rafmacalaba/datause-encoder-data huggingface dataset:
been playing an important role in shaping Chinese export patterns and performance. Based on information from the IO tables for only two years (2007 and 2010), we find evidence of significant increases in SOEs’ VAX ratio, indirect to direct VA export ratio, and share of VA in aggregate exports. These findings have important policy implications. For instance, to the extent that SOEs are less productive than non-state firms (e.g., Zhu, 2012), a deeper privatization of SOEs or lower entry barriers in upstream industries may increase the efficiency of direct exporters in the downstream, which in turn increases the speed of upgrading of Chinese exporters’ along GVC.

We find that SOEs’ dominance in upstream industries is observed not only between industries but also within industries. This fact is established by measuring an industry’s upstreamness by firm ownership type, based on the methods proposed by Antras et al. (2012) and Fally (2012). Using the estimated coefficients of our extended IO table, we measure upstreamness by industry and firm type. Based on the IO table for 2007, Fig. 5 shows that SOEs tend to be more upstream than non-state firms within an industry (see Fig. 8). Figs. 4 and 5 further confirm that SOEs have larger output and export shares in upstream industries, while SMEs exhibit the opposite pattern (see Figs. 6-7). These findings suggest that SOE’s prevalence in upstream industries can be a potential explanation for their high VAX, compared to other firms. Furthermore, we find that the upstreamness measure increases for more than two-third of the 40 sectors from 2007 to 2010 (see Fig. 9). The increase was across the board for all ownership types, suggesting that Chinese firms are “moving up” in GVC, a pattern that is opposite to what is observed for the U.S. (Fally, 2012).

Although SMEs are similar to SOEs in the sense that they also have high value added and indirect export ratios, the sources of the similarities appear to be quite different. In addition to the fact that SMEs are more likely to export through other private firms, their upstreamness measures are generally lower than those of other types of firms within an industry (see Fig. 8). These findings suggest that the high VAX and indirect export share of SMEs are probably due to their higher propensity to sell intermediate inputs and services to other large firms that eventually export, not due to their relative upstream position in the domestic input-output network like SOEs. The findings also highlight a subtle distinction between high upstreamness and high indirect export shares of an industry.

Did the increase in SOEs’ VAX lead to rising profits for the upstream SOEs, as some recent studies claim? Using our split IO table, we can examine how much profit in the Chinese economy could be attributed to exports, both directly and indirectly, and through which type of firms. We find that while total export-related profits declined from 2007 to 2010, the decline fell largely on SMEs. On the other hand, SOEs, FIEs, and LPs all experienced an increase in export-related profits between 2007 and 2010.

4
# **Figure 5: Share of Mortgage Owners and Mortgage Credit to GDP Ratio, 2007**

<!-- Start of picture text --> .6<br>SWE<br>NLD DNK<br>SVK<br>.4 GBR<br>CZE LUX<br>FIN<br>BEL<br>ESP<br>PRT<br>FRA IRL<br>AUT<br>.2<br>LVA<br>HUN CYP<br>ITA EST<br>GRC<br>LTU<br>POL<br>SVN<br>0<br>0 .2 .4 .6 .8 1<br>Mortgage Credit to GDP ratio<br>Proportion of Mortgage Owners<br><!-- End of picture text --> Source: SILC - EU Statistics on Income and Living Conditions, 2007 and ECRI - Lending to Households in Europe, Statistical Package 2008 18
They explain these results by the variation in wages affecting the opportunity cost of conflict, and by the rapacity of armed groups seeking to capture the higher oil rents. Fjelde (2015) and Berman and Couttenier (2015) also find results in line with the opportunity cost channel for labor-intensive agricultural commodities produced in Africa.

