rafmacalaba/datause-encoder-data huggingface dataset:
|||||Year|||
|---|---|---|---|---|---|---|
|**HS**|||||||
|**code**|**_Type of products_**|2001|2002|2003|2004|2005|
|**2 digits**|||||||
|90||8.0|8.0|8.0|8.0|8.0|
|||_(4.5)_|_(4.5)_|_(4.5)_|_(4.5)_|_(4.5)_|
|||63|63|63|63|65|
|91|_CLOCKS AND WATCHES_|27.3|27.8|28.4|27.1|27.9|
|||_(26.0)_|_(27.9)_|_(21.1)_|_(26.9)_|_(27.2)_|
|||33|33|33|33|33|
|92|_MUSICAL INSTRUMENTS_|7.4|7.4|7.4|7.4|7.4|
|||_(4.4)_|_(4.4)_|_(4.4)_|_(4.4)_|_(4.4)_|
|||30|30|30|30|30|
|93|_ARMS AND AMMUNITION_|20.0|20.0|20.0|20.0|20.0|
|||_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||104|104|107|107|111|
|94|_FURNITURE LIGHTING_|22.0|20.7|21.3|21.9|20.6|
||_,_|_(9.8)_|_(14.0)_|_(13.4)_|_(14.1)_|_(12.7)_|
|||81|81|81|81|81|
|95|_Tos and sort oods_|15.5|15.5|15.5|15.5|15.5|
||_y p g_|_(2.0)_|_(2.0)_|_(2.0)_|_(2.0)_|_(2.0)_|
|||72|72|72|72|74|
|96|_MISCELLANEOUS MANUFA_|19.1|19.1|19.5|19.1|18.9|
|||_(3.6)_|_(3.6)_|_(4.5)_|_(3.5)_|_(4.1)_|
|||7|7|7|7|7|
|97|_WORKS OF ART COLLEC_|0.0|0.0|0.0|0.0|0.0|
||_,_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|Notes:|_a Number of lines at 10-digit level_||||||
||_b Simple average over all lines in the su_|_bcategory_|||||
||_c Standard deviation over all lines in the_|_subcategory_|||||

32
Sicherman, N. and O. Galor, 1990, A Theory of career mobility, _Journal of Political Economy_ 98: 169192.

Topel, R., 1991, Specific capital, mobility and wages: Wages rise with job seniority, _Journal of Political Economy_ 99: 145-176.

Wilk, S.L., L.B. Desmarais, and P.R. Sackett, 1995, Gravitation to jobs commensurate with ability: Longitudinal and cross-sectional tests, _Journal of Applied Psychology_ 80: 79-85.

Yamauchi, F., 2001, Does inequality of labor earnings emerge in young days or later? Labor earnings dynamics and learning about individual ability in heterogeneous society, _Journal of Economic Behavior and Organization_ 44: 413-434.

Yamauchi, F., 2004, Are experience and schooling complementary? Evidence from migrants' assimilation in the Bangkok labor market, _Journal of Development Economics_ 74: 489-513.

Yamauchi, F., N. Poapongsakorn and N. Srianant, 2009, Technical change and the returns and investments in firm-level training: Evidence from Thailand _, Journal of Development Studies_ 45: 16331650.

Yamauchi, F. and Y. Liu, 2013, "Impacts of an early stage education intervention on students' learning achievement: Evidence from the Philippines," _Journal of Development Studies_ 49: 208-222.

----------------, 2014, Gender-specific impacts of an early-stage education intervention on transition from school to work in the Philippines, Mimeograph, World Bank.

13
<!-- Start of picture text --> Total Exports Main Destination (largest export value) :<br>nen-CU CIS 7" 4<br>EU28 .<br>Rest of Europe, incl. Turkey<br>MENA<br>China wa<br>RestoNorth America of Asia ic me1 . x ee, fi ‘‘ . .<br>NoROWdata TM " - ‘ nt Es =<br>: a Re . 5<br><!-- End of picture text --> <!-- Start of picture text --> Y ; ey<br><!-- End of picture text -->
convergent wage changes and far weaker correlation with revenues ". This distinction may explain the lack of association, using 3 digit level data, between wages and sales that we report above. The two specifications reported below also indicate little or no feedback to wages from unemployment, An increase in the rate of unemployment has a negative impact on the change in the wage (dW) while a high rate has no or a slight positive effect.

