rafmacalaba/datause-encoder-data huggingface dataset:
without such controls. These somehow suggest that additional heterogeneity analyses can provide further insights into the results presented herein. For instance, full sample estimates may hide some heterogeneity due to cultural and geographical factors, and, more particularly, ethnicity, a highly relevant element in a country such as Bolivia.

The identification of indigenous people in Bolivia is not straightforward as social class and ethnic elements are interrelated and difficult to disentangle, and, in general, the information used to define who the indigenous people are is based on a set of ethnolinguistic characteristics.

We use three specific questions from our survey data aimed at identifying ethnic groups: (i) do you consider yourself as belonging to an indigenous group? (ii) what languages do you speak? and, (iii) what language did you first learn to speak? The first two questions are intended exclusively for household members who are at least 12-years-old, and the third is intended for all members in the household. We follow Molina and Albó (2006), who employ the above three criteria to construct an ethnolinguistic matrix for Bolivia. We classify as non-indigenous those who do not self-identify as indigenous, whose first language was not indigenous, and who did not learn an indigenous language as children. We define as indigenous those who identify themselves as indigenous, whose first language was indigenous and those who learned an indigenous language as children. To simplify our empirical approach, all other combinations are labeled as multiethnic.

Table 4 displays the heterogeneous impacts of the unconditional cash transfer of elders on children’s educational expenditures by ethnicity, region, gender, and age cohorts. The estimated parameters are reported separately for each group. After the implementation of the cash transfer, we find statistically significant and larger impacts for indigenous populations and smaller and statistically insignificant effects for non-indigenous people. Mixed race populations reveal statistically insignificant results that lie somewhere between the indigenous and non-indigenous populations. These results may be because indigenous populations tend to face credit constraints, and therefore, any liquidity shock modifies significantly the allocation of expenditures within households. A similar finding is noted when comparing rural and urban groups. While in the former we find a large and statistically significant impact, urban populations exhibit smaller and non-significant effects. In addition, we es12
association between the measures would increase confidence in the empirical strategy pursued; namely, to use different indicators of corruption to verify the initial results.

The strong correlation between the CPI and the CC has already been noted in several studies (e.g. Treisman, 2007), as has the strong correlation between the CPI and Tickets data (Fishman and Miguel, 2007). These studies have not only consistently found a robust association between these measures, but have also established that, as the number of sources used to create each indicator has increased over time, the correlations between the indicators have also become stronger. Once again, this does not in and of itself prove that the measures are valid, but it is consistent with the logic that as these measures become more efficient at measuring corruption, it would be expected that the correlation between them increases.

If representative measures of subjective corruption data are _consistently_ measuring similar, but not identical, types of perceived corruption, it follows that such measures should be highly correlated. However, measures such as the CPI and the CC – which measure overall corruption– should be less strongly correlated with the GE, which is focused more on bureaucratic corruption. Furthermore, if the subjective measures of corruption are valid, we would also expect them to be negatively correlated with the objective measure of corruption. That is, less perceived corruption (higher CPI, CC and/or GE scores) should be negatively correlated with the per capita number of parking tickets issued to diplomatic teams at the UN.

Tables 7 and 8 show the correlations using the raw data (from the early to mid2000s, so that all sources are from approximately the same time period) between all three subjective indicators<sup>12</sup> and the one representative objective indicator of corruption. As expected from the discussion above, each indicator is correlated with all other measures in the manner anticipated. Specifically, each of the subjective measures is highly correlated with the other two and it is not possible, even at the 1% interval, to reject the hypothesis that these indicators are measuring the same thing. Despite this extremely strong correlation, the CPI (more focused on broad corruption) is much more highly correlated with the CC (0.98) than with the GE (0.93) as would be expected, given their focus on slightly different aspects of corruption. In short, there is very little difference between the indicators, suggesting that they are reliable measures of

> 12 Given the restricted sample size of the GCB, these data are not reported here so as not to significantly restrict the number of observations.

