rafmacalaba/datause-encoder-data huggingface dataset:
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compute the variance ratio. We then run the following regression: Where is a dummy variable that takes on a value of one over the specified time period, and _t_ is the linear time trend. In estimating the coefficients, we correct for auto correlation using Newey-West with three lags.

||**World**|**Developed**|**Developing**|**European**<br>**Union**|**USA**|**Japan**|**Africa**|**Eeastern**<br>**Europe &**<br>**Central Asia**|**East Asia**<br>**& Pacific**|**Latin**<br>**America &**<br>**Carribean**|**Middle**<br>**East & N**<br>**Africa**|**North**<br>**America**|**South**<br>**Asia**|**Western**<br>**Europe**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**int**<br>**1998.01 - 2003.12**|-3.424***|-3.434***|-2.273***|-3.044***|-3.378***|-2.078***|-1.414***|-1.800***|-2.032***|-2.237***|-0.724***|-3.393***|-1.446***|-3.057***|
||(-26.291)|(-26.015)|(-29.887)|(-25.238)|(-29.785)|(-34.073)|(-13.769)|(-44.343)|(-19.055)|(-41.373)|(-7.450)|(-30.109)|(-17.253)|(-24.015)|
|**slope**<br>**1998.01 - 2003.12**|-0.002***|-0.001***|-0.003***|0.000|-0.001***|-0.000|-0.001***|-0.001***|-0.003***|-0.002***|-0.002***|-0.001***|-0.001***|0.000|
||(-3.645)|(-2.839)|(-12.327)|(0.307)|(-3.670)|(-0.499)|(-3.259)|(-10.576)|(-6.981)|(-7.024)|(-7.254)|(-3.373)|(-4.581)|(0.699)|
|**int**<br>**2004.01 - 2007.06**|-6.588***|-6.413***|-2.421***|-7.111***|-5.451***|-2.529***|0.432*|-3.169***|-2.588***|-2.482***|-0.706|-5.336***|-2.640***|-7.941***|
||(-21.959)|(-18.918)|(-4.665)|(-16.365)|(-22.876)|(-10.212)|(1.653)|(-22.073)|(-6.415)|(-12.651)|(-1.093)|(-20.613)|(-7.338)|(-17.706)|
|**slope**<br>**2004.01 - 2007.06**|0.004***|0.004***|-0.004***|0.008***|0.002***|0.002***|-0.005***|0.001***|-0.002**|-0.001***|-0.005***|0.002***|0.001|0.009***|
||(6.608)|(5.897)|(-3.188)|(8.921)|(4.161)|(3.892)|(-8.789)|(4.608)|(-2.361)|(-3.482)|(-3.637)|(3.297)|(1.621)|(10.630)|
|**int**<br>**2007.07 - 2009.12**|-11.953***|-11.616***|-12.650***|-11.422***|-10.315***|-1.344***|-8.000***|-11.175***|-10.057***|-8.404***|-10.293***|-10.443***|-9.735***|-10.694***|
||(-25.455)|(-24.897)|(-19.020)|(-24.689)|(-17.376)|(-6.614)|(-14.525)|(-10.538)|(-37.861)|(-32.621)|(-17.243)|(-17.570)|(-26.888)|(-26.209)|
|**slope**<br>**2007.06 - 2009.12**|0.015***|0.014***|0.015***|0.015***|0.012***|0.000|0.011***|0.014***|0.013***|0.010***|0.012***|0.012***|0.013***|0.014***|
||(19.754)|(19.380)|(14.475)|(19.703)|(12.893)|(0.828)|(12.084)|(8.660)|(29.477)|(23.109)|(12.933)|(13.128)|(21.483)|(20.395)|
|**N**|662|662|662|662|662|662|662|662|662|662|662|662|662|662|
|**lags**|3|3|3|3|3|3|3|3|3|3|3|3|3|3|

