts data, and the fifth column refers to adjusted gaps constructed by Gollin et al (2014). Figure 6b (bottom) shows the ratio between productivity in agriculture and in other sectors based on output per time input.

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# How Conflicting Definitions of "Manufactures" Distort Output and Trade Statistics

by Alexander J. Yeats

# Table of Contents

|I.|Introduction ........|1|
|---|---|---|
|H.|Conceptual Issues<br>.3||
|m.|Alternative Definitions of Manufactured Goods.|6|
|IV.|The Influence on Country Classifications .14||
|V.|Sunmmay and Conclusions .18||
||References .20||
Table 1: Children’s Achievement—First Experiment

||Reading Score|Math Score|Science Score|Overall Index|
|---|---|---|---|---|
|API Original|-0.053|0.083|-0.082|-0.022|
||[0.737]|[0.655]|[0.585]|[0.902]|
||_{_0.750_}_|_{_0.669_}_|_{_0.591_}_|_{_0.910_}_|
||(0.779)|(0.739)|(0.717)|(0.878)|
|Number of clusters|70|70|70|70|
|Observations|599|599|599|599|

_Notes_ : This table shows OLS estimates and the associated _p_ -values on student outcomes measured after two years of exposure to the mentoring program under the first experiment run by the government. For detailed descriptions of the test scores used in this table, see Appendix A.1. The dependent variables are standardized with respect to their means and the standard deviations in the control group. _p_ - values reported in brackets refer to the conventional asymptotic inference. _p_ -values reported in braces are computed using randomization inference (randomization- _t_ ). _p_ -values reported in parentheses are adjusted for testing the null impact of API Original across the five outcomes shown in the tyable through the stepwise procedure described in Romano and Wolf (2005a,b, 2016). All _p_ -values account for clustering at the school level.

standard deviations—a non-negligible effect size that is nonetheless not statistically different from zero. The effect of the _Original_ modality of the mentoring program on the transition rates to lower secondary school are shown in the last two columns of Table 2. Estimated effect sizes are noisy and relatively small in magnitudes, ranging between an increase of seven and eight percentage points out of a basis of 62 percent enrollment rate in seventh grade in the control group.

The second row of Table 2 displays the estimated coefficients for the average impact of the _Plus_ modality of the API program when compared to the control group. Children who are enrolled in a school that received the _Plus_ modality increased their reading scores by 0.32 standard deviations. We can reject the hypothesis of a null effect of API _Plus_ at the 99 percent confidence level across all three different inference procedures. Quantitatively, the API _Plus_ effect is approximately 2.5 times higher than the effect of the API _Original_ . The difference between the two modalities is statistically different from zero at the conventional 95 percent level in two out of three inference procedures.

We find similar patterns when we look at math scores, which show a sizable effect of the _Plus_ modality with an estimated treatment effect of 0.24 standard deviations. This effect is precisely estimated, and we can reject the hypothesis that the two treatment arms have the same effect at the 95 percent confidence level in two out of three cases. The API _Plus_ program also generates a sizable improvement in the socio-emotional score of 0.2 standard deviations.

12
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