rafmacalaba/datause-encoder-data huggingface dataset:
22

# References

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Alonso-Borrego, C. and M. Arellano (1996): “Symmetrically Normalised Instrumental Variable Estimation Using Panel Data,” CEMFI Working Paper No. 9612, September.

Altonji, J. and L. Segal (1994): “Small Sample Bias in GMM Estimation of Covariance Structures,” NBER Technical Working Paper No. 156.

Anderson, T.W. and Hsiao, C. (1982): “Formulation and Estimation of Dynamic Models using Panel Data,” Journal ofEconometrics 18: 47-82.

Andrés, J. and I. Hernando (1997): “Does Inflation harm Economic Growth? Evidence from the OECD”, NBER Working Paper No. 6062, June.

Arellano, M. and Bond, S. (1991): “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies 58: 277-297.

Arellano, M. and Bover O. (1995): “Another Look at the Instrumental Variable Estimation of Error-Component Models, ” Journal ofEconometrics 68: 29-51.

Bailliu, J. and H. Reisen (1998): “Do Funded Pensions contribute to Higher Savings? A Cross-Country Analysis,” OECD Development Centre manuscript, Paris.

Baltagi, B. (1995): Econometric Analysis ofPanel Data, New York: Wiley.

|

Baltagi, B. and Griffin, J. (1997): “Pooled Estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline,” Journal ofEconometrics 77: 303-327.

, Barro, R. (1974): “Are Government Bonds Net Wealth?”, Journal ofPolitical Economy 82: 1095-1117.

Blundell, R. and S. Bond (1997): “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” University College London Discussion Paper 97-07.

Caselli, F., G. Esquivel and F. Lefort (1996): “Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics,” Journal ofEconomic Growth 1: 363-389.

Chamberlain, G. (1984): “Panel Data,” in: Handbook of Econometrics, Vol. 2, Eds: Z. Griliches and M.D. Intriligator, Elsevier, Amsterdam, 1984, pp. 1247-1313.
**Table 3.4. Balance of payments in 2012 and for base simulation by period (Percent of nominal GDP)**

|||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|---|
|**Outflows**|**Crop imports**|0.5|0.5|0.5|0.5|0.5|0.5|
||**Food imports**|9.0|7.9|9.1|8.0|8.6|8.4|
||**Petroleum imports**|15.1|13.4|15.2|13.8|14.5|14.3|
||**Other industry imports**|43.5|29.3|28.3|35.0|27.9|29.9|
||**Non-competive imports**|20.0|5.2|3.1|9.4|3.9|5.4|
||**Service imports**|33.8|33.3|34.8|33.2|34.1|33.9|
||**Factor income mining**|1.9|2.4|5.1|4.9|4.7|4.7|
||**Other factor incomes**|6.7|5.4|5.5|5.9|5.4|5.5|
||**Interest**|0.1|0.2|0.3|0.1|0.2|0.2|
||**Total**|130.7|97.5|101.8|110.8|99.7|102.8|
|**Inflows**|**Trees exports**|8.4|6.2|8.3|6.4|7.4|7.1|
||**Mining exports**|7.7|36.9|23.6|28.4|28.7|28.6|
||**Other industry exports**|1.0|0.6|1.0|0.7|0.8|0.8|
||**Service exports**|20.8|21.8|39.7|19.4|33.0|29.2|
||**Private transfers (net)**|13.8|11.8|14.2|12.2|12.8|12.7|
||**Government transfers**|21.5|14.4|9.1|16.5|11.1|12.6|
||**UNMIL transfers**|25.4|0.0|0.0|7.2|0.0|2.0|
||**Government borrowing**|0.8|2.1|0.9|2.2|1.2|1.5|
||**FDI -- mining**|30.2|1.9|1.5|16.5|1.7|5.8|
||**FDI -- other sectors**|1.3|1.7|3.4|1.2|2.9|2.5|
||**Total**|130.7|97.5|101.8|110.8|99.7|102.8|

# **Note:**

**Columns show values for single years or averages for multiple years (as indicated).**

21
To be aware of our own minds’ propensities, it is at times useful to take cognizance of the very different beliefs found in other cultures, which those cultures consider perfectly reasonable. The Gurung tribesmen of Nepal spend a lot of time climbing dangerously high trees to gather honey. When the French photographer, Eric Valli, who worked extensively among the Gurung, asked them if they do not ever fall down from their high perch, they replied: “Yes, you fall when your life is over.” ( **National Geographic** , vol. 193, no. 6, June 1998, p.92) The above argument warns us that these “conclusions,” about causal links and policy making can be wrong. By the use of examples and a simple argument, I now show they are wrong.

