rafmacalaba/datause-encoder-data huggingface dataset:
Table A.2: Model Endogenous Variables

|Parameter|Description|Units|
|---|---|---|
|_L_<sup>_A_</sup><br>_t_<br>|Agricultural Land Area|GHa|
|_L_<sup>_C_</sup><br>_t_<br>|Commercial Forest Land Area|GHa|
|_L_<sup>_N_</sup><br>_t_<br>|Unmanaged Natural Lands|GHa|
|∆_L_<sup>_N,A_</sup><br>_t_|Flow of Deforested Natural Lands|GHa|
|_L_<sup>_R_</sup><br>_t_<br>|Protected Natural Lands|GHa|
|∆_L_<sup>_N,R_</sup><br>_t_<br>|Flow of Protected Natural Lands|GHa|
|∆_L_<sup>_C,A_</sup><br>_t_<br>|Flow of Managed Forest Land to Agriculture|GHa|
|∆_L_<sup>_C,C_</sup><br>_t_<br>|Replanted Forest Land Area|GHa|
|∆_L_<sup>_C,H_</sup><br>_v,t_|Harvested Forest Land Area of Vintage _v_|GHa|
|_X_<sup>_F_</sup><br>_t_<br>|Stock of Fossil Fuels|Ttoe|
|∆<sup>_F,E_</sup><br>_t_<br>|Flow of Fossil Fuels Converted to Petroleum|Ttoe|
|∆<sup>_F,n_</sup><br>_t_|Flow of Fossil Fuels Converted to Fertilizers|Ttoe|
|_x_<sup>_p_</sup><br>_t_|Petroleum Products|Gtoe|
|_x_<sup>_n_</sup><br>_t_|Fertlizers|Gton|
|_x_<sup>_c_</sup><br>_t_<br>|Food Crops|Gton|
|_x_<sup>_b_</sup><br>_t_|Biofuels|Gtoe|
|_x_<sup>_w_</sup><br>_t_<br>|Raw Timber|Gton|
|_Y _<sup>_f_</sup><br>_t_|Services from Processed Food|billion USD|
|_Y _<sup>_e_</sup><br>_t_|Energy Services|billion USD|
|_Y _<sup>_w_</sup><br>_t_|Services from Processed Timber|billion USD|
|_Y _<sup>_r_</sup><br>_t_|Eco-system Services|billion USD|
|_Y _<sup>_o_</sup><br>_t_|Other Goods and Services|trillion USD|

5
capture potential trends of wage convergence or divergence, the lagged log of monthly wage is included (Topalova, 2010). For instance, because of wage convergence, individuals who enjoyed higher wage levels in 2002, who tended to reside in relatively high wage provinces, may have experienced slower wage growth. If such provinces were exposed to higher trade shock, without properly adjusting for this trend, _β1_ would be biased downward.

For the full sample, the coefficient for the change in Export Index is found to be positive but statistically insignificant. The results of regressions run separately by the subsets of skilled and unskilled workers reveal differential impacts of exports by skill levels. The coefficient for the change in the Export Index for unskilled workers turns out to be positively significant at the five percent level implying that unskilled individuals who resided in a province which experienced more export expansion tend to experience higher wage growth rates. In contrast, the coefficient for the change in Export Index for skilled workers turns out to be insignificant. The coefficients for the lagged wages turn out to be negatively significant implying that Vietnam’s workers appear to have experienced wage convergence, i.e., the individuals who had higher wages in 2002 experienced on average lower growth in wages.<sup>36</sup> Whereas the differencing process controls for any time invariant characteristics that affect the _level_ of wage, it does not rule out a possibility that these variables influence wage _growth_ rates. Thus, I specify an alternative model which includes additional control variables which potentially affect wage growth (Model B). Although many personal characteristics variables do not change between 2002 and 2004, the level of these variables may influence the wage growth

> 36 Some caution needs to be exercised for the inclusion of this variable since the coefficients for the change in the Export Index become insignificant without the lagged log of wage. However, a model with the latter variable is a better model because the coefficient for the lagged log of wage turns out to be highly significant and the addition of the variable improves the overall performance of my regressions; since the initial log wage and the change in Export Index is positively correlated, the coefficient for the change in the Export Index would be biased downward without the initial log wage; and controlling for the initial level of the variable of interest is common both in macro growth literature and in micro literature to adjust for convergence or mean reversion.