24
until case CAN-AD-302 (Cold-rolled steel sheet from Brazil). Furthermore, duties were usually imposed as ad valorem rates. Nevertheless, there were some instances in which the duty was imposed as a tax on any good entering Canada at a price lower than a given selling price reported to customs. Finally, the levels of duty described in the Master spreadsheet are typically the trade-weighted average margin of dumping found across all investigated firms. However, in some cases, the weighted average was not available, so a proxy, such as the un-weighted average, was used instead. We include notes as to how the proxy was constructed.

Next, we were unable to verify the preliminary duty levels on cases appearing before CAN-AD209 and on a few cases afterwards, i.e., CAN-AD-218 through CAN-AD-225. These were gathered from the CBSA Historical Listing, which we found to contain a few minor errors. Second, the dates for CITT cases before CAN-AD-79 were gathered from the _Gazette_ , which means that these dates may be the publication dates for the decision, which usually occurred about 15 days after the actual decision date. The decision dates after CAN-AD-79 have been verified. Third, CITT dates for preliminary rulings before CAN-AD-272 were only available on a select number of cases that went to the final stage. Any cases that were terminated or suspended earlier lacked this information.

There are a few data columns that are redundant in the Canadian data. First, the P_AD_DATE series is largely identical to the P_DUMP_DEC series, as preliminary duties are assessed immediately from the date of the CBSA preliminary decision. Similarly, the F_AD_DATE is largely identical to the F_INJ_DEC, as final duties are assessed as soon as the CITT hands down an affirmative decision. Depending on the specifics of the tribunal’s decision, the final duty may be applied retroactively to goods imported during the provisional duty period. Finally, the INIT_DATE series is identical to the INIT_DATE_CBSA series, as the CBSA always initiates an investigation before the CITT.

# **3.5 Colombia (COL)**

In Colombia, injury, dumping, and causation are handled as a single track process. The main investigative authority is the Subdirección de Prácticas Comerciales (Assistant Director of Trade Practices), which is 21
One possible explanation is that during the period in which households were interviewed in 1995 (April-May), food expenditures were at a seasonal low and nonfood expenditures were at a seasonal high for West Forest export crop farmers. However, the 1985-1988 data do not support this hypothesis. Carrying out regressions similar to those presented in table 24, the regression coefficient for West Forest*cocoa and coffee farners*April-May dummy is positive but not significant with respect to food, and negative but not significant for nonfood expenditures. Using these coefficients to produce a seasonally corrected estimate of food expenditure would reduce food expenditures and increase nonfood expenditures, thus further reducing the food shares of West Forest export crop households in 1995.

A second finding that casts some doubt on the large decline in poverty in the West Forest is the apparent undersampling of cocoa-growing households in 1995 compared to 1993. We suspect that there may have been undersampling because the percentage of cocoa-growing households in the West Forest fell from 63.3 percent to 50.6 percent of the total regional population, a drop of 20 percent.<sup>30</sup> As a result, in the West Forest, mean cocoa revenues per capita showed no increase -- from 16200 to 16032 CFAF -- between 1993 and 1995, in contrast to the East Forest, they increased by 30 percent from 11942 CFAF to 15489 CFAF (table 35). For the subset of households that declared earning cocoa revenues, mean real cocoa revenues went up 17 percent in the East Forest and 24 percent in the West Forest (table 36). Thus the stagnation in cocoa revenues in the West Forest is due to a decline in the percentage of households declaring cocoa revenues, rather than to a decline in the revenues of the households that do grow cocoa.

**Table 35 Mean Export Crop Revenues Per Capita by Region** _(in 1985 CFAFper year)_

|_Region_|_Cocoa_|_Coffee_|_Cotton_|_Total_|
|---|---|---|---|---|
|East Forest|||||
|1993|11943|2197|23|14162|
|1995|15489|8381|0|23870|
|West Forest|||||
|1993|16200|5990|444|22634|
|1995|16032|12646|4069|32747|
|Savannah|||||
|1993|1556|1244|7315|10165|
|1995|852|1247|5387|7486|

**30** lWhile there was also a decline in the mean household size among cocoa growers, this does not account for the drop in the cocoa-growing population. Cocoa-growing households as a percentage of all households in West Forest fell from 55 percent **in** 1993 to 43 percent in 1995.

38
# **<u>Table A.2 Baseline Balance (Unweighted)</u>**

||**Control**<br>**(Mean)**|**Arm 1**<br>**(Mean)**|**Arm 2**<br>**(Mean)**|**Arm 3**<br>**(Mean)**||**Nor**|**malized Mea**|**n Differen**|**ces**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of gram panchayats (GPs)|80|60|60|60|**Control-**<br>|**Control-**<br>|**Control-**<br>|**Arm 1-**<br>|**Arm 1-**<br>|**Arm 2-**<br>|
|Number of households|1,872|1,406|1,394|1,395|**Arm 1**|**Arm 2**|**Arm 3**|**Arm 2**|**Arm 3**|**Arm 3**|
|Tractor|.09348|.09673|.1234|.1004|-.0078|-.0681|-.0164|-.0603|-.0086|.0517|
|Mobile phone|.9578|.9566|.9613|.9663|.0041|-.0124|-.0315|-.0166|-.0356|-.0191|
|Toilet|.5897|.6053|.5947|.5957|-.0224|-.0071|-.0086|.0153|.0138|-.0015|
|**Education of the head of household**|||||||||||
|Secondary|.2014|.1821|.1923|.2122|.0347|.0162|-.0188|-.0184|-.0535|-.0351|
|College/graduate/postgraduate|.09241|.1017|.09254|.09247|-.0222|-.0003|-.0001|.0219|.022|.0002|
|Illiterate|.3595|.3606|.3594|.3477|-.0016|.0002|.0175|.0018|.0191|.0173|
|**Primary occupation of the head of household**|||||||||||
|Cultivation|.1496|.1479|.1829|.1606|.0032|-.0634|-.0215|-.0666|-.0247|.0419|
|Agricultural labor|.09562|.1095|.1212|.09032|-.0324|-.0583|.0129|-.0259|.0453|.0711|
|Nonagricultural labor|.3595|.3848|.3737|.3677|-.037|-.0209|-.0121|.0161|.0249|.0088|
|Salaried work|.1341|.1302|.1176|.1183|.0082|.035|.0336|.0268|.0255|-.0014|
|Business|.0812|.07966|.05811|.09104|.004|.0642|-.0248|.0602|-.0288|-.0888|
|**B. Village characteristics**|||||||||||
|SC/ST majority village|.4594|.4154|.4333|.5104|.0628|.0371|-.0722|-.0256|-.1353|-.1095|
|Village head w/college degree|.086|.1309|.08321|.0509|-.1023|.0071|.0985|.1093|.1986|.0915|
|Village head w/high school degree|.1368|.1351|.1786|.1663|.0033|-.0813|-.0583|-.0847|-.0616|.023|

43
_Figure 6.5: Changes from base case average GDP growth rate (2018-2030)_ <!-- Start of picture text --> Average change (%) from Base Case<br>(2018-2030) (5-30 USD/ton)<br>DTAX STAX SAVINGS CORPOR TRANS<br>0.0%<br>-0.1%<br>-0.2%<br>-0.3%<br>-0.4%<br>-0.4%<br>-0.4%<br>-0.5%<br>-0.6% -0.5% -0.5% -0.5%<br>% change from base<br><!-- End of picture text --> _Figure 6.6: Changes from base case GDP in 2030_ <!-- Start of picture text --> Changes from Base Case GDP in 2030 (30<br>USD/ton)<br>DTAX STAX SAVINGS CORPOR TRANS<br>0.00%<br>-0.20%<br>-0.40%<br>-0.60%<br>-0.80% -0.74%<br>-0.83%<br>-1.00%<br>-1.06% -1.06% -1.07%<br>-1.20%<br>% change from base<br><!-- End of picture text -->

# **6.3 Fiscal Impacts**

Because the government account should balance, and government expenditures are treated as fixed in the analysis, the government recycles the entire change in revenues in each of the five policy scenarios. To assess the amount of revenue raised through carbon pricing in each scenario, we first calculate the amount of fuel imported each year. We then multiply the fuel import by the emission factor (i.e., emission 18
As Otsuka (2013) elaborates, increasing real wages (and transformation of occupational structures in labor markets) challenge Asian agriculture in which the majority of farmers are smallholders, because of the increasing need (i) to reduce the labor force in agriculture (as the opportunity cost of labor increases), (ii) to increase the average farm size (to reduce labor use by introducing labor-saving production methods) and (iii) to generate enough income to retain parity with nonagricultural workers. If land markets and/or institutional mechanisms are imperfect, major inefficiencies in the allocation of farm land will be bound to arise. Otsuka, Liu and Yamauchi (2013) present evidence consistent with the above conjectures using cross-country panel data. Foster and Rosenzweig (2010, 2011) show some evidence to support the second point in India. Yamauchi (2014) also shows evidence from Indonesia that relatively large farms gain more efficiency in production by expanding their farm land and introducing machines.

The key idea of this paper is related to the induced innovations proposed by Hicks (1932), and later elaborated by Hayami and Ruttan (1985) who introduced the idea of induced institutional changes in agriculture. An increase in real wages may induce a technical change to save labor or, simply, a substitution between labor and machines, i.e., mechanization, but also could lead to a new institutional arrangement that saves labor and/or reduces user costs of machines on farms even without land consolidation.<sup>6</sup> For example, if machines can be rented relatively cheaply or the supply of machine services could be accessed without much additional transaction cost, small farmers may be able to effectively save labor by utilizing machine services or machines through rental markets.<sup>7</sup> In this context, we examine whether changes in relative factor prices altered the differences in productivity among small and large farms if the rigid initial land distribution imposes a constraint on labor-capital substitutability.

China provides an interesting setting, in which selling agricultural land is prohibited and expansion of operational farm area can only be achieved through land rental markets. The prohibition on

> 6 Using commodity-wise province-level panel data, Wang et al. (2014) showed clear evidence supporting the capital-labor substitution responding to changes in the relative price of machines to agricultural labor.

> 7 This will be the case in some areas, e.g., lowland agriculture. However, it is more difficult in hilly areas including terraced rice production.
Mwale, M. L., Kamninga, T. M., & Cassim, L. (2022). The effects of the Malawi Farm Input Subsidy Program on household per-capita consumption convergence. _Developement in Practice_ , _32_ (336–348). https://doi.org/10.1080/09614524.2021.1937552 Nisbett, N., van den Bold, M., Gillespie, S., Menon, P., Davis, P., Roopnaraine, T., ... Warren, A. (2017). Community-level perceptions of drivers of change in nutrition: Evidence from South Asia and sub-Saharan Africa. _Global Food Security_ , _13_ (September 2016), 74–82. https://doi.org/10.1016/j.gfs.2017.01.006

- Nkhata, R., Jumbe, C., & Mwabumba, M. (2014). _Does irrigation have an impact on food security and poverty: Evidence from Bwanje Valley Irrigation Scheme in Malawi_ (IFPRI Working paper No. 04). Lilongwe, Malawi. Retrieved from

http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/128180/filename/128391.pdf Nonvide, G. M. A. (2019). A re-examination of the impact of irrigation on rice production in benin: An application of the endogenous switching model. _Kasetsart Journal of Social Sciences_ , _40_ (3), 657–662. https://doi.org/10.1016/j.kjss.2017.12.020 Pace, N., Daidone, S., Davis, B., Handa, S., Knowles, M., & Pickmans, R. (2017). One Plus One can be Greater than Two: Evaluating Synergies of Development Programmes in Malawi. _Journal of Development Studies_ . https://doi.org/10.1080/00220388.2017.1380794

- Palmer-jones, R. (2012). _Impact Evaluation of Mae Lao Irrigation improvement Grantee Final Report_ . Norwich, UK.