Recent empirical evidence suggests that the opportunity cost is not an exclusive transmission channel for labor-intensive goods, nor is rapacity exclusive to capital-intensive goods. Commodities’ lootability and producers’ taxation opportunities can also influence the nature of transmission channels. Rapacity can be a relevant transmission channel for labor-intensive export resources if armed groups can tax producers. In Colombia, Angrist and Kugler (2008) show that the increase in the world price of cocaine (whose production is labor intensive) in the 1990s did increase the quantity produced but had a modest impact on producers’ incomes. Indeed, the wealth created was captured by armed groups and the number of violent events increased in the producing regions. Crost and Felter (2020) also find a higher risk of conflict when the price of bananas (a high-value exported commodity) increases. In the Democratic Republic of Congo, S ́anchez de la Sierra (2020) finds that positive demand shocks on coltan, a labor-intensive mineral, increase violence and suggests that the effect of the opportunity cost channel can be overridden by a taxation-induced rapacity effect.

Income shocks can also have different effects on the risk of conflict when they impact producers or consumers. In Sub-Saharan Africa, McGuirk and Burke (2020) find that higher commodity prices reduce conflict over the control of territory (what they call “factor conflict”) in food-producing areas, and increase conflict over the appropriation of surplus (“output conflict”) in food-consuming areas. Using survey data in Nigeria, Abidoye and Cal`ı (2021) find that higher prices of consumed goods increase the risk of conflict by reducing consumers’ incomes (in line with the opportunity cost channel), while higher oil prices increase the risk of conflict in oil-producing areas (in line with the rapacity effect). Concerning less organized and violent forms of collective actions, a large body of empirical research finds that food prices can act as a trigger for urban riots and social unrest (Bush, 2010; Bellemare, 2015; Hendrix and Haggard, 2015). They generally assume that relative deprivation and grievances are the main explanatory channel, although it is difficult to provide empirical evidence of causality (Martin-Shields and Stojetz, 2019). Another explanatory mechanism is the breakdown of state authority and legitimacy when it fails to provide food security (Arezki and Brueckner, 2014; Buhaug et al., 2015).

7
# **Figure 11: Libor, Chilean Trade Credit Rate and Spread Evolution**

<!-- Start of picture text --> 9,0% 500<br>8,0% 450<br>400<br>7,0%<br>350<br>6,0% 300<br>5,0% 250<br>4,0% 200<br>150<br>3,0%<br>100<br>2,0% 50<br>1,0% 0<br>Trade Credit Rate (90 - 180 days) LIBOR 180 days SPREAD<br>Source : Data from the Central Bank of Chile.<br>Spread (b.p)<br>LIBOR and Chilean Trade Credit Rates<br>Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Jan-09 Feb-09<br><!-- End of picture text --> Other banks have noted that increased capital requirements affected its pricing structure. Banks in India attributed increased pricing to higher capital requirements imposed by the Central Bank of India (RBI), with the majority of banks reporting that LC prices have risen by 10-30 percent and all banks reported that short-term financing has increased by 35 to 150 basis points above LIBOR. Finally, some banks such as in Egypt reported that they have resorted to rationing of credits as a safeguard lack of confidence and risk aversion after September 2008.

**_Yet, banks have not reported the increased pricing as a factor explaining the drop in volumes of trade finance._** Factors that have reportedly contributed to the decline in volumes include (i) a drop in demand for trade loans and credits by firms due to the global economic recession; (ii) difficulty in finding creditworthy customers; (iii) and risk aversion for short-term loans in bank portfolios.

These reports are corroborated by more macro data available for Turkey and Brazil. In Brazil, export finance volume started falling as of October 2008. The average volume of export pre-payment, one of the most important suppliers credit modality used in Brazil, dropped an alarming 42 percent in comparison with the monthly average volume of 2007. In January 2009, the worst month of the historical series, pre-payment reached only $1.5 billion, representing a fall of 63 percent in comparison with January 2008 and 60 percent compared to the average volume of 2007. In Turkey, value of letters of credit issued by the Turkish banking sector declined by 25 percent between September and December 2008. Similarly, export credits provided by the Turkish banking sector decreased by 13 percent during the same period, putting a halt to a positive trend since 2004 (Figures 12 and 13).

23
# **References**

Abraham, Vinoj and S.K. Sasikumar (2011), “Labor Cost and Export Behaviour of Firms in Indian Textile and Clothing Industry,” _Economics, Management, and Financial Markets,_ 6(1): 258–282.