22
**Appendix: Table A2** Mother’s Education, Child and Maternal time use and Child Test Scores

|**Variables**|(1)<br>OLS|(2)<br>IV|(3)<br>OLS|(4)<br>IV|
|---|---|---|---|---|
||All C|hildren|Enrolled|Children|
|Child Time on Educational Activity|42.80|70.03|19.74|67.33|
|(mins/ day)|[4.41]a|[40.04]c|[4.47]a|[33.90]b|
|Observations|4331|4331|3305|3305|
||All M|others|Mothers With N|o Child Above 12|
|Time spent on children’s educational|19.11|11.82|25.11|35.11|
|needs (mins/ day)|[2.47]a|(14.48)|[3.68]a|(33.20)|
|Observations|1437|1437|580|580|
|**Test Scores**|||||
||English|Urdu|Math||
|Heckman Selection Model|0.35|0.35|0.24||
||[0.09]a|[0.09]a|[0.10]b||
|Observations|4218|4218|4218||
|Control Function Approach|0.35<br>[0.11]a|0.35<br>[0.11]a|0.24<br>[0.12]c||
|Observations|663|663|663||

_Notes:_ Robust standard errors, clustered at current village level (a): significant at p<0.01, (b): significant at p<0.05, (c): significant at p<0.1. In addition to controls set in Tables 4A-C and 5, an extra control for whether a school was always present in the village is included in these specifications. All coefficients are similar to those in the previous IV specifications (Tables 4A-C) although precision is reduced in the case of the mother’s time spent on children’s educational needs in families with no child above the age of 12.

40
The credit view, on the other hand, argues that the direct effects of monetary policy on interest rates are augmented by endogenous changes in external finance premium -- the difference in cost between funds raised externally (by issuing equity or debt) and funds generated internally (by retaining earnings). The magnitude of the external finance premium reflects imperfections in the credit market -- imperfections that create a wedge between the expected return received by the lenders and the cost faced by potential borrowers. This theory argues that a change in monetary policy will alter the external finance premium in the same direction. As a result of this additional policy effect on the external finance premium, the effect of monetary policy on the cost of borrowing and on real spending, as well as real activity, is amplified.

Why should policy shifts induced by the central bank have an impact on the external finance premium in credit markets? The credit view offers two distinct linkages arising from information problems in credit markets: those that operate through effects on firms' and households' balance sheets -- _a balance sheet channel --_ and those that operate through effects on bank lending -- _a bank lending channel.5_

**Table 1: The Link Between Monetary Policy and Output**

Types of Monetary Transmission Channels According to the Credit View _Bank Lending Channel Balance Sheet Channel_ Mtznbank depositst=>bank IoansT=*IT=*Y7 MlPet=P>adverse selection;, moral hazard4K>lendingT=lT4=I>YT _Cash Flow Channel Unanticipated Price Household Level Channel Liquidity Effects_ MT=* i4-=>cash flowl'=> MT=1 unanticipated Ptf MT=>PJ=>financial adverse selection4 -, moral =>adverse selectionl, assetsT =:>likelihood of hazardJLoendingt=, IT=: Yt moral hazard-=>lendingT financial distress4-> **=m** rt=> Yt consumer durable and housing expendituret=> Yt Mtznbank depositst=>bank IoansT=*IT=*Y7 MT': expansionary monetaly policy; l: investment spending; Y: GDP; P: price level; Pe: equity prices; i: interest rates mechanism, not a truly independent or parallel channel.

See Table 1. which draws on Mishkin (1996), for a schematic presentation of the transmission of monetary policy according to the credit view.