# **Table 7: Cross-country Regressions**

This table shows the results from cross-country regressions from the following model: . The dependent variable is the variance ratio, , calculated for each country _i_ for each year _t,_ using log changes in default probabilities _._ The dummy variable, , that takes on a value of one if a country in our sample has 31
dI Since the cost of raising external funds C is convex and the production function is concave, is dW positive: investments are sensitive to the availability of internal funds. With a quadratic 1 functional form of C(E, g) = (1 −g)E<sup>2</sup> , we obtain the investment cash-flow sensitivity: 2

**Proposition 2** :  Firms with bureaucratic CEOs have lower investment cash-flow sensitivities

Firms with bureaucratic CEOs are subject to a lower cost of obtaining external funds, and therefore rely less on internally generated cash flows for investment. The investment decisions of firms operated by entrepreneurial CEOs, however, are more sensitive to the availability of internal funds, because internal funds provide a greater cost advantage over external funds compared to firms operated by bureaucratic CEOs.

In short, our model predicts that firms display differential sensitivities of investment to cash flows depending on their government connections. In particular, firms with stronger ties to the government, such as state-owned firms or firms with government-appointed CEOs, benefit from favorable treatment by banks and thus exhibit smaller investment cash flow sensitivities. Our model is therefore in line with FHP (1988) and Cleary (1999) in that it provides a rationale for arguing that the sensitivity of investment to internal funds can be used to gauge firms’ financing conditions. We test the implications of our model by categorizing firms by government involvement in CEO appointment and state ownership. Our results regarding cash flow coefficients lend support to our model predictions, and our inclusion of access to sources of 15
<!-- Start of picture text --> Share of private transport expenditure in total transport expenditure by expenditure quintile<br>(Private transportation: motor cars + fuels and lubricants + maintenance)<br>90%<br>BOuwon ee<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>O86.<br>Poorest quintile Second poorest Middle quintile Second richest Richest quintile<br>ee Allcountries —=—eHighincome —Upper middie income ——Lowermiddleincome — Low income<br><!-- End of picture text -->
**Table 5: Consumption under Alternative Tax Regimes** 1/

|Baseline|||||||||
|---|---|---|---|---|---|---|---|---|
|Quintile|2013|2014|2015|2016|2017|2018|2019|2020|
|1|2227.1|2174.4|2246.1|2274.1|2436.6|2495.0|2534.8|2632.7|
|2|3038.6|2901.8|2801.5|2830.3|2979.4|3058.4|3078.6|3221.3|
|3|3796.0|3655.1|3353.8|3387.0|3503.8|3585.3|3595.3|3733.3|
|4|4805.7|4655.8|4395.7|4427.3|4561.9|4638.3|4673.1|4807.3|
|5|8564.0|8417.9|8257.4|8297.1|8608.0|8697.7|8948.6|9092.1|
|Sales Tax|Increase||||||||
|Quintile|2013|2014|2015|2016|2017|2018|2019|2020|
|1|2225.0|2168.8|2243.4|2276.5|2436.8|2488.7|2537.9|2649.2|
|2|3040.6|2897.9|2799.6|2835.0|2980.8|3050.6|3084.0|3247.2|
|3|3789.0|3645.2|3349.6|3388.9|3503.8|3576.1|3598.8|3756.1|
|4|4798.4|4646.0|4391.0|4428.3|4560.5|4627.9|4675.7|4829.4|
|5|8557.3|8407.2|8250.4|8296.9|8600.5|8678.1|8945.1|9113.4|
|Corporate|Tax Increa|se|||||||
|Quintile|2013|2014|2015|2016|2017|2018|2019|2020|
|1|2203.4|2155.2|2218.7|2260.5|2420.5|2489.7|2531.9|2658.4|
|2|3015.0|2880.1|2765.2|2813.9|2960.7|3055.2|3078.6|3265.0|
|3|3742.7|3611.8|3310.9|3362.4|3479.6|3575.4|3589.4|3768.3|
|4|4745.4|4612.2|4354.0|4401.2|4534.8|4624.2|4661.1|4835.5|
|5|8496.1|8369.4|8206.1|8259.3|8551.0|8653.6|8892.5|9081.9|