Acs, Zoltan J. and David Audretsch (1988), “Innovation in Large and Small Firms: An Empirical Analysis,” _American Economic Review,_ 78 (4): 678-690.

ADB (2006), Asian Development Outlook 2006: Routes for Asia’s Trade, Manila: Asian Development Bank, ADB.

Adhikari, Ratnakar and Yumiko Yamamoto (2007), “The Textile and Clothing Industry: Adjusting to the Post-quota World,” in **Industrial Development for the 21st Century: Sustainable Development Perspectives** edited volume, United Nations, pp: 183-234.

Aguayo-Tellez, Ernesto, Jim Airola and <u>Chinhui Juhn</u> (2010), “Did Trade Liberalization Help Women? The Case of Mexico in the 1990s,” NBER Working Paper No. 16195. Alesina, Alberto and Romain Wacziarg (1998), “Openness, Country Size and Government,” _Journal of Public Economics_ , 69(3): 305-321.

Altonji, J. G. and R. M. Blank (1999), "Race and Gender in the Labor Market," Handbook of Labor Economics, Volume 3, Eds. O. Ashenfelter and D. Card. Amsterdam: Elsevier Science.

Amin, M. and Soh, Y. C. (2020). Does Greater Regulatory Burden Lead to More Corruption? Evidence Using Firm-Level Survey Data for Developing Countries. Forthcoming _World Bank Economic Review_ . <u><mark>https://doi.org/10.1093/wber/lhaa007</mark></u> Amin, Mohammad and Asif Islam (2015), “Does Mandating Nondiscrimination in Hiring Practices Influence Women's Employment? Evidence Using Firm-level Data,” _Feminist Economics_ , _forthcoming_ Amin, Mohammad and Asif Islam (2014), “Imports of Intermediate Inputs and Country Size,” _Applied Economics Letters_ , 21(11): 738-741.

Antecol, Heather (2000), “An Examination of Cross-Country Differences in the Gender Gap in Labor Force Participation Rates,” _Labour Economics_ , 7(4): 409-426.

<mark>Aterido, R., Hallward-Driemeier, M., & Pages, C. (2011). Big Constraints to Small Firms' Growth? Business Environment and Employment Growth Across Firms.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>, 59 (3): 609 – 647.</mark> Ballington, J. (2008) “Equality in Politics: A Survey of Men and Women in Parliaments,” InterParliamentary Union, Reports and Documents No. 54, p.31.

Barth, Erling, and Harald Dale-Olsen (2009) “Monopolistic Discrimination, Worker Turnover, and the Gender Wage Gap” _Labour Economics_ 16:589-597 31
|Fertilizer IndustryRehabilitation Project|
|---|
|Highways Project(02)|
|Highways Project(02)|
|Highways Project(02)|
|Water Supply- ChittagongProject(02)|
|Water Supply- ChittagongProject(02)|
|Water Supply- ChittagongProject(02)|
|Water Supply- ChittagongProject(02)|
|Agricultural Credit Project|
|Agricultural Credit Project|
|Agricultural Credit Project|
|Hand Tubewells Project|
|Hand Tubewells Project|
|Hand Tubewells Project|
|Hand Tubewells Project|
|Bangladesh Shilpa Bank Project(02)|
|Bangladesh Shilpa Bank Project(02)|
|Small Scale IndustryProject(03)|
|Small Scale IndustryProject(03)|
|Bakhrabad Gas Development Project|
|Bakhrabad Gas Development Project|
|Fertilizer Transport Project|
|Fertilizer Transport Project|
|Fertilizer Transport Project|
|Technical Assistance Project(04)|
|Technical Assistance Project(04)|
|Drainage and Flood Control Project(02)|
|Drainage and Flood Control Project(02)|
|Drainage and Flood Control Project(02)|
|Drainage and Flood Control Project(02)|
|Drainage and Flood Control Project(02)|
|Extension and Research Project(02)|
|Extension and Research Project(02)|
|Extension and Research Project(02)|
|Extension and Research Project(02)|
|Extension and Research Project(02)|
|Extension and Research Project(02)|
|Extension and Research Project(02)|
|Textile IndustryRehabilitation Project|
|Textile IndustryRehabilitation Project|
|Textile IndustryRehabilitation Project|
|ChittagongUrea Fertilizer Project|
|ChittagongUrea Fertilizer Project|
|ChittagongUrea Fertilizer Project|
|AshuganjThermal Power Project|
|AshuganjThermal Power Project|
|AshuganjThermal Power Project|
14 _Table 1: Health shock variables_