4
**Table 15: Unorganized Sector Log (Employment): Lag Specification**

||(1)<br>RHS Lag=2|(2)<br> RHS Lag=|(3)<br>2 RHS Lag=4|(4)<br>RHS Lag=4|(5)<br>RHS Lag=9|(6)<br>RHS Lag=9|
|---|---|---|---|---|---|---|
|Horizontal Employment Spillovers|-0.00167|0.0329|-0.120***<br>_Other-sector Em_|0.00713<br>_ployment Spillo_|-0.0990**<br>_vers_|-0.0369|
|from Organized Sector|(-0.07)|(1.38)|(-3.93)|(0.30)|(-3.21)|(-1.35)|
|Forward Employment Spillovers|0.622*|-0.758|0.839**|-0.0450|0.746*|2.088*|
|from Organized Sector|(2.10)|(-1.12)|(2.98)|(-0.07)|(2.48)|(2.41)|
|Backward Employment Spillovers|0.829***|0.673|0.697**|1.061**|0.868**|0.428|
|from Organized Sector|(5.01)|(1.92)|(3.10)|(3.06)|(3.20)|(0.67)|
||||_Other-sector Pr_|_oductivity Spillo_|_vers_||
|Average TFP of Horizontal-linked|0.165|0.0529|0.188|-0.0683|0.0461|-0.0816|
|Organized Plants|(1.34)|(0.46)|(1.63)|(-0.65)|(0.40)|(-0.77)|
|Average TFP of Forward-linked|0.116|-0.0465|-0.594*|-0.943|-0.549**|1.200|
|Organized Plants|(0.84)|(-0.10)|(-2.57)|(-1.27)|(-2.71)|(1.43)|
|Average TFP of Backward-linked|0.704***|0.931|-0.395|-0.585|-1.581***|-0.498|
|Organized Plants|(3.68)|(1.44)|(-1.91)|(-0.65)|(-6.60)|(-0.81)|
||||_Own-sect_|_or Spillovers_|||
|Forward Employment Spillovers|0.494|1.710*|0.455|-1.352|0.353|-1.228|
|from Unorganized Sector|(1.34)|(2.42)|(1.29)|(-1.17)|(1.00)|(-1.41)|
|Backward Employment Spillovers|1.111***|1.238*|1.120***|2.675***|1.178***|0.451|
|from Unorganized Sector|(6.92)|(2.31)|(6.71)|(4.05)|(7.17)|(1.05)|
|Constant|-31.45***|-30.93*|-26.88***|-18.13|-24.72***|-14.67|
||(-5.48)|(-2.02)|(-5.03)|(-1.13)|(-4.39)|(-1.14)|
|N|5888|5888|5888|5888|5888|5888|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. Columns 2, 4 and 6 contain state-year and industry-year fixed effects while Columns 1, 3, and 5 do not Standard errors are clustered at the state-industry level. Significance at the 10% level is indicated by *, at the 5% level by ** and at the 1% level by ***._ 44
25,000 letters delivered

|September 28|October 7|October 9|Timeline|
|---|---|---|---|
|**First day**|**Last day**|**October 2020**|2020|
|<br>**of campaign**|<br>**of campaign**|**bill is due**||

# **5.2.2 Findings**

We begin the empirical analysis by estimating compliance effects on timely payments of the October 2020 property tax bill, along with forward and backward payments.<sup>9</sup> We present visual evidence of the intervention’s effects in Figures 8 through 10, and we summarize the corresponding point estimates in Table 4.

Figure 8 shows the effect of the communication campaign on payment rates of pre- and postintervention bills, for 24 consecutive monthly bills between January 2019 and December 2020. The top figures show payment rates for the control group (women in red and men in blue). The bottom figures report the treatment effects—i.e., the difference between treated and control units—and 95% confidence intervals. The left panels focus on timely payments, defined as bills paid before the 27th of the corresponding month (i.e., any payment made after the 27th is considered unpaid). Hence, pre-intervention bills mechanically exclude any past-due payment triggered by our intervention. The right panels consider on-time and late payments made until December 2020 and, thus, capture backward payments triggered by our intervention (e.g., individuals who, after receiving the letter, pay the October 2020 and previous unpaid bills).

Our analysis, as depicted in Figure 8 panel (a), indicates that men and women in the control group have similar tax payment rates. Approximately 50% of individuals in the control group pay the property tax bill on time during normal times, a figure that increases to about 60% when accounting for late payments. Interestingly, despite the significant drop in payment rates at the onset of the COVID-19 pandemic, there are no noticeable gender-based differences. This indicates that, in our setting, macroeconomic shocks seem to affect the tax behavior of men and women equally.

Additionally, Figure 8 panel (b) shows that men and women respond similarly to personalized property tax letters. The bottom left panel reveals that recipients of the letter are substantially more likely to pay the October 2020 treated bill on time compared to those in the control group, with no significant gender differences. Table 4 summarizes the intention-to-treat effects for both groups: payment rates of treated women are 4.2 percentage points higher than those for control

> 9. Appendix section C.3 confirms that our groups are balanced and comparable.

12
# **5. Concluding Remarks**

The poorer segments of the consumption distribution in Iraq are disproportionately dependent on public and private transfers to supplement their relatively low earnings on the labor market [World Bank (2014)]. Most of these transfers are however, small, and cover a fraction of the poor. The single exception is the Public Distribution System, which guarantees a minimum amount of caloric consumption for not just the poor, but the whole population.

Given the universality of consumption of PDS goods, the lack of a market for ration items, and the low levels of income for much of the population [World Bank (2014)], household consumption of PDS items is relatively inelastic to changes in price. The own quantity elasticities of price are negative and less than 1 in absolute values for all income levels in urban and rural areas. However, they are more inelastic for the poorest quintiles compared with the richest, given their importance in terms of caloric contribution to the diet of the less welloff. In addition, for much of the population, these goods are not inferior, but rather normal goods. Most products show a positive expenditure elasticity of value less than 1 in both rural and urban areas and across quintiles.

