rafmacalaba/datause-encoder-data huggingface dataset:
Table A31: Robustness of impact on decision-making index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|0.07|0.11|0.03|0.06|0.06|0.07|0.07|
|||(0.12)|(0.11)|(0.12)|(0.12)|(0.12)|(0.12)|(0.12)|
|T1|+Leaders|0.25*|0.46***|0.05|0.18|0.21|0.24*|0.25*|
|||(0.13)|(0.15)|(0.13)|(0.13)|(0.13)|(0.13)|(0.13)|
|T2|(Safe Spaces + IGA)|0.16|0.28***|0.03|0.11|0.14|0.16|0.16|
|||(0.13)|(0.11)|(0.13)|(0.13)|(0.13)|(0.13)|(0.13)|
|T2|+Leaders|0.00|0.20*|-0.23**|-0.08|-0.04|0.01|0.00|
|||(0.11)|(0.12)|(0.10)|(0.11)|(0.11)|(0.11)|(0.12)|
|T3|(Safe Spaces + Husbands Clubs)|0.20*|0.39***|-0.01|0.09|0.15|0.20**|0.20**|
|||(0.10)|(0.10)|(0.11)|(0.10)|(0.10)|(0.10)|(0.10)|
|T3|+Leaders|0.40***<br>(0.13)|0.45***<br>(0.14)|0.34***<br>(0.12)|0.37***<br>(0.13)|0.38***<br>(0.13)|0.39***<br>(0.13)|0.40***<br>(0.13)|
|T4|(control spillover)|0.08|0.08|0.08|0.08|0.08|0.08|0.09|
|||(0.11)|(0.11)|(0.10)|(0.11)|(0.11)|(0.11)|(0.11)|
|N||2919|2918|2918|2920|2920|2920|2919|
|Con|trol Mean|0|0|0|.003|.001|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

63
University of Singapore‐Credit Research Initiative using a Merton (1974) model with stability adjustments suggested by Duan (2010) and considering macroeconomic and firm‐specific financial factors.<sup>8</sup> We cover monthly data of 1,786 listed financial institutions in 36 EMDEs.

**_Measuring sovereign stress._** We measure financial stress in the sovereign public sector by using the sovereign EMBI spread for 36 EMDEs. We recognize that the sovereign EMBI spread tracks US‐dollar denominated sovereign debt instruments with minimum face value outstanding of US$500 million, but we consider the sovereign EMBI spread to be a reasonable proxy for the risk of sovereign debt instruments in local currency as well.

**_Quantile regressions to estimate nexus strength at increasing levels of sovereign stress._** To calculate the Nexus indicator, we estimate the following basic model:

Sov. EMBI spread�,� �α�β∗Median PD Financial Institutions�,� �γ∗D� �ε�,� (1) where subscripts _c_ and _t_ represent country _c_ and time _t_ , _Dc_ represents a set of country‐fixed effects, and ε�,� is the error term. β is our Nexus indicator with higher values suggesting a tighter nexus. For example, if β�100 , a 1 percentage point increase in the PD, is associated with a 100‐basis points increase in the EMBI spread. We conjecture that the Nexus strengthens at higher levels of sovereign stress. To estimate the Nexus indicator at different levels of sovereign stress, the coefficient β is estimated using quantile regressions which produces an estimated β for each percentile of the EMBI spread. Figure 7, Panel C shows the estimated _β_ s for 10<sup>th</sup> to 90<sup>th</sup> percentiles using an unbalanced panel of monthly information for 36 EMDEs between January 2000 and December 2017. Similarly, Figure 7, Panel D shows the estimated _β_ s for 10<sup>th</sup> to 90<sup>th</sup> percentiles for each World Bank region.<sup>9</sup>

**_Rolling regressions to estimate the nexus strength over time._** To estimate how the Nexus indicator changes over time, we estimated the model in (1) using rolling median regressions <u>for each country (without country fixed effects) using a 24‐month rolling window. Figure 7,</u> Panels E and F show the cross‐country median of the estimated country‐level β s for each month, and by region as of December 2017.

> 8 For more details see The Credit Research Initiative of the National University of Singapore (2018), Merton (1974), and Duan (2010).

