rafmacalaba/datause-encoder-data huggingface dataset:
emissions from transport if the increase in bus fares causes enough people to switch to cars and two-wheelers.

Previous attempts to estimate the impacts of pollution control policies from passenger vehicles have focused primarily on controlling emissions from automobiles and relied mostly on U.S. data. A key result in this literature (Eskeland 1995) is that a tax on auto emissions can be mimicked by combining command-and-control measures to reduce emissions per mile with a gasoline tax to reduce vehicle miles traveled. At the margin, the cost of emissions reductions should be the same via the gas tax and pollution controls. Holding the marginal cost of pollution controls constant, a higher percent of the total reduction in emissions will come from a tax on gasoline, the more elastic is the demand for gasoline. Using data from the U.S. Consumer Expenditure Survey, Fullerton and West (2000) calculate the welfare improvement from a zero-tax scenario to the ideal Pigouvian tax, and find that 71 percent of that gain can be achieved by the second-best combination of taxes on gas, engine size, and vintage. A gas tax alone attains 62 percent of the Pigouvian gain.

In countries where the share of ridership in public transit, in particular buses, is high, there is a need to evaluate the impacts of policies that reduce emissions from buses. These can take the form of reducing emissions from diesel buses—for example, by installing particle traps if lower-sulfur diesel fuel is available—or replacing diesel buses with CNG buses. Assuming that bus fares will rise to cover the cost of pollution control measures, a potential adverse effect of these policies may be the switch from public to private transportation, which is likely to entail higher emissions per passenger mile traveled. To our knowledge the only study that examines the effect of mode substitution 3
- 27 -

|βWfWf<br>0.0326|(0.0025)|***|βconstant|-8.4402|(2.9614)|***|
|---|---|---|---|---|---|---|
|Obs.|343||||||
|R squared|0.8846||||||
|Wald chi|5652.25||||||
|No. of dummy variables:|||||||
|Industry|4||||||
|Region|16||||||

The dependent variable is ln _C_ -ln _WM_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

**<u>Table 11. Implied Own Price Elasticity of Demand for Input: Three Stage Least Squares</u>**

||3SLS<br>Elasticity|Std.Err.||
|---|---|---|---|
|Labor|-0.663|(0.014)|***|
|Electricity|-0.476|(0.053)|***|
|Fuel|-0.532|(0.046)|***|
|Water|-0.712|(0.063)|***|
|Transport|-0.520|(0.046)|***|
|Communications|-0.641|(0.158)|***|

Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.

# **V. DISCUSSION**

Although the evidence consistently shows that freight rail is beneficial for firms to reduce their overall costs, it remains debatable where the benefits come from. To cast insight on this, two empirical models are considered. First, the inventory model is estimated (e.g., Shirley and Winston, 2004; Iimi et al., 2015). As discussed, economic theory suggests that the optimal level of firm inventory is dependent on the size of demand, ordering and transportation costs and opportunity costs of inventory. Thus, the level of inventory, Z, is regressed on transport costs and sales as well as other firm characteristics. The results are shown in **Table 12** . Freight rail has a clear negative impact on firm inventory. The estimated coefficients are significantly positive. Rail users hold more inventory. This can be interpreted to mean that rail transportation is less reliable, causing high inventory costs, although the same is true for truck transportation as well.
# **Status Goods:**

# **Experimental Evidence from Platinum Credit Cards**<sup>_∗_</sup>

Leonardo Bursztyn<sup>_†_</sup> Bruno Ferman<sup>_‡_</sup> Stefano Fiorin<sup>_§_</sup> Martin Kanz<sup>_¶_</sup> Gautam Rao<sup>_∥_</sup> May 2017 JEL: D03, D12, Z13 Keywords: Status goods, conspicuous consumption, social signaling

> _∗_ We would like to thank Nava Ashraf, Abhijit Banerjee, Roland B ́enabou, Stefano DellaVigna, Benjamin Enke, Robert Frank, Matthew Gentzkow, Ori Heffetz, David Laibson, David McKenzie, Markus Mobius, Ricardo PerezTruglia, Christopher Roth, Andrei Shleifer, Kelly Shue, Lise Vesterlund, David Yanagizawa-Drott, and numerous seminar participants for helpful comments and suggestions. Financial support from the World Bank Strategic Research Program is gratefully acknowledged. The study was approved by the UCLA Institutional Review Board. The opinions expressed in this paper do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

> _†_ University of Chicago and NBER, `bursztyn@uchicago.edu` .

