rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: PM2 _._ 5 and earnings

||ln<br>|(earnings <br>|in last mo<br>|nth)<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|A. % weeks PM 2.5 above|-0.152*|-0.195*|-0.009|0.009|
|35 _μ_g/m<sup>3</sup>|(0.088)|(0.113)|(0.068)|(0.078)|
|B. % weeks PM 2.5 above||0.107||-0.045|
|35 _μ_g/m<sup>3</sup> _×_ has job contract||(0.132)||(0.100)|
|C. p-value H0: _A_+_B_ = 0||0.406||0.685|
|Household has|yes|yes|no|no|
|susceptible individuals|||||
|Observations|2,274|2,274|3,240|3,240|
|R-squared|0.600|0.603|0.625|0.631|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the municipality level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions include household, month and year fixed effects, and the same individual and household controls as the baseline specification (see notes of Table 2). The reference period for explanatory variable “% weeks PM 2.5 above 35 _μ_ g/m<sup>3</sup> ” is weeks _t −_ 1 to _t −_ 4. Row C displays the p-value of the test _A_ + _B_ = 0, where A and B refer to the estimates in two rows.

# **5 Conclusion**

This paper examines the short-term effect of fine particulate matter on labor supply. This issue is important to assess the social cost of pollution and to inform the design of environmental policies.

Using the case of Lima, Peru, we find evidence of a significant and sizable negative effect. The effects are non linear and heterogeneous, affecting mostly individuals in households with small children and elderly adults.

Our findings shed light on the mechanisms linking pollution to labor supply. They suggest that, at moderate levels, caregiving is an important mechanism linking pollution to labor supply. However, at higher levels, pollution affects all individuals suggesting that, at these levels, the mechanism may be more direct: deterioration of workers’ health.

Importantly, our results also point out two important issues not discussed before. First, pollution can have redistributional effects. In our case, the brunt of the pollution externality, in terms of lower labor supply and lower earnings, is borne by households with small children 26
# **A1.3.2 Price-adjustment delays**

Dimson (1979) and Cohen _et al._ (1983)11 consider more persistent priceadjustment delays, due to frictions in the capital market, which may affect quotation as well as trade prices. These authors find a different pattern of biases in beta from that analysed by Scholes and Williams, as shown in Table A1.2. Also, they find that these biases occur even for interval lengths longer than a single day, with estimates becoming asymptotically consistent as the period length is increased.

**Table A1.2: Bias in beta estimation due to price adjustment lags**

|Type|Direction of bias in beta estimate|
|---|---|
|Infrequently traded securities|Downwards|
|Very frequently traded securities|Upwards|

# **A1.3.3 Length of the investment horizon**

Levhari and Levy (1977)12 comment on a different form of bias in calculated beta coefficients: several researchers report a tendency for high-risk (aggressive) stocks to earn lower returns than those estimated by the CAPM, while low-risk (defensive) stocks exceed predictions. Levhari and Levy give an explanation based on a discrepancy between the actual and theoretical length of the investment horizon. The CAPM assumes that investors maximise their returns over a single time period, which may be of any length as long as it is identical for all investors. When the assumed investment horizon differs from the actual one used by investors, the estimated beta coefficient will be systematically biased. The direction of this effect varies according to whether the assumed horizon is too long or too short, as summarised in Table A1.3.

l Dimson (1979), 'Risk Management When Shares are Subject to Infrequent Trading', _Journal of Financial Economics, 7,_ 197-226; Cohen, Hawawini, Maier, Schwartz and Whitcomb (1983), 'Friction in the Trading Process and the Estimation of Systematic Risk', _Journal of Financial Economics,_ 12, 263-278. **12** Levhari and Levy (1977), 'The Capital Asset Pricing Model and the Investment Horizon', _Review of Economic Studies,_ 92-104.

39
shift of resources from tobacco to communication. This shift might be explained by the role of tobacco consumption and mobile phone use as a signal for social status. It is possible that while tobacco consumption used to be a signal for social status, it it now slowly being replaced by mobile phone ownership and use.

# **_5.  Conclusion_**

In this paper, we explore the impact of mobile phone ownership on tobacco consumption. Using household panel data, we find that purchasing a mobile phone leads to a sharp decline in tobacco consumption. This effect is large and materializes through a budget shift from tobacco to communication.

