rafmacalaba/datause-encoder-data huggingface dataset:
|**Pre-Registered Description**|**Description as Used in Analysis**|**Deviation from PAP**|
|---|---|---|
|**Secon**|**dary Analysis Outcomes and Cova**|**riates**|
|**Outcome:**Binary measure whether a<br>specific answer option (a clinical<br>task) was selected.|Same: Yes/no dummies for whether<br>each response is correct.|None|
|**Covariates, of Clinic:**|||
|_Level:_Binary:primaryor secondary|As described in AEA|None|
|_Tercile of the facility’s catchment_<br>_area:_Categorical: small, medium,<br>large. Calculated separately for the<br>primary and secondary facilities|Categories were calculated as equal<br>terciles.|• Terciles were not calculated<br>separately for primary and<br>secondary facilities.<br>• **Note:**This was not intentional<br>and can be revisited based on<br>further discussion and/or the<br>specific analysis being performed.|
|**Covariates, of Health Worker:**|||
|_Gender_: Binary|As described in AEA|None|
|_Age:_Continuous|As described in AEA|None|
|_Education Level:_Categorical:<br>primary or less, secondary or less,<br>,more than secondary|As described in AEA.<br>“More than secondary” included<br>bachelor, master, certificate,<br>diploma, higher national diploma,<br>MBBS (all but “secondary school<br>certificate”).|• This covariate is effectively<br>excluded because all but one<br>respondent was categorized as<br>“more than secondary,” with the<br>other being “secondary.”|
|_Qualification:_Categorical:<br>• _medical professional_[doctor,<br>nurse, nurse midwife]<br>• _lower-level medical professional_<br>[midwife, public health nurse,<br>community health officer,<br>community health extension<br>worker, junior community health<br>extension worker]<br>• _other_[e.g., pharmacist, lab<br>technician]|• _medical professional_(doctor or<br>medical officer, nurse, nurse<br>midwife,**midwife**]<br>• _lower-level medical professional_<br>[public health nurse, community<br>health officer, community health<br>extension worker, junior community<br>health extension worker]<br>• _other_[e.g., pharmacist, lab<br>technician]|• Midwife was assigned to<br>“medical professional.”<br>• Note: There were no respondents<br>in the “other” category.|
|_Received training in labor/delivery:_<br>Categorical: less than 1 year ago,<br>more than 1year ago,never|As described in AEA|None|
|_N/A (added during analysis, not_<br>_specified in AEA)_|Years since clinical qualification.<br>Continuous and ordinal by <1, 1–2,<br>2–3, 3–4, 5–9, 10–14, 15–19, 20–<br>29,30+years.|• Additional to what is specified in<br>the PAP. Added during analysis.|
|_N/A (added during analysis, not_<br>_specified in AEA)_|Tenure (years at current facility).<br>Continuous and ordinal by <1, 1–2,<br>2–3, 3–4, 5–9, 10–14, 15–19, 20–<br>29,30+years.|• Additional to what is specified in<br>the PAP. Added during analysis.|
# **Table 6. Survey Firms’ Aggregated Employment Changes by Export Status**

||Survey|Average Firm|Level Employme|nt Change|Total E|mployment Ch|ange|Average Per|cent Employm|ent Change|
|---|---|---|---|---|---|---|---|---|---|---|
|||2013-2016|2016-2020|2013-<br>2020|2013-2016|2016-2020|2013-2020|2013-2016|2016-2020|2013-2020|
|Always|7|-18.4|239.0|220.6|-129|1,673|1,544|-4.0|53.9|47.8|
|New|17|6.2|57.4|63.6|105|977|1,081|10.8|90.5|111.0|
|Never|141|-35.2|20.8|-14.4|-4,957|2,927|-2,026|-45.8|50.1|-18.7|
|Stopped|33|0.3|12.4|12.7|11|409|420|0.1|3.5|3.5|
|Total|198|-25.1|30.2|5.1|-4,971|5,985|1,019|-18.5|27.4|3.8|

Note: The above table reports the changes in employment levels by export status that we see in the 2013-2016-2020 panel data, unweighted. Export status is defined as follows: (i) “always” are firms which exported in all three years (2013, 2016, and 2020); (ii) “new” are firms that did not export in 2013 but were exporting by 2020 (starting in either 2016 or 2020); (iii) “stopped” are firms that switched from exporting to not; and (iv) “never” are firms that did not export in any of the three years.

