rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 7 – Trace Statistic Matrix for Maize**

|**19**||||||||||||10.33||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**18**|||||||||||10.78|10.80||
|**17**||||||||||6.46|5.74|11.75||
|**16**|||||||||**15.54**|8.88|6.99|9.30||
|**15**||||||||**18.06**|**27.79**|**39.00**|**39.84**|**34.76**||
|**14**|||||||**44.89**|10.84|14.14|**31.56**|**17.31**|11.95||
|**13**||||||**22.17**|**32.17**|10.42|11.02|14.64|12.43|9.15||
|**12**|||||**18.93**|**17.38**|**37.48**|13.48|13.63|13.62|14.89|13.14||
|**11**||||**15.53**|10.87|12.70|**31.01**|8.85|7.28|9.12|9.39|11.41||
|**10**|||8.53|15.36|**23.20**|**22.21**|**50.24**|12.15|9.08|**20.02**|11.28|13.44|adings.|
|**9**||11.70|13.94|**15.56**|8.20|9.24|**51.65**|8.91|4.78|9.39|10.09|**20.22**|in row he|
|**8**||4.24<br>3.55|**17.51**|**18.66**|3.56|3.49|12.78|2.83|5.98|3.33|4.03|6.45|inces as|
|**7**|5.19|14.02<br>6.78|8.68|13.18|10.51|13.27|**25.09**|14.32|12.97|8.66|4.56|12.53|ame prov|
|**6**|3.67<br>4.72|**19.06**<br>**22.37**|7.33|8.24|14.30|**15.94**|**40.22**|7.21|3.80|**15.69**|**19.37**|**20.17**|d to the s|
|**5**|3.58<br>9.78<br>9.55|**18.44**<br>10.11|15.32|14.78|11.60|15.20|**30.51**|9.25|6.21|9.27|4.93|12.23|rrespon|
|**4**|10.07<br>**17.78**<br>8.21<br>5.13|**23.07**<br>11.85|10.15|11.47|8.65|8.91|**33.51**|9.26|9.20|9.91|9.22|11.15|in**bold**<br>adings co|
|**3**|7.43<br>4.49<br>4.85<br>**15.47**<br>5.03|4.57<br>5.92|4.01|8.20|10.21|12.43|**23.45**|11.68|8.62|12.00|4.49|14.13|efficients<br>olumn he|
|**2**|11.28<br>7.10<br>11.46<br>14.84<br>8.95<br>4.42|9.66<br>**17.64**|13.19|14.47|13.43|**26.98**|**39.71**|6.73|9.01|**19.22**|12.77|9.75|ficant co<br>bers in c|
|**1**|9.01<br>12.65<br>**15.79**<br>**18.12**<br>**17.41**<br>**15.77**<br>2.98|**18.96**<br>**15.47**|10.92|**17.89**|13.14|**16.59**|**49.21**|12.95|11.06|14.54|9.70|**19.50**|(1) Signi<br>(2) Num|
|**rovince**|**tral Java**<br>**tral Sulaw**<br>**Java**<br>**Kalim**<br>**th Sulaw**<br>**ulaw**<br>**h Kalim**|**h Sulaw**<br>**Aceh**|**Bengk**|**Jambi**|**Lamp**|**Medan**|**Padang**|**Palemb**|**Riau**|**t Java**|**t Nusat**|**yakarta**|**Notes:**|
|**P**|**1 Bali**<br>**2 Cen**<br>**3 Cen**<br>**4 East**<br>**5 East**<br>**6 Nor**<br>**7 SE S**<br>**8 Sout**|**9 Sout**<br>**10 Sum**|**11 Sum**|**12 Sum**|**13 Sum**|**14 Sum**|**15 Sum**|**16 Sum**|**17 Sum**|**18 Wes**|**19 Wes**|**20 Yog**||

30
# **2.2. Earth observation data sets**

Mapping crop area among smallholder plots across a large geographic scale requires satellite remote sensing data sources with high spatial resolution and temporal cadence. Building on precedents set in the research literature, we designed an array of satellite-derived metrics that can be used by a statistical model to distinguish between crop cover types. We used two types of satellite imagery in our maize area mapping experiments - optical and synthetic aperture radar (SAR). Each data source captures different crop properties useful for crop type mapping. For example, optical imagery records information that can be used to characterize a crop’s phenology, while SAR imagery captures properties of the canopy structure that may signify differences between crops (Robertson et al. 2020). We processed and extracted both optical and SAR data to the survey plot locations for maize area mapping.