|Tariff Incr|ease||||||||
|---|---|---|---|---|---|---|---|---|
|Quintile|2013|2014|2015|2016|2017|2018|2019|2020|
|1|2229.9|2175.2|2245.4|2277.8|2434.4|2493.1|2533.8|2647.2|
|2|3041.6|2902.7|2800.9|2835.8|2976.3|3056.2|3077.6|3243.3|
|3|3797.6|3654.6|3352.5|3391.6|3500.6|3582.7|3594.0|3753.3|
|4|4807.6|4655.8|4394.2|4431.2|4558.7|4635.4|4671.2|4827.5|
|5|8566.6|8418.9|8255.0|8300.6|8602.4|8691.2|8942.6|9117.0|

1/ The units of measurement are billions of real 2013 Pakistan Rs. (National accounts Main Aggregates at current prices), Table 2, 2017.

34
Kleven, H., Knudsen, M., Kreiner, C., Pedersen, S., & Saez, E. (2011). Unwilling or unable to cheat? evidence from a tax audit experiment in denmark [cited By 474]. _Econometrica_ , _79_ (3), 651–692. https://doi.org/10.3982/ECTA9113

- Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. _Proceedings of the National Academy of Sciences_ , _116_ (10), 4156–4165. https://doi.org/10.1073/pnas.1804597116

- Lu, M., Sadiq, S., Feaster, D. J., & Ishwaran, H. (2018). Estimating individual treatment effect in observational data using random forest methods. _Journal of Computational and Graphical Statistics_ , _27_ (1), 209–219. https://doi.org/10.1080/10618600.2017.1356325

- Luttmer, E. F., & Singhal, M. (2014). Tax morale. _Journal of economic perspectives_ , _28_ (4), 149–168. Nie, X., & Wager, S. (2020). Quasi-oracle estimation of heterogeneous treatment effects. _Biometrika_ , _108_ (2), 299–319. https://doi.org/10.1093/biomet/asaa076

- Parikh, H., Volfovsky, A., & Rudin, C. (2022). Malts: Matching after learning to stretch. _Journal of Machine Learning Research_ , _23_ (240).

- Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2018). Some methods for heterogeneous treatment effect estimation in high dimensions. _Statistics in Medicine_ , _37_ (11), 1767–1787. https://doi.org/10.1002/sim.7623

- Robinson, P. M. (1988). Root-n-consistent semiparametric regression. _Econometrica_ , _56_ (4), 931– 954. Retrieved January 26, 2024, from http://www.jstor.org/stable/1912705

- Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. _The Annals of Statistics_ , _6_ (1), 34–58. Retrieved January 25, 2024, from http://www.jstor.org/stable/ 2958688

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28
prevents us from being able to construct work history, thus the sectoral experience parameter can not be estimated. The work history is also required to identify unobserved heterogeneity.

NLSY is widely used for estimation of occupational choice models, since it follows individuals over years and provides detailed information on work history. The sectoral experience variable can be easily constructed from NLSY. Initially, NLSY has 12,686 individuals in the sample, consisting of 6,403 males and 6,283 females. The individuals in our sample are between 14 and 21 years old as of 1979.

Following the previous research, we only use white males. We also take individuals with missing observations out, since their sectoral experience can not be calculated correctly. After this data cleaning procedure, we end up with 1,190 individuals in the sample.

Neal (1999) reports that there are coding errors in NLSY79 regarding occupations. A similar error is also present for industry codings. In order to minimize this problem, we use the following method of Neal (1999): Whenever a sector change is reported, we require the worker to change his employer as well, otherwise it is considered as a coding error and the original sector is kept.

NSLY follows individuals annually until about age 40, so we can not identify parameters for older individuals in the model. Only years 1991-1993 have sufficient number of observations to include both experienced and inexperienced workers at the same time, as most of the individuals in our sample are too young to have sufficient sectoral experience before 1991. We do not have enough observations for college graduates, as the metal sector is a quite small and unskilled labor intensive sector. Because of these data problems, we only estimate the first step equation for non-college graduates with NLSY.