> _‡_ Sao Paulo School of Economics, FGV, `bruno.ferman@fgv.br` .

> _§_ UCLA Anderson, `stefanofiorin@ucla.edu` .

> _¶_ World Bank, `mkanz@worldbank.org` .

> _∥_ Harvard University and NBER, `grao@fas.harvard.edu` .
Sources, Coverage rate data are not collected systematically by any organization although an effort was made during the ILO's most recent (unpublished at time of writing) "Costs of Social Security" survey to gather data on this indicator. Some of these data were used in this report. For the most part, coverage estimates were generated from data from the administering agencies or from World Bank country or sector reports. Table 6 below shows the primary sources available along with a brief comment. Labor force data were generally taken from the World Bank's "Social Indicators of Development". In certain cases, the labor force estimate was not available for the same year as were the data for the number of active contributors. In these cases, the labor force was estimated based on the recent growth rate of the population. Supplementary country notes by region are provided below.

30
APPENDIX Figure B3 (continued): Data coverage by country (prices and bid-ask spreads), 1815-2016 <!-- Start of picture text --> Jamaica<br>Japan<br>Jordan<br>Kazakhstan<br>Kenya<br>Latvia<br>Lebanon<br>Lithuania<br>Malaysia<br>Mexico<br>Mongolia<br>Morocco<br>Namibia<br>Netherlands<br>New Zealand<br>Nicaragua<br>Nigeria<br>Norway<br>Pakistan<br>Panama<br>Paraguay<br>Peru<br>Philippines<br>Poland<br>Portugal<br>Romania<br>Russia<br>Senegal<br>Slovak Republic<br>South Africa<br>South Korea<br>Spain<br>Sri Lanka<br>Sweden<br>Switzerland<br>Tanzania<br>Thailand<br>Trinidad and Tobago<br>Turkey<br>Ukraine<br>Uruguay<br>Venezuela<br>Vietnam<br>Yugoslavia<br>Zambia<br>Zimbabwe<br>1815 1835 1855 1875 1895 1915 1935 1955 1975 1995 2015<br>Prices, returns, and bid-ask spreads Prices and returns<br>Year of independence<br><!-- End of picture text --> _Note_ : For brevity, in case of country mergers or country break-ups, we use the name of the new country and omit the historical name (e.g., “Germany” instead of “Germany-Prussia”, “Yugoslavia” instead of “Serbia”, “Turkey” instead of “Ottoman Empire”). Year of Independence from Reinhart and Rogoff (2009). If independence was earlier than 1815 then that year is marked as the year of independence for presentation purposes.

A9
<!-- Start of picture text --> °<br>(o>)<br>fo)<br>co<br>fo)<br>rs) S<br>rEoO<br>5<br>Ww 2 a fasts BE<br>q erent sr<br>lo ‘gies ia<br>fo) UKR<br>st e SvKe = 88 PRT<br>° GE o (Rute<br>MBB<br>TK. KO? BH<br>oO<br>fo)<br>0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Original HCl<br><!-- End of picture text -->
16 We conducted two treatments, a “low salary” control and a “high salary” treatment, with subjects randomly assigned between the two. We focus on this sector choice decision in order to estimate the effects of high and low salaries on intrinsic motivation. The following probit specification is used to estimate a regression that pools the high and low wage treatments:

# PUBLIC SECTORi = α+ βDONATEi + γABILITYi + δCONTROLSi + εi

The variable _ABILITY_ is again measured using subject performance in the private sector (piece rate) task.

The private sector, reservation wage plays a key role in predictions about the effect of public sector wages on the pro-social motivation and ability of individuals who prefer the public sector. The experimental design allows us to precisely measure the private sector reservation wages of potential private sector workers. This eliminates noise that emerges from possible unobserved differences in the reservation wages of workers with high and low intrinsic motivation.

**Table 2:  Pro-social attitudes and entry into the public sector under low and high salary regimes**

|Dependent Variable: Sector Choi<br>|ce(1 = Join Publ<br>|ic Sector)<br>|
|---|---|---|
|Treatment|Low Salary<br>I|High Salary<br>II|
|Amount Sent in Dictator Game|0.310***|0.072|
||(0.09)|(0.11)|
|Ability|-0.007*|-0.013***|
|(Effort Exerted in Piece Rate)|(0.01)|(0.00)|
|Gender (D)|-0.215**|-0.028|
|(1 = Female)|(0.09)|(0.11)|
|Age (in years)|0.026|-0.038|
||(0.04)|(0.04)|
|Belief that Charity was Paid|-0.011|-0.083|
|(5 = Complete Confidence)|(0.06)|(0.07)|
|Constant|0.228|0.382|
|Pseudo R-squared|0.136|0.083|
|Chi-squared|14.35|10.58|
|P-value|0.014|0.060|
|Observations|92|95|

Note: * p<0.1, ** p<0.05, *** p<0.01. Probit specification, standard errors in parentheses. Table reports marginal effects. All subjects from the University of Indonesia (non-public). The dictator game variable is divided by 1000 for presentational convenience.

Table 2 reports separately the characteristics of subjects who choose the public sector under the two different salary levels. As in Dal Bó, Finan and Rossi (2013), the low salary regime attracts significantly fewer workers to the public sector than the high salary regime: 26 percent of all subjects joined the public sector in the low salary regime versus 39 percent in the high salary regime
> Some macro information was available before the crisis, such as the stock of loans in each East Asian country **by** BIS (Bank for International Settlements)-reported banks, which indicated that some countries had large bank exposure that exceeded foreign exchange reserves. However, investors ignored such information in their investment decisions. Better information can guide international investors to efficient and productive investment, if sufficient incentives are created for the investors to use such information.

The international financial system also needs to increase its transparency on the lenders' side and coordinate its regulatory frameworks to limit the tendencies towards boom-bust behavior and to create a more -level playing field. Specifically, information disclosure **by** large international investors, including **highly** leveraged institutions, is necessary to prevent them from imprudent decision making. An internationally coordinated approach is imperative to better regulate hedge funds and other **highly** leveraged institutions.

_Foreign exchange reserve policy._ As capital movements have become larger, foreign exchange reserves have become an increasingly important line of defense. The adequacy of foreign exchange reserves can no longer be measured **by** their coverage of imports but **by** the magnitude of potential capital outflows. Guidotti **(1999)** concludes that reserves should equal the amount of capital contractually due in the coming year. But reserves should arguably be larger if current account deficits are large (e.g., more than 2 percent of **GDP),** if the exchange rate is fixed and hence more vulnerable to shocks, or if the economy has historical tendency toward instability, e.g., because of commodity dependence and chronic terms of trade shocks (see World Bank, **2000b).** The optimal amount of reserves must also be judged against the opportunity cost of carrying them.

**26**
simple model, city growth could more effectively “trickle down” to the rural poor. It is an empirical question as to which type of urban growth is better for the rural poor.

Section III describes our data for addressing that question, in which we have formed a regional panel data set, combining results from household surveys with data on the extent of night light. Section IV explains our econometric model, which is calibrated to the panel data. Alternative models are also described, and shown to be testable restrictions on our preferred (encompassing) specification. Our results are then presented in Section V, which provide strong support for the hypothesis that economic growth in secondary towns has more impact on rural poverty than does growth of the big cities. Section VI concludes.

# **II. A Simple Theoretical Model**

The purpose of the following model is to illustrate one source of urban-rural linkage, namely through the labor market, for which urban economic growth emanating from cities brings different gains to the rural poor than growth in towns.

We suppose that the urban economy comprises two sectors, a town and a city. In the spirit of the classic Harris-Todaro (1970) model of rural-urban migration in the presence of an urban labor-market distortion, we assume that the wage rate in the city is fixed above the market clearing level, but the wages in the town and the rural economy are fully flexible, and come into parity. Technical progress in the town is assumed to increase the marginal product of labor there, which will lead to higher wages in the town, and also in the rural hinterland due to the integrated labor markets; indeed, in equilibrium the wage gains will be the same. Technical progress in the cities will increase employment there, which will attract workers out of unemployment and from both the town and rural areas. This will bring gains to wages in the latter sectors.

In the context of this model, we ask whether technical progress in the town has more impact on poverty than that in the city. There is no inequality within sectors (although this can be relaxed to assume an inequality-neutral growth processes). The poor in this model are taken to be all workers except those who get a job in the city (i.e., rural plus township workers, plus the urban unemployed). In other words, the poverty line is below the city wage rate but above the rural and town wage rates.

In more formal terms, the model is as follows: The production functions are _Ai Fi_ ( _Nie_ ) _e_ ( _i_ = _c_ , _t_ , _r_ ) for the city, town and rural areas respectively, where _N i_ denotes employment in sector _i_ and<sup>_A_</sup> _i_<sup>is the shift parameter for (Hicks-neutral) technical progress; we will only</sup> 4
# **References:**

Adams, Alayne, Jindra Cekan and Rainer Sauerborn. 1998. ―Towards a Conceptual Framework of Household copping: Reflections from Rural West Africa,‖ _Africa_ 68(2): 263-283.

Appleton, Simon. 1996. ―Women-Headed Households and Household Welfare: An Empirical Deconstruction for Uganda.‖ _World Development_ 24(12): 1811-1827.

Backiny-Yetna, Prospere, Franck Adoho, Issa Bouaré, Virginie Briand, Kassim Dabitao, Assa Gakou, Estelle Sommeiller, Dramane Traoré, Clarence Tsimpo, amd Quentin Wodon. 2007. ―Tendances et Déterminants de la Pauvreté au Mali 2001-2006,‖ World Bank.

Buvinic, Mayra and Geeta Rao Gupta. 1997. ―Female-headed Households and FemaleMaintained Families: Are they Worth Targeting to Reduce Poverty in Developing Countries?‖ _Economic Development and Cultural Change_ 45(2): 259-280.

Case, Anne, Christina Paxson and Joseph Ableidinger. 2004. ―Orphans in Africa: Poverty and School Enrollment,‖ _Demography_ 41(3): 483-508.

Chen, Martha. 2000. _Perpetual Mourning: Widowhood in Rural India_ . New Delhi: Oxford University Press.

Christiaensen, Luc and Richard Boisvert. 2000. ―On Measuring Household Food Vulnerability: Case Evidence from Northern Mali,‖ Working Paper 2000-05, Cornell University, Department of Applied Economics and Management, Ithaca, NY.

Croson, Rachel and Uri Gneezy. 2009. ―Gender Differences in Preferences,‖ _Journal of Economic Literature_ 47(2): 448-474.

Dercon, Stefan and Pramila Krishnan. 2000. ―In Sickness and In Health: Risk Sharing within Households in Rural Ethiopia,‖ _Journal of Political Economy_ 108(4): 688-727.

Doepke, Matthias and Michele Tertilt. 2010. ―Does Female Empowerment Promote Economic Development?‖ Policy Research Working Papers, World Bank, forthcoming.

Drèze, Jean and P. V. Srinivasan. 1997. ―Widowhood and Poverty in Rural India: Some Inferences from Household Survey Data,‖ _Journal of Development Economics_ 54: 217-234.

Duflo, Esther. 2003. ―Grandmothers and Granddaughters: Old Age Pension and Intra-household Allocation in South Africa.‖ _World Bank Economic Review_ 17(1): 1–25.

Dutta, Puja, Stephen Howes and Rinku Murgai. 2010. ―Small but Effective: India‘s Targeted Unconditional Cash Transfers,‖ ASARC Working Paper 2010/1, Canberra, Australia.

Ezememari, Kene, Nazmul Chaudhury and Janet Owens. 2002. ―Gender and Risk in the Design of Social Protection Interventions,‖ Social Protection Discussion Paper Series No. 0231, World Bank, Washington, DC.

28
10-B Figure 2.b <!-- Start of picture text --> Exports, Real Exch. Rate and Premium<br>Tanzania: 1970-88<br>PREMIUM<br>1300 -600<br>1200<br>1100 -500<br>1000<br>900 -400<br>80030<br>700<br>600 -200<br>300~ ~ ~ ~ ~ ~ ~~~~~~~~~~-0<br>500<br>300<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>-u-<br>Volume of Exports, mill 1980 $<br>-E-<br>Parallel Premium (%)<br>Real Effective Exch. Rate, 1980=1 00<br><!-- End of picture text -->
20 _Table 7: Results for log per capita unearned income_

|VARIABLE||_log per ca_|_pita unearn_|_ed income_||
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|0.031||-0.031|0.037|0.036|
||0.850||-0.610|0.880|0.860|
|SAH_POS_CH_LGE||0.102||||
|||0.460||||
|SAH_POS_CH_SML||0.002||||
|||0.020||||
|SAH_NEG_CH_SML||0.001||||
|||0.010||||
|SAH_NEG_CH_LGE||**0.341**||||
|||**1.800**||||
|POOR X SAH_CHANGE|||**0.139**|||
||||**1.740**|||
|HI X SAH_CHANGE||||-0.027||
|||||-0.290||
|URB_HI X SAH_CHANGE|||||-0.072|
||||||-0.670|
|RUR_HI X SAH_CHANGE|||||0.067|
||||||0.450|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|0.108|||||
||0.856|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||0.097||||
|||0.361||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||0.339||||
|||1.428||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||**0.108**|||
||||**1.891**|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||0.010||
|||||0.131||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||-0.036|
||||||-0.366|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||0.103|
||||||0.745|
|N|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t- statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (twosided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_
# (see the Appendix for details).