30
|Table 7Dynamic effects of M|alawidrought onschoolin|g progression|
|---|---|---|
|Dependent: Change ingrade|from 2000 to 2004||
|Drought 2001<br>|-0.0172 -0.1328<br>|-1.0057 -1.1000<br>|
||(0.130) (0.980)|(4.440) (7.500)|
|* total asset<br>|0.00002<br>(3.460)||
|* max edu<br>|<br>|0.1107 0.1120<br>(6.160) (5.070)|
|* land<br>|<br>|0.0082 0.0109<br>|
|||(0.190) (0.240)|
|* household size<br>|<br>|0.0366 0.0373<br>(1.470) (2.420)|
|* livestock||-3.28E06 -2.90E06|
|||(1.020) (1.210)|
|* male<br>|-0.0540 -0.0683<br>|-0.0707 0.0119<br>|
||(0.300) (0.410)|(0.410) (0.060)|
|Drought 2001 * flood 2001<br>|<br>|0.5657<br>(0.980)|
|Flood 2001||-0.2861|
|||(1.510)|
|* max edu<br>|<br>|-0.0104<br>(0.320)|
|* land<br>|<br>|0.0342<br>(0.410)|
|* household size<br>|<br>|0.0152<br>(0.140)|
|* livestock<br>|<br>|-6.26E06<br>(1.030)|
|* male<br>|<br>|0.3250<br>(2.600)|
|Region FE|yes yes|yes yes|
|Number of observations|449 435|433 433|
|Number of regions|4 4|4 4|
|R squared (within)|0.0520 0.0849|0.1176 0.1287|

Numbers in parentheses are absolute t values, using robust standard errors with region clusters. Age and male dummies are included in all specifications.

29
> _N_ ( 0, _σ v_ 2 ) . All _v_ ' _s_ and _u_ ' _s_ are assumed to be independent of each other and of the

_it_ regressors. The _u s_ are non-negative random variables assumed to be independently distributed such that _u it_ is the truncation (at zero) of the normal distribution with mean _μ it_ and variance _σ u_ 2 . The mean _μ it_ is further specified as The parameter _λ PR_ measures the effect that private ownership has on the mean of the distribution of the inefficiency term prior to truncation. If _λ PR_ < 0 , then inefficiency is reduced because, on average, private firms use fewer employees than public firms to produce an equal amount of outputs.<sup>7</sup> Maximum likelihood (ML) estimates of the stochastic labor requirement frontier are reported in column (1) of Table 3.<sup>8</sup> To derive the likelihood function, we use the 2 2 2 parameterization proposed by Battese and Corra (1977): _γ_ = _σ u_ ( _σ ν_ + _σ u_ ) . The parameter _γ_ must lie between zero and one, where zero indicates that the deviations from the frontier are due entirely to noise—the OLS assumption.

**Table 3: Alternative specifications**

||Dependent vari|able: number of em|ployees, in logs|O&M, in logs|
|---|---|---|---|---|
||Stochastic|Between firms|Model in||
||frontier|variation|differences||
|Variable|(1)|(2)|(3)|(4)|

> 7 See Battese and Coelli (1995) for a discussion on these types of models.

> 8 We use the computer program FRONTIER 4.1 (Coelli, 1996) to estimate the stochastic frontier parameters.