An interesting avenue for further analysis is to understand the role of social status in explaining the observed shift between tobacco and communication. Indeed, it might be that smoking used to be a signal for social status and this signal is slowly being replaced by mobile phone ownership and use.

Finally, it appears important to assess if those results hold in other countries. First, in countries at similar levels of incomes, other goods might get crowded out by mobile phones. Second, in richer countries, the budget constraints might not be as strong and thus mobile phone ownership might not lead to such a crowding-out.

10
13

**Figure 5.  Latin American Import Tariff Liberalization (1985-1999)**

(Average and Deviations in Percentages) <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1986 1988 1990 1992 1994 1996 1998<br><!-- End of picture text --> _Note: The solid line represents the simple average tariff rate for Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, Peru, Uruguay and Venezuela. The area represents the average deviation of the tariff rate across countries. Costa Rica and Guatemala are excluded from the deviation calculations. Source: Lora (2001)._

**Figure 6.  International Financial Liberalization Index (1973-2002)**

<!-- Start of picture text --> 3 Full Liberalization<br>Developed<br>2.5 Countries East Asia<br>2<br>1.5<br>Latin America<br>Full Closure<br>1<br>1973 1977 1981 1985 1989 1993 1997 2001 2005<br>More liberalization<br><!-- End of picture text --> _Note: The liberalization index is calculated as the simple average of three indices (liberalization of the capital account, domestic financial sector and stock market) that range between 1 and 3, where 1 means no liberalization and 3 means full liberalization. The regional averages are simple country averages._ _Source: De la Torre and Schmukler (2007)._
# **Box 3. Indicators in US university results-based funding systems**

<mark>Most states in the US use some form of performance funding or reporting based on different indicators reflecting the preferences of policy makers in different states. Burke and Minassians (2002b) have made a survey of the indicators used to evaluate performance in US higher education. Some of the most commonly used indicators include:</mark>

- <mark>Administrative/academic costs/staff (input)</mark>

- <mark>Tuition and fees (input)</mark>

- <mark>Financial aid (input)</mark>

- <mark>Research sponsored by external sources (input)</mark>

- <mark>Technology/ distance learning (process)</mark>

- <mark>Student transfers (output)</mark>

- <mark>Time-to-degree (output)</mark>

- <mark>Graduation/ retention rate (output)</mark>

- <u><mark>Job placement (outcome)</mark></u>

_Source: Burke et al. (2002c)_ Each type of indicator reflects a tradeoff between accountability and measurability. Focusing exclusively on inputs and processes facilitates the monitoring of what and how means are used but neglects the purposes for which resources are obtained. Output indicators are relatively simple to observe and quantify but may be only vaguely associated with societal impact. Outcome indicators are generally more relevant, but also less useful for assigning responsibilities. For most purposes it is preferable to rely on a combination of indicators rather than focusing on any single measure. Yet, an abundance of data adds complexity, and risks making the performance system unwieldy and difficult to understand. Hence, most results-based funding systems attempt to strike a balance between manageability and comprehensiveness.

An indicator should preferably meet the following requirements: (i) easy to calculate; (ii) difficult to manipulate; (iii) give a reliable estimate of the institution’s value added and (iv) not be subject to noise. First, it should be transparent for institutions and their employees how their performance is evaluated. If people do not know how to influence their performance, results-based funding is unlikely to work. Second, the indicator must be difficult to manipulate. For instance, test scores can be manipulated in several ways, such as teaching-to-the-exam and keeping marginal students out of the pool of tested students. Third, students learn inside and outside tertiary institutions. The indicator should measure the performance of the institution, correcting for external influences. Finally, random factors should not have a major impact on selected indicators.

Several OECD countries have experimented with establishing distinctive indicator subsystems for the funding of tertiary education. Separating e.g. indicators for teaching and research, as in the United Kingdom, may reduce complexity and allows evaluation criteria and follow-up processes to be closely tailored to each sub-system (Box 4). Nonetheless, decoupling indicator sub-systems adds to the challenge of creating incentives that support a balanced approach to institutional improvement (Jongbloed 2001).

