rafmacalaba/datause-encoder-data huggingface dataset:
associated with agglomeration economies. This can happen, for instance, if manufacturing firms produce locally traded goods.

More recently, Jacobsen and Parker (2014) extend Black, McKinnish, and Sanders’ (2005) study to the Western United States. They focus on the oil and gas boomtowns created during the 1970s increase in energy prices. They also use county-level data but observe a longer time span, that is, 1969 to 1998. This allows them to explore the effect of a bust after a longer period.

Similar to Black et al. (2005), they also find evidence of improvement in several economic indicators during boom years (such as nominal income and wages, employment, and population). Most of these effects are transitory and disappear after the boom. But, there are some long-lived negative effects after the bust: lower nominal income, lower profits and earnings, and higher unemployment compensation. The authors interpret these results as evidence that the bust created long-lasting economic hardships in the form of joblessness and depressed local income. In contrast to Michaels (2011) and Allcott and Keniston (2014), however, they find no evidence of any effect on manufacturing employment in either the short or long term.

These studies suggest that extractive industries (such as coal mining, oil, and gas) do generate positive employment spillovers: booms seem to increase the number of jobs and wages in other industries.<sup>18</sup> These spillovers are, however, modest in size.

These studies provide a less clear answer regarding the crowding out of manufacturing, which is a necessary condition for Dutch disease arguments. While some studies find a reduction in the relative size of manufacturing, others document an actual increase in manufacturing activity. A possible explanation is that resource booms may generate agglomeration economies that benefit local manufacturing firms. Similarly, part of manufacturing output is actually destined to local markets. Thus, some manufacturers could benefit from the growth of local demand. These factors may offset the increase in input prices associated with the resource boom. There is also not conclusive evidence on the long-term effects, at the local level, of resource booms. This is still an active area of research.

The studies discussed above have two important limitations. First, they are not very informative about the effects on real income or other measures of welfare. In particular, it is

> 18 Similar employment spillovers have been documented using other sources of demand shocks, such as construction of a pipeline (Carrington 1996), closure of army bases (Hooker and Knetter 2001), or opening of manufacturing plants (Greenstone, Hornbeck, and Moretti 2010). 26
**Table 1 – Development level and growth rate of Chinese provinces**

|||GD<br>|Ppercapita <br>|(yuan 197<br>|8)<br>A<br>|verage annual growth<br>rate<br>|
|---|---|---|---|---|---|---|
|**code**|**province**|1978|1985|1992|1999|79-99|
|**_Coastal regi_**|**_on_**||||||
|1|Beijing|1249|2109|3358|6222|7.65%|
|2|Tianjin|1141|1941|2714|5656|7.62%|
|3|Hebei|362|586|994|2239|8.68%|
|6|Liaoning|675|1107|1759|3164|7.35%|
|9|Shanghai|2484|4032|6161|14065|8.26%|
|10|Jiangsu|427|872|1722|4082|10.75%|
|11|Zhejiang|330|805|1529|3796|11.63%|
|13|Fujian|271|567|1102|2895|11.29%|
|15|Shandong|315|614|1099|2614|10.08%|
|19|Guangdong|365|725|1637|3600|10.90%|
|**_Central regi_**|**_on_**||||||
|4|Shanxi|363|637|905|1611|7.10%|
|5|Nei Mongol.|318|630|964|1747|8.11%|
|7|Jilin|381|668|1099|2115|8.16%|
|8|Heilongjiang|558|835|1218|2102|6.31%|
|12|Anhui|242|483|657|1548|8.84%|
|14|Jiangxi|273|488|781|1623|8.48%|
|16|Henan|231|453|702|1480|8.85%|
|17|Hubei|330|662|973|2108|8.83%|
|18|Hunan|285|463|702|1363|7.46%|
|**_Western reg_**|**_ion_**||||||
|20|Guangxi|223|334|534|1062|7.44%|
|21|Sichuan|252|475|737|1361|8.03%|
|22|Guizhou|174|326|472|765|7.07%|
|23|Yunnan|223|395|650|1141|7.77%|
|24|Shaanxi|292|500|782|1415|7.52%|
|25|Gansu|346|515|844|1487|6.94%|
|26|Qinghai|426|627|805|1355|5.51%|
|27|Ningxia|366|618|885|1531|6.82%|
|28|Xinjiang|317|621|1098|1769|8.19%|

Data source: _China Statistical Yearbook_ (various editions) and author’s calculation. The data of Chongqing, a municipality established in 1997, is included into its original province Sichuan; and Hainan (an island province separated as an special economic zone from Guangdong in 1988) and Xizang are excluded for their special characteristics.

