rafmacalaba/datause-encoder-data huggingface dataset:
|here|subsamples<br>at least<br>at least<br>one dau<br>one son<br>(11)<br>(12)<br>daughter<br>son<br>elsewhere<br>elsewhere<br>-0.043***<br>0.054***<br>(0.015)<br>(0.011)<br>0.064***<br>-0.044***<br>(0.012)<br>(0.014)<br>9156<br>9246<br>0.061<br>0.058<br>_∗_, and _∗_indicate signifcance at<br>d lives elsewhere. Other controls<br>d his wife (or woman head), a<br>ren ever born, # of daughters and|
|---|---|
|ing elsew|at least<br>one child<br>(10)<br>child<br>elsewhere<br>-0.015<br>(0.012)<br>-0.000<br>(0.012)<br>12477<br>0.068<br>ghts. _∗∗∗_, _∗_<br>ousehold hea<br>male head an<br>old, # child|
|ge 6-14 liv|(9)<br>son<br>elsewhere<br>0.124***<br>(0.008)<br>-0.021***<br>(0.005)<br>20578<br>0.201<br>ng survey wei<br>hter of the h<br>d age of the <br>eaded househ|
|r or son a|full sample<br>(8)<br>daughter<br>elsewhere<br>-0.021***<br>(0.005)<br>0.135***<br>(0.008)<br>20578<br>0.213<br>rentheses. Usi<br>d, son or daug<br> education an<br> and female h|
|, daughte|(7)<br>child<br>elsewhere<br>0.150***<br>(0.009)<br>0.162***<br>(0.009)<br>20578<br>0.193<br>e level in pa<br>iological chil<br>city, years of<br>usehold head|
|gical child|at least<br>one son<br>(6)<br>son<br>elsewhere<br>0.058***<br>(0.006)<br>-0.007<br>(0.005)<br>9246<br>0.063<br>g at the villag<br>at least one b<br>ion and ethni<br>olygynous ho|
|sion: biolo|subsamples<br>at least<br>one dau<br>(5)<br>daughter<br>elsewhere<br>-0.007<br>(0.005)<br>0.078***<br>(0.006)<br>9156<br>0.072<br> for clusterin<br>qual to one if <br>ousehold) relig<br>a dummy for p|
|-out deci|at least<br>one child<br>(4)<br>child<br>elsewhere<br>0.048***<br>(0.005)<br>0.062***<br>(0.005)<br>12477<br>0.083<br>rors adjusted<br> dummies e<br>le headed h<br>agriculture, <br>male).|
|fostering|(3)<br>son<br>elsewhere<br>0.060***<br>(0.002)<br>0.003*<br>(0.002)<br>20578<br>0.233<br>t standard er<br>bles are three<br> head if fema<br>d working in <br> (male and fe|
|Household|full sample<br>(2)<br>daughter<br>elsewhere<br>0.003<br>(0.002)<br>0.068***<br>(0.003)<br>20578<br>0.257<br>ported. Robus<br>ependent varia<br>wife (or woman<br>e and husban<br> older than 15|
|**Table 11:**|Households with:<br>(1)<br>dep. var:<br>child<br>elsewhere<br># biol. sons<br>0.062***<br>(0.003)<br># biol. daughters<br>0.071***<br>(0.003)<br>more biol. sons<br>more biol. daughters<br>Observations<br>20578<br>Pseudo R-squared<br>0.218<br>_Notes._ Probit estimates, marginal efects re<br>1%, 5% and 10% levels, respectively. The d<br>include region fxed efects, urban dummy, <br>wealth index, household land ownership, wif<br>sons alive age 0–5, # of household members|

33
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53
**Figure 2: ‘Map’ of Financial Services-Related Trade Commitments in LCR (mid-2006)**

