rafmacalaba/datause-encoder-data huggingface dataset:
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# Table C.1: The Effect of VAT Rebates on Tax Compliance Difference-in-Difference Estimates, Annual Data

(a) Balanced Sample

||Taxabl|e Sales|Outpu|t VAT|Net VAT|Liability|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Retailer _·_ PostReform|-.059|-.056|.047|.037|-.022|-.065|
||(.046)|(.046)|(.041)|(.041)|(.074)|(.077)|
|State _·_ Year FE|No|Yes|No|Yes|No|Yes|
|Firm Size Decile _·_ Year|No|Yes|No|Yes|No|Yes|
|N Treated|2822|2822|2822|2822|2822|2822|
|N Control|3014|3014|3014|3014|3014|3014|

## (b) Unbalanced Sample

||Taxabl|e Sales|Outpu|t VAT|Net VAT|Liability|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Retailer _·_ PostReform|.032|.018|-.026|-.045|.019|-.01|
||(.04)|(.041)|(.036)|(.037)|(.049)|(.051)|
|State _·_ Year FE|No|Yes|No|Yes|No|Yes|
|N Treated|17356|17356|17356|17356|17356|17356|
|N Control|13077|13077|13077|13077|13077|13077|

Notes: This table examines the robustness of the DiD estimates from equation 2. The estimation compares retail firms (treatment group) and wholesale firms (control group) between 2011 and 2015 to capture the effect of VAT rebates for electronic purchases introduced in August 2014 in different tax compliance outcomes (as per the column titles). Estimations in panel (a) use a balanced sample of firms that file a VAT return at least once in every quarter between 2010-2016. Estimations in panel (b) include all firms that file a VAT return at any time between 2010-2016. For each outcome we display the baseline specification and an additional specification introducing group-specific (state and firm-size decile) trends as controls. The firm-size deciles are constructed using the average annual sales during the pre-reform period (2011-2013). Outcome variables are winsorized at the 99th percentile. Standard errors are robust to heteroskedasticity and clustered at the firm level. This table is discussed in Section 5.3.

68
The muil.collinearity and the resulting r.on-identification of the key parameters of the aggregate product n function c be overcoir. by pooling the time-series data of several countries and by standardizing the measurements of the inputs. Such an attempt was made by Jamison and Lau<sup>in an</sup> unpublished study of the aggregate real outputs of the countries of sub-Saharan Africa for the period 1960-1985. They found, contrary to expectation, that education has had little effect on the aggregate real **GDPs** of **these** countries. **This** finding **may,** however, **be** attributed to the unfavorable local conditions, the lack of complementary inputs, insufficient institutional capability, or failure to reach a critical threshold, rather than the general ineffectiveness of education per **se.** It is t!erefore of both academic and practical interest to ascertain whether the finding for subSaharan Africa is applicable elsewhere. In this study, the data of 58 developing countries from 1960 through 1986 are pooled to estimate an aggregate production function with the quantities of capital, labor and land, average educational attainment of the labor force, and chronological time as the independent variables.

# 2. The Model

Aggregate real output, the dependent variable of the production function, is measured as the real GDP in constant 1980 U.S. dollars. The independent variables consist of the quantity of the capital stock at the end of the previous year, also in 1980 U.S. dollars, multiplied by an estimated country-specific rate of utilization for the current year, and the quantities of labor force ar.d arable land for the current year. In addition, there is a vdriable measuring the average nun ber of years of schooling, both separately and together for primary and secondary education, per person of working age, defined to be 15-64. Tertiary education was omitted because data are available for very few countries.

The basic maintained hypothesis of the study is that all countries have the same underlying production function in terms ef standardized inputs. However, countries may differ in terms of the 4
Table 11: Alternative crosssectional IV: First-stage estimates of completion based on course

|Outcome:||**Completed**|**course [0/1]**|
|---|---|---|---|
|Specifcation:|Month FE|Class FE|Class FE<br>+ educ<br>Class FE<br>+ educ + _Et−_1_,_3_,_6_,_9|
||(1)|(2)|(2)<br>(3)|
|Course ofer|0.427<br>(0.000)***|0.398<br>(0.002)***|0.397<br>0.398<br>(0.002)***<br>(0.002)***|
|Worker education (years)|||0.009<br>0.009<br>(0.001)***<br>(0.001)***|
|Employment 6 mos.|||0.015|
|before course start|||(0.001)***|
|Month fxed efects|Y|N|N<br>N|
|Class fxed efects|N|Y|Y<br>Y|
|_N_|335,300|334,954|334,954<br>334,249|
|_R_<sup>2</sup>|0.049|0.229|0.231<br>0.231|

Notes: Table presents coefficients from the crosssectional first-stage regression estimating completion with course offer status across specifications indicated in the column headings. Heteroskedasticity-consistent robust standard errors clustered by course reported in parentheses. All specifications include an unreported constant term. Significance indicated by: _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01.