However, in the last two decades, the growth rate of the coastal provinces has been much higher than that of the western provinces. For example, Guangdong, Shanxi and Ningxia shared the similar development level in 1978, while thanks to the doubledigit average annual growth in more than 20 years, the GDP per capita of Guangdong was more than twice of its two inland partners in 1999.<sup>6</sup> The coastal region became far

> 6 According to the regional classification of the "Statistics of China's Fixed Assets and Investment 19501985, _"_ the Chinese provinces are classified into three categories: coastal, central, and west. The coastal region includes Liaoning, Hebei, Beijing, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian,

5
Table 3: **Effects of ASP Social-Emotional Learning on Behavior and Academic Outcomes** _(Intention-to-Treat Estimates)_

||Behavior<br>at School<br>(1)|School<br>Dropout<br>(2)|Math<br>Grades<br>(3)|
|---|---|---|---|
|Psychology-based curricula|0.232***|-0.010|-0.017|
||(0.078)|(0.023)|(0.076)|
||[0.015]|[0.640]|[0.860]|
|Mean pure club group|0.650|0.037|0.587|
|MDE|0.068|0.037|0.099|
|Observations|887|868|855|
|R-squared|0.076|0.076|0.019|

_Notes:_ This table shows the estimated impacts of the ASP’s socialemotional learning on behavioral and academic outcomes. _Psychologybased intervention_ is a dummy equal to 1 if the student was enrolled in a school that was randomly assigned to the _Virtue_ or _Mindful_ intervention, and 0 if assigned to _Clubs_ . _Mean pure club group_ is the mean of the outcome for the group that only participates in _Clubs_ , without any extracurricular activity. We present the estimated coefficient _μ_ ˆ2 from specification (1). The description of dependent variables is available in Section 3.2. _Behavior at school_ is a dummy that equals 1 if the student’s behavior report is above the school median. _School dropout_ is a dummy variable that equals 1 if a student dropped out of any school in our study in the 2019 academic year. _Math grades_ is a dummy that equals 1 if the student’s behavior report is above the school median. All outcomes were obtained from administrative data sources (namely, teachers’ reports). Sample size in each specification varies according to the number of observations available for each outcome. Estimations include all individual controls for which there is an imbalance at baseline. All regressions include randomization-block (strata) fixed effects. Strata are defined as country and violence level (high or low) of the community in which the school is located. Wild bootstrap standard errors are shown in parentheses, and adjusted _p_ -values are in brackets. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01 _, p <_ 0 _._ 05 _, p <_ 0 _._ 1 48
els. Scaling our average effect by average mask manufacturing exposure, our results imply 51.6% of the decrease in prices from March to August is explained by mask manufacturing, offsetting 13% of the January-to-March increase. Second, as mask manufacturing exposure is constructed from differences across sub-Districts in nonmask textile trade flows, we interpret these impacts as being driven by persistent differences in costs of trade between sub-Districts. Building on this interpretation, we combine our estimated impacts of mask manufacturing exposure on prices with estimates from a gravity regression of log textile trade flows on log distance which we present in Appendix H; the resulting back-of-envelope calculation implies that a 10% decrease in distance to mask manufacturing at average mask manufacturing exposure causes a 0.6% decrease in mask prices.<sup>10</sup> Second, the persistent decrease in mask prices caused by exposure to mask manufacturing caused large, temporary increases in mask quantities purchased from manufacturers prior to the enforcement of quality standards nationally. As shown in Figure 3, impacts on mask quantities purchased from manufacturers are large and significant in April, but fade over time and vanish completely by August. We argue that our results in the initial months reflect substitution away from informal non-certified masks into domestically manufactured certified masks in sub-Districts with high mask manufacturing exposure, while the declining impacts are driven by this same substitution occurring in sub-Districts with low mask manufacturing exposure following the implementation of quality standards for masks. The June gazetting of quality standards for masks sold in Rwanda coincides with these declining impacts; quality standards were already met by masks produced by licensed manufacturers, as we discuss in Section 2.2. As a result, these standards primarily targeted the informal sale of non-

> 10For comparison, Donaldson (2018) estimates a 10% decrease in distance to the source of salt causes a 0.9%–1.7% price decrease in salt prices in India, statistically indistinguishable from our estimate.