To what extent are wage settlements government by firm-specific financial conditions or ability to pay ? Here, it is useful to distinguish between changes to wages and wage inclusive of bonuses. The.latter are generally paid in the last two months of any year and are reasonably sizable; for 1993 nominal industrial wages at end-year jumped over 25% as a result of bonus payments. The wage-revenue association pinned down in the wage equation reported in Table 8 using the LMFS dataset is confirmed by the Ministry of Finance dataset. Table 9 provides information on both the average wage increase and bonuses in 1992 in terms of both firm size and profit or loss-making status. The conclusions are evident. The change in wages does not vary very much by firm size but the variation increases dramatically in inverse association to size. (Wage levels are also far more dispersed among small firms.) Further, while for most size classes the change in wages in 1992 disaggregated over profit and loss makers suggests that the latter sanctioned lower wage increases, the gap is not that large. In short, using this dataset, it does not appear that small firms tie wages that much more closely to revenues. But introducing bonuses tells a somewhat different story. The latter are far larger among small firms and are clearly and positively correlated with firm profitability, particularly among smaller firms. This can probably be related to the presence of co-ownership or partnerships among the smaller units.

The presence of partnerships among the small firms may explain the fact that labor costs tend if anything to be higher in this group. As Table 10 shows, part of this can be attributed to the effect of bonus payments but it holds true when excluding bonuses. This is not inconsistent with the fact of higher wage increases in 1993 in large firms, as indicated by "Note that in the MoF dataset, for the largest loss makers in 1992, wages as a share of total costs were around 33% (the share is highest for large SOE loss-makers); as against 16-19% for other loss-making categories and 11/12% wage shares in the economy as a whole. This smacks of decapitalization.

23
and are not frequently updated. Migrants in urban areas who are living in worksites, in dormitories, or even less formal housing, are less likely to be enumerated. The Rural Urban Migration in China, developed and headed by Xin Meng from Australian National University from 2008 through 2016, addressed this weakness by using geography-based survey techniques and sampling through workplaces judged to employ migrants (Akguc et al 2014; Kong 2010).

# **3.2 Long-Term Migration Trends**

With the abolition of the production team system and the establishment of the household responsibility system in agricultural production in the late 1970s, China’s farmers not only increased agricultural production and earned higher incomes (Lin, 1992), they also gained freedom to allocate their labor to higher-earning non-agricultural activities locally -- first in local township and village enterprises, and later enterprises in urban areas within their prefectures or provinces (Bao et al., 2009). The establishment of the Special Economic Zones, starting in the mid-1980s, spurred foreign investment flows into eastern and southern coastal regions, creating an increase in demand for workers in new factories. Millions of migrants responded and moved for employment, most noticeably to the Pearl River Delta region in the south and the Yangtze River Delta in the east (Liang, 1999; Shen and Chiang, 2011).

After the mid-1990s private businesses also offered employment to migrants in closer cities (Chen and Coulson, 2002). Since the mid-1990s, rural-urban migration patterns have been dominated by intraprovincial movement (Kanbur and Zhang, 1999; Song and Liang, 2016), but inter-provincial migration increased steadily over time (Liang and Song, 2016) before stagnating since 2006, as shown in Figure 1. Recent studies on migration trends have not noted this slowdown. Most studies using 2000 and 2010 census data (Liang et al., 2014; Liang and Ma, 2004) fail to pick up the rate of change over these years. Longer distance migration is likely driven by differences in growth rates, and consequent increases in regional inequality and wage differences, between the coastal and the inland regions (Kanbur and Zhang, 1999; Liu and Xu, 2017), though internal migration spells to large cities within home provinces are less costly and easier to arrange. Recent studies also provide evidence that China’s access to WTO in 2001 and the resulting reduction in trade policy uncertainty faced by Chinese exporters to the U.S. facilitated economic structural transformation in China (Erten and Leight, 2021; Facchini et al., 2019). Increases in trade after 2001 are associated with more migration of skilled urban labor, not unskilled rural labor (Facchini et al., 2019). Based on the RCRE household data from 2009-2017 in which migration destination information was collected in a consistent manner, Guangdong, Zhejiang, Shanghai, Jiangsu, and Beijing 10
spread of 0.36%. The value-weighted returns show the similar pattern. Therefore, the predicitive power of the relative ES-implied beta does not hinge on the empirical setting.