38
Gladstone, M., Lancaster, G.A., Umar, E., Nyirenda, M., Kayira, E., Van den Broek NR, et al. (2010) The Malawi Developmental Assessment Tool (MDAT): The Creation, Validation, and Reliability of a Tool to Assess Child Development in Rural African Settings. PLoS Med 7(5): e1000273.

Gollwitzer, P. and Moskowitz, G. (1996). Goal effects on action and cognition. First publ. in: _Social psychology: Handbook of basic principles_ . New York: Guilford, 1996, pp. 361-399.

Grantham-McGregor, Sally, Yin Bun Cheung, Santiago Cueto, Paul Glewwe, Linda Richter, and Barbara Strupp. 2007. “Developmental Potential in the First 5 Years for Children in Developing Countries.” _The Lancet_ 369 (9555): 60–70.

Hall, C.C., Zhao, J., Shafir, E. (2013).Self-affirmation among the poor: cognitive and behavioral implications. _Psychological Science_ , 25: 619-25. PMID 24357617 DOI: 10.1177/0956797613510949 Houshofer, J. and Shapiro, J. (2013). Policy Brief: Impacts of unconditional cash transfers. Princeton University, USA Huff, Amber, M. Gregg Smith, Keetie Roelen, Razafiharison Andriamanantena, Hajasoa Patricia, Mianndrito Mampiray Mbola, Hadeer El Shafie (2019). Qualitative evaluation of the Social Safety Net Drought Response (SSNDR) program in Southern Madagascar: Program processes, social dynamics and impacts. Technical report from the Institute for Development Studies, World Bank.

Instat Madagascar. https://www.instat.mg/ Antananarivo, Madagascar.

Janmohamed, A., Sohani, N., Lassi, Z.N., Butta, Z.A., 2020. “The Effects of Community Home Visit and Peer Group Nutrition Intervention Delivery Platforms on Nutrition Outcomes in Low and Middle-Income Countries: A Systematic Review and Meta-Analysis. Nutrients 12(12):440 Kabeer, N., Piza, C., & Taylor, L. (2012). What are the economic impacts of conditional cash transfer programmes? A systematic review of the evidence. _Technical report. EPPI-centre, Social Science Research Unit_ . Institute of Education, University of London, London.

Kabeer, N. and Waddington, H. (2015). Economic impacts of conditional cash transfer programmes: A systematic review and meta-analysis. _Journal of Development Effectiveness_ , 7 (3). pp. 290-303. ISSN 19439342 Kahneman, D. and Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. _Econometrica_ , 47, issue 2, p. 263-91.

Kuhl, J. (1984). Volitional aspects of achievement motivation and learned helplessness: Toward a comprehensive theory of action control. In Progress in experimental personality research (Vol. 13, pp. 99-171). Elsevier.

Lagarde, M., Haines, A., and Palmer, N. (2007). “Conditional Cash Transfers for Improving Uptake of Health Interventions in Low- and Middle-Income Countries: A Systematic Review” _Journal of the American Medical Association_ 298 (16) Loewenstein, G. (1996). Out of control: Visceral influences on behavior. _Organizational Behavior and Human Decision Processes_ , 65(3), 272-292.

18
collective action that combines both formal and proposed CFs into one variable called CForProposed. Empirical methods used are the same for all collective action measures.<sup>15</sup>

# **6. Results**

We begin this section with simple descriptive statistics. Table 5 presents means and standard deviations for our four forest quality measures, which are our dependent variables. Table 6 breaks total carbon down by CF/NCF and hill/ _Terai_ . Average carbon per hectare in CF forests is similar to those in NCFs and the difference is not significantly different from zero. The difference between hill and _Terai_ forests is, however, significant, with _Terai_ forests having on average 42% more carbon than hill forests. This difference reflects the generally more productive ecosystems and differing species compositions in the _Terai_ .

Table 7 presents descriptive statistics delineated by whether village leaders can identify the formation years for their forest user groups. As discussed in the previous section, we analyze only NCFs to avoid confusion with CF status and to better balance our samples.

