rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3**

# **Climate Regressions for the United States**

|Independent||Dependen|t Variable: Cr|opland Value|in 1997|
|---|---|---|---|---|---|
|Variables|Wea|ther Stati|on||Satellite|
||Temp|Climate|All|Temp|Climate All|
|Constant|-2520|3780|4780|8070|6530 5550|
||(2.36)|(3.47)|(5.04)|(11.22)|(9.29) (8.63)|
|April Temp|589.|757.|553.|579.|591. 393.|
||(9.52)|(13.03)|(10.43)|(10.29)|(10.45) (8.32)|
|July Temp|350.|-242.|-406.|-601<br>|-484. –476.|
||(3.70)|(2.44)|(4.75)|(8.79)|(7.42) (8.18)|
|October Temp|-186.|-628.|-414.|-166.|-261. –76.|
||(2.12)|(7.49)|(5.48)|(2.52)|(3.94) (1.40)|
|Apr Temp Squared|-29.9|-39.9|-25.0|-26.9|-30.4 -16.6|
||(13.82)|(18.66)|(12.66)|(11.28)|(12.74) (8.20)|
|Jul Temp Squared|-15.1|2.37|7.1|6.5|6.0 7.0|
||(7.61)|(1.15)|(4.02)|(4.31)|(4.03) (5.28)|
|Oct Temp Squared|23.6|35.8|18.8|17.7|23.3 7.6|
||(8.31)|(12.09)|(6.99)|(6.36)|(8.19) (3.21)|
|April Prec|...<br>|239.|177.|...<br>|6.6 -39.6|
|||(8.53)|(6.97)||(0.97) (1.65)|
|July Prec|...|-57.|-80.|...<br>|60.2 3.0|
|||(3.30)|(5.30)||(7.26) (0.09)|
|October Prec|...<br>|147.|48.|...<br>|-36.6 0.7|
|||(4.69)|(1.74)||(3.89) (0.02)|
|Apr Prec Squared|...<br>|-5.62|-3.10|...<br>|1.14 6.01|
|||(3.89)|(2.37)||(3.04) (4.50)|
|Jul Prec Squared|...|3.95|5.35|...<br>|-1.83 2.77|
|||(4.47)|(6.95)||(2.48) (0.83)|

46
32 the reader can easily translate them to the length of the period that he or she thinks is appropriate.

The compensated labor elasticity is a key parameter in the calculations of the deadweight loss of income tax. Browning (1987) uses the compensated labor elasticity of 0.2-0.4. Feldstein (1999) uses an elasticity of 1.04. Here we use a compensated elasticity of 1.

Income distribution matters for our calculations. The Congressional Budget Office provides data about income distribution in the US.<sup>5</sup> We use the income distribution for 2011 described in Table 1. The share of income of the top 1 percent is 14.6 percent while the share of income of the lowest quintile is only 5.3 percent. We use these data to create 100 individuals indexed 0.01, 0.02,...,1. The income of the highest earning individual (indexed 1) is 14.6. The income of each of the four individuals indexed 0.96-0.99 is 12.7/4=0.03175 and so on. In the absence of distortions the income of each individual is <sup>2</sup> . We use 14.6 as a proxy for the <sup>2</sup> of the individual indexed 1 and 0.03175 as a proxy for each of the individuals indexed 0.96-0.99 and so on. The last row in Table 2 is the imputed <sup>2</sup> per individual in each income bracket. Note the difference from Figure 6 that assumes a uniform distribution.

Table 2: The <u>distribution of income in the US (2011)</u>

|Percentile|1-20|21-40|41-60|61-80|81-90|91-95|96-99|100|
|---|---|---|---|---|---|---|---|---|
|Share|5.3|9.6|14.1|20.4|14.6|10.0|12.7|14.6|
|<sup>2</sup>|0.00265|0.0048|0.00705|0.0102|0.0146|0.02|0.03175|0.146|

We adopt the lower estimate of Philippon (2015) for the proportional cost and assume  0.015.

> When  is "too high" no one uses bonds and when  is "too low" no one uses

> money. We choose  subject to the constraint that when the quarterly nominal interest is

> 5 https://www.cbo.gov/publication/49440
countries in order to make comparisons, countries could presumably have the same true value of IOp but have different estimated values. This problem implies that lower bound estimates of IOp can have different levels of measurement error, rendering them incomparable.