> 9 Country coverage: EAP: China, Indonesia, Malaysia, Philippines, Vietnam; ECA: Croatia, Georgia, Kazakhstan, Romania, the Russian Federation, Serbia, Turkey, Ukraine; LAC: Argentina, Belize, Brazil, Chile, Colombia, Jamaica, Mexico, Peru, the República Bolivariana de Venezuela; MENA: the Arab Republic of Egypt, Jordan, Morocco, Oman, Tunisia; SAR: India, Pakistan, Sri Lanka; SSA: Ghana, Kenya, Namibia, Nigeria, South Africa.

23
the information was somewhat new and the movie was deemed by the facilitators to have generated a lot of discussion in 29% of the showings.

# **4. Impacts**

We focus mainly on the single difference estimates of the impact of showing the movie in a household’s village. Given the randomized assignment, the single difference estimates are unbiased in large samples. Small sample properties mean that there may be some dissimilarities between the randomized in and out villages. The difference-in difference (DD) estimator corrects for time-invariant selection bias that may be present due to any small sample bias. However, we found that the DD impact estimates were generally very similar to those found for the single difference; we comment on the exceptions below.

Table 2 reports the estimated impacts of the movie on knowledge of BREGS, for both men and women separately, as well as for the full sample. These are regression coefficients of knowledge on the village assignment of the movie.<sup>15</sup> The dependent variable is a dummy variable taking the value 1 if the respondent was aware of BREGS or got the right answer to the relevant question and 0 otherwise. Thus the regression coefficient is the difference in the mean knowledge score between those who live in a village that was assigned the movie and those who live in a control village. The constant gives the mean knowledge score for the control group. Given that the village assignment is random by design, and there was complete compliance, the estimated difference in means is unbiased.

The movie had a significant impact on knowledge of the existence of BREGS for the sample as a whole. The magnitude of the effect is small (a 3% point gain), although that is not surprising as knowledge of the existence of BREGS was high to begin with. Also note that a larger and more significant effect on knowledge is found amongst women, who (as noted above) were less aware initially. The DD estimate was even larger for women.

The movie helped improve knowledge of employment related rights (as measured by the work score), for both men and women. For most questions, the impacts on men are larger than those on women, even though women had less information coming in to the information intervention.

> 15 The “svy” command in STATA was used to assure that the regression coefficient on the assignment dummy variable is equivalent to the difference in weighted means.

12
square of the correlation between the dependent variable and the estimated conditional mean), and the p-value of the HPC test of the sample selection estimator against the other model and vice-versa.

It results that both models perform similarly when comparing the R-Squared measures. When comparing the p-values, the HPC tests reject the PPML specification for all types of expenditures, while providing no evidence of departures of the ZIP model in the direction of its competitor. In the rest of the paper, we therefore present and discuss the results of the ZIP model only. The results of the regressions using both PPML and ZIP models are available in the Appendix.

Table 3: HPC tests between PPML and ZIP models

||All Tra|nsport|Serv|ices|Priv|ate|
|---|---|---|---|---|---|---|
|Ho: Model .. is valid|PPML|ZIP|PPML|ZIP|PPML|ZIP|
|Prob > t|0.149|0.496|0.000|1.000|0.000|0.958|
|R Square|.384|.387|.209|.203|.336|.343|
|Country FE|N|o|N|o|N|o|
|Subnational FE|Ye|s|Ye|s|Ye|s|

# **4.3 Extensive margin: first-time users of transport goods and services**

Tables 4 and 5 present the results of the estimation of the ZIP model when all observations across countries are pooled and including subnational fixed-effects and the square expenditure per capita as a variable in the regression. Table 4 reports the estimation results of the inflate logit estimation part, and Table 5 the main Poisson estimation.

25
countries with large fiscal space is up to 1 percentage point higher than in countries with small fiscal space, on average.<sup>25</sup>

# **Figure 4. Effects of public investment on output conditional on fiscal space**

## **A. Large fiscal space B. Small fiscal space**

<!-- Start of picture text --> Percent Baseline response Percent Baseline response<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>0.5 0.5<br>0.0 0.0<br>-0.5 -0.5<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>Year Year<br><!-- End of picture text --> _Source:_ Authors’ estimates.

