rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2: Agencies’ environmental responsibility, non-environmental responsibility, and top priorities**

||**Environmental**<br>**responsibility**|**Non-environmental**<br>**responsibility**|**Top priorities**|
|---|---|---|---|
|**Development and**<br>**Reform Commission**<br>**(DRC)**|Energy efficiency,<br>climate change, low<br>carbon city, master<br>planning for ecological<br>improvement and<br>environmental<br>protection|Overall social and<br>economic planning,<br>market regulation,<br>implementation of<br>fiscal, financial,<br>industrial, land, and<br>social policies,<br>investment attraction|Industry development<br>and investment in<br>public infrastructure<br>development|
|**Environmental**<br>**Protection Bureau**<br>**(EPB)**|Pollution control,<br>environmental<br>monitoring, and<br>ecological conservation||Environmental<br>protection|
|**Water Resource**<br>**Bureau**|Water resource<br>protection and water<br>quality control|Water infrastructure<br>planning and<br>construction, reservoir<br>and dam construction<br>and management, flood<br>and drought prevention|Water infrastructure<br>construction, especially<br>dams and reservoirs,<br>collecting water<br>resource fees|
|**Land Resource**<br>**Bureau**|Land, mineral, and<br>marine resource<br>protection, natural<br>disaster prevention|Land sale, collecting<br>land and mineral<br>resource fees, land<br>requisition in rural<br>areas, and demolition<br>projects in urban areas|Collecting land and<br>mineral resources fees|
|**Economic**<br>**Commission**|Industrial energy<br>efficiency (11<sup>th</sup>FYP),<br>elimination of outdated<br>technologies|Industrial restructuring,<br>enterprise regulation,<br>technology upgrades,<br>management training|Enterprise management<br>and development|
|**Transportation**<br>**Bureau**|Energy-efficient public<br>transportation,<br>methanol fuel pilot<br>projects|Railway, road, air, and<br>water transport<br>infrastructure<br>improvements and<br>regulation|Highway infrastructure<br>improvements;<br>management of<br>transport regulation|
|**Construction**<br>**Department**|Energy-efficient<br>building projects<br>(building codes)|Management of urban<br>construction (e.g.,<br>usage, demolishing,<br>renovation and<br>relocation); public<br>utilityconstruction|Real estate industry<br>development|

_Source: Adapted from Ran (2013)_ 24
15 the World and the Europa Year Book). We therefore first asked whether the orientation of a party was immediately obvious from its name. If it was not, or if our primary sources disagreed about orientation, we consulted a European website mantained by Agora Telematica (http://www.agora.stm.it/elections/parties.htm) edited by Wilfried Derksen. Its coverage is non-historical, however. Party orientation was also cross-checked against with information in Political Parties ofAfrica and the Middle East and Political Parties ofEastern Europe, Russia, and the Successor States, both published by Longman Current Affairs series. In the rare cases where conflict emerged among these sources, we used the web site and noted the conflict in the Excel spreadsheet.

The first categorization refers to preferences regarding greater or less state control of the economy - the standard left-right scale. Parties on the right are therefore those with the terms “conservative” or “Christian democratic” in their names, or that are labeled right-wing in our sources. Similarly, we classify parties as left if their names reveal them to be communist, socialist, or social democratic or if the sources label them as left-wing. We classify parties as centrist that are called centrist by our sources or if their position can best be described as centrist (e.g., because the party advocates the strengthening of private enterprise but also-supports a redistributive role for government, for example in the form ofa tightly knit safety net). Any party that cannot be classified in any of the three categories is categorized as “other”. These are frequently parties in non-competitive electoral systems.