<!-- Start of picture text --> CHILE TAIWAN<br>COSTA RICA (CHINA) SINGAPORE<br>DR-CAFTA<br>USA DOM. REP.<br>CARICOM<br>countries<br>EL SALVADOR<br>PANAMA<br>NAFTA GUATEMALA<br>PERU COLOMBIA<br>CACM<br>Mexico –<br>ECUADOR COMMUNITYANDEAN HONDURAS Northern<br>Triangle<br>CANADA (CAN)<br>BOLIVIA<br>VENEZUELA NICARAGUA<br>(prior to 2006)<br>G3<br>BRAZIL<br>URUGUAY<br>MEXICO ARGENTINA<br>MERCOSUR<br>EC<br>VENEZUELA<br>EFTA JAPAN PARAGUAY (2006)<br><!-- End of picture text --> _Source: Own analysis, SICE database._ _Note: Lines indicate the existence of a financial services chapter/Annex in a PTA between the relevant countries (dashed lines indicate that the agreement has not yet been ratified or implemented), while the ovals indicate the presence of a trade agreement for the creation of a common market or customs union._ 20
Table 3: The decision to become an exporter: before and after the crisis

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Crisis period?|-0.016|0.063|0.085|-0.041|-0.020|-0.183|0.186|
|(1=Yes)|(0.083)|(0.094)|(0.094)|(0.085)|(0.082)|(0.408)|(0.352)|
|Subj. fnancial||0.028|0.028|||||
|constraint||(0.028)|(0.028)|||||
|Subj. fnancial||-0.034|-0.033|||||
|constraint _×_ Crisis?||(0.029)|(0.029)|||||
|Unable to obtain a||||0.014|0.013|0.190|0.536|
|loan? (Yes=1)||||(0.038)|(0.038)|(0.581)|(0.451)|
|Unable to obtain a||||0.007|0.009|0.256|-0.304|
|loan? (Yes=1) _×_ Crisis?||||(0.041)|(0.041)|(0.693)|(0.661)|
|Foreign shock|0.016***||0.017***||0.016***||0.007|
|(%∆GDP)|(0.006)||(0.006)||(0.005)||(0.009)|
|Foreign shock|-0.009||-0.010||-0.010||-0.004|
|_×_ Crisis?|(0.007)||(0.007)||(0.007)||(0.014)|
|Domestic shock|-0.007***||-0.007***||-0.007***||-0.006***|
|(%∆GDP)|(0.001)||(0.001)||(0.001)||(0.002)|
|Domestic shock|0.002||0.002||0.002||0.000|
|_×_ Crisis?|(0.003)||(0.003)||(0.003)||(0.004)|
|Log(Labor|0.039***|0.040***|0.039***|0.040***|0.039***|0.048***|0.042***|
|Productivity)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.008)|(0.008)|
|Firm Age|0.001*|0.001|0.001|0.001|0.001|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Small Firm? (Yes=1)|-0.120*** <br>(0.017)|-0.120*** <br>(0.017)|-0.120*** <br>(0.017)|-0.118*** <br>(0.017)|-0.118*** <br>(0.017)|-0.079**<br>(0.032)|-0.099**<br>(0.039)|
|Foreign Ownership?|0.218***|0.216***|0.218***|0.217***|0.219***|0.226***|0.226***|
|(1=Yes)|(0.027)|(0.027)|(0.027)|(0.027)|(0.027)|(0.031)|(0.030)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.269|0.269|0.269|0.269|0.269|0.269|0.269|
|F stat||||||50|53|
|Kleibergen-Paap LM test||||||0.037|0.196|
|Hansen J stat||||||0.022|0.000|
|Endog. test||||||0.000|0.000|
|N|23342|23342|23342|23342|23342|23342|23342|

The dependent variable is the _Prob_ ( _Exportit >_ 0). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

17
thus need to look at the role of weathvr variables (temperature and rain), role of an overwintering population, availability of host plants-sequence and suitability, role of predators, influence of cropping patterns-monoculture versus multicropping, continuous cropping versus period of fallow, role of source crops, migration and uncultivated hosts.

In general most of the factors mentioned above are poorly researched and there is little if any evidence of the effect of these factors on armigera populations. Some entomologists claim that a hot dry spell is conducive to rapid pest multiplication partly by reducing the length of the pest cycle and partly by increasing the nutritional quality and susceptibility of many wild and cultivated host plants (Pimbert & Srivastava, 1991). Others claim that high rainfall influences the pest population positively by increasing host plant abundance (Fitt,,1989). Evidence from ICRISAT (Pimbert & Srivastava, 1991) shows that rainfall deficit and high abundance of armigera are positively correlated. Thus it seems that, in the net, a long dry spell is conducive to armigera growth. It was also shown in the Pimbert & Srivastava study that long range migration had only a limited role in the population dynamics during the period of analysis.

