rafmacalaba/datause-encoder-data huggingface dataset:
variable. Without loss of generality, assume that _ε_ has a zero mean, that is = 0, where _f_ (·) is the probability density function of _ε_ and is known to the firm’s manager at the time of choosing the factor inputs. _ε_<sup>_max_</sup> and _ε_<sup>_min_</sup> represent the upper and lower bounds of the random term.

To simplify the notation, it is convenient to assume that the level of productive capital ( _k_ ) is fixed, although relaxing this assumption does not affect our main conclusions. Utilities had mainly preferred two compliance strategies under the Acid Rain Program: substituting low‐sulfur coal for high‐sulfur coal, and/or purchasing allowances in addition to their initial allocation.<sup>5</sup> The firm chooses low‐ and high‐sulfur coal inputs before observing the allowance price. The transaction of allowances is then decided _ex post_ . If total emissions during the production period exceed the initial allocation of allowances ( _A_ ), the firm purchases extra allowances [ _e_ ( _l,h_ ) – _A_ ] to meet the emissions constraint; if total emissions are less than the endowed allowances [ _e_ ( _l,h_ ) _< A_ ], the firm sells the surplus of allowances to make a profit.

Under rate‐of‐return regulation, the firm maximizes expected profits subject to a profit constraint: after subtracting its expected operating expenses from gross revenues, the remaining revenue should be just enough to compensate the firm for its capital investment at an allowed rate of return ( _s_ ). If the profit constraint is violated, the regulatory agency may intervene by changing the output price. Finally, define the market interest rate as _r_ . Following the previous literature, we assume that the regulatory agency would allow the firm to earn some profit, implying that _s > r_ .

This model presents a simple framework for analyzing the dynamic effect of rate‐of‐return regulation on the trading of emissions permits when their price is uncertain. This model is in nature 5. According to the Energy Information Administration (1997), 52 percent of the Phase I units chose to switch or blend fuels, 32 percent chose to purchase allowances, while less than 6 percent installed scrubbers.

7
# Table 13. OLS

||Blue Collar<br>|Households<br>||Households<br>|with Low Edu<br>|cation<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Proportion of Foreign-Born in the Population|1.123*|-0.128|-0.0154|1.098*|-0.108|0.0367|
|Constant|(0.512)<br>0.279***|(0.0857)<br>0.190***|(0.0763)<br>-0.724|(0.517)<br>0.258***|(0.0890)<br>0.161***|(0.0707)<br>0.238|
||(0.0447)|(0.00121)|(0.553)|(0.0465)|(0.00126)|(0.459)|
|Observations|52|52|52|52|52|52|
|R-squared|0.095|0.959|0.995|0.086|0.959|0.994|
|Year Controls||X|X||X|X|
|Regional Controls||X|X||X|X|
|Labor and Demographic Controls|||X|||X|

Notes: * p<0.10, ** p<0.05, *** p<0.01. _Source_ : LFS 2011 and 2013. World Bank author’s calculations.

# Table 14. OLS

||Female Hea<br>|ded Household<br>|s<br>|Male Heade<br>|d Households<br>|<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Proportion of Foreign-Born in the Population<br>Constant|0.898**<br>(0.325)<br>0.153***<br>(0.0352)|0.00125<br>(0.209)<br>0.0520***<br>(0.00296)|0.0348<br>(0.0653)<br>0.555**<br>(0.247)|0.969**<br>(0.436)<br>0.211***<br>(0.0388)|-0.107<br>(0.112)<br>0.119***<br>(0.00159)|-0.0393<br>(0.0675)<br>0.601**<br>(0.211)|
|Observations|52|52|52|52|52|52|
|R-squared|0.095|0.910|0.973|0.092|0.948|0.993|
|Year Controls||X|X||X|X|
|Regional Controls||X|X||X|X|
|Labor and Demographic Controls|||X|||X|

Notes: * p<0.10, ** p<0.05, *** p<0.01. _Source_ : LFS 2011 and 2013. World Bank author’s calculations.

29
Given these facts, this paper asks what the effects of the interplay between size-dependent distortions and bribery are on the plant size distribution and aggregate output. To answer this question, I build a model based on the environment of Guner _et al._ (2008) which uses a Lucas (1978) span of control framework. Agents in the model are heterogeneous in terms of their managerial ability and they can either be a worker or a manager. My innovation to the span-of-control model is that agents are assigned to a corrupt official with some probability. Managers who encounter the corrupt official face size-dependent distortions as well as the fixed cost of dealing with corrupt officials. In other words, managers who encounter the corrupt official have to pay the fixed cost and they are subject to a distortion (output tax) depending on their production level. However, since there is a bribery opportunity (the official is corrupt), managers can choose to decrease the distortion level by paying a bribe to evade distortionary size-dependent policies. Although the existence of the corrupt official is exogenous, the amount of the bribe paid by a manager is endogenously chosen by each manager.