However, there are some signs that with improvements in welfare levels, and faced with well-functioning markets, some segments of the population are substituting away from the PDS and increasing their consumption of market substitutes. For instance, most ration items are marginally “inferior” goods in the Kurdistan region irrespective of the level of per capita consumption, while the opposite occurs in the rest of Iraq where ration items are considered “normal” goods.

Overall, our findings suggest that any one-shot reform of the PDS will have adverse and sizeable welfare impacts. The removal of all subsidies will require compensating the poor households by 74.4% of their expenditures compared with nearly 40% for the richest in urban areas. However, over time, and with increases in welfare, some households may not be as significantly affected.

18
<mark>Douenne, Thomas. "The vertical and horizontal distributive effects of energy taxes: A case study of a french policy."</mark> _<mark>The Energy Journal</mark>_ <mark>41, no. 3 (2020).</mark> <mark>Douenne, Thomas, and Adrien Fabre. "Yellow vests, pessimistic beliefs, and carbon tax aversion."</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>(2020).</mark> <mark>Eyer, Jonathan, and Matthew E. Kahn. "Prolonging coal’s sunset: Local demand for local supply."</mark> _<mark>Regional Science and Urban Economics</mark>_ <mark>81 (2020): 103487.</mark> <mark>Gentzkow, Matthew, and Jesse M. Shapiro. "Competition and Truth in the Market for News."</mark> _<mark>Journal of Economic perspectives</mark>_ <mark>22, no. 2 (2008): 133-154.</mark> <mark>Gertler, Paul J., Orie Shelef, Catherine D. Wolfram, and Alan Fuchs. "The demand for energyusing assets among the world's rising middle classes."</mark> _<mark>American Economic Review</mark>_ <mark>106, no. 6 (2016): 1366-1401.</mark> <mark>Glaeser, Edward L., and Matthew E. Kahn. "The greenness of cities: Carbon dioxide emissions and urban development."</mark> _<mark>Journal of urban economics</mark>_ <mark>67, no. 3 (2010): 404-418.</mark> <mark>Goulder, Lawrence H., Marc AC Hafstead, GyuRim Kim, and Xianling Long. "Impacts of a carbon tax across US household income groups: What are the equity-efficiency tradeoffs?."</mark> _<mark>Journal of Public Economics</mark>_ <mark>175 (2019): 44-64.</mark> <mark>Graff Zivin, Joshua, and Matthew Neidell. "Environment, health, and human capital."</mark> _<mark>Journal of Economic Literature</mark>_ <mark>51, no. 3 (2013): 689-730.</mark> <mark>Grossman, Michael.</mark> _<mark>The relationship between health and schooling: What’s new?</mark>_ <mark>. No. w21609. National Bureau of Economic Research, 2015. Grossman, Gene M., and Alan B. Krueger. "Economic growth and the environment."</mark> _<mark>The quarterly journal of economics</mark>_ <mark>110, no. 2 (1995): 353-377.</mark> <mark>Holian, Matthew J., and Matthew E. Kahn. "Household demand for low carbon policies: Evidence from California."</mark> _<mark>Journal of the Association of Environmental and Resource Economists</mark>_ <mark>2, no. 2 (2015): 205-234.</mark> <mark>Jacobsen, Grant D. "The Al Gore effect: an inconvenient truth and voluntary carbon offsets."</mark> _<mark>Journal of Environmental Economics and Management</mark>_ <mark>61, no. 1 (2011): 67-78.</mark> 18
prices for irrigation water is included in the model, linking the micro and macro aspects of irrigation water management.

# **3.1 Structure of the Agricultural Computable Equilibrium Model for Turkey**

TACOGEM-W is a Walrasian CGE model that includes the behavior of the three main sectors in the Turkish economy: the production activities, the institutions, and the foreign sector. In order to study the impact of a range of policy simulations, TACOGEM-W further disaggregates the economy into 20 agricultural and 9 non-agricultural activities. Agricultural activities are categorized by field crops, livestock, fishing and forestry (classified as ‘other agriculture’). Nonagricultural activities include mining, consumer manufacturing, food manufacturing, intermediates and capital goods, electricity and gas, water, construction, private services and government services. Table 3.1 provides a break-down of the Turkish GDP in activities for the year 2003. Accordingly, about 12 percent of gross output was devoted to agriculture, about 25 percent to industry, and the remaining 63 percent to services.