Recently, Niehues and Peichl (2014) address the problem of partial observability of circumstances by using panel data for Germany and the United States. They use a fixed-effects regression to estimate the amount of inequality that comes from time-invariant circumstances and show that “the existing lower bound estimates substantially underestimate IOp [...]” In this paper I contribute with further evidence on this regard. However, I focus on exploring an outcome for which effort (arguably) plays no role: _height-for-age_ for children 0 to 2 years old. This outcome allows me to identify the size of measurement error coming from the empirical impossibility of including all circumstances when estimating IOp since I do not have to make any assumptions about the distribution of effort. By including a comprehensive set of observable circumstances in my analysis I find that measurement error is large and that it varies considerably across countries, implying that making the “traditional” cross-country comparisons using lower bounds estimates of IOp can be misleading.

The paper proceeds as follows. In the next Section I describe the analytical framework. In Section 3 I describe the data and present the results of my analysis. In section 4 I conclude.

# **2 Analytical framework**

Let us consider a society of _N_ individuals. Each individual is described by two classes of variables: circumstances ( _C_ ) and efforts ( _e_ ). Let _X_ = _{x_ 1 _,..., xi,..., xN} ∈_ R<sup>_N_</sup> +<sup>bethevectorofoutcomesinthe</sup> population with where _f_ : _C × e →_ R+. I consider, however, the special case where _x_ is completely independent of efforts and where circumstances are partially observable.

Let _C_ = _C_<sup>_o_�</sup> _C_<sup>_u_</sup> be the full set of circumstances, where the superscripts _o_ and _u_ stand for observable and unobservable. Traditionally, the measurement of IOp is based on a partition of the population into _K_ = _{_ 1 _,..., k}_ mutually exclusive types based on observable circumstances (ex-post approach to compensation), so that ∑ _K N j_ = _N_ with _j ∈ K_ . In this sense, a type is a group of individuals sharing the same observable circumstances.

Once the population is partitioned according to observable circumstances, we can rewrite the overall outcome as follows: 2
29 happened. Rather autonomization appears to have left the cost structure of Vietnamese hospitals unchanged.

Oddly, given these results and given the apparent positive effects of autonomization on patient volumes seen in Table 4, we see no significant effects – upwards or downwards – of autonomy on total hospital costs or on costs per case for any level of hospital.

Oddly too, despite the positive impacts we saw on inpatient days in provincial and district hospitals, and on outpatient visits in district hospitals, we see a significant impact on total user fee and out-of-pocket fee revenues only in the case of provincial hospitals. However, Table 5 shows that – for both central and provincial hospitals – autonomy increases user fee revenues _per case_ and out-of-pocket revenues _per case_ , after controlling for casemix; oddly, this is not evident for district hospitals.<sup>5</sup> The patterns of the individual-level results in Table 6 are not wholly consistent with the hospital-level results, but they do point to autonomization exerting some upward pressure on out-of-pocket payments. Hospital autonomization in a household‘s district appears to have led to higher out-of-pocket spending on hospital care: there is fairly strong evidence that this reflects higher utilization rates, and a suggestion that it may also reflect higher spending per contact.

# _Quality_

We find no evidence in Table 4 or Table 5 of autonomization leading to changes in hospital death rates or complications. Our results suggest a mixed picture, however, on the

> 5 In the case of the district hospitals, the impact on inpatient days is driven in part by an increase in length of stay; it may be that the extra length of stay has not translated into higher revenues. It may also be that that the additional outpatient visits have not translated into extra revenues. In both cases, though, the finding is a little strange. It may well be a data quality issue – even with the extensive data-cleaning that we did prior to the estimation, the quality of the revenue data may still be inadequate for the task. One would have to argue, of course, that the district hospital data are in worse shape than the central and provincial hospital data since we do see effects there, but this is not implausible.
# **The Rise of China and Labor Market Adjustments in Latin America**<sup>_∗_</sup>