24
# **5 Conclusion**

This paper has sought to focus attention and find insights regarding a most remarkable PISA 2012 result - the superlative performance of Vietnam, a country with the lowest per capita income amongst all PISA participants. Vietnam, with a mean PISA math score of 511 is not one of the very top performers. However, when compared with other lower middle income countries that took part in PISA 2012, Vietnam is a clear outlier. The following three concluding points can be made as a result of the analysis presented in this paper.

**1. Half the gap can be explained:** Even though the PISA dataset is rich and covers many aspects related to the achievement of student scores with international standardization of measures, with all the available variables, we could explain at best about 50% of the performance gap of Vietnam. The PISA 2015 application will be especially interesting to study as it will provide another important data point and enable a trend analysis to be conducted.

**2. Cultural factors are likely very important:** A combination of three sets of factors appear to be the most potent explanation for Vietnam’s performance: First, Vietnamese students work harder - we see they have less instances of skipped classes and being late for school, spend about the same time or more learning in school and substantial extra time studying after school. While at school, Vietnamese students are more disciplined and focused on their studies. Second, Vietnamese teachers appear to benefit from a closer supervision of their work by the school principal and others, and there may be a stronger harmony between the hard working students and their teachers. Third, parents may have an important role to play, by taking an active part in combining high expectations of their children, following up with their children’s teachers and contributing at school.

**3. Resources do appear to matter:** When we compare PISA performances across the range from lower income non-OECD countries to the high income OECD countries, we find a clear positive trend. Vietnam has so far been the only PISA outlier, with a performance on par with much wealthier countries, and in fact one of the top performing countries in Science. The analysis indicates that Vietnam may be reaping the benefits of policies regarding investments in education - the most important factor probably being the higher level of access to pre-school. A second factor is the investment in school infrastructure, especially in cities and small towns.

The unique combination of focused educational investments beyond its income level and a cultural heritage that has positive behavioral implications for students appear to be part of the story behind Vietnam’s educational success.

37
individuals are employed for a wage, either as production workers or managers. Managerial roles in the public sector receive a gender-specific premium. In equilibrium, the most skilled women operate businesses in the private sector, mid-high skilled women transition between managerial roles in the public sector and home production, mid-low skilled women engage in production at home, while the least skilled join the production labor force. Regarding men, the lowest-skilled individuals work as production workers, some with mid-skills assume managerial roles in the public sector, and the most skilled run private businesses.

We calibrate the model to match data from the Arab Republic of Egypt. This country is an interesting case study since it exhibits a disproportionate share of workers in the public sector. Public sector employment is close to 22% of total employment (18% for men and 42% for women), about 7 percentage points higher than the average of 108 non-MENA economies.<sup>3</sup> Many of these employees are highly educated: among workers with college education, around 25% of men and two-thirds of women work in the public sector. Even though the model structure is simple, we show that it is capable to match several features of the allocation of talent in the Egyptian economy. Moreover, the tractability of the model allows to easily extend the analysis to several other developing economies.

We compute aggregate productivity gains, in terms of output per worker and total factor productivity (TFP), from adjusting the current public sector premium and the size of the public sector to their optimal levels. We show that reducing the public sector premium from the current to the optimal level would yield aggregate gains of 12% for output per worker and 8% for TFP if Egypt exhibits the elasticity of the private sector to public goods of the average developed economy (0.1, Bom and Ligthart 2014). The lower the value of this elasticity, the higher the productivity gains in our baseline economy from reducing the current public sector premium and the current share of public sector employment.