Suppose researchers come to a town and do a randomized control trial on the town population to check if the injection of a green chemical improves memory and has adverse side effects. And suppose it is found that it has no side effects and improves memory greatly in 95% of cases. If the study is properly done and the random draw is truly random, it is likely to be treated as an important finding and will, in all likelihood, get published in a major scientific journal.

Now consider a particular woman called Eve who lives in this town and is keen to enhance her memory. Can she, on the basis of this scientific study, deduce that there is 0.95 probability that her memory will improve greatly if she takes this injection? The answer is no, because she is not a random draw of an individual from this town. All we do know from the law of large numbers is that for every randomly drawn person from this population the probability that the injection will enhance memory is 0.95. But this would not be true for a specially chosen person in the same way that this would not be true of someone chosen from another town or another time.

To see this more clearly, permit me to alter the scenario in a statisticallyneutral way. Suppose that what I called the town in the above example is actually 7
34

# IABL& 4

# THE COMPOSITION EFFECT: RESULTS OF F-TESTS

|Equation|F-stat|istics||I|nterpr|etation|
|---|---|---|---|---|---|---|
|J-1|F(2,5)|-<br>0.1501|do|not|reject|Ho0:21j031°Ob|
||F(3,5)|-<br>1.6282|do|not|reject|HO:al-021-03l_Ob|
|J-2|F(2,5)|-<br>0.9281|do|not|reject|Ho-<br>0<br>22<sup>-0</sup>32 <sup>_Ob</sup>|
||F(3,5)|- 4.1056|do|not|reject|HO:a2-022-032<sup>0</sup>|
|j-3|F(1,6)|- 0.2530|do|not|reject|Ho:23<sup>_Ob</sup>|
||F(2,6)|- 36.584|||reject|Ho0a3-023-0a|
|j-4|F(2,5)|0.9631|do|not|reject|H0:o24_P34 0ob|
||F(3,5)|-<br>16.485|||reject|Ho<br>0 <sup>: </sup>4 <sup>-0</sup>24<sup>-0</sup>34 <sup>_O</sup>|
|j-5|F(3,4)|- 3.8578|do|not|reject|Ho<br>0<br>23 <sup>-.-</sup><br>045-Ob|
||F(4,4)|- 20.377|||reject|Ho<br>0 <sup>: </sup>5<sup>-0</sup>25<sup>-</sup><br>45|
|j-6|F(2,5)|- 0.5861|do|not|reject|Ho<br>0 26<sup>-0</sup>36 <sup>Ob</sup>|
||F(3,5)|-<br>9.7241|||reject|Ho:(6-026-036_O|
|j-7|F(2,5)|-<br>1.8174|do|not|reject|Hx:O2 7-#3 7 _ob|
||F(3,5)|-<br>101.53|||reject|H9 a7-027-f37-°|
|j-8|F(4,3)|-<br>2.9018|do|not|reject|H0:028-<br>5<br>.<br>8-0b|
||F(5,3)|-<br>5.8012|do|not|reject|H0:a8-028- .. _p58|
|j-9|F(2,5)|- 1.4450|do|not|reject|Ho<br>0<br>29<sup>-0</sup>39 <sup>_Ob</sup>|
||F(3,5)|-<br>5.0231|do|not|reject|Ho:a9-029-A3<br>9_b|
|j-10|F(1,6)|- 3.1932|do|not|reject|Ho:02<sup>lO_,</sup><br>b|
||F(2,6)|-<br>943.15|||reject|Ho:*10 02,10 -°a|

a At the 1 per cent level. b At the 5 per cent level.
5 gender gaps in land productivity, may support the same conclusion. Additionally, there may be many other reasons for fostering female agricultural productivity, beyond boosting agricultural output, such as female empowerment. The key objective here is to put the policy dialogue on solid empirical footing.