There are two key mechanisms that govern an optimal bribe for a plant: the return on the bribe and the distortion rate. The return on the bribe shows how effective the bribery mechanism is. For example, a high return on a bribe indicates an effective bribery mechanism, where managers are able to ‘get things done’ with comparatively smaller bribes. On the other hand, a low return on a bribe implies that managers need to spend more resources on bribery to solve the problems they face. Similarly, high distortion rates lead managers to spend more resources on bribery activities, because there are more problems to solve. Since managers of plants with high levels of output face distortion rates proportional to plant size, they pay higher bribes than managers with low output levels.

The existence of the corrupt official creates a misallocation of resources via two different channels: First, it distorts managers’ optimal input decisions. To be more specific, managers who are assigned to the corrupt official demand fewer inputs because the corrupt official creates distortions. Second, the corrupt officials distort the optimal occupational sorting of agents. Since agents who are assigned to the corrupt official have to pay an output tax and a fixed cost, their managerial income would be smaller than the agents who have the same ability level but who are not assigned to the corrupt official. Therefore, some of the agents become workers instead of managers.

In order to quantify the interplay between the size-dependent distortions and bribery, I design two quantitative experiments. First, I calibrate model parameters to generate the U.S. plant size distribution by assuming the U.S. is free of distortions. Then I introduce the sizedependent distortions with and without bribery opportunities separately. The purpose of 2
# **3.1 Results**

The section presents the results from the model-based simulation experiments where the goal is to compare the performance of the different methods. Marhuenda et al. (2017) consider multiple scenarios where they simulate different values for _σa_<sup>2and</sup><sup>_σ_</sup> _ac_<sup>2.Theauthorsnotewhatmattersare</sup> the relative values. In this instance the interest is to assess how results differ when the random cluster effect is considerably smaller than the random area effect and when the random cluster effect is considerably larger than the random area effect. ELL would commonly specify its random location effect at the cluster level and then aggregate results to the area level. Consequently, we expect ELL to perform better when the random cluster effect is larger than the area random effect. We consider two scenarios: Simulation results under the two considered scenarios are presented respectively in Figures 1 and 3 for bias, and Figures 2 and 4 for MSE.

The results for bias and MSE are presented at the area level. With the exception of a few areas in which the ELL and unit-context methods demonstrate a slightly higher bias, all examined methods perform better than the direct estimators, in most cases by a substantial margin. For MSE, the result varies between the two simulation scenarios. Under scenario 1 with results shown in Figure 2, where the variance of cluster effects is double that of the area effects, the methods considered, including ELL and the unit-context methods, perform relatively well and in nearly all cases now do as well as or better than direct estimates, though again ELL and the unit-context methods perform worse than the other options.<sup>16</sup> However, in Figure 4, where the variance of the area effects is now much larger, ELL in particular performs poorly in terms of MSE likely due to the error misspecification and the contextual variables not explaining sufficiently the variability of the area effects. To a lesser extent a similar effect can be observed for the CensusEB estimator, based on a model with only cluster effects and contextual variables ( _CEBc_ with context), which performs well in terms of MSE under scenario 1 but under-performs in scenario 2.

The twofold model results are aligned to the results presented by Marhuenda et al. (2017); the bias and MSE of estimates obtained under twofold fitting and onefold CensusEB fitting at the area level

> 16Despite the result, other issues from the implementation of ELL (2003) noted by CMN (2020) like the underestimation of the MSE still remain, unless the method to estimate MSE is adjusted.