First, using CPS, we estimate a special case of the model without sectoral experience and unobserved heterogeneity. Then, using NLSY, we estimate parameters of the general model for only unskilled workers who are less than 37 years old. Fortunately, we are able to see the complete picture when we combine estimates from both datasets, as the changes in worker mobility across different subgroups are fully identified.

17
_comunas_ .<sup>19</sup> Second, in the rapidly expanding _comunas_ , the population density was highest – crowding and congestion were the consequences. Third, poverty had a clear racial connotation in Cali – while the black population makes up about one-third of the overall population, they represented more than half of the poor. The black population is heavily concentrated in _comuna_ 14 _._ Lastly, high levels of homicide were concentrated in the most marginalized neighborhoods in the city.

# _4.2. Incidence of Municipal Expenditures_

We now turn to an analysis of municipal expenditures in the city, which was one of the key questions the EPSOC was to answer. Who benefited from these municipal expenditures? And how were they distributed within the city?

At the end of the 1990s, by far the largest share of municipal expenditures in Cali went to servicing the city debt; social expenditures in health, education, pacification and social welfare were a distant second. Of total municipal expenditures, only 26 percent benefited the population in individual _comunas_ as -- in addition to the debt service – the municipal administration absorbed a large part of the available resources.

These geographically targeted expenditures, however, went largely to the better off _comunas_ . We examined the spatial distribution of expenditures in three categories: social expenditures (social welfare, pacification, education, health, sports, social housing); transport (roads, maintenance, transit) and all <!-- Start of picture text --> Figure 3: Social Expenditure Distributioin Against Poverty<br>Distribution, 1997<br>20.00<br>10.00<br>0.00<br>-10.00<br>-20.00<br>Communities communities<br>w/ most poor w/ least poor<br>Source: Own calculations based on EPSOC (1999)<br><!-- End of picture text --> other expenditures. Figure 3 compares the distribution of the social expenditures against the distribution of the poor in the city. That is, we calculated the share of social expenditures flowing to a specific _comuna_ and subtracted from this the share of the total poor living in the same _comuna_ . A positive bar will hence signal that the _comuna_ obtained a higher share of expenditures than it would have obtained had expenditures been distributed strictly according to a poverty map. In the figure, _comunas_ are ranked from left to right by the share of the poor

> 19 According to Urrea and Ortiz (1999, p.7) the population growth rate from 1993 to 1998 has been the highest in _comunas_

18
poverty rates, which fall to just 15 percent in 2008/09. There is also some apparent spillover from urban to rural areas, with poverty in the rural South falling from 70 percent in 1996/97 to about 45 percent in 2008/2009. As a result the rural South becomes the least poor rural area in the country and one of the regions in which poverty rates decline fastest. In the rest of the country substantial declines in urban poverty are not accompanied by corresponding improvements in adjacent rural areas. A critical feature that distinguishes Southern Mozambique, and the Maputo area in particular, from the rest of the country is the simultaneous decline of both urban and rural poverty rates. By contrast, urban areas in the Central and Northern regions remain islands of relative prosperity surrounded by rural areas mired in severe and persistent poverty. In conclusion, with the notable exception of the urban Central region, overall poverty reduction seems to be driven by consistent reductions in urban poverty nationwide and by the strong performance of the Southern region as a whole.

**Figure 5: Revised Poverty Headcount, by Consolidated Regions**

<!-- Start of picture text --> Revised poverty headcount<br>by macro regions<br>74.7 74.9<br>70.5 71.1 71.2 70.6<br>65.4<br>57.2 56.2<br>52.8 53.1<br>44.2 44.7<br>38.7 38.9<br>33.8<br>27.4<br>15.9<br>1996/7 2002/3 2008/9<br>North Urban North Rural Central Urban Central Rural South Urban South Rural<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text -->

**Revised** : national 1996/97 based basket PL with 13 location price deflators and 2002/03 and 2008/09 data partially re-estimated (set A and B table1)

An analysis of the distribution of real consumption (see Figure 6, below) obviates the potential disadvantages of considering only one measure of welfare, the poverty line, while overlooking the rest of the consumption distribution. The horizontal axis represents revised consumption measured as a percentage of the poverty line. The vertical axis represents the population percentage. Each point on the distribution function shows the share of the population below a specified consumption level.<sup>33</sup> The

> 33 Given our focus on the performance of the poorest segments of the population we cut off the distribution at 200% of the poverty line.