|VARIABLE|Description|1991<br>|1993<br>|1997<br>|2000<br>||Ally<br>|ears<br>||
|---|---|---|---|---|---|---|---|---|---|
|||Mean|Mean|Mean|Mean|Mean|sd|Min.|Max.|
|SAH=1 (EXCELLENT)|Self-assessed health (SAH) reported by<br>|0.128|0.120|0.137|0.133|0.130|0.336|0.000|1.000|
|SAH=2 (GOOD)|head of household|0.602|0.606|0.592|0.505|0.576|0.494|0.000|1.000|
|SAH=3 (FAIR)||0.237|0.234|0.230|0.307|0.252|0.434|0.000|1.000|
|SAH=4 (POOR)||0.033|0.040|0.041|0.055|0.042|0.201|0.000|1.000|
|SAH_CHANGE|Difference between SAH in current period<br>and SAH in last period. A positive value<br>corresponds to a worsening of health status||0.002|0.030|0.133|0.060|0.875|-3.000|3.000|
|SAH_POS_CH_LGE|Dummy variable for whether head of<br>household has experienced an improvement<br>of more than one rating in SAH||0.029|0.038|0.029|0.032|0.175|0.000|1.000|
|SAH_POS_CH_SML|Dummy variable for whether head of<br>household has experienced an improvement<br>of one rating in SAH||0.220|0.206|0.191|0.205|0.404|0.000|1.000|
|SAH_NEG_CH_SML|Dummy variable for whether head of<br>household has experienced a worsening of<br>one rating in SAH||0.229|0.226|0.261|0.240|0.427|0.000|1.000|
|SAH_NEG_CH_LGE|Dummy variable for whether head of<br>household has experienced a worsening of<br>more than one rating in SAH||0.025|0.043|0.060|0.044|0.204|0.000|1.000|

_n = 2,719 (1991), 2,715 (1993), 3,089 (1997), 2,798 (2000)_ _Note: No data available for 1991 for the variables that are constructed using lagged SAH_
**Figure 2:  Pension Contributions**

||1995|2000|2004|
|---|---|---|---|
|Total(DKKbillion)|44.02|70.91|89.53|
|% ofGDP|4.3%|5.5%|6.1%|
|ATP (% of total)|10.5%|8.9%|7.5%|
|SP||9.7%||
|Occupational Pension Plans|54.1%|58.6%|70.3%|
|L&P Institutions|46.4%|52.5%|62.5%|
|Banks|7.7%|6.1%|7.7%|
|Personal Pension Plans|35.4%|22.9%|22.3%|
|L&P Institutions|14.4%|10.1%|9.2%|
|Banks|20.9%|12.8%|13.1%|
|Total|100.0%|100.0%|100.0%|

Source: Statistics Denmark and the Danish Financial Supervisory Authority An important characteristic of the Danish pension system is the extensive use of guaranteed minimum benefits in the second and third pillars. Plans operated by insurance and pension companies offer both guaranteed minimum benefits and guaranteed annuity conversion factors in both the second and third pillars, but banks only offer guaranteed minimum investment returns since they are not allowed to offer annuities.

The offer of guaranteed benefits raises some crucial regulatory issues since the value of any guarantees depends on the solvency and integrity of the institution providing them. In Denmark there is no government guarantee fund to absorb the consequences for members of different pension plans if a pension institution were to become insolvent.