Cropping patterns can have a profound influence on the abundance of armigera. The spread of continuous cropping is often quoted as being an important reason for the emergence of armigera as a major pest. Some rerearchers claim that monoculture exacerbates the pest problem whereas others support the opposite 40
# References

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14
Freund, Caroline and Bineswaree Bolaky (2008), “Trade, Regulations, and Income,” Journal of Development Economics, 87, pp.309-321.

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13
Table A.13: Innovation Capacity and Post-Sanction Innovation Performance

||(1)<br>**Patent A**<br>Total|(2)<br>**pplication **<br>Intl.|(3)<br> **(Intensive Margin)**<br>High-Tech|
|---|---|---|---|
|Treatment_×_Post|-0.237***|-0.260**|-0.214***|
||(0.042)|(0.107)|(0.064)|
|Treatment_×_Post_×_Top|0.260***|0.033|0.083***|
||(0.039)|(0.070)|(0.027)|
|No. Obs|5,442|2,040|3,690|
|R-squared|0.653|0.515|0.610|
|Firm FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS on a set of patenting firms. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

Table A.14: Knowledge Distance and Post-Sanction Innovation Performance

||(1)<br>**Patent **|(2)<br>**Application **|(3)<br> **(Intensive **|(4)<br> **Margin)**|
|---|---|---|---|---|
|Treatment_×_Post|-0.851***<br>(0.238)|-2.107***<br>(0.543)|-4.193***<br>(1.375)|-12.987***<br>(4.246)|
|Treatment_×_Post_×_TechDist|0.042**<br>(0.010)||||
|Treatment_×_Post_×_Sourcing||0.035***<br>(0.009)|||
|Treatment_×_Post_×_Adoption|||0.052***<br>(0.016)||
|Treatment_×_Post_×_Collaboration||||0.136***<br>(0.044)|
|No. Observation|6,334|6,334|6,334|6,334|
|R-Squared|0.654|0.655|0.654|0.653|
|Firm FE|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|

Note: We estimate the coefficients using OLS on a set of patenting firms. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

60
investment will be ambiguous.

We test the predictions of the model using data on early childhood stimulation — both the overall amount of stimulation received by each young child, and the amount of stimulation done by different family members (e.g. the mother, the father, the older siblings, etc.). Estimates of the impact of having an older daughter on the overall level of early childhood stimulation are reported in Panel C of Table 3. We find large and statistically significant impacts of older sisters on the level of early childhood stimulation a child experiences. Having an older sister increases the number of different stimulating activities (out of 12) over the three days prior to the survey by between 0.637 (without controls, p-value 0.006) and 0.703 (with controls, p-value 0.002). Among households where the older child is male, the mean number of stimulating activities if 5.147; hence, the estimated treatment effect of having an older sister represents more than a ten percentage point increase in early childhood stimulation. From a theoretical perspective, this suggests that we can rule out the possibility that both _δo_<sup>_G_=</sup><sup>_δ_</sup> _o_<sup>_B_and</sup><sup>_λG_</sup> _o_<sup>=</sup><sup>_λB_</sup> _o_<sup>—sinceweobservea</sup> clear treatment effect of older sibling gender on early childhood stimulation.

Next, we test whether parents are the main channel of impact. In Figure 3, we summarize the estimated treatment effect of having an older sister on 10 different outcome variables. First, we present the treatment effect on the overall level of early childhood stimulation (replicating the specification in Panel C of Table 3 that was discussed above). Then, we present treatment effects on the amount of early childhood stimulation done by parents and the amount done by siblings. Finally, we present treatment effects on the amount of early childhood stimulation done mothers, by fathers, by sisters, by brothers, by grandmothers, by grandfathers, and by other individuals. Having an older sister does not impact the amount of stimulation young children receive from their parents, nor does it impact the amount received from the mother or father specifically, from either grandparent, or from others. All estimated coefficients are relatively precise zeros. Instead, having an older sister leads to a significant increase in the amount of stimulation received from siblings. Having an older sister increases the amount of stimulation done by sisters and 23
Table E3: Share of Significant Rainfall Coefficients in Niger