Table 5 Descriptive <u>Statistics for Dependent Variables</u>

|Variable|Observations|Mean|StandardDeviation|
|---|---|---|---|
|Total carbon in kg per hectare|130|92410.33|76074.89|
|Number of treesper hectare|130|560.6894|402.1309|
|Canopycover inpercent|130|48.61507|19.73628|
|Seedlingsper hectare|130|30356.32|26124.46|

> 15 For proposed CFs we test whether 1990 NDVI adequately adjusts for baseline carbon levels by running a plot level regression of total carbon on 1990 NDVI.  We find that 45 of the 108 proposed CF plots (out of a total of 620 plots) are below the regression line (i.e. actual carbon is less than the full sample would predict).  We do, however, reject the hypothesis that adjusting for 1990 NDVI the difference between the actual and predicted carbon is zero (p<0.01).

**23 |** P a g e
T tt tt PP = YYPPYY ∙NNPPYY∙AAPP (7) where represents the yield of jatropha seed per plant in the year _t_ (kg/plant), is the number of jatropha plants on a hectare of land (No. of plants/ha), and tt is the total area of jatropha plantation YYPPYY NNPPYY (ha). AAPP

# **_2.1.2. Economic Analysis of Biodiesel Production_**

The energy equivalent cost of biodiesel production is estimated by dividing per unit cost of biodiesel production by the energy equivalent factor.

E =<sup>P</sup> (8)<sup>PPE</sup> PPE where E EEFFE is the energy equivalent cost of biodiesel production (NRs/liter), P is per unit cost of biodiesel production (NRs/liter), and is the energy equivalent factor of biodiesel. The PPE PPE energy equivalent factor in the case of jatropha biodiesel is 0.88. The average per unit cost of biodiesel EEFFE production (P ) is equal to the sum of discounted total costs divided by the sum of discounted total quantity of biodiesel production. PPE ∑ <u>t ( )</u><sup>tt</sup> P = (9) ∑tt TTTPPEE <u>t</u> 1 + rr PPE tt where _t_ represents the production year, and (1 + rr)<sup>tt</sup> _r_ represents the discount rate. biodiesel production in the year _t_ (NRs), and T E tt<sup>is the total cost of</sup> TTPPE the year _t_ (liter). The total cost of biodiesel production (tt<sup>is the total quantity of biodiesel production in</sup> variable cost of biodiesel production. tt<sup>) includes the total fixed cost and total</sup> TTPPE tt<sup>= FFPPE</sup> tt<sup>+ VVPPE</sup> tt<sup>(10)</sup> TTPPEwhere the total variable cost in biodiesel production in the year tt<sup>represents the total fixed cost in biodiesel production in the year</sup> _t_ (NRs). The total fixed cost of biodiesel<sup>_t_(NRs), and VVPPE</sup> tt<sup>is</sup> FFPPE production in the year _t_ ( extraction plant and the land rent. Certain amount of land is required to establish and operate a tt<sup>) is estimated by summing up the annual investment for biodiesel</sup> FFPPE biodiesel plant.

9
This mix of structural and macroeconomic policies proved at once attractive to foreign capital-and thus was associated with large capital inflows--and, in combination with tight monetary policy, was largely successful in preventing the emergency of macroeconomic overheating, at least early in the inflow period. Most importantly, across countries an important correlation existed during the capital-inflow period between the avoidance of excessive real exchange rate appreciation and a mix of aggregate demand oriented toward investment rather than consumption (Table 11.3). This link can be interpreted naturally as the outcome of the policy mix undertaken. Since the effects of tight money tend to fall disproportionately on investment, an outward-oriented strategy in which tight fiscal policy supports a depreciated real exchange rate exerts a systematic effect on the compositionl of aggregate demand favoring investment over consumption.

**Table 11.3 Disposition of Capital Inflows during Inflow Episodes (%** of GDP, except for columns 7 and 8 which are in percent)