The paper proceeds by describing the data in more detail and discussing the key methodological considerations in section 2. The empirical findings regarding the female labor share in crop production in Africa, their robustness, and the key correlates are presented in section 3. Section 4 concludes.

# **2 DATA AND METHODS**

# **_Understanding the information base_**

Nationally-representative time use surveys or labor force surveys that depict the relative labor contributions of men and women in agriculture within an appropriate reference period remain largely lacking (FAO, 2011). As such, the nationally-representative household surveys conducted under the LSMS-ISA initiative present a rather unique opportunity to study and compare the female labor share in agriculture across diverse settings. These data form the information base of the paper. In particular, the analysis uses the data from Ethiopia Socioeconomic Survey 2011/12, Malawi Third Integrated Household Survey 2010/11, Niger <mark>l'Enquête Nationale sur les Conditions de Vie des Ménages et l'Agriculture 2011, N</mark> igeria General Household Survey – Panel 2012/13, Tanzania National Panel Survey 2010/11, and Uganda National Panel Survey 2010/11.<sup>8</sup>

> 8 All survey rounds, with the exception of Ethiopia Socioeconomic Survey (ESS) 2011/12, are representative at least at the national, rural and urban levels. The ESS 2011/12 round provides representative statistics for rural areas and small towns, which, based on population estimates from the 2007 Population Census, are defined as towns with populations of less than 10,000. See Appendix A for basic information regarding each survey used in the analysis.
Table 3: Descriptive statistics - budget execution

||CE|MG|PB|PE|PR|RS|SP|Total|
|---|---|---|---|---|---|---|---|---|
|**_Commitments_**|||||||||
|Observations|7,634,745|39,015,657|20,323,928|8,338,666|35,792,154|52,368,944|90,882,992|254,357,086|
|Distinct commitments|7,634,743|39,015,657|20,319,861|-|35,792,154|52,332,427|89,886,641|244,981,483|
|Related to procurement (%)|26.7|30.3|19.1|19.9|29.3|26.6|31.7|28.5|
|Greater than zero (%)|97.8|96.5|98.7|95.9|96.9|96.4|96.6|96.8|
|Has verifcation information (%)|96.0|95.0|63.0|-|98.0|97.0|97.0|94.0|
|Has payment information (%)|82.0|89.0|95.0|-|97.0|96.0|92.0|93.0|
|**_Verifcations_**|||||||||
|Observations|15,189,831|63,757,882|13,787,845|15,229,631|40,457,210|65,789,353|87,894,462|302,106,214|
|Distinct verifcations|-|63,753,322|-|-|40,457,210|34,941,675|81,410,584|220,562,791|
|**_Payments_**|||||||||
|Observations payment|13,930,424|64,127,137|22,113,067|21,449,922|52,545,469|74,023,098|83,436,799|331,625,916|
|Distinct payments|13,243,955|64,127,137|22,109,079|-|52,545,469|72,428,292|69,293,735|293,747,667|
|Total amount of payments (billion BRL)|132.2|514.9|155.4|196.5|407.4|501.7|1691.6|3,599.7|
|Number of distinct sellers|548,503|1,710,005|1,326,612|-|758,735|1,541,115|3,141,460|9,026,430|
|Number of distinct municipalities|182|853|223|184|399|497|644|2,798|

**Notes** : This table presents the descriptive statistics from the budget execution dataset. The variables “Distinct commitments”, “Distinct verifications” and “Distinct payments” are simply the count of the identifiers that uniquely identify each table - _empenho_ , _liquidacao_ , _pagamento_ . In the states of CE, PB and PE we can not count the number of distinct verifications because we were unable to build a unique identifier for this table. Likewise for distinct commitments and distinct payments in the case of PE. The variables “Has verification information” and “Has payment information” refer to the percentage of commitment identifiers with any verification or payment information, respectively, in the current year or later. This means that they can be followed throughout the execution process. The state of PB has a low percentage of commitments with verification information due to different temporal coverage of those two tables - while the first starts on 2003, the later starts on 2008. The commitments related to procurement restricts the data to three categories: consumption material (code 30), material for free distribution (code 32) and, equipment and permanent material (code 52). Also, the number of distinct sellers is missing for PE because we don’t have information about suppliers in the payment table. The variable “Total amount of payments” is in 2021 prices.