Erhan Artu ̧c, Daniel Lederman and Diego Rojas

## JEL Codes: E24, F13, F16

Keywords: Informal and Formal Employment, China, Latin America, Labor Mobility

> _∗_ We would like to thank William Maloney, Pravin Krishna, Juan Carlos Hallak and Sergio Urzua for their insightful comments. We also like to thank comments from participants of the LACEA Labor Market Network’s 2<sup>_nd_</sup> meeting held in Colombia, the Authors’ Workshop for the World Bank’s LAC 2014 Flagship and participants of the CEMLA-Banco de Mexico Conference on“The Insertion of Asian Economies into International Trade: Challenges, Opportunities and Productivity Policies in Latin America & the Caribbean”. Artuç is an Economist with the Development Research Group, International Trade and Integration Unit (DECTI), Lederman is Deputy Chief Economist for Latin America and the Caribbean of the World Bank, and Rojas is a Consultant with the Office of the Chief Economist for Latin America and the Caribbean.
This finding implies the existence of a trade-off: while financial deepening plays a vital role in stimulating economic growth, it can also contribute to higher CO2 emissions per dollar of GDP. Importantly, we find evidence that some dimensions of a country’s institutional environment mitigate this trade-off. Specifically, more robust environmental regulation, stronger rule of law, and a financial system that is relatively more market- than bank-based have helped countries mitigate the unconditional adverse effect of financial deepening on CO2 emissions per dollar of GDP. We find no significant role of foreign bank ownership in managing the trade-off. Moreover, our results show that countries with an initially lower carbon intensity of production can mitigate the effect of financial deepening on CO2 emissions per dollar of GDP by improving their general institutional environment as proxied by the rule of law index. There is also somewhat more limited evidence that having a relatively more market-based financial system can help mitigate the potential trade-off. By contrast, countries with an initially higher carbon intensity of production could manage the trade-off from financial deepening by improving their environmental regulations. Our results hold up in robustness checks, including using an alternative measure of financial deepening, restricting the sample to episodes of credit booms, addressing the Nickell bias and cross-sectional dependence, and using alternative empirical specifications.

The remainder of the paper is organized as follows. Section 2 describes the data and presents some stylized facts. Section 3 outlines the empirical methodology, while section 4 discusses the results. Section 5 includes several robustness checks. Section 6 concludes the paper.

# **2. Data and stylized facts**

- **2.1. Data**

We use an unbalanced panel dataset of 125 advanced and emerging economies covering 19902019.<sup>1</sup> The frequency of the data is annual with an average time span of 20 years per country. The list of countries included in our sample is reported in Table A.1 in the Appendix. We aim to include all countries for which data is available so that we can provide comprehensive evidence for a large sample of countries. Such a large country sample also allows us to exploit its heterogeneity across institutional factors that may influence the relationship between financial deepening and CO2

> 1 As we include several lags of both the dependent and explanatory variables in our regressions, 1994 is the first year included in our regression analysis.

5
Table 2: Lagged Shift in Household Consumption Patterns

||HH Cons|umption|||Share of|HH Consum|ption on...||
|---|---|---|---|---|---|---|---|---|
||Naira|(log)|Fo|od|Non|-food|Edu.|& Health|
|_δlag_|-0.0272|-0.124|0.0865**|0.0342|-0.0401|0.000115|-0.0464*|-0.0343|
||(0.106)|(0.112)|(0.0409)|(0.0474)|(0.0253)|(0.0308)|(0.0275)|(0.0329)|
|_βlag,pv_||0.106||-0.0497**||0.0573***||-0.00761|
|||(0.0745)||(0.0233)||(0.0221)||(0.0151)|
|_δlag,pv_||0.603**||0.168***||-0.101*||-0.0673|
|||(0.307)||(0.0608)||(0.0534)||(0.0574)|
|Constant|12.56***|12.55***|0.651***|0.655***|0.269***|0.265***|0.0794***|0.0799***|
||(0.0235)|(0.0235)|(0.00533)|(0.00571)|(0.00523)|(0.00544)|(0.00449)|(0.00460)|
|Obs.|5,020|5,020|5,020|5,020|5,020|5,020|5,020|5,020|
|R<sup>2</sup>|0.597|0.598|0.370|0.372|0.387|0.389|0.169|0.169|
|_δpv −βpv_||0.497||0.218***||-0.158***||-0.0597|
|S.E.||0.324||0.0655||0.0592||0.0628|

_Notes_ : * p _<_ .1, ** p _<_ .05, *** p _<_ .01. Household-level data. S.E.s allow for clustering at the level of enumeration area (EA). All columns include a co-variate adjustment for the number of household members, as well as fixed effects for if a household was located within 10 km. of a population center with at least 20,000 inhabitants, the state-level administrative center, and a market. Additionally EA-level, state-level, month, and river sub-basin fixed effects are included, as is a dummy for whether the household was engaged in agriculture. Borno state is excluded.

31
similar in terms of their observable characteristics, but treatment households are slightly bigger despite having the same number of young children (0-3 years of age). In both groups, household heads are on average 37 years of age, and over 90 percent of them are men. About half of the household heads are engaged in agricultural labor, and fewer than 10 percent hold nonagricultural jobs. Asset ownership is similar across the two groups (land, cattle, tube well, fan, television, bicycle, number of rooms) except for house ownership (slightly higher for the control group), and one in 7 households (treatment and control) receive remittances. Treatment and control houses have the same probability of having young infants, but control houses have less children on average (0.74 as compared to 1.27 for treatment families), leading to slightly smaller households (4.5 members on average as compared to 5 for treatment families).