The Danish Financial Supervisory Authority (Finanstilsynet), which regulates all types of financial institutions, is responsible for regulating and supervising pension institutions and ensuring that they are in sound financial condition to be able to honor their commitments. The Danish FSA is an institution under the responsibility of the Minister for Economic Affairs. However, the supervision of the various statutory supplementary pension schemes (ATP, SP and LD) comes under the responsibility of the Minister of Labor.

The social pension scheme provides a universal benefit that aims to prevent poverty in old age. Over 700 thousand people were recipients of the social pension in 2003. This corresponded to 13 percent of the total population or 25 percent of the labor force. The total cost of the social pension absorbed 4.4 percent of GDP in 2003, corresponding to 7.5 percent of the wage bill. The average benefit amounted to 30 percent of the average wage. However, these figures include the pension supplements paid to lower-income 4
**Table 7. IV Regressions with Law and Order**

|**_Dependen_**<br>**_t Var_**||**_Private_**|**_Credit_**|||**_NPL/tota_**|**_l loan_**||
|---|---|---|---|---|---|---|---|---|
||IV1|IV1|IV2|IV2|IV1|IV1|IV2|IV2|
|T_245|26.5***|15.8|29.9***|-9.6***|-11.8***|19.2|-11.2***|-8.1**|
||(2.6)|(1.188)|(2.8)|(-2.83)|(-2.8)|(1.4)|(-2.9)|(-2.59)|
|legor_uk|12.2|7.63|12.7|1.18|1.17|6.5|1.6|1.02|
||(.56)|(.28)|(.58)|(.61)|(.51)|(.24)|(.77)|(.54)|
|legor_fr|-7.4|-8.63|-6.6|4.5*|3.6|-11.1|4.09|3.79|
||(-.36)|(-.33)|(-.33)|(1.9)|(.94)|(-.43)|(1.10)|(1.6)|
|legor_ge|20.7|22.9|19.3|4.3*|3.6|18.6|4.17|3.02|
||(1.0)|(.82)|(.95)|(1.69)|(1.0)|(.68)|(1.35)|(1.28)|
|legor_so|-57.2***|-24.1|-59***|8.2**|10.3***|-36.2|10.8***|4.29|
||(-2.9)|(-.8)|(-3.13)|(2.3)|(2.6)|(-1.35)|(2.71)|(1.46)|
|apmindex|-1.5||-2.15||2.22***||1.76**||
||(-.52)||(-.76)||(2.6)||(2.29)||
|ospower|-.16||.14||-.43||-.54||
||(-.11)||(.10)||(-.91)||(-1.29)||
|pmindex_p||9.96||-.13||9.5||.33|
|c||(1.26)||(-.07)||(1.3)||(.23)|
|Supoer_pc||-2.1||-1.15||-.26||-.64|
|||(-.33)||(-1.2)||(-.05)||(-.8)|
|Law0204|10.6***|10.3**|10.2***|-1.03|-.55|10.03**|-.66|-1.39|
||(3.9)|(2.55)|(3.7)|(-.9)|(-.42)|(2.57)|(-.55)|(-1.38)|
|Constant|-101**|-60.7|-116**|62.6***|-.55|-75.8|58.8***|56.2***|
||(-2.1)|(-.76)|(-2.23)|(4.08)|(-.42)|(-.93)|(4.05)|(3.89)|
|# obs|83|61|93|54|64|68|74|61|
|R<sup>2</sup>|.82|.80|.82|.62|.53|.80|.54|.64|