10
- 11 -

# Table 3

Jamaica, Tax Burden by Income Groups, Excise Taxes and General Consumption Tax on Goods Also Subject to Excises

|Excises|C|onsi'notionTa|x on Goods Als|o Sub..ct to Excises||||
|---|---|---|---|---|---|---|---|
|InomeDecile|Cigarettes|Boor|Spirits|Income Decile|Alcoholic<br>Beverages|Tobacco<br>Products|Motor<br>Fuels|
|UnderJ$3,674|1.16|0.42|0.06|Under J$1,232|0.42|1.06|0.62|
|3,675 - 6,048|1.46|0.41|0.25|1,233- 1,943|0.69|1.20|0.61|
|6,049 - 8,734|1.32|0.51|0.41|1,944 - 2,825|0.57|1.40|0.59|
|8,735 - 12,704|1.54|0.58|0.10|2,826 - 3,759|0.89|1.46|0.59|
|12,705 - 16,780|1.15|0.52|0.13|3,760 - 4,848|0.96|1.47|0.57|
|16,781-21,895|1.10|0.50|0.25|4,849 - 6,336|*<br>1.09|1.14|0.71|
|21,896-27,701|0.91|0.41|0.22|6,337 - 8,543|1.03|_.. L4_|0.83|
|27,702- 35,882|0.80|0.41|0.16|8,544 - 12,120|1.22|0|0.97|
|35,883 - 54.928|0.78|0.37|0.22|12,121 - 19,995|0.79|0.Ca|1.07|
|Over J$54,929|0.41|0.27|0.10|Over J$19,996|0.82|0.59|1.20|
|All Households|1.06|0.44|0.19|All Households|0.85|1.10|0.78|

Source Michael Wayslenko, Tax Burden in Jamaica Before and After Tax Reform, Jamaica Tax Structure Exanination Project, Metropolitan Studies Program, Syracuse University and Jamaica Board of Revenue (Syracuse and Kingston 1987).
|**Table 3: **<br>|**Change in Inters**<br>Change in X [201<br>|**ectoral Link**<br>3–2004] in th|**ages, 2004–2013**<br>e south–change i<br>|n X [2013–2004] in the north<br>|
|---|---|---|---|---|
|X =|Value-added<br>share|Domar<br>weight|Total factor<br>productivity|Intermediate use<br>share<br>Input<br>downstreamness<br>index|
||(1)|(2)|(3)|(4)<br>(5)|
|AGR|0.181|0.162|–0.021|0.005<br>0.005|
|MIN|**0.067**|**0.070**|**0.004**|**0.019**<br>**0.033**|
|O-IND|–0.118|–0.092|0.355|–0.007<br>–0.025|
|WRT|0.026|–0.058|0.067|–0.248<br>–0.308|
|O-SER|–0.155|–0.253|–0.065|–0.023<br>–0.116|

_<mark>Source</mark>_ <mark>: Authors’ elaboration based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021).</mark> _Note:_ TFP = total factor productivity. AGR = agriculture. MIN = mining; O-IND = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. O-SER = services other than wholesale and retail trade; they include transport, communication, finance, commerce, government services, and private services. 5 Following Antras et al. (2012), input downstreamness is measured as = 1 × <u>i</u> + 2 × <u>j j j</u> + 5 5 5 5 VV ∑VV AA jii 3 × <u>j j j</u> + 4 × <u>j j j</u> + ⋯. DDii XXii XXii ∑∑VVjj AA AAjjii ∑∑∑VVjj 5ss AA AAjjssAAssii XXii XXii 24
operation, which contains 201,288,833 observations for 62 funds between July 1996 and July 2008.<sup>8</sup> The daily data have the same fields included in the monthly database.

The data on Chilean insurance companies comprise monthly portfolio holdings from June 2002 to December 2005. The database contains 2,156,576 observations corresponding to the fixed-term assets of 36 insurance companies. Information on security type, maturity date, and currency, among others, are available in this dataset.

The data on the maturity structure of US bond mutual funds come from Morningstar. The available data consist of the fraction of the total portfolio invested at different maturities (up to three years, between three and five years, five and seven years, seven and ten years, ten and 15 years, 15 and 20 years, 20 and 30 years, and above 30 years). We use the universe of 186 US bond mutual funds (multi-sector, short-term, and emerging market mutual funds) operating between 2003 and 2005. Due to limited data availability we use annual data with a total of 11,440 data points.<sup>9</sup>

# **3. Maturity Structure**

We describe the demand for long-term assets by concentrating first on the maturity structure of Chilean mutual funds. Figure 1 plots the fraction of investments in fixed-term assets per year-to-maturity, both within each maturity range and accumulated.

The figure is built by determining at each point in time (each month) the term to maturity

> 8 The difference between the number of funds in the monthly and daily datasets is due to the extended period the daily dataset covers.