33
Figure 2 <!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>Others<br>60.0<br>Avantel<br>50.0<br>Alestra<br>40.0<br>30.0 Telmex<br>20.0<br>10.0<br>-<br>1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> The Panel mostly agreed with the U.S. claims, but found that Mexico had not committed to allow international simple resale and thus was not in violation of its obligations on that count. The final report of 250 pages explains in detail how the Panel reached these conclusions (WTO 2004). For illustration, the following are some of the arguments, related primarily to the Reference Paper, cast in plain language.

_Definition of cross-border services_ . A key question related to compliance with Sections 2.1 and 2.2 of the Reference Paper was whether the services at issue were supplied crossborder, as the U.S. claimed, or, as Mexico argued, were merely services provided by U.S. firms up to the Mexican border from where they were provided by Mexican firms. Article I:2 of the GATS defines four modes of trade in services, of which the first mode, usually referred to as cross-border, comprises “...the supply of a service...from the territory of one Member into the territory of any other Member...”. The distinction mattered because Mexico had committed to cross-border market access for the provision of the services at issue.<sup>12</sup> The Panel concluded that a telephone call originated in the U.S. and terminated in Mexico was indeed a cross-border service irrespective of whether the U.S. firm had its own facilities in Mexico or made arrangements with Mexican firms to carry the call from the border to its final destination. Summarizing part of the argument, the Panel explained that the supply of “...telecommunications services normally involve or require the linking with another operator to complete the service, and the operation, or presence in some way, of the supplier on both ends of the service cannot therefore be a necessary element of the definition of cross-border supply.”<sup>13</sup> “More generally, a supplier of services under the GATS is no less of a supplier solely because elements of the service are subcontracted to another firm, or are carried out with assets owned by another firm. What counts is the service that the supplier offers and has agreed to supply to a customer.”<sup>14</sup> Had the panel agreed with Mexico's position, there would have been no grounds to consider the U.S. claims and, as a consequence, most cross border traffic flows would have been considered to fall outside the ambit of the GATS.

7
- 44 -

2.71 Policies in many countries concerning cereals and livestock are in need of review and modification. Countries with surplus rural labor, for example, could investigate the feasibility of labor-intensive livestock production to meet equity objectives of increased employment as well as provide greater supplies of livestock products to meet market demand from higher-income consumers. Historically, most governments created disincentives toward domestic production of feed grains and erected barriers against imports. If gaps between the projected 42mand and supply of cereals used for livestock feed cannot be met by imports, however, market forces could lead to higher prices for meat and feed grains. This would adversely affect food consumption of the poor if grains such as sorghum and corn play a significant role in their diets. Keeping wheat and rice prices low will not help these low-income consumers very much. Consequently, a price policy that protected producers of feed grains or restricted imports while it transmitted low international prices of more preferred grains into the domestic economy will discriminate against the poor in two ways: it will raise the cost of their basic foodstuff; and it will limit the growth of income opportunities from a more dynamic livestock sector. Only surplus producers of feed grains would benefit. Although some of the rural poor may be in this category, with respect to grain production most are net deficit households.

# Tree Crops and EzDort-Oriented Crops 1

2.72 The export-oriented crop subsector is second in size to staple cereal production in most Asian developing countries. The export of coconut, rubber, palm oil, cotton, jute, coffee, tea, sugar, and other tropical products provides a major share of foreign exchange earnings. Many of these crops are processed or used as raw materials by industry and are therefore important for growth in both agriculture and industry. 11/ This section is drawn extensively from Vijay S. Vyas and William E. James, "Asian Agriculture' in S. Ichimura, Editor, Development Strategies and Productivity Issues in Asia (Tokyo. Asian Productivity Organization, 1988).
22 Preqin. (2012). _2012 Preqin Global Private Equity Report._

- Private Sector & Development. (2011a). _Technical Assistance: A Development Serving the Private Sector_ . July.

(2011b). _Achieving Impact Through Technical Assistance_ . July.

- Shell Foundation and Said Business School, Oxford University. (2011). _Growth Finance in Sub-Saharan Africa: Routes to Scale and Sustainability_ . August

Small Enterprise Assistance Funds. (2011). _2011 Development Impact Report_ . March.