Note: _1) Legor_uk is those with English legal system origin, legor_fr – French legal system origin; legor_ge – German legal system origin; legor_so – socialist legal system origin. Latitude is the Absolute average latitude of country, ethnic diversity is the avelf index. Ospower is official supervisory power (summation of the 14 dummries), apmindex is adjusted private monitoring index, pmindex_pc is the principal component for the private monitoring index, supower_pc is the principal component of the supervisory power index. Law0204 is the ICRG index on law and order, average over 2002~04 (see the appendix table for source and variable description)_ _(2): numbers in parentheses are t statistic._ _(3): Coefficients with * means it is significant at .10 level, ** at .05 level and *** for.01 level. It is the same for the other regression tables._ _(4):For IV1, the instruments for T are avelf, lat_avg, cathshare, and muslshare. For IV2, the instruments are lat_avg, indep, cathshar, and muslshare (see Appendix 2 for variable description and source). (5):The instruments passed the over-identification test, with the Hansen J statistic quite small (p-value for the χ_<sup>_2_</sup> _larger than .10). We also looked at the first step regression in the 2SLS, to make sure the instruments are correlated with the endogenous explanatory variable, with the p-value for the F-test smaller than .05._ 25
# **6 Applications**

## **6.1 Consumption Poverty and Inequality as Correlates of Malnutrition**

The first application we illustrate in this paper is the analysis of the relationship between consumption poverty, inequality and malnutrition. We utilize the estimates of consumption poverty and inequality derived by Fujii (2004) using the standard ELL approach. One way to look at the relationship is to overlay maps. Figure 5(a) and Figure 5(b) are the stunting and underweight maps overlaid with the poverty map. Table 6 gives the number of communes by poverty rate and prevalence of underweight. While there is not an obvious pattern between poverty and malnutrition, there are at least two notable features on these maps. First, the northern part of Siem Reap and southern part of Prey Veng have high levels of stunting, underweight and poverty. These places have food security problems because many people lack means of production there. Southern Prey Veng has been repeatedly hit by drought and flood. Second, the northeastern provinces of Kratie, Stueng Treng and Ratanakiri have low levels of poverty and high levels of malnutrition. This may be partly because of diseases and poor child care in this region (See Hardy and Health Unlimited Ratanakiri Team (2001) for the case of Ratanakiri), both of which are not direct results of poverty. By overlaying nutrition map with other maps, we can investigate the plausible causes of malnutrition in different parts of the country.

To analyze the correlational structure of aggregate indicators, we first regressed estimates of malnutrition indicators on the estimates of the logarithm of the commune-level mean consumption _Y_<sup> ̄</sup> and the generalized entropy inequality measure with parameter 0 (GE(0)).<sup>11</sup> Table 4 shows the results.<sup>12</sup> Not surprisingly, we found that the logarithmic mean consump-

> 11GE(0) is also known as the Theil’s L index. See Shorrocks (1980) for the discussion of generalized entropy measures.

> 12The standard errors in Table 4 are underestimated because they do not take into account the fact that

19
# **1.  Introduction**

Many country studies have found that forest clearing is lower in protected areas. To illustrate the number and country range of such studies, we include the near-exhaustive bibliography of Geldmann et al (2013) and Nelson and Chomitz (2011) in the references for this paper. Most studies have tested the effects of protection status in regressions that include other determinants of forest clearing (Table S5 in Geldmann et al 2013). More sophisticated tests (e.g. Nelson and Chomitz, 2011, Joppa and Pfaff 2011; Gaveau et al 2009, Andam et al. 2008) have matched pairs of protected and unprotected areas that are similar in characteristics relevant for deforestation analysis. Although these studies have produced many useful insights, the scarcity of reliable time series data on deforestation has forced them all to take a cross-sectional approach. Without studies that measure deforestation before and after protection, the basic question of causality remains: Is deforestation lower in protected areas because of their protected status, or do other factors explain the difference?

A theoretically-appropriate test of protection would resemble quasi-experiments that have been performed in fields where data have been more plentiful. It would require observations on changes in protected areas and comparable non-protected areas, both before and after the establishment of legal protection. Such a study could employ a spatial panel of high-resolution data that permits measurement of forest clearing ex ante and ex post, with observations inside and outside of park boundaries in contiguous zones that are sufficiently narrow (and therefore indistinguishable on locational grounds) for near-precise matching to be achieved.

With the recent publication of global forest clearing data at 30 m resolution by Hansen ~~,~~ et al. (2013), such quasi-experiments are now possible. The Hansen data identify 30 m cells as forested or cleared in every year from 2001 to 2012. This paper mobilizes the Hansen data to estimate 1