The second dimension we consider is “nationalism”. A party or the chief executive is coded as nationalist if (i) a party is listed or described as nationalist in our sources, or (ii) a primary component of the party’s platform is the creation or defense of a national or ethnic identity. With respect to the latter, parties that fought for independence froma colonial
Table 2: Placebo Tests

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Baseline<br>Model|Travel Scaled<br>by Avg<br>Monthly Incid|Avg Travel<br>Scaled by<br>Monthly Incid|Travel Scaled<br>by Non-Malarial<br>Case Incid|Eect on<br>Non-Malarial<br>Case Incid|
|Imported Incidence|1.094***|0.0290|0.00118|0.323|27.14|
||(0.357)|(0.0566)|(0.0579)|(0.337)|(25.09)|
|Lag Incidence|0.563***|0.588***|0.588***|0.574***|0.202*|
||(0.129)|(0.130)|(0.130)|(0.144)|(0.116)|
|Rain in cm|-0.000654|-0.00530|-0.00538|0.0444|-0.605|
||(0.00790)|(0.00741)|(0.00733)|(0.0560)|(0.843)|
|Lag Rain in cm|0.0269|0.0290|0.0288|0.0595|-0.198|
||(0.0181)|(0.0179)|(0.0176)|(0.0564)|(0.640)|
|Lag 2 Rain in cm|0.0319**|0.0382**|0.0381**|0.0542|-1.340**|
||(0.0156)|(0.0160)|(0.0161)|(0.0399)|(0.559)|
|Constant|-0.140|-0.389|-0.375|0|0|
||(0.161)|(0.269)|(0.263)|(0)|(0)|
|Month FE|Yes|Yes|Yes|Yes|Yes|
|Health Post x Month Obs|432|432|432|432|432|
||Robus<br>***|t standard error<br> p_<_0.01, ** p_<_|s in parentheses<br>0.05, * p_<_0.1|||

Notes: Augmented version of the Arellano-Bond Generalized Method of Moments estimator used in all specications. Columns 2-4 show regressions where imported incidence has been constructed in an alternative way that does not incorporate both incidence in the origin and the timing of the particular traveler. In Column 5, the dependent variable is non-malarial disease incidence while imported malaria incidence is calculated as is done in the baseline specication.
- **Dey, Matthew S, and Christopher J Flinn.** 2005. “An Equilibrium Model of Health Insurance Provision and Wage Determination.” _Econometrica_ , 73(2): 571–627.

- **Egebark, Johan, and Niklas Kaunitz.** 2018. “Payroll Taxes and Youth Labor Demand.” _Labour Economics_ , 55(1): 163–177.

- **Golosov, Mikhail, Pricila Maziero, and Guido Menzio.** 2013. “Taxation and Redistribution of Residual Income Inequality.” _Journal of Political Economy_ , 121(6): 1160–1204.

- **Gruber, Jonathan.** 1997. “The Incidence of Payroll Taxation: Evidence from Chile.” _Journal of Labor Economics_ , 15(3): S72–S101.

- **Hall, Robert E, and Alan B Krueger.** 2012. “Evidence on the Incidence of Wage Posting, Wage Bargaining, and On-the-Job Search.” _American Economic Journal: Macroeconomics_ , 4(4): 56–67.

- **Harasztosi, P ́eter, and Attila Lindner.** 2019. “Who Pays for the Minimum Wage?” _American Economic Review_ , 109(8): 2693–2727.

- **Hendren, Nathaniel, and Ben Sprung-Keyser.** 2020. “A Unified Welfare Analysis of Government Policies.” _Quarterly Journal of Economics_ , 135(3): 1209–1318.

- **Hungarian Central Statistical Office.** 2016. “Unionization Rate.” `https://www.ksh. hu/docs/hun/xstadat/xstadat_evkozi/e_szerv9_01_16.html` .

- **Hungarian Central Statistical Office.** 2022. “Real Earnings (1992-).” `https://www. ksh.hu/docs/hun/xstadat/xstadat_eves/i_qli042.html` .

- **Huttunen, Kristiina, Jukka Pirttil ̈a, and Roope Uusitalo.** 2013. “The Employment Effects of Low-Wage Subsidies.” _Journal of Public Economics_ , 97(1): 49–60.

- **J ̈ager, Simon, Benjamin Schoefer, Samuel Young, and Josef Zweim ̈uller.** 2020. “Wages and the Value of Nonemployment.” _Quarterly Journal of Economics_ , 135(4): 1905– 1963.

- **Kaminski, Bartlomiej.** 1999. “Hungary’s Integration into European Union Markets: Production and Trade Restructuring.” World Bank Policy Research Working Paper 2135.

- **Katz, Lawrence F.** 1986. “Efficiency Wage Theories: A Partial Evaluation.” _NBER Macroeconomics Annual_ , 1: 235–276.