# **2.2.1 Synthetic aperture radar imagery**

Sentinel-1 (S1) satellites carry a Synthetic Aperture Radar (SAR) sensor that operates in a part of the microwave region of the electromagnetic spectrum which is insensitive to water vapor and thus unaffected by clouds or haze. Sentinel-1 Interferometric Wide swath mode (IW) provides images with dual polarization (VV and VH) centered on a single frequency. Google Earth Engine provides S1 images at 10m resolution which are corrected for noise. To use this imagery, we applied Local Incidence Angle (LIA) correction, and computed RATIO and DIFF bands (Table 5).

# **2.2.2 Optical imagery**

Sentinel-2 (S2) satellites provide multispectral imagery for 13 spectral bands with a 10 m resolution for red, green, blue, and near infrared bands used to compute common vegetation indices. We used S2 Level-2A imagery hosted in Google Earth Engine (Gorelick et al. 2017) in our analysis. Image pre-processed included masking out pixels containing clouds, cloud shadows, haze, and snow using quality masks provided with the imagery product (see Appendix Table A1 for summary statistics on imagery counts). Once preprocessed, five vegetation indices (VIs) were calculated for all available S2 images (Table 5).

11
Some observers have questioned whether China’s new PPP is credible. Bhalla (2008) argues that, when combined with the official growth rates, the new PPP implies that China was too poor to be believed in (say) 1950; in Bhalla’s words, the World Bank’s numbers imply that “most Asians (were) dead in 1950.” Maddison and Wu (2008) and Deaton and Heston (2010) raise similar objections, leading Maddison and Wu to claim that the new PPP for China is “weird” and “implausible.” It is far from clear whether these extrapolations back in time constitute a sound basis for validating the new PPP for China. Ravallion (2010) points out that the new PPP only implies that China was as poor (in terms of GDP per capita) in 1950 as the Democratic Republic of the Congo is today, and that about 400 million people in the world (40 million of them in China) currently live below that income level; they survive, albeit at very meager levels of consumption. Nonetheless, given the size of this data revision, the subsequent controversy, and the importance of the global importance of the Chinese economy, it is of interest to look more closely at the data for China.

How much of the observed change in China’s price level index implied by the 2005 ICP is accountable to the dynamic Penn effect? If one adds a dummy variable for China to equation (6) or (8) the coefficient is 0.278 (s.e.=0.077), while it is 0.239 (s.e.=0.083) when added to (7). Yet the observed change in the log of China’s price level index is 0.795. So the model can account for 65-70% of the change in China’s price level between 1993 and 2005.

This assessment is affected little by allowing for over-estimation of China’s growth rate. Maddison (2007) has claimed that China’s long-run growth rate is over-estimated by possibly two percentage points per year (though also see Holz’s, 2006, comments on Maddison’s assumptions). However, even cutting two percentage points off China’s annual growth rate and re-estimating the regressions,<sup>31</sup> the China coefficient is still 0.320 (s.e.=0.066) in equation (6) and 0.282 (s.e.=0.071) in equation (7).

The rest of the change in China’s price level could well stem from the sampling bias in the 2005 ICP’s price surveys for China. The 2005 round of the ICP was the first time that China had officially participated in the ICP; priors had been based on an estimate of the country’s PPP for 1993 that was not based on a 1993 price survey, but rather was an updated version of an older

> 31 Given that the 2% is annual, the term in ln( _Y_ 05 _i_ /<sup>_Y_</sup> 93 _i_ ) for non-benchmark countries was replaced by ln( _Y_ 05 _i_ / _Y_ 93 _i_ )  .024 _China_<sup>where</sup><sup>_China_is a dummy variable for China, which also appears as a</sup> separate regressor for the purpose of this test.

16
Third, although various secondary sources pertaining to VTE in China have been consulted, data gathering in the field has been focused on only two provinces, Liaoning and Hubei, both rather well developed by Chinese standards. Liaoning, in particular, is heavily industrialized. Thus the description, analysis and conclusions below must be interpreted cautiously for China as a whole, and then only for urban training for industrial and service sector employment and self-employment.

Lastly, vocational and technical education in the two provinces studied represents the current system at or near its strongest. Thus any analyses or conclusions which rely primarily on data from these provinces should be taken as relevant to possible policy directions for the better developed areas of China in the immediate future, though they may also be broadly indicative of directions for the system as a whole over the longer term.