- Tanzania Daily News (Dar es Salaam). (2012). _SMEs Role on Economy Gets Investment Agency’s Nod_ . June.

- TechnoServe. (2011). _Technical Assistance Facility Supporting the African Agriculture Fund_ . Washington, DC, October.

- Uganda Bureau of Statistics. (2011). _Report on the Census of Business Establishments, 2010/11._

- UNECA. (2012a). _Assessing Regional Integration in Africa V: Towards an African Continental Free Trade Area_ .

- (2012b). _Tracking Progress on Macroeconomic and Social Developments in the Eastern Africa Region_ . USAID. 2006. _The Enterprise Funds in Europe and Eurasia: A USAID/USG Success Story_ . Washington, DC.

- ( 2007). _The Enterprise Funds, Exchange of Experiences_ . Washington, DC.

- USAID and DFID Department for International Development. (2007). _Evaluation of Africap Microfinance Fund._ Washington, DC, September.

- World Resources Institute. (2009). _On the Frontiers of Finance: Scaling Up Investment in Sustainable Small and Medium Enterprises in Developing Countries_ .

- World Bank. (2010). _Impact Evaluation of SME Programs in Latin America and Caribbean_ . Washington, DC.

- (2012a). _Global Economic Prospects: Sub-Saharan Africa._ Washington, DC.

- (2012b). _Tanzania Economic Update_ . Washington, DC.

- International Finance Corporation. (2012). _Doing Business in the East African Community_ . Washington, DC.
**Figure 2 Evolution of central government expenditure (share of GDP at constant 2000 prices)**

<!-- Start of picture text --> Transport Health<br>4.000 5.0000<br>3.500 4.5000<br>4.0000<br>3.000<br>3.5000 High income<br>2.500 High income 3.0000<br>2.000 Low income 2.5000 Low Income<br>Lower middle income 2.0000 Lower middle income<br>1.500 Upper middle income 1.5000 Upper middle income<br>1.000 1.0000<br>0.500 0.5000<br>0.0000<br>0.000<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>time time<br>Energy and Fuel Education<br>2.5000 6.0000<br>5.0000<br>2.0000<br>High income 4.0000<br>1.5000 Low income High incomelow income<br>Lower middle income 3.0000 Low er middle income<br>1.0000 Upper middle income 2.0000 Upper middle income<br>0.5000<br>1.0000<br>0.0000 0.0000<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>time time<br>e a n<br>m ean m<br>m ean m ean<br><!-- End of picture text --> It is clearly difficult to offer much more than a basic description of trends across categories. A much better analysis would consider all the major changes that have taken place in the organization of government in each country but this data is not available although we know it is relevant. Indeed, **c** lear potential explanations for any changes observed over the past 15 years are the increased involvement of the private sector and of subnational governments in all of these sectors. Among national policies that might explain the changes are the deregulation or regulation of the sector and increased (or changed) demand for subsidies directed at the poor. However, very little data on these two dimensions has been collected in a comparable way across sectors.

Supranational changes, too, would seem to be relevant in this regard; shifting priorities among bilateral and multilateral donors would probably contribute as much as shifts in national policies—some would argue that there is a strong correlation between the national and international changes. The supranational changes are particularly important for the poorest small countries, because such a large share of many of their investment programs is financed by donor loans and grants.