- **Katz, Lawrence F, and Lawrence H Summers.** 1989. “Industry Rents: Evidence and Implications.” _Brookings Papers on Economic Activity: Microeconomics_ , 1989: 209–290.

40
regional analysis before the pandemic reveals that Santa Cruz had the highest employment quality level, reaching 0.54 throughout the entire period. This value surpasses the national average and other departments, and even after the lifting of pandemic lockdown measures, job quality in Santa Cruz remained unchanged. Regions with similar worker participation, such as La Paz and Cochabamba, reported job quality levels of 0.42 and 0.45, respectively, placing them at the average national level. Conversely, Potosi and Chuquisaca had low job quality, with levels below the national average. During the pandemic, the quality of jobs in Potosi decreased by almost 10 points, making it the region with the lowest job quality.

In addition to this analysis, Figure 2a illustrates the distribution of job quality among regions by comparing two different periods: 2011 and 2019. The analysis based on the full 2011 household survey shows that the llanos orientales (Santa Cruz, Pando, and Beni) had higher job quality, while the rest of the departments had an average Job Quality Index (JQI) of 0.44. Comparing this situation to the 2019 one (see Figure 2b), the pattern seems to remain the same, with the exceptions of Oruro, which increased its job quality, and Cochabamba, which experienced the opposite change. In summary, these regional disparities remained relatively similar between 2011 and 2019. These findings align with the distribution of salaried workers in medium and large companies, which is higher in Santa Cruz and lower in the southern departments of Potosi and Chuquisaca.

Table 9: Average Job Quality between 2011-2021, by departments

|||||De|partments||||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Chuquisac|(2)<br>a La Paz|(3)<br>Cochabamba|(4)<br>Oruro|(5)<br>Potosi|(6)<br>Tarija|(7)<br>Santa Cruz|(8)<br>Beni-Pando|
|2011|0.38|0.42|0.47|0.43|0.30|0.46|0.57|0.53|
|2012|0.41|0.42|0.47|0.45|0.34|0.57|0.57|0.51|
|2013|0.38|0.43|0.49|0.50|0.37|0.51|0.59|0.55|
|2014|0.37|0.42|0.44|0.49|0.31|0.49|0.52|0.54|
|2015|0.43|0.43|0.43|0.42|0.33|0.48|0.54|0.50|
|2016|0.34|0.46|0.43|0.46|0.29|0.49|0.56|0.49|
|2017|0.35|0.42|0.44|0.45|0.33|0.50|0.57|0.49|
|2018|0.38|0.41|0.47|0.44|0.33|0.48|0.55|0.52|
|2019|0.39|0.42|0.45|0.46|0.38|0.49|0.54|0.51|
|2020|0.35|0.45|0.45|0.46|0.29|0.44|0.54|0.50|
|2021|0.35|0.45|0.41|0.45|0.31|0.42|0.54|0.50|

_Notes:_ Based on data from EH 2011-2021. Estimation using a rescaled score from the first principal component of the PCA. Following INE’s methodology, the estimation for the Beni and Pando regions was done together.

Table 11 explores the evolution of job quality across different types of employment 22
shown in Figure 1.7, it is estimated that out of the over 1,700 currently exporting companies, 15% are microbusinesses and 58% are small and medium-sized enterprises (SMEs).<sup>5</sup> Equally important is the increasing number of products exported by Costa Rica, which amounted to 3,565 in the year 2003, according to official data from the Promotora de Comercio Exterior de Costa Rica (Costa Rica’s Foreign Trade Promotion Bureau).

<!-- Start of picture text --> Micro, 15%<br>Large, 27%<br><!-- End of picture text --> Source: Prepared by the authors based on figures from PROCOMER.

Finally, it should be noted that Costa Rica’s economic performance, linked to trade liberalization, has achieved three more major successes. First, it facilitated a reduction of the high levels of unemployment and underemployment that resulted from the foreign debt crisis of the late 1970s and early 1980s; second, it has made it possible to absorb a large increase in the supply of unskilled workers that was the consequence of the important migration flows of people from Central America, mainly Nicaragua, that occurred in the last few years; and finally, it has managed to bring down the poverty level from 29% in 1987 (a year after the liberalization process began) to 18.5% in 2003 (Figure 1.8).<sup>6</sup> In addition to the positive outcomes of the economic liberalization model regarding trade flows, foreign investment, employment and poverty, it should be noted that although Costa Rica’s income distribution did not improve over the liberalization period, it did not worsen. In fact, according to the family distribution of income based on the per capita income of Costa Rica’s households, Gini’s coefficient has not changed much between the years 1987 (0.401) and 2003 (0.425), with an average of 0.395 for the period and a coefficient of variation of only 4.9%.<sup>7</sup>

> 5 According to the records of Costa Rica’s Foreign Trade Ministry, the country had 1744 exporting businesses in 2003.