In the overall assessment of the novel intervention examined in our paper, we aim to take a quite nuanced perspective. On the one hand, our results show that extending contraceptive provision to community health workers has the potential to effectively increase adoption of the latest generation of injectables in rural villages. By educating and training community health workers to administer injections locally, the scale-up of the new family planning services can be expedited. These results underscore the promising potential of the self-administration attribute of the injectable. On the other hand, the intervention did not significantly increase overall contraceptive coverage, meaning it failed to better match contraceptive options with the preferences of women in the study. While the rise in new contraceptive injections suggests an improved fit of the menu of contraceptive services for women in the area, the decline in the use of longer-acting methods is a concern for public health and economic development, especially when follow-up injections are missed.<sup>17</sup> Our findings highlight the need for further research. First, individual-level data will be required to identify which sub-populations are gaining protection coverage and which are losing it as a result of the intervention. Second, a more comprehensive intervention should be tested that exploits the potential associated with the self-administration attribute of the new injectable method while ensuring that the adoption of longer-acting contraceptive methods is not penalized. This may involve a better integration of service delivery models and associated health worker incentives, i.e., actively offering the entire contraceptive choice set and measuring health center performance based on women-months of coverage. A promising component is educational campaigns that highlight the benefits of the various contraceptive methods with an emphasis of the importance of maintaining continuous protection. Third, future research should aim at separately exploring the relevance of the various barriers to contraceptive utilization. This knowledge will likely stimulate development of tailored contraceptive technologies and research on effective service delivery strategies.

> 17 This becomes even more critical considering evidence that injectables’ discontinuation rates are roughly double those of implants. According to Burundi Institute of Statistics and Economic Studies (2017), implants have a discontinuation rate of 21%, contrasting with injectables at 47%. This discrepancy likely stems from the requirement to physically remove the implant for discontinuation, whereas discontinuation of injectables often occurs when women fail to receive timely renewals when the efficacy of the last injection wanes.

18
# Table 8: Additional gains from input-trade liberalization

|Depend|ent variable: loga|rithm of profts or s|ales or electricity|of frm_i_in_t_.|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Pr|ofts|Sa|les|Electricity|consumption|
|Importer capital goods|0.128***|0.308***|0.098***|0.268***|0.091***|0.213***|
||(0.025)|(0.057)|(0.010)|(0.029)|(0.011)|(0.027)|
|Input tariff(s)(t-1)_×_Importer capital goods||-0.427***||-0.409***||-0.297***|
|||(0.123)||(0.055)||(0.057)|
|Input tariff(s)(t-1)||-0.200||0.169||-0.194*|
|||(0.308)||(0.113)||(0.106)|
|Capital goods and output tariff(s)(t-1)|Yes|Yes|Yes|Yes|Yes|Yes|
|Herfndahl index(s)(t-1)|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry level controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm level controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm fxed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fxed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|11,945|11,945|14,680|14,680|14,662|14,662|
|R-squared|0.178|0.182|0.604|0.607|0.708|0.709|

_Notes_ : The dependent variable is the logarithm of firms’ profits (columns 1 and 2) or sales (columns 3 and 4) in year _t_ . All control variables are defined in table 3. Heteroskedasticity-robust standards errors are reported in parentheses. Errors are corrected for clustering at the 3-digit industry level.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and _∗_ indicate significance at the 1, 5 and 10 percent levels respectively.

Finally, we test the assumption that Indian firms are upgrading their technology by importing capital goods. More advanced technologies are more likely to be reliant on electricity. Thereby, we expect that Indian firms increase their consumption of electricity as a result of input tariff cuts and importing capital goods. Columns (5) and (6) of Table 8 show that this was indeed the case.<sup>36</sup> 36We thank an anonymous referee for suggesting this test.

47