**<u>Table 3.1. Gross national product by kind of activity, 2003</u>**

||Productinc<br>|urrent prices<br>|
|---|---|---|
||Value|Sector share|
|ISIC,Rev. 2|(Billion TL)|(%)|
|Agriculture|42 126 246|11.8|
|Agriculture and livestock|39 550 179|11.1|
|Forestry|1 268 139|0.4|
|Fishing|1 307 928|0.4|
|Industry|88 813 240|24.9|
|Mining and quarrying|3 858 087|1.1|
|Manufacturing|71 910 797|20.2|
|Electricity, gas, water|13 044 356|3.7|
|Trade|71 329 760|20.0|
|Transportation and communication|53 846 171|15.1|
|Financial institutions|17 884 644|5.0|
|Ownership of dwelling|14 653 025|4.1|
|Business and personal services|12 429 089|3.5|
|(Less) Imputed bank service charges|7 911 747|2.2|
|Government services|36 561 477|10.3|
|Private non-profit institutions|3 610 383|1.0|
|Import duties|13 758 630|3.9|
|GDP (in purchasers’ value)|359 762 926|100.9|
|Net factor income from rest of the world|-3 082 038|-0.9|
|GNP(inpurchasers’value)|356 680 888|100.0|

_Source: TurkSTAT (2005)_ 22
# 1 Introduction

A decision-making process is considered democratic if it results in an outcome that re‡ects the “will of the people”. Democracy’s central challenge is to discern this will, particularly among people with di¤erent preferred outcomes. The theory of democracy proposes, according to Jon Elster(1986), two solutions to this challenge. The ...rst solution, the subject of social choice theory, aggregates preferences across individuals. In this view of the world individuals do not interact with each other, they simply express their preferences, as they would do in a market transaction. The main ...nding of social choice theory is a negative one: Arrow’s impossibility theorem states that a rule for aggregating individual preferences, satisfying a set of reasonable conditions, does not exist. The second solution to the democratic challenge is deliberation. Instead of aggregating preferences across individuals, the ideal deliberative process consists of discussions during which some individuals can be persuaded by others to change their preferences and at the end of which “unanimous preferences”(Elster, 1986, p. 112) emerge. To Elster, the distinction between the two decision making processes is akin to the distinction between "the market and the forum". In this paper we use data extracted from transcripts of village meetings, coupled with household surveys, to empirically explore the mechanism of deliberation. In particular, we look at the extent to which individual preferences for public goods are matched by discussion of public goods in the meetings.

There is a large literature on processes that aggregate individual preferences - particularly on voting behaviors, but the literature on deliberative processes is relatively sparse: Osborne, Rosenthal, and Turner(2000) study participation in meetings from a theoretical perspective. Their model assumes that individuals have favorite policies represented by a point in a multidimensional space, with valuations depending only on the Euclidean distance between the implemented 2
not pay much into the system as of 2017, as personal income taxes have a high exemption threshold and sales taxes only apply to a selected number of goods that are more likely to be consumed by richer households. Due to the lack of direct transfers, the net cash position is negative for poor households, though the magnitude is very small. Simulations suggest that recent reductions in personal income taxes and the planned introduction of the GST would alter these distributional impacts. With the reduction in PIT, direct taxes have become slightly more progressive; however, the inequality-reducing impact is slightly dampened than before. The GST as a broad-based tax likely leads to an increase in indirect taxes paid by households across the distribution, including those in the bottom 40 percent. At the same time, the GST is much less progressive than the existing sales tax. While the GST is poverty-increasing as a result, the poverty rate reverts to levels similar to the baseline in 2017 after considering the growth of income/GDP during this period. This suggests that the negative welfare impact of the new GST is likely to be temporary. More broadly, some of the additional revenues raised from the GST could effectively be used to offset the temporary deterioration in household welfare, such as through direct transfers to poor households.

This study shows that fiscal incidence analysis tools can provide important insights into the distributional impact of the fiscal system, including major fiscal reforms. While Bhutan has achieved significant poverty reduction in the past, effective fiscal policies can help further accelerate progress.

The exercise faced severe data challenges. In particular, the household survey did not contain information on incomes and information on labor market activities was also not available. Additional limitations in data availability prevented the use of other methodologies employed in the literature, such as survey-to-survey imputations. Instead, this study uses a sophisticated consumption-based modeling approach to estimate individual-level market income and simulate direct taxes and social contributions at the same time. This is the first such study to our knowledge and could be a useful example for low-income countries facing similar data challenges. The model described in this paper uses the latest household survey available at time of publication, but a future update of the model is expected to rely on updated data from the 2022 BLSS.

27