> 6 In fact, according to figures from the last population census (2000), immigrants make up one fourth of Costa Rica’s population.

> 7 Due to a change in the methodology for conducting the household survey in 1987, figures from previous years are not comparable. For that reason, the income distribution and poverty figures used in this paper are from 1987. However, for the purposes of our analysis, this situation is not a serious restriction, since Costa Rica’s liberalization period began in 1986.

10
# **9. REFERENCES**

Bhat, Ramesh and S. Vasanthi (1999) Mycotoxin Contamination of Foods and Feeds. Third Joint FAO/WHO/UNEP International Conference on Mycotoxins. Tunis, March 3-6, 1999.

Boutrif, E. (1997) Aflatoxin Prevention Programmes. FAO. Rome.

Bureau, J.C. (2004) Raising the Bar on Products and Process Standards : Economic Principles. Prepared for World Bank training seminar on Standards and Trade, January 27-28, 2004, Washington, D.C.

Bureau, J., Marette, S. And Schiavina, A. (1998). Non-Tariff Trade Barriers and Consumers’ Information: The Case of the Eu-US Trade Dispute over Beef. _European Review of Agricultural Economics_ , 25, 437-462.

Bureau, J., Gozlan, E. and Marette, S. (1999). _Food safety and Quality Issues: Trade Considerations_ . Organization for Economic Cooperation and Development, Paris.

Bureau, J. and J. Beghin (2001) “Quantitative Policy Analysis of Sanitary, Phytosanitary and Technical Barriers to Trade,” Economie Internationale (87): 107-130.

Buurma, J, M and others (2001) “Developing Countries and Products Affected by Setting New Maximum Residue Limits of Pesticides in the EU. Agricultural Economics Research Institute, The Hague.

Buzby, J. (editor) (2003) International Trade and Food Safety: Economic Theory and Case Studies. United States Department of Agriculture. Agricultural Economic Report No. 828. Washington, D.C.

Calvin, L. (2003) Produce, Food Safety, and International Trade: Respose to U.S. Foodborne Illness Outbreaks Associated with Imported Produce, in Buzby (editor).

Calvin, L., L. Flores, and W. Foster (2003) Case Study: Guatemalan Raspberries and Cyclospora, in Food Safety in Food Security and Food Trade, edited by L. Unnevehr. International Food Policy Research Institute, Washington, D.C.

Cato, J. and S. Subasinge (2003) Case Study: The Shrimp Export Industry in Bangladesh, in Food Safety in Food Security and Food Trade, edited by L. Unnevehr. International Food Policy Research Institute, Washington, D.C.

Cato, J. and C. Lima dos Santos (2000) Costs to Upgrade the Bangladesh Frozen Shrimp Processing Sector to Adequate Technical and Safety Standards and to Maintain a HACCP Program, in HACCP: New Studies of Costs and Benefits, edited by L. Unnevehr. St. Paul, Minn.: Eagen Press.

39
## Table 6: **Evidence on Gender-based Crowding Out: Effects of Inpres on Senior High Completion**