> 9 We use Morningstar’s categories to group US bond mutual funds (short-term, multi-sector, and emerging market funds). Short-term bond mutual funds invest in a variety of bonds, from the most creditworthy, such as government bonds, to mortgage and corporate bonds (they may invest in more speculative high-yield and emerging market debt on rare occasions). Multi-sector bond mutual funds are usually more diversified than other types of bond mutual funds, investing in a wide range of foreign and domestic government and corporate bonds. Emerging market mutual funds invest more than 65% of their portfolios in bonds from developing countries. Data are available for 125 short-term mutual funds, 42 multi-sector mutual funds, and 19 emerging market mutual funds.

10
disorder of the small intestine, due to “faecal bacteria ingested in large quantities by young children living in conditions of poor sanitation and hygiene” (Humphrey, 2009, 1032), could cause widespread malabsorption of nutrients among children living in places where open defecation is common (Petri et al., 2008; Mondal et al., 2011; Korpe and Petri, 2012). In a recent study in rural Bangladesh, Lin et al. (2013) show that children exposed to less sanitary conditions show more biologically measured markers of enteropathy and do not grow as tall (also see Kosek et al., 2013). These epidemiological and medical studies, combined with a growing literature within economics that documents effects of poor sanitation on earlylife health,<sup>2</sup> suggest that diminished human capital and cognitive skills may be a further consequence of open defecation.

# **1.2 Outline**

This paper continues in eight further sections. Section 2 first offers descriptive statistics from the India Human Development Survey as motivation: children in this survey who are exposed to more local open defecation perform worse on cognitive tests similar to those we study. Section 3 provides introductory context about the Total Sanitation Campaign (TSC), a government program in India. Section 4 details our difference-in-differences empirical strategy. Section 5 presents our main result, that children exposed to a sanitation environment that had been improved by the TSC by their first year of life subsequently showed greater cognitive skills at age six. Section 6 reports evidence that pre-program trends in learning outcomes and related measures were similar in districts that did and did not receive the TSC. Section 7 verifies that our findings are consistent with the hypothesized mechanism of effect, by showing that the sanitation-achievement gradient is steepest for exposure in the first year of life. Section 8 reports a simulation exercise designed to account for possible un-

> 2A growing set of studies in economics documents the health importance of sanitation. Cutler and Miller (2005) show an effect of adoption of clean water technology in U.S. cities on mortality rates in the twentieth century. Watson (2006) documents an effect of improving sanitation in U.S. Native American populations on infant mortality. Galiani et al. (2005) show that privatizing water supply in Argentina reduced child mortality.

5
Vulnerability in Consumption, Education and Health: Evidence from Moldova during the Russian Crisis'

> Edmundo Murrugarra a

Jose Signoret b

> ' Paper prepared for the Non Income Dimensions of Poverty Regional Study.

**a** The World Bank. Human Development Department, Europe and Central Asia Region. e-mail: emurrugarra(worldbank.org.

b U. of California, Berkeley, Dept. of Economics. e-mail: signoretXecon.berkeley.edu
Table A. 3 and Table A. 4 also confirm that the instruments are not weak. In addition, the low correlations between export values and geographical features (see Table A. 5) support the fact that they meet the exclusion restriction criteria.

To control for the potential reverse causality of other explanatory variables such as the depth of preferential trade agreements, we instrument PTA depth between country _i_ and country _j_ with the trade weighted average depth of all the agreements signed by _i_ and _j_ with third countries. This type of instrumental variable approach has already been used in the literature (see, for instance, Orefice and Rocha, 2014 and Laget et al., 2018). We aggregate tariffs at the HS4 level, so the degree of endogeneity between trade and tariffs at the product level decreases.<sup>12</sup> Table 5 presents the results of the impact of trading times on export values once we control for endogeneity. Estimations excluding nodal countries and products from the extractive industries are very similar to those in the baseline regression and suggest that a one‐day decrease in trading times increases trade by 5.2‐6.6 percent on average (columns 1, 2, 4 and 5). The second‐stage results of the instrumental variable regressions are presented in column (3). The magnitude of the coefficients on trading times is larger compared to those in the baseline results and show that a one‐day decrease in trading times increases exports by 12 percent on average. One potential reason for this is that the variable, GIS time, might be subject to measurement error as it might not fully reflect the role of geography in explaining the connectivity between countries.