- Pauw, K., & Thurlow, J. (2015). Prioritizing Rural Investments in Africa: A Hybrid Evaluation Approach Applied to Uganda. _European Journal of Development Research_ , _27_ (3), 407– 424. https://doi.org/10.1057/ejdr.2015.24

Qureshi, M. E., Dixon, J., & Wood, M. (2015). Public policies for improving food and nutrition security at different scales. _Food Security_ , _7_ (2), 393–403. https://doi.org/10.1007/s12571015-0443-z Ragasa, C., & Mazunda, J. (2018). The impact of agricultural extension services in the context of a heavily subsidized input system: The case of Malawi. _World Development_ , _105_ , 25–47. https://doi.org/10.1016/j.worlddev.2017.12.004 Ricker-Gilbert, J., & Jayne, T. S. (2012). _Do Fertilizer Subsidies Boost Staple Crop Production_ 33
# Table **4:** EFFECT OF A00ITIOiAL M14P1Si0NS FOR DEVELOPING CoWTRY DEBT

||Developing<br>Country<br>Exposure|<br>Additional<br>Provision|Second<br>Quarter<br>Earnings|1967<br>Earnings|Total|1-ESERVES<br>ilnvelop-<br>Ing<br>iountry|<br>Other|Rrserves/<br>Loans<br>**S**|Reserves/<br>Developing<br>Country<br>Exposure<br>**S**|Non-<br>Devloping<br>Country<br>Reserves/<br>NPLs<br>**S**|Comon<br>Equity/<br>Assets<br>**S**|Total<br>Equlty/<br>Assets<br>**S**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|CITlOWP|15,590|3000|(2,500)|(1,000)|4,900|3,500|1,400|3.7|22.5|41.2|3.30|4.01|
|NK AiERICA COW.|10,000|1,100|(1,000)|(750)|3,300|10|1,500|4.5|18.0|37.2|2.U|3.13|
|iFACTtiERS<br>iANDVER|8,400|1,700|(1,400)|(1,050)|2,700|1,850|850|4.8|22.0|39.8|2.74|3.62|
|CHASEMNAITTAN|86700|1,600|(1,400)|(850)|2,700|2,000|700|4.1|23.0|36.1|3.45|4.09|
|J. P.<br>iAN *|||||||||||||
|CHEICALNEWYOW|5,900|1,100|(1,100)|(710)|2,074|1,300|694|4.1|24.8|47.9|3.27|3.79|
|iANERS TRUIST|4,000|;C)|(570)|(175)|1,300|1 ,00|300|4.5|25.0|34.1|4.36|4.36|
|FIAST CHICAGO|2,600|SW|(700)|(435)|1,370|935|435|5.4|33.4|53.1|3.89|4.74|
|SECURITY PACIFIC|1,850|500|(175)|150|1,300|650|650|2.9|35.1|54.3|4.19|4.68|
|WELLS FARO&OD|I,900||||||||||||
|FIRST INTERSTATE|1 600|750|(455)|(200)|1,200|530|670|3.5|33.1|65.6|4.45|4.46|

Nute: Nther ei of those banks had announced a resrve Increas at the time this table was completed, though they did subsequently.

Several of the banks stated that their developing country reserves were 25 peri*nt of developing country exposure, but this was accomplished by adding back the "Allocated Transfer Risk" to reserves. Our table does not aod back this rioserve and thus the ratios are slightly lower.

Bank Aerica Indicated that It. reserve was for 25 percent of Its S10 billion developing country **exposure,** but that ratio Is achieved only after adding back about **5600** million of "Allocated Transfer Risk" reserves and prior charg-oftfs of developing country loans.