- 10 -
**Figure 11. Fiscal Impoverishment and Fiscal Gains in Mali and Niger, 2014**

<!-- Start of picture text --> Mal i Niger<br>37.89 45.94<br>31.98<br>21.46<br>20.93<br>13.13<br>5.51<br>0.25 1.7 0.43 2.68 1.08<br>From Market From Market From Market From Market From Market From Market<br>Income to Income to Income to Final Income to Income to Income to Final<br>Disposable Income Consumable Income Disposable Consumable Income<br>Income Income Income<br>Fiscal Impoverishment Fiscal Gains Fiscal Impoverishment Fiscal Gains<br>Source: Authors<br><!-- End of picture text --> Direct taxes have a relatively neutral effect on fiscal impoverishment. When moving from market income to disposable income and thus considering direct transfers net of direct taxes, almost no one becomes poor or is fiscally impoverished in the two countries. This is because direct transfers are targeted solely at the poor (for Mali), and only the richest members of the population pay direct taxes. Alternatively, when indirect taxes net of indirect subsidies are considered, 37.9 percent of the population is fiscally impoverished in Mali and 45.9 percent in Niger (figure 11). Hence, indirect taxes have a strong impoverishing effect. People pay more in indirect taxes than they receive in indirect subsidies.

The benefits of in-kind transfers in education and health somewhat attenuate the strong impoverishment effect of indirect taxes. Education and health spending greatly decrease fiscal impoverishment; from 37.9 percent at consumable income, fiscal impoverishment drops to 21.5 percent at final income in Mali and from 45.9 percent to 13.1 percent in Niger.<sup>4</sup> Moreover, with a small proportion of fiscal gainers at consumable income, fiscal gains rise significantly because of in-kind transfers.

# **5. Conclusion**

This paper aimed to assess the effect of spending and taxes on poverty and income distribution in Mali and Niger. We analyzed the incidence of two-thirds of total tax revenue, including the WST, VAT, import taxes, and other indirect taxes. We also analyzed the effect of spending on education

> 4 In most cases, spending on education and health is not included when assessing fiscal impoverishment and gains.

17
Poioy **Researcth Working Paper Series**

||s**itie**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|---|
|;'VPS<br>P<br>><br>-;|Promoting Growth i Sri Lanka<br>1-essonsfrmn East Asia|Amadr<br>e<br>A tmed<br>_Prva_Rar-win|June1995|A. Bhalla<br>82168|
|WPS,1479|IsThere a Com:nercial Case<br>~<br>Tropical Timber Cedtlficatio.|Panayot:sN. Vara:iq.<br>Hachel Crossley<br>2arlos A. Primo F3raga|June 1995|J.**Jacobson**<br>33710|
|WPS 1480|Debt as a Control Device irr<br>Transitional Economre: The<br>Experiences oflHungaryand Pola-!|H!etbert L. Baer<br>C eryl W. CGray|June 1995|G. Evans<br>85783|
|.VPS1481|Corporate Control in Central Europe<br>and Russia: '-rhuld Banks 3. r!<br>Shares"'|Peter Dittus<br>Stepher Prowse|June 1995|G. Evans<br>85783|
|'V FS 1482|A Measure of Stock Market<br>Integration for Developed and<br>Emerging Markats|Robert A. Korajczyk|June 1995|P. Sintim-Aboagye<br>38526|
|/VPS1483|Costa Rican Pension Systemn<br>Options for Reform|Asli Demirg:c-Kunt<br>Arnita Schwarz|June 1995|P. Sintim-Aboagye<br>38526|
|VPS1484|The Uruguay Round and SouthAs;ia<br>An Overview of the Impact and<br>Opportunities|Nicier Majd|July 1995|J. Ngaine<br>37947|
|WPS1485|Aggregate Agricultura! Supply<br>Response in Dcvelop;ng Countrie-.<br>A Survey of Selected Issues|Maurice Schiff<br>Claudio E. Montenegro|July 1995|J. Ngaine<br>37947|
|WPS1486|The Emerging Legal Framework for<br>Private Sector Development in<br>Viet Nam's Transitional Economy|Pham van- Thuyet|July 1995|G. Evans<br>85783|
|WPS1487|Decomposing Social Indicatcrs lUsing<br>Distributional Data|Benu Bidani<br>Martin Ravaliion|July 1995|P. Sader<br>33902|
|WPS 1488|Estimating the World at Worl,|Doon Filmer|July 1995|M. Geller<br>31393|
|AE/PS148|9 Educationai Attainment in Developing<br>Countries Now Estimates and<br>Projections Disaggregated bv Gt.r tder|Virnod Ahuja<br>Deon Filmer<br>|July 1995|M. Geller<br>31393|
Table 9: Change in population age structure, 1996-2011