_Note:_ Responses of real GDP (cumulative change in year _t_ relative to year _t_ = -1, in percent) to a public investment shock equivalent to 1 percent of GDP, conditional on fiscal space. _t_ = 0 is the year of the shock. High-efficiency and low-efficiency samples are based on the top and bottom quartiles of the IMF (2021b) public infrastructure efficiency index, which ranges from 0 to 100 (the values above 81 and below 47, respectively).

Fiscal space influences the output effects of public investment through two channels. The first is associated with the effects on private sector, as additional public spending in countries with weak fiscal positions may lead to lower disposable income of liquidity-constrained households, as well as increased tax burdens for the private sector in the future (which may also be anticipated). The second channel relates to the interest rate effect, as scaling up of government expenditures in countries with high levels of debt may lead to higher international interest rate spreads, on account of higher sovereign risk and inflation, thus increasing borrowing costs for the private sector (Blanchard 1990; Huidrom et al. 2020; Sutherland 1997).

Infrastructure investment projects, given their large upfront costs and long time horizons, are often financed by borrowing rather than from current government revenues. Larger fiscal space implies that the sovereign has more capacity to service its borrowing and therefore is more creditworthy, allowing it to finance such investment at a lower interest rate.

> 25 The high-debt and low-debt states are defined using the smooth transition function that reflects the historical dynamics of public-debt-to-GDP ratios on a country-by-country basis. For a median EMDE, a low-debt state over the sample period corresponds to about 30 percent of GDP and a high-debt state corresponds to about 80 percent of GDP for EMDEs that experienced a public investment shock.

19
20

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- Repression," National Bureau of Economic Research, Working Paper No. 4062, 1992.

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since project benefits are a multiple of national income, suggesting poorer countries in general may struggle to fund infrastructure; and (iv) transportation infrastructure can be an international public good that creates benefits far from its location.

Taken together, our findings do suggest the Belt and Road has the potential to create substantial mutual benefit for China and countries along the Eurasian transportation network. However, to achieve this, rigorous economic feasibility analysis is required, and some projects must be cancelled, as is already happening in select markets.

# 6. REFERENCES

Adao, R., Costinot, A. and Donaldson, D., 2017. “Nonparametric counterfactual predictions in neoclassical models of international trade.” American Economic Review, 107(3): 633-89.

Alder, S. 2019. “Chinese Roads in India: The Effect of Transport Infrastructure on Economic Development.” University of North Carolina at Chapel Hill manuscript.

Allen, T. and Arkolakis, C., 2014. Trade and the Topography of the Spatial Economy. The Quarterly Journal of Economics, 129 (3): 1085-1140.

Allen, T. and Arkolakis, C., 2019. “The welfare effects of transportation infrastructure improvements.” National Bureau of Economic Research Working Paper No. 25487.

Asher, S.E. and Novosad, P. 2020. “Rural Roads and Local Economic Development.” American Economic Review. forthcoming.

Baum-Snow, N., Brandt, L., Henderson, J.V., Turner, M.A. and Zhang, Q., 2017. “Roads, railroads, and decentralization of Chinese cities.” Review of Economics and Statistics. 99(3): 435-448.

33
**Table 1: Diet quality and obesity,** _ordered logit model, average marginal effects_