In addition, we show that, in Egypt, an optimal talent allocation (assuming an elasticity of private sector output to public goods of _γ_ = 0 _._ 1) requires a decrease in the average public sector premium from 22% to 13%, but the premium would remain positive for women and close to zero for men. This wage structure reduces the size of the public sector from 22% to about 8%, and fosters female entrepreneurial activities. Moreover, compared to a wage

> 3MENA: Middle East and North Africa. See International Labor Organization (ILO) and `https://theforum.erf.org.eg/2020/08/31/public-sector-employment-mena-comparison-world-indicators/`

4
# **1 INTRODUCTION**

Trade liberalization is good for growth, and growth is good for the poor. This argument is simple but powerful. It has served as the departure point for discussion of the link between trade and poverty among economists and policy-makers, regardless of whether and to what extent they buy this argument. Krueger (1998) considers the inefficiencies that import substitution strategy creates and argues that trade liberalization undertaken at a period of low or negative growth rates can normally lead to a period of higher growth rates. Bhagwati and Srinivasan (2002) emphasize the empirical evidence of China and India. That is, these two giant economies achieved faster growth and poverty reduction through greater integration into the world economy. Dollar and Kraay (2002, 2004) use cross country regression to support this argument.

However, there are also many researchers who have strong reservations about this argument for at least two reasons. The first is methodological; Rodriguez and Rodrik (2001) for example severely criticized earlier studies supporting this argument because the measurement or the method is flawed. Ravallion (2001) points out that working with aggregate numbers can be misleading.

The second reason is the possibility of an adverse impact of trade liberalization on the poor. As pointed out by Winters (2002), there are a number of reasons why the poor may be adversely affected by trade liberalization. Important links include the change in prices of goods and services that poor households transact in relatively large amounts. Trade liberalization and poverty are also connected through government revenue and vulnerability of the economy to negative external shocks. Winters et al. (2004) provide an extensive survey on the relationship between trade liberalization and poverty. While they find no simple relationship, the empirical evidence broadly supports the notion that trade liberalization alleviates poverty in the long run and on an average basis. Yet, trade liberalization almost always creates winners and losers, and the losers may well include poor people.

2
- 20 -

Table 2: Means and standard deviations of student-level variables used in HLM analysis for private, government and local schools in the Philippines, 1983

||Priv<br>(N -<br>29|ate<br>60)|Gove<br>(N-|rnment<br>3470)|Loc<br>(N-<br>|al<br>2306)|
|---|---|---|---|---|---|---|
|Variables|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|Student-level Dependent Variables|||||||
|Science test score|11.86|4.30|11.88|4.80|10.11|4.10|
|Mathematics test score|11.37|3.66|10.55|3.39|8.65|3.58|
|Positive science attitude|0.12|0.91|0.07|0.97|-0.30|1.10|
|Negative science attitude|-0.12|0.89|-0.13|0.92|0 .3|1.14|
|Positive school attitude|0.07|0.94|0.04|0.97|-0.15|1.08|
|Negative school attitude|-0.13|0.96|-0.02|1.01|0.14|0.99|
|Student-level Predictors|||||||
|Male (X)|41.96|49.36|40.49|49.09|41.07|49.21|
|Age in months|188.91|23.11|190.60|22.56|195.18|34.11|
|SES factor score|0.20|1.00|0.07|0.98|-0.58|0.70|
|English spoken at home (x)|0,41|6.36|0.35|5.87|0.22|4.65|
|Philipino spoken at home (Z)|36.01|48.01|30.55|46.07|21.34|40.98|
|Dialect spoken at home (2)|38.61|48.69|44.12|49.66|54.03|49.85|
|Books in the home|2.71|1.29|2.51|1.23|1.99|1.12|
|Large family|63.16|48.25|67.41|46.88|73,45|44.17|

41. Social Composition. As a result of these family background differences, the social composition of private, government and local schools also differ from one another (Table 3). Local schools have a higher proportion of students that come from large families and that speak a local dialect; they have a lower proportion of students that speak English and have more than two books in the home. More of their classmates come from lower SES backgrounds.