10
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73

# TABLE 14: Maize: Relation of Sown to Harvested Area

||Private|Ejldo|Mixed|
|---|---|---|---|
|Maize: 1980-1981*||||
|(1) Sown area ('000 has)|2,933|4,813|721|
|(2) Harvested area ('000 has)|2,456|4,273|-<br>645|
|(3) Percent of (2) under irrigation|13.9|14.9|21.7|
|(4) (2)/(1) (X)|83.7|88.8|89.5|

* Refers to the sum of two crop cycles: Autumn-Winter (1980-81) and Spring-Summer (1981)

# SOURCE: Same as Table 4.
**Figure 6: Change in FLFP Rate Over Time Across Social Groups**

<!-- Start of picture text --> 5 0.9<br>0<br>‐5 ‐3.5 ‐3 ‐3.2 ‐1 ‐1.6‐2.3‐3.9<br>‐10 ‐6.4‐5.5 ‐6 ‐7 ‐7<br>‐9.2<br>‐15 ‐12 ‐13 ‐12.4<br>‐15.1 ‐15<br>‐20<br>‐18.6<br>ST SC OBC UC ST SC OBC UC<br>Rural Urban<br>1993‐94 and 2004‐05 2004‐05 and 2011‐12 1993‐94 and 2011‐12<br><!-- End of picture text --> Source: Authors’ own estimate based on NSSO data.

# **3.5 Household Income and Female Labor Force Participation**

A comparision across income deciles suggests that during the period between 1993-94 to 200405, there was a rise in the FLFP rate across almost all deciles, in both rural and urban areas. However, the drop is more for the lower deciles, and it is the large drop in the FLFP rate which occurred in the second period (2004-05 to 2011-12) that led to an overall decline in the total FLFP rate across the entire period.<sup>6</sup> The drop is more pronounced at the lower strata of the consumption decile, and it gradually declined with increases in per capita consumption during the second — period a trend that is not visible during the first period.

As can also be seen from Table 12, the change in the FLFP rate during 1993-94 to 2011-12 was brought about largely by a change in the FLFP rate in rural areas, rather than in urban areas. In all deciles, the contribution of the decline in the rural FLFP rate is larger than that of urban areas. Drawing from the above analysis, it is reasonable to state that the large drop in the FLFP rate during 1993-94 to 2011-12 was mainly due to the drop which occurred in rural areas during the

> 6 There was in fact a drop in FLFP across all deciles between 1993-94 and 1999-00. Since then, the FLFP rate rose between 1999-00 and 2004-05, and subsequently began to decline again after 2004-05. Hence, the decline in FLFP occurred from 1993-94 to 1999-2000, and then from 2004-05 to 2011-12. Since we are considering two periods—the first between 1993-94 and 2004-05, and the second between 2004-05 and 2011-12—the fluctuations in between are thus covered, and the overall change is reported.

22
terms of risk aversion would predict that relatively more risk averse individuals will be less likely to choose the highest variance lotteries. Second, constructing a measure of risk aversion that does not rely on functional form assumptions allows us to use data from all subjects, including those whose choices were not perfectly consistent with a specific utility representation.

We estimate probit regressions of indicators for operating one’s own business and having ever migrated seeking work on our index of risk aversion: the number of times (out of five) that a respondent chose the riskiest (highest expected value) lottery. Results are reported in Table 1. In all specifications, the number of times a respondent opted for the riskiest lottery is significantly associated with the likelihood of operating one’s own business or migrating for work. After controlling for gender, education level, age, the month in which the survey took place, and the survey enumerator who administered the lottery choice module, an additional risky choice is associated with a 0.5 percentage point (4.3 percent) increase in the likelihood of operating a business and a 0.2 percentage point (6.7 percent) increase in the likelihood of having ever migrated for work. Given the many factors underlying occupational choices, we interpret these robust associations as clear evidence that our lottery choice measure does predict outcomes associated with risk aversion in the cross-section.

# **4 Analysis**

## **4.1 Empirical Approach**

We exploit the fact that Kenya’s post-election crisis occurred in the middle of the KLPS2 data collection effort to estimate the impact of the violence on risk preferences. The KLPS2 survey was launched in August of 2007, and 1,180 respondents were surveyed in 2007 prior to the elections. The crisis led to a two-month suspension of survey activities, which resumed in March of 2008 and continued through the end of 2009. We employ two complementary identification strategies. First, we estimate the impact of the crisis in a straightforward linear framework, controlling for month-of-the-year fixed effects. In these specifications, we also include enumerator fixed effects and controls for sociodemographic characteristics such as respondent age, educational attainment, value lottery as a risky alternative.