# **Panel A: Full Sample**

||**Sons**|||**Daughters**|
|---|---|---|---|---|
||(1)|||(2)|
|Born 1968-72_×_Inpres|0.076*|||-0.084*|
||(0.039)|||(0.049)|
|Father’s Edu_×_Born 1968-72_×_Inpres|-0.006|||0.008|
||(0.011)|||(0.011)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|0.000|||-0.000|
||(0.001)|||(0.001)|
|R2|0.270|||0.343|
|Observations|1199814|||848350|
||**Panel B:**|**Matrilineal a**|**nd Patrilin**|**eal Samples**|
||**Mat**|**rilineal**|**Pat**|**rilineal**|
||**Sons**|**Daughters**|**Sons**|**Daughters**|
||(1)|(2)|(3)|(4)|
|Born 1968-72_×_Inpres|-0.749*|0.130|0.082**|-0.073|
||(0.367)|(0.628)|(0.040)|(0.049)|
|Father’s Edu_×_Born 1968-72_×_Inpres|0.144|-0.071|-0.006|0.008|
||(0.108)|(0.062)|(0.011)|(0.011)|
|Father’s Edu Sq_×_Born 1968-72_×_Inpres|-0.014**|0.003|0.000|-0.000|
||(0.006)|(0.007)|(0.001)|(0.001)|
|R2|0.239|0.257|0.270|0.343|
|Observations|19901|18381|1179913|829969|

Notes: Robust standard errors are in parentheses, clustered at the district of birth (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). We define West Sumatra as a matrilineal society and the rest of Indonesia as a Patrilineal society. Quadratic model is adopted following Becker et al. (2015). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). Senior high completion takes the value of 1 if the child has completed senior high and 0, otherwise. Father’s Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of 43 schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).
payments, and _then_ decide to purchase inputs on credit, perhaps in order to use their payables to finance the provision of receivables. We interpret this evidence as further support to our competition-matching story.

Our result on the role of market competition raises another question: if competition is a main driver behind the decision to offer trade credit, why do firms not simply reduce the product price? In many developed countries, laws prohibit firms from selling identical products to customers at different prices (i.e. the Clay Act in the U.S.). However, China has no such laws. Alternatively, if firms are able to finance their extension of accounts receivable with accounts payable, firms might be able to both increase their total assets (relative to a price discount), and reduce their leverage ratio (i.e. total debt), which might be more favorable for loan officers and equity analysts. We leave a more in depth discussion of these institutional questions to future research.

# **6.  Conclusions**

This paper uses firm-level data on about 2,500 Chinese firms to study the decision to extend trade credit. Supplier financing is often overlooked in the capital structure literature, although it is arguably the most important source of small and medium sized financial intermediation – particularly in countries with less developed financial and information systems. We show that firms are likely to offer trade credit as a competitive gesture. We also find that firms are likely to depend on credit from their own suppliers to finance the extension of trade credit to their customers and to match credit terms between accounts payable and accounts receivable; in particular, firms with stronger market power in the input market and facing strong competition in the output market. Furthermore, we find evidence that firms match their ex-post payment decisions. Finally, we find that for 31
protected aggregate sectors of the economy. In table 4, we show that tariff peaks (tariffs above 20 or 30 percent) are most commonly found in light industry, food and in mechanical engineering and metalworking.

Based on data from the State Customs Service, the value of imports has fallen in these two sectors from 2001. Based on an index of 100, the value of textile and footwear imports (the principal light industry sectors) was 125 in 2001 and fell to 117 in 2003 and 110 in 2005. Similarly, but less dramatically, the value of food sector imports fell from 122 in 2001 to 109 in 2003 and 115 in 2005. The decline in imports in these sectors, however, did little to boost production. The index of production of textiles was essentially unchanged between 2001 and 205, while there was a three percent increase in the production of leather goods in the same time period. In the food sector, output fell by 2.5 percent between 2001 and 2005.

# **5. There are numerous sectors at the two digit industry level with tariffs greater than 20 percent. Meat, sugar, apparel, footwear, leather products and motor vehicles are among the most highly protected.**

Considerable variance in the tariff rates exists at the two digit level of aggregation. The following sectors that have an average unweighted tariff of 20 percent or more (the average is over all tariff lines within the two-digit category): meats, edible offal (29%); meat and fish preparations (35%); sugar (24%); beverages and vinegar (21%); glues (20%); articles of leather (28%); carpets (20%); apparel (21%); footwear (27%); hats (20%); umbrellas (20%); clocks and watches (29%); furniture (21%); feathers (23%). On the other hand, most mineral products had low tariff barriers throughout the period. Appendix table A1 provides the data for all the two digit industries.

# **6.  Tariff peaks at the** **_tariff line_ level (ten digit level) are very high. In 2005, the MFN tariff rates exceeded 100 percent for 27 tariff lines and exceeded 50 percent for 113 tariff lines.**

7
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