|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|
|---|---|---|---|---|---|---|---|
|_Country_|_Inflow_<br>_Period_|_Net_<br>_Private_<br>_Inflows_|_Net_<br>_Official_<br>_inflows_|_Current_<br>_Account_<br>_Deficit_|_Reserve_<br>_Accum._|_Change in_<br>_current_<br>_Account_|_Change in_<br>_reserve_<br>_Accum._|
|East Asia||||||||
|China|1993-96|2.65|0.35|1.04|1.96|34.7|65.3|
|India|1992-96|1.03|-0.58|-1.05|1.51|-231.1|331.1|
|Indonesia|1990-96|2.22|-1.08|0.14|1.00|12.2|87.8|
|Korea|1991-96|5.10|0.59|6.17|-0.48|108.5|-8.5|
|Malaysia|1989-96|8.08|0.11|6.05|.2.14|7:3.9|26.1|
|Pakistan|1992-96|2.60|0.31|1.90|1.00|65.4|34.6|
|Philippines|1990-96|5.38|-0.65|3.37|1.36|71.3|28.7|
|Thailand|1988-96|6.72|-1.19|2.81|.2.72|50.8|49.2|
|Other co|untries|||||||
|Argentina|1991-94|2.13|0.11|1.03|1.21|45.9|54.1|
|Brazil|1992-96|2.65|-0.01|0.80|1.84|30.5|69.5|
|Chile|1989-96|1.46|-3.38|-4.19|2.28|219.3|-119.3|
|Colombia|1992-96|5.20|-0.83|4.81|-.0.44|110.0|-10.0|
|Mexico|1989-95|4.93|0.46|5.03|0.37|93.2|6.8|
|Peru|1990-96|4.75|0.04|2.11|2.68|44.0|56.0|

Columns 3-6: average during inflow period minus average during the immediately preceding 5-year period.

Column 5: a minus sign means an improvement in the current account balance.

Column 6: a minus sign means a decrease in reserve accumulation.

Column 7: column 5 as a percentage of the sum of columns 3 and 4.

Column 8: column 6 as a percentage of the sum of columns 3 and 4.

_Source:_ World Bank data; IMF, _International Financial Statistics._ 8
overwhelmingly concentrated in rural areas with higher share of rural population than the national average in Bangladesh and India, except in Darjeeling, which is slightly below the national average. Despite the relatively high density, there are few significant towns – across the districts in the three countries studied, the only settlements with a population above 100,000 are Siliguri and Darjeeling (India).This population distribution is likely to reflect the agricultural nature of the districts. At the same time it is also likely to affect the future economic specialization of the district: the absence of large centers may make it difficult to achieve those economies of agglomeration needed for many non-primary resource based sectors to develop. Thus, these regions are likely to be failing to take advantage of the benefits of agglomeration for growth and development.

**Table 3: Population in the districts**

||**2001**<br>Pop (|**2011**<br>'000)|**2001**<br>Density (p|**2011**<br>op/km<sup>2</sup>)|**2001**<br>% r|**2011**<br>ural|Area (km<sup>2</sup>)|
|---|---|---|---|---|---|---|---|
|Panchagarh|874|1,026|622|731|91%|90%|1,405|
|**Bangladesh**|**130,523**|**144,044**|**884**|**976**|**76%**|**72%**|**147,570**|
|Darjeeling|1,609|1,842|511|585|68%|61%|3,149|
|Uttar Dinajpur|2,442|3,001|777|955|88%|88%|3,142|
|**India**|**1,027,015**|**1,210,193**|**312**|**368**|**72%**|**69%**|**3,287,263**|
|Ilam|283|290|166|174|n/a|n/a|1,703|
|Jhapa|688|810|428|505|n/a|n/a|1,606|
|**Nepal**|**23,151**|**26,494**|**157**|**181**|**86%**|**83%**|**147,181**|

_Sources: Population census data: Indiastat; Nepal Central Bureau of Statistics; Bangladesh Bureau of Statistics_

**Table 4: The education profile in Indian and Nepalese districts**

||**Period**|**Literacy**<br>**rate**|**At least**<br>**primary**|**At least**<br>**higher**<br>**secondary**|**Graduate**|
|---|---|---|---|---|---|
|Darjeeling|_2004-05_|59.7%|41.0%|9.3%|4.6%|
||_2009-10_|76.1%|56.1%|14.9%|8.9%|
|Utt Di|_2004-05_|36.2%|29.7%|4.1%|1.9%|
|ar najpur|_2009-10_|59.7%|39.5%|4.8%|1.1%|
|**India**|**_2004-05_**|**61.8%**|**51.8%**|**12.8%**|**5.7%**|
||**_2009-10_**|**68.3%**|**59.5%**|**16.6%**|**7.3%**|
|Ilam|1998||24%|1.3%|0.3%|
||_2008_|65.7%|46.9%|4.6%|1.0%|
|Jhaa|1998||34%|4.4%|2.0%|
|p|_2008_|58.9%|46.8%|6.5%|0.9%|
|**Neal**|**_1998_**||**24.4%**|**3.0%**|**1.1%**|
|**p**|**_2008_**|**52.9%**|**41.7%**|**6.0%**|**2.0%**|
|**Panchagarh**|2010|50.4%*|38.5%|5.6%|3.3%|
|Bangladesh|**2010**|**50.4%***|**38.6%**|**5.6%**|**3.3%**|