# **6 Conclusion**

Downside risk plays a crucial role in asset pricing. As early as 1952, economists have realized that investors are more concerned about losses than gains. If the distribution of returns is symmetric, the analysis based on the whole distribution would be identical to the analysis based on low returns. However, it has been long recognized that return distribution is asymmetric. The recent literature, thereby, places the emphasis on low returns, and recommends the usage of downside beta, a beta conditional on returns falling below a threshold, in addition to the CAPM beta. Existing downside betas show a close relation to the cross-section of stock returns over the same period, but their predictive power over future returns is limited. Therefore, this paper proposes an alternative measure of downside beta using the expected shortfall, referred to as the ES-implied beta.

This paper compares the performance of the ES-implied beta with a downside beta that is widely used in the literature, calculated as covariation between asset returns and market returns conditional on market returns falling below market average and referred to as the usual downside beta. Consistent with the literature, the usual downside beta exhibits strong explanatory power for the cross-sectional returns over the same period. Returns of portfolios sorted by the relative usual downside beta over the same period increase monotonically from the low to high portfolios. The spread between the highest and lowest portfolios is 0.7% per month and highly significant. However, the relative usual downside beta demonstrates very limited power for predicting returns in the future. Returns of portfolios sorted by the relative usual downside beta in the past 12 months do not exhibit an increasing pattern. Furthermore, although the relative usual downside beta generates a spread of 0.2%, significant at 5%, between the highest and lowest portfolios in the unadjusted returns, it only generates a spread around 0.12% and significant at 10% in risk-adjusted returns. Results from Fama-MacBeth regressions also suggests that the predicitve power of the relative usual downside beta over futures is limited. The relative usual downside beta implies a significant risk premium of 0.2% when only controlling for the CAPM beta; however, this risk premium becomes insignificant after controlling for other variables.

In contrast, the performance of the relative ES-implied beta is relatively stable. Its in-sample performance is as good as the relative usual downside beta. The spread between the highest and lowest portfolios sorted by the relative ES-implied beta over the same period is 0.66% for equalweighted portfolios and 0.61% for value-weighted portfolios. More importantly, the relative ESimplied beta is highly persistent and demonstrates strong predictive power over future returns. A trading strategy that buys stocks with high relative ES-implied beta and sells stocks with low 17
# <u>Figure 2 : Estimated distribution of Income in Morocco in 1995.</u>

<!-- Start of picture text --> Estimated Income distribution - 1995 (Dhs)<br>18000<br>17000<br>16000<br>14000<br>12000<br>10800<br>10000<br>8000<br>6900<br>6000 5600<br>4800<br>4000 4000<br>3300<br>2700<br>2000 2100<br>1400<br>600<br>0<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Decile of expenditure<br>Dhs)<br>Estimated monthly Expenditure(1995<br><!-- End of picture text --> <u>Figure 3 : Borrowing capacity and maximum dwelling value by decile of income, Morocco, 1995</u> <!-- Start of picture text --> Borrowing Capacity and Maximum Dwelling Value (1995)<br>700<br>600<br>500 478<br>400<br>304<br>300<br>194<br>200<br>158<br>135<br>113<br>93<br>100 59 76<br>39<br>0<br>10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Decile of expenditure<br>Borrowing Capacity Maximum Dwelling Value<br>Value (Thousands of MDhs)<br><!-- End of picture text --> 25
to factors Y, and X2. Altematively, unequal technical efficiency, such as shown by fim C and D, should r :vc. I a positive correlation between retums to X, and Xp That is, in firm D, retums to both factors are low, whereas in C, they are both high. If, over time, clusters of firms around A and B migrate toward point C, the correlation between returns to Xl and X2 should become less negative, while, the drift of a cluster abound D toward a cluster around C. should cause the correlation between factor returns to become less positive.

6.9 Table 3 shows these correlation coefficients for the full sample for the years 1980, 1985 and 1989. These patterns indicate that during, the decade of the 1980s, labor and capital productivity have become only slightly more positively correlated, suggesting that gains in allocative efficiency have somewhat exceeded gains in the combined technical efficiency of capital and labor. Conversely, as between labor and materials, and capital and materials, increasingly negative correlations indicate that, relative to 1980, as a source of growing productive efficiency, gains in the combined efficiency of labor and materials and capital and materials have been large relative to gains in the efficient allocation of each of these sets of inputs.

# 7. Interpreting the Convergence of Factor Returns

7.1 In this section, we investig-te two issues. First, we examine the implications for our analysis of differences between average and marginal prices resulting from the assignment of multiple prices for the same product within the same enterprise. Second, we investigate the extent to which changes in relative product prices account for the observed tendency for technical efficiency to converge.

Pricng Regime: Intuitively, changes in marginal revenue products across two or more sectors or enterprises result from two types of shifts. The first, affecting physical marginal products, includes changes in relative factor intensities, differential rates of productivity growth, and technological change which affects the magnitudes of the output elasticities. The second type of change affecting inter-sectoral or inter-enterprise factor returns leaves physical products unaffected but, by changing relative prices, affects marginal revenue products. This latter category includes both changes in relative product prices and changes in factor input prices across sectors or enterprises.<sup>1</sup> '

**t6** See Jefferson (1991) for a framework which decomposes the sources of convergence of factor returns.

_Assesng_ **_Gais_** _in_ **_Efficient_** _Production_ **_Among_** _China's_ **_Indwfrlo Enterprises,_** by Gary H. Jefferson **and** Xu Wenyi 11
**Figure 1: The Ayala Group**

<!-- Start of picture text --> 23 l 1o<br>100l 1ool 401 43 S41 74 77<br>_l 5 05 9 5?, q<br>64<br>Note BPI denotes th Bank of the _6 seC_ t_at czv..<br>Philippine Isl:mds sl_<br><!-- End of picture text --> The first set of examples is based on the organizational chart of the Ayala group, the largest conglomerate in the Philippines (Figure 1). We identify 46 companies in our database whose ultimate owner is the Ayala family. First, we look at the ownership of the Ayala Corporation, the second largest publicly-held company on the Manila Stock Exchange in terms of market capitalization. Note that the largest publicly-owned company (Ayala Land) and the fifth-largest publicly-owned company (Bank of the Philippine Islands) also belong to the Ayala conglomerate. The principal owners of the Ayala corporation are the privately held Mermac Inc. (58% of total Ayala Corp. shares), and the Mitsubishi Bank (20%). Each other owner of Ayala Corp. has less than 10% of the stock. We next trace the owners of the owners of Ayala Corp. The Ayala family has 100% of the control of Mermac Inc., while Meiji Life Insurance of Japan has 23% control of Mitsubishi Bank. There are no other significant owners of Mitsubishi Bank. We now can say that the ultimate owners of the Ayala Corp. are the Ayala family (with 58% of control rights) and Meiji Life Insurance with 20% of the control rights.

Next, we study the ultimate control structure of Globe Telecom, another member of the Ayala conglomerate. The two principal owners of Globe Telecom are the ITT corporation (32%) and the Ayala corporation (40%). We have, however, already 9
<!-- Start of picture text --> 100% @ Others<br>@ Transfers<br>80% @ Business<br>Ee @ Non-ag wage skilled<br>9 @ Non-ag wage unskilled<br>c 60%<br>= @ Ag wage<br>fc}<br>8 @ Fishing<br><!-- End of picture text --> <!-- Start of picture text --> 5 40% @ Forestry<br>2 @ Livestock<br>oO<br>=“20% @ By-productsi<br>@ Fruits<br>@ Industrial<br>0% Other staple<br>S$sscéegzg& SSEGEGZEFa FSSESESZES|i ekice<br>2010 2012 2014<br><!-- End of picture text -->
**Table 9. Robustness check: Domestic credit to private sector**

||[1]|[2]|[3]|
|---|---|---|---|
|2nd stage:Output informality||||
|Real GDP per capita (in logs)|-6.90***|-6.98***|-6.86***|
||(1.66)|(1.62)|(1.70)|
|Control of Corruption|-0.38|-0.36|-0.41|
||(0.47)|(0.46)|(0.47)|
|Regulatory Quality|-0.21|-0.21|-0.22|
||(0.57)|(0.56)|(0.57)|
|Rule of Law|0.58|0.51|0.64|
||(0.61)|(0.61)|(0.64)|
|Domestic credit to private sector|-0.04**|-0.04*|-0.05*|
||(0.02)|(0.02)|(0.03)|
|1ststage: Domestic credit||||
|Domestic credit to private sector in the region|0.53***<br>(0.15)|||
|Distance-discounted domestic credit to private sector<br>(regional)||0.70***<br>(0.18)||
|Distance-discounted domestic credit to private sector<br>(global)|||2.21***<br>(0.65)|
|Observations|2,511|2,511|2,521|
|R-squared|0.99|0.99|0.99|
|<br>Kleibergen-Paap rk LM statistic|11.90***|12.55***|11.47***|
|Cragg-Donald Wald F statistic|12.37|14.48|11.63|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). Domestic credit to private sector is in percent of GDP (taken from WDI). See sections 3 and 4 for detailed data descriptions and empirical methods. GDP per capita is in constant 2010 U.S. dollars. Data are between 2000 and 2018.