13
there have been real success stories, a strong connection between taxation, responsiveness, accountability and state building is far from guaranteed.<sup>3</sup> Questions remain about whether current reform efforts have focused sufficiently on fostering broader development gains.<sup>4</sup> The limited focus on building fiscal contracts is evident in scant fiscal redistribution in many lowincome countries and correspondingly limited trust in tax systems. OECD data point toward far lower levels of fiscal redistribution among new members from the Global South.<sup>5</sup> Looking more deeply, while simulation estimates are inevitably imperfect, the Commitment to Equity Institute has sought to estimate total fiscal redistribution through taxes and direct transfers at the country level. The pattern is striking: while middle-income countries sometimes achieve significant reductions in extreme poverty, this is not the case for lower-income countries, where direct fiscal redistribution is limited by heavy reliance on indirect taxes and weak transfer systems (Figure 2). While the impact of taxes on poverty reduction likely looks more encouraging when the in-kind value of all public services funded by government revenues is considered, there remains ample evidence that service delivery targeting the poor is frequently ineffective.<sup>6</sup> Unsurprisingly, evidence of inequitable tax burdens and poor translation of revenue into services is reflected in limited trust in tax systems in much of the low-income world (Figure 3).

**Figure 2: Estimated Reductions in Extreme Poverty from Taxes and Transfers**

<!-- Start of picture text --> 20%<br>0%<br>-20%<br>-40%<br>-60%<br>-80%<br>-100%<br><!-- End of picture text --> _Notes_ : The poverty line is defined as $1.9 PPP/day. Positive values imply an _increase_ in extreme poverty after taxes and transfers.

_Source_ : Jellema, Lustig, and Papelbon (forthcoming).<sup>7</sup>

> 3 Prichard, W. (2015). _Taxation, responsiveness and accountability in developing countries: The dynamics of tax bargaining_ . Cambridge: Cambridge University Press.

> 4 Ibid; Prichard, W. (2016). What have we learned about taxation, state building, and accountability? _ICTD Summary Brief 4._ Brighton: International Centre for Tax and Development.; Prichard, W. (2010). Taxation and state building: Toward a governance focused tax reform agenda. _IDS Working Paper 341_ . Brighton: Institute of Development Studies.

> 5 OECD (2019). Income inequality (indicator). doi: 10.1787/459aa7f1-en (Accessed on 14 June 2019).

> 6 Hirvonen, K., Mascagni, G., & Keetie Roelen. (2018). Linking taxation and social protection: Evidence on redistribution and poverty reduction in Ethiopia. _International Social Security Review_ , _71_ (1), 3-24.

> 7 <mark>Lustig, N., Jellema, J., & Martinez Pabon, V. (Forthcoming).  Leaving No One Behind: Can Tax-funded Transfer Programs Provide Income Floors in Sub-Saharan Africa? in Kharas, H and McArthur, J. (Eds.).</mark> _<mark>Leave No One Behind</mark>_ <mark>. Washington: Brookings Institution Press.</mark>

3
and log capital. The residual from this regression for each plant is taken as its TFP. We then take the average of these residuals across plants for a district.

As our data are repeated cross-sections, rather than panels with unique plant identifiers, there are limits with respect to some of our analyses. Perhaps most notably, we do not have accurate measures of exiting plants. Our data do, however, allow us to measure and study new entrants. Plants are distinguished by whether or not they are less than four years old. We will use the term “young” plant or new entrant to describe the activity of plants that are less than four years old. We aggregate young plant activity at the district level, similar to metrics of total activity.

Our core sample for the organized sector contains 312 districts. This sample is roughly half of the total number of districts in India of 630, but it accounts for over 90% of plants, employment, and output in the manufacturing sector throughout the period of study. The reductions from the 630 baseline occur due to the following. First, the ASI surveys only record data for about 400 districts due to the lack of organized manufacturing (or its extremely limited presence) in many districts.<sup>1</sup> Second, we drop states that have a small share of organized manufacturing.<sup>2</sup> Last, we make an additional restriction for our regression sample that manufacturing activity in terms of plants, employment, and output in districts be observed at all points from 1994 to 2007.

The requirements with respect to continuous measurement of districts are motivated by a desire to have a consistent sample before and after the GQ upgrades. The requirements with respect to minimum share of states in organized manufacturing are motivated by a desire to have reasonably measured plant traits, especially with respect to labor productivity and plant TFP. With respect to the latter, we also exclude plants that have negative value added, which accounts for 6%-7% of employment. These restrictions are again not very significant in terms of economic activity, with our final sample retaining more than 90% of Indian manufacturing activity.

> 1 For instance, the ASI surveys the entire country except the states of Arunachal Pradesh, Mizoram, and Sikkim and Union Territory of Lakshadweep, so these states are naturally excluded.

> 2 These states are Andaman and Nicobar Islands, Dadra and Nagar Haveli, Daman and Diu, Jammu and Kashmir, Tripura, Manipur, Meghalaya, Nagaland and Assam. The average share of organized manufacturing from these states varies from 0.2% to 0.5% in terms of establishment counts, employment or output levels.

7
_Wgttnkit_ : the weight of the imports of Japan from country _i_ with the HS codes from25 to 27 at the year _t_ . The range of the code covers stones, cement plaster, ores, slag, mineral fuel, oil, and so on. These bulky goods are normally transported by tankers or tramps. This divided by _Wgt_ makes the ratio of bulky goods shipments in weight to be used to control the bulky goods shipments.

_PIndexit_ : the indexes representing the efficiency of the ports of the exporter country _i_ at the year _t_ . We use our port congestion index, the infrastructure index in _GCR_ , and the water transportation index in _WCY_ . In addition, the port efficiency measurements by Blonigen and Wilson is used in this regression of Japanese data to test this measurements estimated from the U.S. data.

_β1t_ : the time dummy variables to control the effects of time-varying factors throughout the countries, such as the fuel prices, exchange rates and overall technological progress.

_β 8t_ : the dummy variables for controlling the extraordinarily large trade costs estimated for the data in Hong Kong from 2003 to 2006.

_i_ : the importing country/region to Japan, including each country of ASEAN5, China, Korea, Hong Kong, Taiwan, Viet Nam, United States, Canada, Australia and New Zealand.

As indicated above, we control shipments via air; and those of bulky commodities of HS#25-27, to single out the effects of the improvement of ocean container port capacities. The interaction variable, distance in log times value per weight in log, is included in the regressors to control the special geographical feature in Japanese imports, namely, the remote countries across the Pacific Ocean, such as Australia and Canada, are rich in natural resources and materials, and tend to export the bulky goods via cheaper transportation<sup>25</sup> .

# _Results of the Trade Cost Regressions of Japan_

Table 6 below summarizes the results of regression (2). The four columns in the table correspond to the uses of each indicator on ports. The estimation periods in some cases differ from the

> 25 We have also tested the containerization ratio both in values and weights, but they do not have significant coefficients in most of the specifications.

26
(at the 10 percent level or less) contribution of the remaining firm-level variables to the productivity gap via the endowment effect.

# _Structural effect_

The structural effect refers to the role of the returns to production factors or attributes of firms that lead to the widening or narrowing of the productivity gap. Focusing on the variables that contribute significantly (at the 10 percent level or less) to the productivity gap through the structural effect, the biggest contribution comes from differences between the UMICs and the HICs in returns to market size followed by secondary education attainment, law and order, firm size and exports. That is, differences between the UMICs and the HICs in returns to market size serve to increase the productivity gap by 670 percent (significant at the 1 percent level); differences in returns to secondary education attainment and law and order increase the productivity gap by 590 percent and 139 percent (significant at the 1 percent level), respectively. These are extremely large effects, but they are countered by other unidentified structural effects that serve to reduce the productivity gap. This can be seen from the fact that the coefficient of the constant term in the column for structural effects (column 2, table 7) is negative, large, and significant at the 1 percent level. Thus, the overall contribution of the structural effects to the productivity gap is limited to less than 50 percent.

For the firm-level variables, the difference in the return to firms’ size between the UMICs and the HICs reduces the productivity gap by 32.5 percent (significant at the 10 percent level), implying that the diminishing returns to labor is weaker in the UMICs than the HICs. On the other hand, the difference in the return to exports increases the labor productivity gap by 5.9 percent 24