|Dep. Var.: change in share|(1)<br>Age<br>0-9|(2)<br>Age<br>10-19|(3)<br>Age<br>20-39|(4)<br>Age<br>40-59|(5)<br>Age<br>60+|
|---|---|---|---|---|---|
|Homeland (=1)|-0.066|0.147**|0.041|-0.087|-0.047|
||[0.047]|[0.057]|[0.102]|[0.061]|[0.031]|
|Share Employed mining1996|0.025*|0.047|-0.039|-0.039**|-0.005|
||[0.014]|[0.034]|[0.043]|[0.016]|[0.014]|
|Homeland*Share Employed|-0.034**|-0.052|0.038|0.060***|0.002|
|mining1996|[0.016]|[0.033]|[0.043]|[0.020]|[0.013]|
|Observations|9,598|9,598|9,598|9,598|9,598|
|R-squared|0.443|0.557|0.359|0.418|0.396|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|
|MunicipalityFE|Yes|Yes|Yes|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in the percentage of population in given age group for 2011-1996. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
Households in the treatment group also experienced a rainfall shock in the agricultural year previous to the baseline period. This shock was less pronounced in the control group. Finally, Table 1 reports the participation status with the NREGS at the time of the baseline data collection. As can be seen, 66% of the households in the treatment districts report having registered with the NREGS in 2007.

For the calculation of the dependent variable - a risk index of each households’ crop portfolio - survey data on input allocation to each crop is combined with District-level crop production statistics. The time series of crop production statistics are used to calculate the coefficient of variation of each crop’s yield. With this information, a risk index _Ri_ of each household’s crop portfolio is constructed given the reported allocation of inputs to each of the crops.<sup>21</sup> The risk index for household _i_ given input allocation _k_ to crop _m_ is defined as _Ri_ =<sup>�</sup> _rmkm/_<sup>�</sup> _kn_ , where _rm_ is the coefficient of variation of the yield of crop _m_ .<sup>22</sup> Note here, that _rm_ is only available for a subset of all crops _n_ (26 out of 42), such that _m ⊆ n_ . Still,<sup>�</sup> _km_ represents roughly 90% of the total allocation of inputs in the sample. To reduce potential bias, I drop all observations from the sample which have no crop in their portfolio for which risk information is available, e.g.<sup>�</sup> _km_ = 0 or _Ri_ = 0, in one or both of the survey rounds.<sup>23</sup> As can be seen in Table 1, the risk index at baseline is higher in the treatment group (0.36) than in the control group (0.26). The difference is statistically significant at the 1% level.

There are two main caveats associated with the choice of the dependent variable. First, risk in crop returns not only stems from yield fluctuations but also from the variability in prices. Also, for domestically traded crops it is quite likely that yield and prices are negatively correlated, such that prices are high in years with a bad harvest and low in years with a good harvest. This would reduce the variability in returns. If these crops are also the crops that display the highest variability in yields, I would be grossly misinterpreting

> 21Allocation of inputs refers to the share in total variable inputs such as seeds, fertilizer and pesticides that is allocated to each crop in a household’s portfolio. This is the only information collected in the survey that gives information about the relative importance of each crop in a household’s production.

> 22The distribution of the risk index as well as of the change in this variable between survey rounds is plotted in Figures D.2 and D.3 respectively in the appendix.

> 23Appendix B provides more information on how the variable is constructed. The robustness of my findings to different methods of aggregating the risk index is shown in Table E.2 in the appendix.