# The Context of Economic Reform

China's economy has been the object of many rapid and far-reaching changes in direction since 1949. Current reforms have led to sign'ficant changes in both the rural and urban economies. While the government asserts that China will remain solidly within the socialist form of society, it has nevertheless mandated changes in the forms of economic control bearing many esf'ential hallmarks of a competitive, market economy. China in 1987 thus stands poised somewhat ambiguously between its heavily controlled, orthodox-socialist past and a more loosely managed, experimental market-socialist future as it establishes a "planned commodity economy." China's long-term plan for economic growth emphasizes increased investments in new technology and incre_sed efficiency of enterprises. An economy historically dominated by agriculture and industry is to be diversified by expanding the commercial and service sectors to create revenue and employment as well as to support a number of reforms aimed at establishing opea commodity and labor markets in at least a portion of the economy.

Current economic reforms are directed toward establishing the profitability of enterprises in a more competitive environment as the prime criterion of success. Enterprises are to be allowed to retain a larger share of profits for investment in the firm and worker incentives. At the same time, enterprises, even those owned by the State, are to be allowed to go bankrupt, if they remaiv. unprofitable over a relatively long period. In addition, a revival of the private sector of the economy is being encouraged as an important sector in its own right, and as a valuable spur to greater efficiency in the State-and collectively-owned enterprises.

# Labor Market Implications

In general, managers report that they _ea3_ short of adequately skilled labor, and overwhelmed by abundant supplies of inadequately skilled labor. At the same time, pre-employment training opportunities in secondary vocational and technical schools have been severely

-3-
Fernando, A. Nilesh, Niharika Sing, and Gabriel Tourek. 2023. “Hiring Frictions and the Promise of Online Job Portals: Evidence from India.” _American Economic Review: Insights_ 5(4): 546–62.

Finkelstein Shapiro, Alan, and Federico S. Mandelman. 2021. “Digital Adoption, Automation, and Labor Markets in Developine Countries.” _Journal of Development Economics_ 151 (June): 102656.

Fort, Teresa C. 2017. “Technology and Production Fragmentation: Domestic versus Foreign Sourcing”, _The Review of Economic Studies_ 84 (2): 650–687.

Freund, Caroline, and Diana Weinhold. 2002. “The Internet and International Trade in Services.” _American Economic Review_ 92 (2): 236–40.

Freund, Caroline L., and Diana Weinhold, 2004. “The Effect of the Internet on International Trade.” _Journal of International Economics_ 62 (1): 171–189.

Freund, Caroline, Alen Mulabdic, and Michele Ruta. 2022. “Is 3D Printing a Threat to Trade? The Trade Effects You Didn’t Hear About.” _Journal of International Economics_ 138 (September).

Fukao, Kyoji, Tsutomu Miyagawa, Kentaro Mukai, Yukio Shinoda, and Konomi Tonogi. 2009. “Intangible Investment in Japan: Measurement and Contribution to Economic Growth.” _Review of Income and Wealth_ 55 (3): 717–36.

Gal, Peter, Giuseppe Nicoletti, Theodore Renault, Stéphane Sorbe, and Christina Timiliotis. 2019. “Digitalisation and Productivity: In Search of the Holy Grail – Firm-Level Empirical Evidence from EU Countries.” OECD Economics Department Working Papers 1533, OECD Publishing, Paris.

Goldfarb, Avi, and Catherine Tucker. 2019. “Digital Economics.” _Journal of Economic Literature_ 57 (1): 3–43.

Goldfarb, Avi and Daniel Trefler, 2018. “AI and International Trade.” NBER Working Papers 24254, National Bureau of Economic Research, Inc.

Goos, Maarten, and Alan Manning. 2007. “Lousy and Lovely Jobs: The Rising Polarization of Work in Britain.” _The Review of Economics and Statistics_ 89 (1): 118–33.

Goos, Maarten, Alan Manning, and Anna Salomons. 2014. “Explaining Job Polarization: RoutineBiased Technological Change and Offshoring.” _American Economic Review_ 104 (8): 2509–26.

Graetz, Georg, and Guy Michaels. 2018. “Robots at Work.” _The Review of Economics and Statistics_ 100 (5): 753–68.

Grieco, K., A. B. Kamara, N. F. Meriggi, J. Michel,W. Prichard, and G. Stewart-Wilson. 2019. “Simplifying Property Tax Administration in Africa: Piloting a Points-Based Valuation in 32
those with sales less than 2.5 and 10 million dollars, respectively. Furthermore, the definition provided by banks is remarkably similar to the average annual sales reported by small and medium-sized firms in recent enterprise surveys conducted by The World Bank for the countries in our sample. The annual sales of small firms (defined in the enterprise surveys as those with less than 20 employees) average 1.8 million dollars and those of medium-sized firms (those with 20 to 99 employees) average 14.5 million dollars. Since the enterprise survey samples are constructed to be representative of the universe of firms in each country, this suggests that banks are defining SMEs in a way that is consistent with the actual size distribution of firms. In other words, these statistics reduce the concern that banks’ definition of SMEs refers to firms that are substantially larger than those operating in the countries in our sample.