23
**Table 1. Original and actual allocation of villages to batches (number of villages)**

||Orig<br>|inalallocati<br>|on<br>|||
|---|---|---|---|---|---|
|Actual allocation|batch 1|batch 2|batch 3|matched comparison|Total|
|Batch 1|89|10|6|0|105|
|batch 3|9|10|93|1|113|
|matched comparison|2|0|1|89|92|
|Total|100|20|100|90|310|

Note: Each cell shows number of villages

**Figure 1. The timing of surveys**

|Jan-08|March 2009:<br>baseline survey<br>Jan-09<br>Jan-1|April 2010 Midline<br>survey<br>0<br>Jan-11|March 2013<br>Endline survey<br>Jan-12<br>Jan-13|
|---|---|---|---|
||Batch 1 villages<br>in project|Batch 3 villages<br>in project||

Note: Comparison group villages that never receive the project are interviewed at the same time but are not show in the figure above. The baseline survey were fielded 6 months after batch 1 villages received their block grants. Most of batch 3 villages received their block grants about five months after the baseline survey which is about nine months before the midline survey. Thus, the average difference in exposure between batch 1 and 3 villages is about 11 months while the average difference in exposure between batch 3 and the comparison group is about nine months at midline, and 40 months at endline with no project exposure in the comparison group villages.

**Table 2. Timing of project and surveys**

|||baseline|midline|endline|
|---|---|---|---|---|
|batch 1 villages|||||
|(actual allocation)|Percent of villages that received block grant|100|100|100|
||Avg. Nr of months since receipt of block grant<sup>a</sup>|6|20|51|
|batch 3 villages|Percent of villages that received block grant|7|96|100|
||Avg. Nrof months sincereceipt ofblockgrant<sup>a</sup>|2|9|39|
|a: Note: W|e include villages that did receive block grant||||

29
_estimate on poverty; conversely, if this trend is negative, a lower bound estimate on poverty results._ While Proposition 2 appears to require much additional information, it is relevant in such cases as where no data on household assets are available. Since assets are positively correlated with household consumption (see, e.g., Filmer and Pritchett (2001)), additional knowledge about the trend of asset ownership over time (say, from macroeconomic data or qualitative surveys) can be useful in helping determine the bias of estimates.

# **III.2. Validation in the Jordanian Context**

We turn in this section to discussing poverty imputation using the 2008 and 2010 rounds of the HEIS. Since we have the actual consumption data in 2010, we can validate our imputation method by imputing from 2008 into 2010 to obtain imputation-based poverty estimates pretending that consumption data did not exist in the latter year, and then compare these estimates with the design-based (true) estimates based on the actual consumption data. We provide an overview of the country background and data description before discussing estimation results.

# **III.2.1. Country Background: Poverty in Jordan**

The official poverty line in Jordan is constructed based on a “cost of basic needs” approach with a common food and non-food basket for all households, where the food consumption is anchored to a national caloric level of 2,347 calories per capita per day. Since consumption habits of rich and poor households may differ greatly, the poverty line was based on the revealed consumption patterns of the bottom 30 percent of the population (regarded as poor or near-poor) as reflected in the 2010 HEIS (World Bank, 2012b). The national annual poverty line for 2010 is thus set at 813.7 JD per individual,<sup>17</sup> yielding the official poverty rate of 14.4 percent for this year. This poverty line is then fixed for 2010 and is adjusted for changes in the cost of living

> 17 This line is equivalent to 3.42 US Dollars per day in 2005 PPP terms.

19