Since much of the impact of any project depends on how well the project is implemented, it is important to also assess these processes if possible. Since the _Shombhob_ project had a dual objective of setting up a local level cash transfer modality _and_ improving poor children’s nutrition and education outcomes, the survey instruments included many questions related to these project processes. Therefore before assessing the impact of the project on the above outcomes variables, we first look at whether the processes such as registration and payments were implemented properly. Following which we assess the impact on: (ii) household consumption variables; (ii) school attendance of primary-school-aged children; and (iii) nutrition outcomes for children aged 0 to 36 months. However we limit the analysis to the Jaldhaka sample only, and use the first control group, C1 (i.e. those applied but were not selected).

# **4.2 Process Assessment**

Results from the panel of _Shombhob_ participants suggest that the project was implemented reasonably well. To select beneficiary households from Jaldhaka, the PMT eligibility threshold used for the rural areas was 660. This represented a cut-off at the 19<sup>th</sup> percentile on the actual expenditure distribution. An application booth was set up in each union 21
ment errors in the impact estimate cannot be overemphasized. An alternate measure of the impact of outages can be obtained by estimating an econometric model that corrects for the endogeneity between outage intensity and firms’ revenue. Mensah (2016) estimates the impact of outages on firms’ revenue and productivity using the instrumental-variable approach with firm-level panel data from the Enterprise Surveys from several SSA countries between 2003 and 2015. To validate the self-reported impact estimate, we compare the tax-revenue simulation results from the self-reported estimates with the simulation results based on estimates from Mensah (2016). It must be emphasized that the data on South Africa as used in Mensah (2016) date back to 2003, which falls outside our preferred data period used in the computation of the self-reported impact estimates. Therefore, for ease of comparison and without loss of generality, we re-estimate the model in Mensah (2016) using the same data while excluding South Africa. Additional data on the willingness of informal firms to register and their reasons for not registering are obtained from the Firm Informality Surveys, which is a variant of the Enterprise Surveys data focusing solely on informal firms.

# **3.2.3 Supplementary Data Sources**

We complement the above data sets with data on country-level indicators. In the analysis of the effects of electricity access and reliability on tax compliance and the potential mechanisms, country-level indicators such as total population, GDP at purchasing power parity (PPP), share of urban population, population density, value added of agriculture and service sectors as a share of GDP, and natural resources rents (% of GDP) from the World Development Indicators database; the Mo Ibrahim Index of African Governance (IIAG) from the Mo Ibrahim Africa Foundation; the Polity IV Index on Democracy; and the ethno-linguistic fragmentation score (Fearon, 2003). The IIAG provides an annual assessment of the quality of governance in every African country. The index ranges from 0 to 100; the higher, the better. The ethno-linguistic fragmentation score provides a measurement of ethnic, linguistic, and religious fractionalization. It reflects the likelihood that two chosen at random will be from ethnic The Index on people different groups. Polity Democracy ranges from -10 (hereditary monarchy) to +10 (consolidated democracy). Additionally, we utilized georeferenced data on the electricity transmission network in Africa from the Africa Infrastructure Country Diagnostic database.

In the analysis of the tax-revenue implications of improved and reliable supply of electricity, we complement the impact estimates from the Enterprise Surveys data with data on corporate income tax (CIT) revenue as a percentage of GDP from the Worldwide Gov14
**Table 5: Export Ban on Maize Depresses Tanzanian Maize Prices**

|**Year**|**Direction of**<br>**Tanzania’s**<br>**Production**<br>**Deviation from**|**Regime 1**|**Regime 2**|**Additional Effect**<br>**of Export Ban**<br>**(Regime 2 -**<br>**Regime 1)**|
|---|---|---|---|---|
||**Trend**|**I**|**II**|**III**|
|1995|Ti|-23.03|-25.94|-2.90|
|1971|anzana +|-21.74|-23.10|-1.35|
|1980|Tanzania 0|0.72|0.51|-0.22|
|1982|Ti|28.54|28.74|0.20|
|1983|anzana -|43.79|43.72|-0.07|

Source: Authors’ simulations In 1980, Tanzania’s maize production was essentially on trend, such that domestic supplies were little changed by idiosyncratic events such as climate shocks. In Scenario Baseline 1980, the changes in Tanzanian maize markets are largely driven by factors in the importing country that affect aggregate import demand and price. For example, Uganda’s maize production is almost 30% below trend in this year, and there is a resulting high demand for maize imports generally. Almost all the increase in Ugandan imports of Tanzanian maize is driven by this higher demand. Let us contrast this with the case of East Asia, where maize production is also below-trend, and aggregate maize import demand also increases. However, the aggregate import price of maize declines while Tanzania’s maize price rises. This leads East Asia to source its maize imports from countries that have experienced positive supply shocks and thus have lower prices, e.g. China. Overall however, Tanzanian maize exports would have expanded by almost 7% in this Baseline 1980.