# **III. Banks’ perception of the SME segment**

Our survey includes a number of questions related to how banks perceive the SME segment. Banks are asked about the size and prospect of the market, the drivers, and obstacles to their involvement with SMEs, and the role of government programs and regulations. Figures 2a and 2b show that most banks (80% or more), independent of where they operate and of their ownership type, perceive the SME segment to be big and with good prospects. In terms of drivers, Figure 3a reveals that the perceived profitability of the segment is the most important driver for banks’ involvement with SMEs. 81% of banks from developed countries and 72% of banks from developing countries indicate that this is the most important determinant for their involvement. However, as seen in Figure 3b there are some differences across bank ownership types. While private and foreign-owned banks point to the profitability of the segment as the key driver, government-owned banks are less driven by the profitability of the market (only 47% rank profitability as the key driver compared to 83% of foreign and 75% of private banks). Among government-owned banks, competition faced in other segments is also an important consideration (18% and 12% of government banks rank competition for large corporates and retail customers, respectively, as the key driver to SME involvement).

While most banks seem to agree on the factors driving their involvement with SMEs and perceive prospects in this segment to be very good, there is more disagreement regarding the obstacles to SME financing. Among banks in developed countries (see Figure 4a), the top 6
# **Table 2. The effect of victimization on food insecurity**

||||CSI||Rely on less<br>foo|preferred<br>ds|Borrow o<br>he|r rely on<br>lp|Limit po|rtion size|<br>Restri<br>consu|ct adult<br>mption|Reduce<br>m|number of<br>eals|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||Assistance=0|||||||||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|
|Conflict events|0.052***|0.044**|0.046***|0.038**|0.058***|0.036*|0.012|0.025|0.055**|0.059**|<br>0.056**|0.051**|0.060**|0.047**|
||(0.019)|(0.018)|(0.015)|(0.016)|(0.016)|(0.019)|(0.020)|(0.020)|(0.027)|(0.024)|<br>(0.025)|(0.023)|(0.028)|(0.018)|
|Observations|3,550|3,457|3,457|3,253|3,550|3,457|3,550|3,457|3,550|3,457|3,550|3,457|3,550|3,457|
|R-squared|0.013|0.029|0.040|0.040|0.005|0.027|0.015|0.054|0.021|0.038|0.005|0.028|0.008|0.030|
|Number of<br>households|717|717|717|674|717|717|717|717|717|717|717|717|717|717|
|Controls|NO|YES|YES|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-round FE|NO|NO|YES|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note:_ Dependent variable is the standardized CSI and the (standardized) components of that index. In column 4, households that report having received assistance have been removed from the sample. Data used are from the six visits of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** p < 0.05; * p < 0.1 34
4 owned firms and banks, subsidization of infant industries, central planning, widespread price controls, high import tariffs and quotas, administered interest rates and directed credit.<sup>3</sup>

**Figure 1.  Latin America and East Asia: Relative GDP per capita (1900-2008)** (Ratio to US GDP per capita, in constant 1990 USD)

<!-- Start of picture text --> 70%<br>Gold Standard Period<br>60% Washington<br>Interwar Dissensus<br>Period and Great WWII and Post War Washington<br>Depression Recovery Consensus<br>50%<br>Alliance Lost<br>for Progress and Decade<br>Chile/US Import Substitution<br>40%<br>30%<br>LAC/US<br>20%<br>Asian Tigers/US<br>10%<br>0%<br>1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> _Note: GDP PPP figures, LAC is the weighted average of Argentina, Brazil, Chile, Colombia, Mexico, Peru, Uruguay and Venezuela. Last two years using WDI growth rates. Source: Historical Statistics of the World Economy, Maddison (2006) and World Development Indicators (2010)._ LAC did grow robustly during the height of the ISI period but so did the world; hence, its per capita income did not on average converge significantly towards that of the rich countries—a fact that often goes unnoticed (Figure 1). In any case, ISI showed signs of exhaustion around the late 1970s, as the initial growth dynamism of inward-looking industrialization subsided and associated macroeconomic imbalances mounted. While this was a heterogeneous process and with different timings across countries in the region,<sup>4</sup> it was the Debt Crisis of the 1980s that intensified and synchronized LAC countries’ movement away from ISI to embrace the rising 3 ISI was politically attractive and ideologically appealing for key interest groups that were increasingly entrenched. Overvalued exchange rates ensured cheap imports of capital goods for the rising industrial class, along with protection of their consumer products in the domestic market. Cheap borrowing on external markets and easy fiscal and monetary policies allowed for expanding job creation in urban areas, including in the public sector and state enterprises. By protecting activities oriented towards the domestic market and using an overvalued exchange rate to keep urban prices low, ISI policies acted as a tax on exporting agriculture. This dislodged the traditional power of large hacienda owners and contributed to increasing rural-urban migration, while creating a powerful new set of political constituencies in the rapidly growing urban areas (Lipton, 1977).

4 For example, Argentina and Brazil borrowed heavily to finance import-substitution while Mexico mainly to increase public spending. By contrast, public borrowing had little impact in Chile and Colombia.
Recent literature suggests that there are several views on what could happen to ownership structure on cross-listing. One view, supported by theories of functional _convergence_ (Coffee 1999) and by the _rent-protection theory_ of ownership (Bebchuk 1999), predicts that migrating to a corporate law system that effectively limits private benefits of control can produce more efficient choices of ownership structure.

For instance, Coffee (2001) argues that dispersed ownership could arise and persist if there is significant improvement in protection of minority rights in European and transitional economies. Therefore, if cross-listing on the US market accords at least some of this improvement in investor protection for firms listing from these countries, a change in their ownership structures would be expected. John and Kedia (2000) in fact, make the prediction that firms from weak legal regimes should experience _rapid_ changes in corporate governance on cross-listing.

A second view is the _path-dependence_ theory of ownership, proposed by Bebchuk and Roe (1999), which states that the ownership structure of a firm at any point is influenced by the initial ownership pattern due to complementarities, network externalities, and sunk costs. If a firm starts out with a concentrated ownership structure, the controlling owner is likely to retain control or sell the control block to a new incoming shareholder, rather than sell voting rights to a dispersed group of shareholders. Therefore, the theory predicts that the initial ownership patterns of foreign firms persist after cross-listing.

This paper tests the two theories by using a unique database of 425 firms from 42 countries that cross-listed on a major exchange in the United States. I examine time-series changes in ownership and control structures around the date of cross-listing. The aggregate picture shows that a majority of the firms that cross-list have an initial controlling shareholder (73%) and family ownership is the most dominant form of control–roughly one in two firms has a family as the controlling owner. There also exists a high degree of separation of ownership and control rights in these firms through the use of pyramidal structures and other control-enhancing 2
Fi ure 3c: Correlation in female activit for services b industr <u>g y y y</u> <!-- Start of picture text --> A. Female-owned firms vs. female employees D. Female employee share vs. segmentation in female-<br>70% owned firms<br>100%<br>60%<br>90%<br>50% 80%<br>70%<br>40%<br>60%<br>50%<br>30%<br>40%<br>20% 30%<br>20%<br>10%<br>10%<br>0% 0%<br>0% 10% 20% 30% 40% 50% 60% 0% 10% 20% 30% 40% 50% 60%<br>Share of establishments in industry that are female-owned Share of employees in industry that are female<br>B. Female-owned firms vs. segmentation in female-<br>E. Female employee share vs. segmentation in male-<br>owned firms<br>owned firms<br>100%<br>100%<br>90%<br>95%<br>80%<br>90%<br>70%<br>60% 85%<br>50%<br>80%<br>40%<br>75%<br>30%<br>70%<br>20%<br>10% 65%<br>0%<br>60%<br>0% 10% 20% 30% 40% 50% 60%<br>0% 10% 20% 30% 40% 50% 60%<br>Share of establishments in industry that are female-owned<br>Share of employees in industry that are female<br>C. Female-owned firms vs. segmentation in male-<br>F. Segmentation in female-owned vs. male-owned<br>owned firms<br>firms<br>100%<br>100%<br>95%<br>95%<br>90%<br>90%<br>85%<br>85%<br>80%<br>80%<br>75%<br>75%<br>70%<br>70%<br>65%<br>65%<br>60%<br>0% 10% 20% 30% 40% 50% 60% 60%<br>20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Share of establishments in industry that are female-owned<br>Share of female employees in female-owned establishments<br>female establishments<br>Share of employees in industry that are<br>Share of female employees in female-owned<br>establishments<br>establishments<br>Share of female employees in female-owned Share of male employees in male-owned<br>establishments<br>establishments<br>Share of male employees in male-owned<br>Share of male employees in male-owned<br><!-- End of picture text -->