26
The 21<sup>st</sup> century climate model experiments suggest that Tanzania is likely to experience nondry years in most of the years in which her key African trading partners experience dry years (Table 8). For example, although South Africa exhibits increasing occurrence of dry years throughout the 21<sup>st</sup> century period, a minimum average of 84.6% of those dry years in a given decade co-occur with non-dry years in Tanzania, including greater than 97% in the 2060s, 2070s and 2080s. Likewise, between 87% and 100% of the dry years in Madagascar and Zimbabwe co-occur with non-dry years in Tanzania, as do between 79% and 85% of the dry years in Mozambique. Kenya exhibits the lowest co-occurrence of dry years with Tanzania non-dry years, including the three lowest mean decadal co-occurrences of any of Tanzania’s key trading partners (47.1%, 56.2% and 63.9%).

Outside of Africa, Mexico experiences the most substantial intensification of dry year occurrence of Tanzania’s key trading partners, including a minimum average of 1.68 dry years per decade in the 2000s and a maximum of 4.64 dry years per decade in the 2070s (Table 8). Australia likewise exhibits at least 1.2 dry years per decade in each decade of the 2040-2089 period, including an average of 1.6 dry years in the 2080s. Conversely, Canada, China, India and the Russian Federation all exhibit decreases in dry year occurrence over the 21<sup>st</sup> century, including near-zero occurrence in Canada and the Russian Federation beginning in the 2020s, and in China after the 2050s.

Despite the very high occurrence of dry years in Mexico, the mean co-occurrence of those dry years with non-dry years in Tanzania is at least 90% in all decades of the 2000-2089 period, including greater than 97% in the 2060-2089 period, in which Mexico experiences the highest occurrence of dry years (Table 8). Australia, which also experiences increasing dry year occurrence in the 21<sup>st</sup> century, exhibits lower co-occurrence of dry years with Tanzania non-dry years than does Mexico, although the mean decadal co-occurrence is greater than 88% in all decades of the 2000-2089 period. In contrast, the mean co-occurrence of dry years with non-dry years in Tanzania is 100% for Canada and the Russian Federation throughout the 21<sup>st</sup> century, 100% for China after the 2030s, and 100% for India and the USA after the 2040s.

# **4.2.4 – DISCUSSION**

The CMIP3 suite of global climate model projections for the 21<sup>st</sup> century suggests that further global warming is likely to both increase the mean seasonal precipitation (Christensen et al., 2007;
# **2. Extreme Poverty**

The definition of extreme poverty by drawing a ‘poverty line’ at $1.25 (pppadjusted) is familiar and well established. It has been enshrined in the United Nations-led commitment to the Millennium Development Goals. What is new, however, is the World Bank’s analysis and conclusion that it is ambitious but feasible to end chronic poverty by 2030. But before going into that, it is useful to take stock of where we stand today and how we got here. A summary view of this for some major emerging economies is shown in Table 1.

**Table 1:  Headcount Extreme Poverty Rates for Selected Countries (%)**

($1.25 day PPP)

||**c1980**|**c1990**|**c2000**|**c2010**|
|---|---|---|---|---|
|**Congo, Dem. Rep**||||87.7|
|**Ethiopia**|66.2||60.5|30.7|
|**Nigeria**|53.9|61.9|63.1|54.4|
|**South Africa**||24.3|26.2|13.8|
|**Bangladesh**|60.6|70.2|58.6|43.3|
|**China**|84.0|60.2|35.6|11.8|
|**India**|55.5|49.4||32.7|
|**Indonesia**|62.8|54.3|47.7|18.1|
|**Brazil**|13.6|17.2|11.8|6.1|
|**Colombia**||6.3|17.9|8.1|
|**Mexico**|12.8|4.0|5.5|0.7|

# Source: World Bank (PovcalNet).

For the world as a whole, poverty as a percentage of population has declined over the three decades displayed in Table 1 from just below 50% to 17.7% and this decline has been remarkably monotonic over time (Ravallion and Chen 2012). Many nations mirrored the global trend by seeing a steady decline 4