_Table 5: Sorting out endogeneity_

|||**OLS**||**P**|**PML**|
|---|---|---|---|---|---|
||**Eliminate**<br>**CHN‐EU**|**Eliminate**<br>**Extraction**|**IV**|**Eliminate**<br>**CHN‐EU**|**Eliminate**<br>**Extraction**|
||(1)|(2)|(3)|(4)|(5)|
|GIS Time|‐0.00264***|‐0.00233***|‐0.00499***|‐0.00273***|‐0.00215***|
||(0.000198)|(0.000188)|(0.000995)|(0.000166)|(0.000153)|
|Ln(PRF+1)|‐1.166***|‐1.314***|1.401|‐1.686***|‐1.889***|
||(0.367)|(0.377)|(0.974)|(0.276)|(0.285)|
|PTA Depth|0.0228***|0.0208***|0.00803|0.0226***|0.0194***|

> 12 As an extra check, regressions are conducted aggregating tariffs at the hs2 level. Results presented in appendix Table A. 6  are very similar to those in the baseline regression.

15
# VI. COST-BENEFIT ANALYSIS OF SPATIALLY DISAGGREGATED PRICE SURVEYS

The results in sections IV and V show that deflators from the food Engel curve appear to be a poor proxy for those obtained from the WCPD benchmark price indexes; compared to those benchmarks, estimates of the level, location, and change in poverty would be distorted if the Engel method deflator was used. We also note that researchers may turn to the Engel curve method, in part, because needed prices for time-space deflation are unavailable. In this section we join these two points in a cost-benefit analysis that asks the following question: could an analysis in the absence of prices (and instead using the Engel curve method to get the deflators) be so incorrect that it is so costly that it would have been better for a government to spend the money to gather the spatially disaggregated prices needed for the first-best deflators.

Cost-benefit analyses of data infrastructure in poor countries are sorely lacking (Jerven 2014), in part because it is difficult to link data to outcomes, and it is not clear if bad policies are any less likely with better data. Despite those caveats, we proceed as follows: we assume that the goal of the price survey is to deflate in order to measure the total poverty gap, so that Vietnam’s authorities can budget the exact amount to eliminate poverty (with costless and perfectly targeted transfers). We obtain this budgetary figure from the product of the poverty gap index, the value of the poverty line, and the size of the population. The results from table 3 show that, if the Engel curve deflator is used, the poverty gap index in 2010 was 0.130, while it was just 0.079 if the WCPD-vw price index is used. The difference in the total value of the poverty gap is US$2.4b (at market exchange rates since World Bank funding to the GSO for the SCOLI survey was also at market exchange rates). Even if we assume just a one percent social loss from paying poverty alleviation funds to nonpoor people, the overstated poverty gap would have an annual social cost of US$24 million per year. In contrast, the cost of the SCOLI survey was just US$0.25m, and the 32
# **Table A1: First-stage results**

For communities with 100,000 or less population (column 6, Table 3)

||Migration<br>Network|Migration Network<br>*Education|
|---|---|---|
|_Instruments_|||
|1924 state migration rate|0.1656***<br>|0.3447***<br>|
||(0.0160)|(0.1060)|
|1924 state migration rate * years of education|-0.0035**<br>|0.0834***<br>|
||(0.0015)|(0.0258)|
|_Regression controls_|||
|Years of Education|-0.0011|0.1304***|
||(0.0016)|(0.0248)|
|Years of Education Squared|0.0001|-0.0007|
||(0.0001)|(0.0012)|
|Age|-0.0036***|-0.0269***|
||(0.0009)|(0.0073)|
|Age Squared|0.0001***|0.0004***|
||(0.0000)|(0.0001)|
|Married dummy|-0.0037|-0.0352|
||(0.0039)|(0.0317)|
|Head of household dummy|-0.0033<br>|-0.0018<br>|
||(0.0047)|(0.0381)|
|Proportion of rural households owning land in 1910|0.0001**<br>(0.0000)|0.0009***<br>(0.0003)|
|Male school attendance rate in 1930|0.0000<br>|0.0001<br>|
||(0.0000)|(0.0001)|
|Gini of household income 1960|-0.0001|-0.0033|
||(0.0009)|(0.0069)|
|Schools per 1000 in 1930|0.0007**|0.0050**|
||(0.0003)|(0.0022)|
|Gini of Male schooling in 1960|0.0101***|0.0781***|
||(0.0025)|(0.0201)|
|Average Male years of schooling 1960|0.0003|0.0029|
||(0.0003)|(0.0022)|
|Constant|-0.0062***|-0.0573***|
||(0.0023)|(0.0182)|
|Observations|25531|25531|
|R-squared|0.32|0.42|
|Shea Partial R-squared|0.27|0.24|
|F-statistic on instruments|54.14|40.51|

# _Notes:_

Robust standard errors in parentheses with standard errors clustered at the municipality (community) level All coefficients apart from the constant are multiplied by 100 for display purposes *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively.