rafmacalaba/datause-encoder-data huggingface dataset:
in any of the three treatments received the information part of the intervention at the time of receipt of the baseline survey. For the information treatment effect, the ITT is therefore equal to the treatment effect on the treated. For the Senegal and Vocational Training treatments, the parameters 2 and 3 measure the impact of receiving the information video treatment and then receiving the offer of assistance to travel to Senegal or the offer of vocational training respectively. We do not ββ ββ estimate the TOT for these two treatments, since those who do not take the bus to Senegal or takeup the vocational training still have received the information part of their treatments, which could affect outcomes, and the offer of the migration alternatives could have also caused them to consider other alternatives to backway migration. This precludes the use of assignment to treatment as an instrument for take-up of these parts of the treatment.

Note that the specification in equation (3) does not control for the baseline value of the outcome. While an Ancova specification typically can boost power, it is not possible in our setting since our key outcome variables are ones where all individuals have the same baseline value (i.e. they have not migrated). McKenzie (2019) discusses this situation, and we follow the recommendation there to boost power by ex-ante stratifying on geographic region and village-level migration experience and intentions. Then to further boost power, we use the post-double-selection lasso approach of Belloni et al. (2014), using 40 pre-specified control variables as inputs.<sup>21</sup> This approach potentially chooses different control variables for each different outcome, selecting to control for the set of variables that either help strongly predict the outcome (thereby replacing the typical lagged dependent variable in Ancova) or that strongly predict treatment (which occurs if there is baseline imbalance arising from chance or attrition).

We use three approaches to address different aspects of multiple hypothesis testing. First, we prespecified our primary outcomes, broken up into domains, and aggregate variables into prespecified index measures, particularly in the well-being domain. Single-estimate p-values from this approach are then useful for understanding results in a pre-specified primary hypothesis domain, as well as for comparing results for this outcome to those for the same treatment and

> transferred across villages, and the information treatment is also unlikely to diffuse quickly, especially given the limited effects we find of this treatment.

> 21 We use the village, socioeconomic, and migration history variables shown in our balance tables 1A, 1B, and 1C, along with four additional baseline variables that we believed may affect willingness to migrate (health, risk attitude, patience, and self-efficacy). We partial out the randomization strata fixed effects before selecting these variables.

26
needs. These costs may come in the form of reduced convenience due to the need to conserve wood, increased monetary costs as households shift to higher cost, less GHG-intensive fuels like LPG (e.g. see Smith et al., 2000) or greater time costs as they plant trees on their own lands, because they cannot harvest enough wood from forests.

An estimated 25% of developing country forests are managed by communities (Agrawal et al., 2008; World Bank, 2009) and about 15.5% of global forest area is under the formal control of communities (RRI, 2014). These observations make formal, informal, _de jure_ and _de facto_ communitycontrolled forests (Bluffstone et al, 2013) especially important sources of GHG emissions _or_ carbon sequestration, particularly as most community forests are in low and lower- middle income countries where net forest biomass loss is occurring.

Nepal has for many decades been a leader in formal community control of forests. It has over 19,000 formal, registered forest user groups that include over 35% of the population. As of 2015, in hill areas that are home to the majority of Nepal’s population, over 78% of households were formal community forest user group (CFUG) members.<sup>3</sup> ~~.~~ For example, in the hill district of Salyan (population approximately 250,000) in 2014 there were 558 CFUGs.<sup>4</sup> These institutions have characteristics and practices that may differ from those outside the program. It is therefore important in any analysis to distinguish community forests (CFs) and non-community forests (non-CFs).

These CFUGs in Nepal implement management structures in consultation with government officials and impose collection restrictions, including on fodder, firewood and timber. Communities

> 3 National information available from the Ministry of Forest and Soil Conservation (MoFSC) Department of Forestry (DoF) Community Forestry Division website

> <u>http://dof.gov.np/dof_community_forest_division/community_forestry_dof.    Hill district information based on</u> author calculations and data from the Community Forests Database (August 2015) available at <u>http://dof.gov.np/image/data/Community_Forestry/Summary.pdf and the 2013 Nepal Statistical Yearbook (most</u> recent available), which is available at <u>http://cbs.gov.np/publications/statisticalyearbook_2013.  All accessed August 16,</u> 2016.

> 4 See <u>http://www.dfosalyan.gov.np/eng/images/pdf/database/database_of_cfugs.pdf for details.</u>

5
**statistics that suggest that the PK instruments are invalid.** This is the same claim made in the current version of their paper, and refuted below.  However, in the 2009 version and in blog updates to that version, RM used different methods to argue this point.  As in the current version of the paper, the methods used in the earlier version are faulty.  The particular errors of those previous attempts to disprove causality in PK include invalid Sargan test statistics and wrong Hansen J-tests critical values, both as a consequence of their neglect of the implications of sample weights. Furthermore, the incorrect two-stage setup used by RM means that their Sargan and Hansen J-tests would be wrong even if there were no need to use sample weights. This error is discussed at length in this paper in the context of Kleibergen-Paap test statistics (Kleibergen and Paap, 2006), the test statistics adopted in this most recent iteration of RM, but it applies as well to Sargan and Hansen J tests.

# **3.  Weak Instruments**

The essential nature of the model as formulated in PK is quite simple, although we are unaware of any previous models of this sort in the literature. Some individuals are randomly offered treatment.<sup>8</sup> Those offered treatment choose a level of treatment c (credit), which can be zero. Other individuals are randomly not offered treatment, and their level of treatment is deterministically zero (c = ε = 0, where ε is an error term). The upper portion of the vector contains the level of treatment chosen by those offered treatment, and the bottom portion contains the non-stochastic zeros of those not offered treatment.<sup>9</sup> Analogously, the upper portion of the matrix contains a set of K exogenous variables (which may include dummy variables for place) that affect the level of treatment c among those with choice. The bottom portion of this matrix is zero, as is the bottom portion of the vector of errors , consistent with the deterministic nature of c=ε=0 of those without choice.

> to Finance” authored by Morduch and Dean Karlan in the widely respected _Handbook of Development Economics_ (Karlan and Morduch, 2009):

> “Roodman and Morduch (2009) attempt to find closure to the issue by returning to the data and rebuilding the analysis from scratch. They are unable to replicate results from Pitt and Khandker (1998) or Khandker (2005). In fact, their estimates carry the opposite sign. Rather than concluding that microcredit harms borrowers, however, they unearth a raft of identification issues which are not solved with panel data. Their revised analysis casts doubt on all of the findings from the related set of papers, including Morduch (1998)’s finding on consumption smoothing. “

> 8 More generally, we require only that the offer of treatment be random conditional on a set of exogenous covariates that might include location fixed-effects, the assumption actually made in PK.

> 9 Thus, elements of c can be stochastically or deterministically zero depending on if individuals are offered treatment.

6
**Figure 1: Estimated trade costs for services**

<!-- Start of picture text --> 400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Year<br>Construction Distribution Transport<br>Post & Telecom Finance Business<br>Percent Ad Valorem Equivalent<br><!-- End of picture text --> Source: Miroudot and Shepherd, 2016.

Regulatory policies vary across countries for any given sector and the resulting heterogeneity is an important source of international trade costs (Nordas, 2016; Miroudot and Shepherd, 2016; OECD, 2017; WTO, 2019). As illustrated in Figure 2, the problem for firms is that they must address a set of idiosyncratic regulatory requirements in each market they wish to contest. The associated costs for each market increase overall production costs at the level of the firm and prevent them from capturing economies of scale or scope. There are several dimensions relevant here: (i) (asymmetric) information on the applicable rules and requirements; (ii) the associated certification/conformity assessment processes; and (iii) uncertainty/variability in administration of (i) and (ii). These factors generate trade costs even if regulation is applied on a nondiscriminatory basis.

4
|(0.628)|(0.672)|(0.671)|(0.673)|
|---|---|---|---|
|Power Outages|-0.056|-0.074|-0.072|
|(days)||||
||(0.080)|(0.082)|(0.083)|
|Courts Obstacle|-0.454|-0.599|-0.494|
|(cell average)||||
||(0.991)|(1.034)|(1.040)|
|Crime Losses Y:1|-1.341*|-1.285*|-1.299*|
|N:0||||
||(0.741)|(0.733)|(0.734)|
|Firm Inspected Y:1|-0.170|-0.190|-0.188|
|N:0||||
||(0.746)|(0.754)|(0.752)|
|How Much Of An<br>|-0.418|-0.448|-0.438|
|Obstacle: Tax Rates||||
||(0.276)|(0.276)|(0.275)|
|How Much Of An|-0.140|-0.082|-0.076|
|Obstacle: Labor||||
|Regulations?||||
||(0.362)|(0.360)|(0.359)|
|Population (logs)||-100.948***|-94.482***|
|||(36.755)|(36.360)|
|Urbanization (%)||-1.582**|-1.486**|
|||(0.745)|(0.736)|
|WBL Index||17.887|17.764|
|||(11.050)|(10.931)|
|Women can work||1.877|1.469|
|the same night hours||||
|as men Y:1 N:0||||
|||(3.140)|(3.172)|
|Women can work in||-27.171***|-25.654***|
|the same industries||||
|as men Y:1 N:0||||
|||(3.744)|(3.879)|
|Ratio of Female to||-11.870**|-11.328*|
|Male Population||||
|||(5.789)|(5.785)|
|Fertility Rate||-46.903***|-46.026***|
|||(7.940)|(7.955)|
|Starting a Business||0.035|0.035|
|(DTF)||||
|||(0.081)|(0.081)|
|Inflation (%)||0.640**|0.677**|
|||(0.284)|(0.282)|
|Year fixed effects<br>Yes<br>Yes<br>Yes<br>Yes|Yes|Yes|Yes|

68
79 EUROPE **AND** U.S.S.R. Population projection (thousands)

# Sulury for 25 Year Periods

|....................<br>AGE GROUP<br>..............|.......................................<br>1985<br>...........................|..................................................<br>2000|.................................................<br>2025<br>....<br>......|.....<br>2050<br> <br>..<br>. ...<br>....|2075<br>....<br>.....<br>.|2100<br>.....<br>...<br>....|2125<br>...............|...<br>2150<br>......................<br>... ..|
|---|---|---|---|---|---|---|---|---|
|TOTAL N F|770751|820747|863674|865251|870352|878531|880799|881371|
|MALES|||||||||
|0-4|28951|27846|27972|28134|28234|286|28256|28256|
|5-9|28985|28412|27905|28070|28198|28238|28236|28235|
|10-14|29297|28853|27926|28029|28164|28223|28220|28219|
|15-19|30121|28625|27758|27991|28109|28193|2819?|28193|
|20-24|32320|28678|27512|27880|28024|28144|28149|28151|
|25-29|31566|28896|27385|27659|27928|28081|28103|28103|
|30-34|29175|29635|27910|27557|27837|28017|28057|28055|
|35-39|25766|31695|28217|27490|27736|27937|27996|27993|
|40-44|21320|30778|27823|27202|27610|27812|27895|27894|
|45-49|24681|28129|27610|26752|27337|27580|27698|27703|
|50-54|21388|24369|27329|26209|26769|27160|27308|27330|
|55-59|20722|19451|27082|25887|25965|26404|26575|26613|
|60-64|15260|21097|27160|24735|<br>24654|25116|25299|25352|
|65-69|9735|16666|23712|22186|22398|23050|23219|23288|
|70-74|10033|13844|18292|18827|19038|19832|20009|20095|
|75+|12162|14072|<br>22202|29659|30467|31949|32559|32779|
|TOTAL|371482|401046|423795|424268|428469|433991|435772|<br>436260|
|FEMALES|||||||||
|0-4|28002|26861|26847|26891|26889|26892|26892|26892|
|5-9|27769|27543|26879|26888|26892|26892|26890|26889|
|10-14|27873|28094|26975|26897|26896|26890|26888|26888|
|15-19|28900|27936|26886|26922|26892|26885|26885|<br>26885|
|20-24|31027|27711|26757|26901|26879|26877|26881|<br>26883|
|25-29|30552|277m|26770|26793|26864|26874|26878|26878|
|30-34|28534|28791|27434|26813|26855|26873|<br>26872|<br>26871|
|35-39|25483|<br>30842|27935|26878|26847|26865|<br>26860|26857|
|40-44|21518|30254|27698|26736|26839|26837|26830|26830|
|45-49|25529|28073|27335|26506|26747|26765|26763|26768|
|50-54|22778|<br>24813|27175|26325|26497|26627|26638|26643|
|55-59|24159|20580|27711|<br>26625|26250|26383|26402|26404|
|60-64|20948|23652|2852|2642|25833|25946|25966|25963|
|65-69|14443|20041|26846|25115|24771|25083|25085|25083|
|70-74|16679|19217|22582|22745|22783|23301|23323|23327|
|75.|25076|27496|35199|45465|47149|48549|<br>48973|<br>49052|
|TOTAL|399269|419700|439879|440963|441883|444539|445027|445112|
|BIRTH RATE||14.4|13.2|12.8|12.8|12.7|12.6|12.6|
|DEATH RATE||10.2|11.2|12.8|12.6|12.3|12.5|12.6|
|NET MIGRATI|ON RATE|.1|.0|.0|**.0**|**.0**|.0|.0|
|**GROWTH**RATE||.42|.20|.01|.02|.04|.01|.00|
|TOTAL FERTI|LITY|1.963|2.008|2.069|2.066|2.063|2.063|2.063|
|eCO)**-** BOTH|SEXES|73.79|75.63|77.28|78.75|79.59|79.60|79.60|
|**IN- 90TH**S|EXES|20.0|14.1|9.7|6.8|5.3|5.3|5.3|
42

**Table 13: Share of Sensitive Goods** in the **Baltic Exports to the** EU **and the Nordics (1992)**

|**%**|Finland|Norway|Sweden|EU|
|---|---|---|---|---|
|Estonia|||||
|Labor-intensive|26|20|22|11|
|Resource-intensive|53|56|50|40|
|Capital-intensive|12|6|15|20|
|<br>Other|9|18|13|29|
|Latvia|||||
|Labor-intensive|20|3|10|4|
|Resource-intensive|68|51|81|73|
|Capital-intensive|4|45|3|13|
|Other|8|1|6|10|
|Lithuania|||||
|Labor-intensive|35|20|11|5|
|Resource-intensive|24|64|62|67|
|Capital-intensive|4|-|10|20|
|Other|37|16|17|8|

Source: Comtrade. Labor-intensive=Textiles (65+84), footwear (85), fiurniture (82); resource-intensive=wood (24+63), fish (03), oil (3), metals (28); capital-intensive=chemicals (5), machinery (7), iron and steel (67+68).

**the** Baltics and Western Europe, trade creation is likely to exceed trade diversion in a regional trade agreement. Integration also brings security to market access and helps to lock in reforns at home, which in turn may help attract foreign investments.

Gravity models predict that the West would absorb most of the Baltic exports - over 90% of **the** Baltic trade would be with the non-FSU countries. Initial exports are likely to be labor-and resource-intensive goods, because of easier adjustment to Western standards in these goods. In the longer run, the Baltics are likely to specialize in skill-intensive manufactures - their skiU levels are
<u>Table 1: Precision of Aid Locations</u>

|Precision|code|Percentage of locations|
|---|---|---|
|Precision|1: specifc place|40.9|
|Precision|2: within 25 km of specifc place|2.4|
|Precision|3: municipality (ADM1)|25.7|
|Precision|4: province (ADM2)|19.7|
|Precision|5: imprecise|1.9|
|Precision|6: country-wide projects|4.4|
|Precision|7: unclear|0|
|Precision|8: state or national capitals|5|

_Source_ : Authors’ summary based on AidData, World Bank geocoded research release, version 1.3. _Note_ : The sample is composed of 1,900 projects in Africa.

province). They do _not_ refer to imprecisely georeferenced point locations. Precision 5 locations are imprecisely geocoded, so only approximate coordinates are reported. Precision 7 locations are “unclear” in the sense that it was only possible to identify the country in which the project is located. Finally, precision 8 locations correspond to capital cities (both national and local) and also include projects aimed at government institutions (such as a ministry or the central bank). The distribution of location precision categories is reported in table 1.

Most projects are implemented across several locations (around 40 on average), often belonging to different precision categories. Consider, for example, a project that aims to build a road connecting two towns in two different provinces. In this case, at least four locations are assigned to the project: the two towns as precision 1 locations and the two provinces as precision 4 locations, plus any province crossed by the road as an additional precision 4 location, and any town crossed by the road as an additional precision 1 location. For the purpose of the analysis, only locations of precision 1 or 2 are retained: precisions 3, 4, and 6 are too coarse to be uniquely assigned to a cell in the grid-level analysis, and precision 8 locations are removed to prevent the results from being driven by capitals. This leaves 768 projects (40 percent) and 12,318 project locations (74 percent) in the analysis.

These sample restrictions raise the question of whether the excluded projects are systematically different from the retained ones. This question is 9
A large body of work demonstrates strong associations between early nutrition status and child development (Prado & Dewey, 2014). Children with poor early nutrition fail to reach expected linear growth milestones, leading to stunting, long-term cognitive and motor deficits, and eventually worse economic outcomes (e.g., Galasso, Wagstaff, Naudeau & Shekar, 2017; Sudfield et al., 2015). Some work similarly demonstrates that cognitive stimulation can ameliorate some of the effects of poor early nutrition on child development, especially when coupled with parenting interventions (Paxson & Schady, 2007; Grantham-McGregor, Powell, Walker & Hines, 1993). Most evidence to date has been produced through intervention studies, with less work conducted using large-scale samples, where effects may be more representative of typical interactions between parenting and nutrition status.

The present study examines the emergence of wealth gradients in child development and the joint roles of nutrition status and parenting in explaining the gaps between high- and lowwealth children. We had three hypotheses: first, we expected to find gradients in child development based on family wealth in a low-income country. Second, based on existing parenting literature, we expected to find that there are different types of parenting, such as cognitively-stimulating and emotionally-responsive, with unique implications for child development, and that parenting quality is associated with both family wealth and child development. Third, we hypothesized that parenting and nutrition status would jointly influence child development, and that parenting could have different impacts on child development depending on nutrition status. In sum, we hypothesized that family assets would exert a strong and consistent impact on child development, but that parenting quality and nutrition status could account for a portion of the variance attributable to family wealth, demonstrated by a reduction in the impact of family assets on child development when parenting and nutrition status are 6
**Imputing Country of Residence** One limitation of the PATSTAT dataset is the absence of location information. For example, for patents applied to Chinese patent office, around 37% of patent applicants/inventors have missing information on their country of residence. To address this gap, we utilize the methodology proposed by De Rassenfosse et al. (2013) and Menon and Tarasconi (2017) to recover and assign country codes to each inventor/applicant with missing location information using the following steps.

First, we use the location information associated with each inventor/applicant id to recover their country of residence. Next, we utilize the standardized names by PATSTAT. Due to variations in how names are recorded across different patent offices, the same inventors/applicants may appear under different names. PATSTAT regroups name that likely represents the same individual under a unique standardized name and assigns them a unique PSN ID. Inventors/applicants linked by the same PSN ID are assumed to reside in the same country within a specific year. If some inventors/applicants id lack country information, but others under the same PSN ID have, we infer the missing country information based on the available location information. In some rear cases, multiple country codes are associated with a single PSN ID, the country code corresponding to the highest number of patents is selected for all linked inventors/applicants.

Second, we use the priority patent information, when, for example, a patent filed in China lacks the location data of its applicants/inventors, but its priority filings in EPO contain the location information. The location information recorded by EPO is used to infer the country of residence of applicants/inventors that lack location information in China. In some cases, where the first filings lacks location information, whereas the subsequent filings contains the location information, we use the country code in the subsequent filings to impute country information for applicants/inventors.

Lastly, for the rest of applicants/inventors with missing country code, we use the information on patent office locations. Following Rassenfosse and Seliger (2021), we assume that inventors/applicants would first file patents in their domestic patent office. Therefore, 44
Figure A6: Event-study analysis. The graphs present estimates of year-specific coefficients of ln(irrigarea) and the 95 percent confidence interval for the different sub-regions of Africa, using a specification similar to that employed in Table A3.

<!-- Start of picture text --> Rest of West Africa<br>G5 Sahel<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>year year<br>Rest of SSA North Africa (MENA)<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>year year<br>.15<br>.1<br>.1<br>.05 .05<br>0 0<br>-.05 -.05<br>-.1<br>.06<br>.04 .04<br>.02 .02<br>0 0<br>-.02 -.02<br>-.04<br><!-- End of picture text --> 27