_* literacy is measured according to whether the individual has passed at least one class as the non response rate to the question on literacy is high. Sources: Nepal LSS; India NSS; Bangladesh HIES_ 7
(b) does not address all failures to act in a commercial manner, but only those related to discrimination. The Appellate Body pointed out that “Article XVII:1 imposes an obligation on Members not to use STEs in order to discriminate in ways that would be prohibited if undertaken directly by Members.”<sup>47</sup>

# **4.4 Competition and STE Behavior**

The central question addressed in this case by the Appellate Body was whether the non-discriminatory treatment prong of (a) is independent of the commercial considerations prong of subparagraph (b). The U.S. argued that these were independent, several obligations, either of which could be the basis for a successful attack. The Appellate Body concluded that subparagraph (b) clarifies the scope of the nondiscrimination obligation in subparagraph (a). According to the Appellate Body, the types of discrimination listed in subparagraph (b) are the most likely, but do not exhaust the category of measures that might violate subparagraph (a).<sup>48</sup> Therefore, panels must begin by analyzing subparagraph (a), and will usually, but not always, also have to evaluate subparagraph (b).<sup>49</sup> The Appellate Body stated that “[t]he disciplines of Article XVII:1 are aimed at preventing certain types of discriminatory behaviour. We see no basis for interpreting that provision as imposing comprehensive competition-law-type obligations on STEs, as the United States would have us do.”<sup>50</sup> Thus, subparagraph (b) does not establish separate requirements independent of subparagraph (a).<sup>51</sup> The Appellate Body quoted approvingly the Canada-FIRA panel report to the effect that subparagraph (b) does not establish a separate obligation to act in accordance with commercial considerations.<sup>52</sup> Importantly, Article XVII is therefore not a broad-ranging requirement that STEs emulate private sector behavior. Rather, STEs may engage in actions that are not commercially motivated—that reflect public policy objectives—so long as the implementation of that public policy is not discriminatory. In other words, STEs may act

> 47   Appellate Body Report, para. 98.

> 48   Appellate Body Report, para. 99.

> 49   Appellate Body Report, para. 109.

> 50   Appellate Body Report, para. 145.

> 51   Appellate Body Report, para. 100.

> 52   Appellate Body Report, para. 104. _See_ Canada—Administration of the Foreign Investment Review Act, L504, adopted 7 February 1984, 30S/140, para. 5.16.

16
household. However, additional elements were incorporated in T4 to potentially improve the accuracy of the household-level reporting of FAFH. For all new treatment arms (T2, T3, T4) a separate FAFH module was designed and implemented whereas FAFH in T1 was collected as additional items inside the overall VHLSS consumption module.

Before providing details on the FAFH component of each treatment arm, we describe now how this component of food consumption fits into the broader survey. Total food consumption can be split into (1) at-home, (2) FAFH, and (3) take-away meals. For this study, FAFH is defined as food purchased _and_ consumed outside the home, such as at restaurants, work, or a friend’s house. **Take-away** meals are meals that are produced outside of the home but brought back and consumed at home. Any food that was prepared at home regardless of where it is consumed is considered to be **at-home** consumption. The at-home portion of the food consumption module implemented in the study was exactly the same across all treatment arms. Take-away meals consumption was asked in a separate subsection within the household-level food consumption module only in treatment arms T2, T3 and T4. In T1, which followed the original VHLSS protocol, take-away meals were not separately identified. It was therefore very important that interviewers described in detail what each of these components capture in order to avoid double-counting or omitted consumption.

FAFH can be further split into adult FAFH, child FAFH, and FAFH of absent members.<sup>7</sup> The one-line recall arm (T1) collected FAFH on a single line for all members of the household (adults and children) present in the household. However, this aggregation could potentially lead to accidental omission of some FAFH, especially for children. Therefore, we differentiated between adult and child FAFH consumption in the other three arms (T2, T3, and T4). Adult FAFH was collected in a separate module (described in detail below) which varied across treatment arms. Child FAFH consumption continued to be asked at the household level but was disaggregated into two categories: (1) lunch at school and (2) any other child FAFH. The consumption of absent members is likely to be largely composed of FAFH since the member has not been present in the home. This category is critically important for accurate measurement of FAFH and thus the consumption of these members is explicitly captured separate from present members. Following the standard VHLSS methodology, consumption of absent members is included as another line item in the consumption module in T1. However, in T2, T3, and T4, consumption of absent members was

> 7 Absent household members are defined as those persons who would not be present at the household during the entire day of the interview. Typically, these were individuals who were temporarily traveling, boarding at school, or in the hospital.

8