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.67|0.67|0.42|0.67|0.83|0.33|
|Median Daily Rainfall|0.00|0.00|0.58|0.00|0.00|0.58|
|Variance of Daily Rainfall|0.75|0.83|0.50|0.67|0.58|0.25|
|Skew of Daily Rainfall|0.42|0.33|0.42|0.42|0.42|0.33|
|Total Rainfall|0.67|0.67|0.42|0.67|0.83|0.33|
|Deviation in Total Rainfall|0.58|0.92|0.83|0.67|0.67|0.25|
|Z-Score of Total Rainfall|0.58|0.83|0.83|0.67|0.50|0.17|
|Rainy Days|0.42|0.42|0.58|0.75|0.67|0.67|
|Deviation in Rainy Days|0.58|0.42|0.50|0.25|0.00|0.67|
|No Rain Days|0.50|0.33|0.58|0.67|0.58|0.67|
|Deviation in No Rain Days|0.58|0.42|0.50|0.25|0.00|0.67|
|% Rainy Days|0.42|0.42|0.58|0.75|0.67|0.67|
|Deviation in % Rainy Days|0.58|0.42|0.50|0.25|0.00|0.67|
|Longest Dry Spell|0.58|0.75|0.75|0.42|0.58|0.75|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Niger.

Table E4: Share of Significant Rainfall Coefficients in Nigeria

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.58|0.42|0.58|0.50|0.83|0.50|
|Median Daily Rainfall|0.58|0.33|0.58|0.58|1.00|0.58|
|Variance of Daily Rainfall|0.75|0.50|0.25|0.58|0.83|0.58|
|Skew of Daily Rainfall|0.67|0.42|0.33|0.42|0.17|0.42|
|Total Rainfall|0.58|0.33|0.67|0.42|0.83|0.58|
|Deviation in Total Rainfall|0.67|0.50|0.33|0.50|1.00|0.50|
|Z-Score of Total Rainfall|0.67|0.92|0.33|0.17|1.00|0.67|
|Rainy Days|0.67|0.50|0.75|0.67|0.33|0.75|
|Deviation in Rainy Days|0.50|0.58|0.50|0.58|0.58|0.58|
|No Rain Days|0.50|0.42|0.67|0.42|0.33|0.58|
|Deviation in No Rain Days|0.50|0.58|0.50|0.58|0.58|0.58|
|% Rainy Days|0.83|0.58|0.75|0.83|0.33|0.92|
|Deviation in % Rainy Days|0.42|0.75|0.50|0.33|0.67|0.58|
|Longest Dry Spell|0.42|0.58|0.33|0.25|0.58|0.50|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Nigeria.

132
<!-- Start of picture text --> 5%<br>0%<br>5% —Baseline scaliing-up scenario<br>Scenario with only labor inputs<br>“10% nario with only intermediary inputs<br>-15%<br>-20%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 5%<br>O%<br>5%<br>“10%°<br>-15%<br>-20% —Baseline’Scaling up scenario<br>—Scenario with only labor inputs<br>-25% —Scenario with only intermediary inputs<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
Policy ReseaRch WoRking PaPeR 4538

# **Abstract**

This paper studies the effect of business registration regulation on economic activity using micro-level data. The identification strategy exploits the fact that a recent business registration reform in Mexico was introduced in different municipalities at different points in time. Using panel data from the Mexican employment survey, I find that the reform increased the number of registered businesses by 5 percent in eligible industries. This increase was due to former wage earners opening businesses. Former unregistered business owners were not more likely to register their business after the reform. Moreover, employment in eligible industries went up by 2.8 percent, and people who were previously unemployed or out of the labor force were more likely to work as wage earners after the reform. Finally, the results imply that the competition from new entrants lowered prices by 0.6 percent and decreased the income of incumbent businesses by 3.2 percent.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand the impact of business regulation reforms on economic development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mbruhn@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team