13
in the IE-LFS surveys. As expected, the sample of phone owners comprises households more likely to be urban, with higher expenditure levels and lower poverty rates (Table 5, panel b). To adjust for this sampling bias, the AWMS R3 relies on a reweighting process to adjust sampling differences between the AWMS and the original full IE-LFS 2019/20 surveys. Panels a and c show that, to a great extent, the reweighting process narrows sampling differences in the spatial distribution, asset ownership, and dwelling conditions of the AWMS R3 and the IE-LFS sample. Additionally, Panel b shows that the weighted AWMS R3 sample closely reflects the expenditure distribution of the full IE-LFS 2019/20 sample.

**Table 5: Sample composition AWMS rounds**

|**Panel a. Sample size and hous**|**ehold spatial distribu**|**tion of IE-LF**|**S and AWM**|**S R3 round**||
|---|---|---|---|---|---|
||**Full IE-LFS 201**|**9/20**|**Quarter 3**|**IE-LFS 2019/20**|**AWMS R3**|
|Fieldwork|October19-Septemb|er 2020|June-A|ugust 2022|April-June 2023|
|**Households**|Unweighted<br>W|eighted|Unweighte|d<br>Weighted|Unweighted<br>Weighted|
|Central|22.2|24.6|23|.1<br>28.2|27.1<br>26.2|
|East|11.7|9.2|11|.7<br>10.8|14.9<br>11.2|
|North|13.6|13.8|14|.6<br>15.6|15.4<br>16.1|
|North-East|11.8|13.9|12|.2<br>13.3|12.8<br>12.5|
|South|11.0|10.7|10|.2<br>8.9|7.7<br>5.7|
|South-West|13.3|11.7|11|.3<br>9.5|5.2<br>6.2|
|West|7.4|10.5|6|.6<br>6.9|7.1<br>11.1|
|West-Central|9.0|5.7|10|.4<br>6.8|9.7<br>11.0|
|**Panel b. Welfare differences b**|**etween original IE-LF**|**S sample an**|**d AWMS R**|**3 round**||
|**Source sample**||**Poor**|**Urban**|**Q1 **<br>**Q2 **|**Q3 **<br>**Q4 **<br>**Q5 **|
|Full sample||47.1|25.1|20.0<br>20.0|20.0<br>20.0<br>20.0|
|<br>With aphon|e|43.0|30.4|16.6<br>18.9|19.8<br>20.8<br>23.9|
|_Weighted_<br>Full sample|Q3|52.2|29.4|21.5<br>21.0|22.2<br>19.0<br>16.3|
|AWMS R3|participants *|46.2|24.9|17.9<br>21.2|20.3<br>21.1<br>19.5|
|**Panel c. Comparison of key ch**|**aracteristics at baseli**<br>|**ne: IELFS 20**<br>|**19/20 infor**<br>|**mation** <br>|<br>|
||**Full sample**<br>**(IE-LFS weights)**|**Phone**<br>**(IE-LFS w**|**owners**<br>**eights)**|**AWMS R3 pan**<br> **(IE-LFS weigh**|**el***<br>**ts)**<br>**AWMS R3 panel***<br>**(AWMS weights)**|
|**Household Characteristics**||||||
|Household Size|7.25|7.|39|7.59|7.52|
|**Asset Ownership**||||||
|Refrigerator|16.7%|22.|7%|25.9%|20.1%|
|TV|40.5%|49.|2%|52.2%|48.0%|
|Bicycle|12.4%|13.|7%|13.6%|12.3%|
|Motorcycle|21.3%|20.|7%|18.2%|20.7%|
|Car|9.4%|10.|8%|11.9%|10.7%|
|WashingMachine|13.0%|17.|7%|19.1%|15.4%|
|Vacuum Cleaner|7.1%|9.2|%|9.6%|7.7%|
|Small livestock|33.6%|31.|4%|30.5%|34.3%|
|Large livestock|44.2%|39.|4%|38.7%|42.8%|
|**Dwelling Characteristics**||||||
|Roof material: Concrete|22.5%|27.|7%|29.1%|25.1%|
|Wall material: Concrete|20.3%|25.|6%|27.2%|22.1%|
|No Toilet|10.5%|6.3|%|5.2%|7.8%|

_Source: World Bank estimations using IE-LFS households surveys and AMWS rounds. Note: *Includes households that participated in both the IE-LFS 2019 and the AWMS R3. Shows characteristics at baseline using the AWMS R3 panel weights._ 11