The main point to be made here is that any assessment of the performance of governments based on international datasets is going to be limited by the lack of documentation of these sorts of institutional changes. Measuring reforms is as important as 10
# **Table 4 Trade-off between the** **<u>bribe tax and the time tax</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent Variable: Log Output|OLS|IV|OLS|IV|
|Bribe tax|0.019|-0.509|0.021|-1.151|
||[0.075]|[0.261]*|[0.099]|[0.859]|
|Time tax|0.001|-0.008|0.001|-0.008|
||[0.002]|[0.008]|[0.002]|[0.008]|
|**Time tax * Bribe tax**|||**-0.000**<br>|**0.050**<br>|
||||**[0.004]**|**[0.062]**|
|L|0.311|0.314|0.311|0.306|
||[0.046]***|[0.050]***|[0.046]***|[0.049]***|
|M|0.418|0.422|0.418|0.421|
||[0.031]***|[0.032]***|[0.031]***|[0.032]***|
|K<sup>Equipment</sup>|0.074|0.076|0.074|0.084|
||[0.017]***|[0.017]***|[0.017]***|[0.021]***|
|K<sup>Land</sup>|0.011|0.002|0.011|0.003|
||[0.013]|[0.014]|[0.013]|[0.015]|
|Exporter|0.206|0.226|0.206|0.237|
||[0.054]***|[0.055]***|[0.053]***|[0.062]***|
|Age|-0.000|-0.000|-0.000|-0.000|
||[0.001]|[0.001]|[0.001]|[0.001]|
|SizeM|-0.455|-0.432|-0.455|-0.440|
||[0.109]***|[0.121]***|[0.109]***|[0.123]***|
|SizeL|-0.257|-0.235|-0.257|-0.230|
||[0.064]***|[0.071]***|[0.065]***|[0.076]***|
|Foreign Owned|0.181|0.203|0.181|0.166|
||[0.073]**|[0.078]***|[0.073]**|[0.093]*|
|Innovator|0.072|0.053|0.072|0.056|
||[0.056]|[0.055]|[0.056]|[0.058]|
|Competition|-0.016|0.000|-0.016|-0.004|
||[0.015]|[0.018]|[0.015]|[0.018]|
|Courts|-0.067|0.026|-0.067|-0.001|
||[0.055]|[0.073]|[0.055]|[0.087]|
|Political Stability|0.036|0.047|0.037|0.021|
||[0.049]|[0.049]|[0.049]|[0.059]|
|Constant|YES|YES|YES|YES|
|Country Dummies|YES|YES|YES|YES|
|Sector Dummies|YES|YES|YES|YES|
|R squared|0.87|0.80|0.87|0.78|
|Wu-Hausman [p-value]||0.06||0.10|
|Hansen J [p-value]||0.78||0.90|
|F-Stat [time tax]||11.14||11.14|
|F-Stat [bribe tax]||29.25||29.25|
|F-Stat [time tax * bribe tax]||||6.83|
|Observations<br>_Note_<br>_: Standard errors clustered by c_<br>_5%; *** significant at 1%._|1519<br>_ountry-industry in_|1490<br>_brackets. * signif_|1519<br>_icant at 10%; **_|1490<br>_significant at_|

_<u>Note: Instruments used in column (2) are the same as in Table 4. In column (4), we include the interaction</u> of industry level bribe tax and industry level time tax as additional instrument in order to be able to test for overidentification restrictions using the Hansen J test._ 26
Table A6: Impact of the decree on decision phase (common linear trends)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Duration<br>of decision<br>stage (in<br>days)|Hazard<br>ratio -<br>fnishing<br>decision<br>stage|Likelihood<br>of decision<br>comple-<br>tion in 1<br>month<br>|Number<br>of decision<br>stage<br>hearings|Decision<br>stage<br>likelihood<br>of being<br>heard|
|Entered after decree|5.762|1.150|-0.017|-0.070|0.047|
|application (_β_)|(8.727)|(0.101)|(0.055)|(0.372)|(0.038)|
|Trend|0.835***|0.985***|-0.008***|0.023***|-0.006***|
||(0.198)|(0.002)|(0.001)|(0.008)|(0.001)|
|Interaction|-2.733***|1.003|0.006**|-0.056**|0.005*|
||(0.546)|(0.006)|(0.003)|(0.025)|(0.003)|
|P-value 6pWCBoot|0.749||0.871|0.854|0.353|
|Chamber FEs|Yes|Yes|Yes|Yes|Yes|
|Calendar month FEs|Yes|Yes|Yes|Yes|Yes|
|Without adj. period|Yes|Yes|Yes|Yes|Yes|
|Pre-mean|63.119||0.499|2.599|0.774|
|Pre-sd|82.701||0.500|3.399|0.246|
|R-Squared|0.066||0.153|0.029|0.325|
|Observations|3608|3844|3844|3844|3844|

<u>Notes:</u> Estimates of case-level impacts of the reform on decision-stage proceedings. See Notes on Table 3. 3844 observations for decision stage outcomes, except col 1 (censoring).

60