The non-performing loan totals are for year-end 1986 and thus do not include tilie Brazilian loans placed on nonaccrual status In the first quarter. The adjusted equity to assets ratios assume that the holding companies pay the sam dividends In 1967 as In 1986 and that asset totals at yeoar-nd 1987 aro the sas as at year-end 1966.

Adjustmnts have been made In the data for Chemical to reflect the acquisition of Texas Comerce In May 1967.

Source: International Bank Credit Analysis Group (ISCA), London.
Table 3.3

# Chinese Industrial Discharge Standards

# Table 3.3a: Chinese Discharge Standards in Twvo Areas (mg/i)

# Guanedon! (1990)

|Water Body Class'|A|B||C||D||
|---|---|---|---|---|---|---|---|
|Discharne Scaleb||1|2|1|2|1|2|
|TSS|70|100|100|200|200|200|**_250_**|
|BOD|30|50|60|60|70|70|80|
|COD|100|110|130|130|150|150|200|
|Beiiine (1985)||||||||
|Water Bodv Classe|A||B||C|||
|TSS|30||50||80|||
|Paper & Leather|||||100|||
|BOD|5||20||60|||
|COD|15||60||100|||
|Paper & Leather|||||160|||

- Classes refer to the protection status of the receiving water bodv. A is for high-quality use; C-D for low-quality uses.

- bDischarge scale is grouped into two categories: I = < 1000 m<sup>3</sup> /d; 2 = > 1000 m3/d)

Table 3.3b: Discharge Standards vs. Factory EMuent Concentrations (mg/I)

||St<br>Beiiinz!|andards<br>Guanzdon2|Effluent<br>**lst**Quartile|Concentrations<br> <br>Median|
|---|---|---|---|---|
|TSS|80(C)|100 (B)|44|93|
|COD|100 (C)|200 (D)|82|184|
|BOD|60(C)|60(B)|17|49|

Source: NEPA 14
corporate governance index with measures of legal efficiency and shareholder rights. The main contribution of this paper is our finding that good governance practices are more important in countries with weak shareholder rights and inefficient enforcement. This finding has strong policy implications and suggests that recommending to firms to adopt good governance practices is even more important in countries with weak legal systems.

An important caveat of this study is the likely endogeneity of corporate governance practices. For example, a growing firm with large needs for outside financing has more incentive to adopt better governance practices in order to lower its cost of capital. We test this proposition by using the growth rate of sales as one of the determinants of governance and find significantly positive correlation. These growth opportunities should also be reflected in the market valuation of the firm, thus creating a positive correlation between governance and Tobin's-Q.<sup>4</sup> Since our governance data have no time-variation we cannot address the issue of causality directly and leave this issue for future research. However, we attempt to mitigate this problem by adding several control variables that could proxy for growth opportunities such as size, average growth in sales and the rate of investment and find that our governance results are not spuriously caused by these omitted variables.

An additional source of endogeneity could arise as a result of the differences in proportion of intangible to fixed assets. For example, the composition of a firn's assets will affect its contracting environment because it is easier to monitor and harder to steal fixed assets (i.e. machinery and equipment) then "soft" capital (intangibles, R&D capital, and even some short-term assets, such as inventories). Therefore, a firm operating with a

> 4 Similar endogeneity problem arises in the studies of ownership and performance, as argued by Himmelberg, Hubbard and Palia (1999) who propose panel data techniques and instrumental variables to address this question.

_5_
contributes to agricultural outcomes. Our results indicate that agricultural productivity is on average higher where transaction costs imposed by regulations are lower and where countries adhere to a higher number of regulatory good practices. In particular, this relationship is stronger when low transaction costs and regulatory good practices are combined. The outcome of our research is encouraging, but points at the need for improved agricultural data. Better coverage and quality of data on agricultural productivity and regulation are needed to measure their relationship with higher precision. In this regard, one limitation of our study is represented by its purely crosssectional nature. A panel analysis is necessary to further test the robustness of our hypothesis and will be conducted once the necessary data are available.

18