42. Inputs. The three types of schools differ in a number of other respects as well, with local schools consistently disadvantaged. Teachers in local schools have fewer years of post-secondary science education and teaching experience; they teach less in laboratories and less frequently use small groups for instruction; their students report that their teachers are less likely to use a teaching style that emphasizes advance organizers, summaries and demonstrations.
In addition to being the<sup>major importer of precious metals, Asia wasalso a</sup> producer of gold and silver.<sup>In fact,while theEuropeansweremajor exportersof</sup> precious metals, they produced<sup>very little of it themselvesand, thus,had to obtain it</sup> elsewhere. The major and<sup>minorproducersworldwide of thesemetals are listedin Table</sup> 1.10.

Table 1.10

||I<br>**_Major_**_Producers_<br>c|_Linor_<br>_Producers_<br>|
|---|---|---|
|Siiver|klexico|NorthestEurope|
||Per&L<br>Japall|Persia<br>CenctalAsia<br>Burma/SiamiViemam|
|Gold|WestandSoutheastAfrica|Japan|
||SpanishAmerica(in1<sup>6'hc.)</sup>|Persia|
||Brazil(in18thc.,from<sup>1690)</sup>|China|
||SoutheastAsia||
|C7op-p-f|la-p:ln||
||Sweden||
|Tin'|Malaya||

'In addition, Prakash (1986-<sup>84) mentionsBolivia and Columbia as large SouthAmericansilver producers.</sup>

> 2Copper and tin were sometimes alloyed; both were used for low-value coinage. Source: Frank (1998:<sup>Table3.1,140)</sup>

Earlier on, copper was predominantly<sup>usedin Asiaascurrency but silver</sup> eventually displaced copper<sup>(and alsogold) as suppliesincreased.During this period,</sup> there was "at least a trimetallic<sup>world market,which however was predominantlyon a de</sup> facto silver standard."<sup>90For this reason(and becauseof limitationsof time andspace in</sup> this study), our focus will<sup>be primarilyonsilver.</sup>

# **Silver**

Rich deposits of silver<sup>discoveredandminedin the Americasconstituted the</sup> "most imnportant component<sup>of the increased total availability"of precious metalsin the</sup> 16th century.<sup>9t</sup> This silver<sup>traveled toAsia via twomain routes:(1) from South America</sup> via Spain to Europe and<sup>then on to Asiaand(2) from Mexico acrossthe Pacific to</sup> Manila.

Asia's producers included<sup>Japanas the major supplier in the regionstartingin the</sup> 16th century. Silver had<sup>becomea veryimportantcommodityinJapan in the16th and17'h</sup> centuries with only Spanish<sup>America beinga laiger producer and thusa majorcompetitor</sup> in the global market at<sup>that time.92Estimatesof Japan's productionare 50 tonsper year</sup> between 1560 and 1600,<sup>and 150to190tons per year between1600and 1640.9</sup> 19
Patterns of industrial employment have changed rather dramatically in rural India over the past two decades. As seen in Figure 2, from around three‐quarters in the early‐1990s, the share of rural labor employed in agriculture had, by 2011, declined to around one‐half. The two main rural growth industries are services and construction, with the latter’s employment share more than quadrupling over the last two decades. By contrast, the urban picture is one of relative stasis, with more modest expansions of services and construction over the same period.

<!-- Start of picture text --> Rural Urban<br>1993‐94 1993‐94<br>1.00 1.00<br>0.75<br>0.75<br>0.50<br>0.50<br>0.25<br>0.25<br>0.00<br>0.00<br>2011‐12 2011‐12<br>1.00 1.00<br>0.75 0.75<br>0.50 0.50<br>0.25 0.25<br>0.00 0.00<br>Uneducated young male Educated young male Uneducated young female Educated young female<br>Uneducated old male Educated old male Uneducated old female Educated old female<br>Services<br>Services<br>Construction Professional<br>Construction Professional<br>and Utilities<br>Utilities<br>Agri.+ Forestry+Fishing Mining, Manufacturing<br>Agri.+ Forestry+Fishing Mining, Manufacturing and<br>Construction Professional Services Construction Professional Services<br>and Utilities Utilities<br>Agri.+ Forestry+Fishing Mining, Manufacturing Agri.+ Forestry+Fishing Mining, Manufacturing and<br><!-- End of picture text -->

**Figure 3:  Share of demographic group in each industry**

5