The case of Scenario Baseline 1982 is the converse of Scenarios 1995 and 1971. In this case, Tanzanian maize production was 13% below trend, pushing up domestic prices by more than 28%. The supply shocks in other countries were such that this supply price effect dominated the effects of the other import determinants in equation 1 and Tanzanian exports are predicted to fall across the board.

The case of Baseline 1983 is more complicated. Even though total Tanzanian maize exports in Scenario Baseline 1983 decrease by about 28%, Tanzania is able to increase its exports to some countries that experienced even more negative deviations in their production from trend. For example, Tanzanian exports of maize to the USA – increase by 5%. The USA accounts for 15% of Tanzania’s maize exports and experienced production 40% below-trend in the Scenario Baseline 1983. These results are driven by events in the USA. Since the USA is responsible for more than 41% of the global maize trade in the benchmark database, this has dramatic implications for the aggregate maize import price in all 17
**Table A.2: Land Market Restrictions, Female Labor Force participation and Wages: Robustness Checks for IV Results: Estimates from 2SLS**

|||**L**<br>Ad<br>|**abor Force Pa**<br>ditional Contro<br>|**rticipation**<br>ls<br>|||
|---|---|---|---|---|---|---|
||Farm Size|Populaion<br>Density|Pop. in 5 hrs<br>travel time|Non-farm<br>Share|Irrigation|LDO less<br>than 30%|
||1|2|3|4|5|6|
|**Area Under LDO**|**1.042*****|**0.971****|**1.061*****|**1.149*****|**0.847****|**1.732*****|
||**(2.639)**|**(2.433)**|**(2.743)**|**(2.802)**|**(2.236)**|**(3.245)**|
|**Instrument Strength**|||||||
|Angrist Pischke F|11.98|11.27|13.24|11.83|10.84|9.326|
|Stock-Yogo 10% bias|9.08|9.08|9.08|9.08|9.08|9.08|
|No. Of Observations|10850|10850|10850|10850|10850|9767|
||||**Log(Real**<br>|**Wage)**<br>|||
|**Area Under LDO**|**-1.556****|**-1.481****|**-1.511****|**-1.351****|**-1.388***|**-2.014****|
||**(-2.339)**|**(-2.192)**|**(-2.273)**|**(-1.960)**|**(-1.881)**|**(-2.343)**|
|**Instrument Strength**|||||||
|Angrist Pischke F|13.50|12.53|15.32|13.18|11.49|12.25|
|Stock-Yogo 10% bias|9.08|9.08|9.08|9.08|9.08|9.08|
|No. Of Observations|2,918|2,918|2,918|2,918|2,918|2,708|

(1) All regressions include full set of regressors as in columns (2) and (4) of Table 3.

(2) Robust t statistics in parentheses. Standard errors corrected for clustering at sub-district (DSD)

* significant at 10%; ** significant at 5%; *** significant at 1%

# **Table A.3: Female Labor Force Participation and Male** **<u>and Female Wages</u>**

||**Male**<br>**Log(real**<br>**Wage)**|**Female Labor**<br>**Participation**<br>Older<br>Women<br>(age=>40yr)|**Force**<br>Younger<br>Women<br>(25-40yr)|**Log( Real**<br>Older<br>Women<br>(age=>40yr)|**Wage)**<br>Younger<br>Women<br>(25-40yr)|
|---|---|---|---|---|---|
|||2SLS|2SLS|2SLS|2SLS|
||1|2|3|4|5|
|Proportion of Area Under LDO|**-0.978***|**1.327*****|**0.855****|**-1.656****|**-1.344***|
||**(-1.942)**|**(2.818)**|**(2.261)**|**(-2.109)**|**(-1.773)**|
|**Instrument Strength**||||||
|Kleibergen-Paap/Angrist Pischke F|10.95|12.59|11.24|11.83|13.14|
|Stock-Yogo 10% max. rel. IV bias|9.08|9.08|9.08|9.08|9.08|
|No. of observation|6,492|4,352|7,017|1,205|1,867|

(1) All regressions include full set of regressors as in column (3) of Table 4.

(2) Robust t statistics in parentheses. Standard errors corrected for clustering at sub-district (DSD)

* significant at 10%; ** significant at 5%; *** significant at 1%