|**Vibl**|**(1) BMI**<br>**t**|**(2) BMI**<br>**t**|**(3) BMI**<br>**t**|**Relative difference**<br>**(2)(3) l i**|
|---|---|---|---|---|
|**araes**|**caegory**<br>**obesity**|**caegory**<br>**obesity**|**caegory**<br>**obesity**|**– p‐vaues n**<br>**brackets**|
|Low socioeconomic status (1/0) (neighborhood)||0.137***|0.117***|_−14.60%_|
|||(0.0413)|(0.0333)|_[0.061]_|
|Mid‐low socioeconomic status (1/0) (neighborhood)||0.0682*|0.0588*|_−13.80%_|
|||(0.0402)|(0.0330)|_[0.070]_|
|Mid socioeconomic status /1/0) (neighborhood)||0.0678*|0.0608*|_−10.30%_|
|||(0.0365)|(0.0317)|_[0.068]_|
|PDRNI restricted (neighborhood)|||_0.170**_||
||||_(0.0709)_||
|_Socioeconomic characteristics_|||||
|Secondary education (1/0)|−0.0343*|−0.0335|−0.0300||
||(0.0203)|(0.0265)|(0.0260)||
|Higher education (1/0) [r_eference: primary education_]|−0.0671*|−0.0626*|−0.0616*||
||(0.0350)|(0.0351)|(0.0348)||
|2nd quantile household wealth index (1/0)|0.0295|0.0189|0.0183||
||(0.0272)|(0.0272)|(0.0272)||
|3rd quantile household wealth index (1/0)|0.0132|−0.000127|−0.00112||
||(0.0305)|(0.0288)|(0.0290)||
|4th quantile household wealth index (1/0)|−0.0523|−0.0189|−0.0216||
||(0.0296)|(0.0289)|(0.0290)||
|Age 20–29 [_Ref. cat. Age 15‐19_]|0.116***|0.116***|0.115***||
||(0.0240)|(0.0238)|(0.0237)||
|Age 30–39|0.227***|0.224***|0.223***||
||(0.0306)|(0.0286)|(0.0295)||
|Age 40–49|0.281***|0.283***|0.282***||
||(0.0268)|(0.0252)|(0.0261)||
|Single (1/0) [_reference: divorced, widowed_]|−0.0320<br>|−0.0325<br>|−0.0325<br>||
||(0.0217)|(0.0215)|(0.0210)||
|Married (1/0)|0.0634**|0.0636**|0.0618**||
||(0.0258)|(0.0264)|(0.0261)||
|Number of household members|0.00740|0.00747*|0.00698||
||(0.00459)|(0.00454)|(0.00442)||
|_Lifestyle_|||||
|<br>Smoking (1/0)|0.0668**|0.0632*|0.0616*||
||(0.0325)|(0.0325)|(0.0321)||
|Daily television usage (1/0)|0.0219*|0.0221*|0.0236**||
||(0.0118)|(0.0116)|(0.0115)||
|Not working (1/0)|0.00782|0.00828|0.00757||
||(0.0191)|(0.0186)|(0.0187)||
|_Health_|||||
|Sick last two weeks (1/0)|0.0162|0.0158|0.0192||
||(0.0264)|(0.0267)|(0.0268)||
|Currently breastfeeding (1/0)|0.00669|0.0119|0.0104||
||(0.0260)|(0.0263)|(0.0265)||
|Contributory health insurance (1/0) [_reference: no_|***|***|***||
|_insurance_]|−0.0496|−0.0489|−0.0520||
||(0.0149)|(0.0160)|(0.0161)||
|Non‐contributory health insurance (1/0)|<br>−0.0252|<br>−0.0278*|<br>−0.0285*||
||(0.0166)|(0.0158)|(0.0158)||
|_Environment_|||||
|Share of women daily FAFH (neighborhood)|0.110|0.0953|0.0776||
||(0.168)|(0.242)|(0.235)||
|Average menu price (neighborhood)|−0.00379|−0.0181|−0.0249**||
||(0.00983)|(0.0113)|(0.0103)||
|N|2,106|2,106|2,106||
|Pseudo R2|<br>0.087|<br>0.092|<br>0.096||

_Note:_ The variable _PDRNI restricted_ is the diet quality index that measures the proportional difference of relative nutrient intake (PDRNI). A one-unit increase expresses a higher distance to the ideal value and thus implies a less nutritious diet. * p < 0.1, ** p < 0.05, *** p < 0.01. Standard errors in parentheses are clustered by district.

17
However, one research grant allowed us to fill some geographical gaps, such as in West Africa and Southeast Asia. Overall, due to a special interest in forests, it was a precondition that some forest cover was still present; on the other hand, very remote indigenous hunting and gatherer cases with close to 100% forest cover were also omitted as too special scenarios. In addition, Africa is as a continent somewhat oversampled in PEN (slightly over half of the households). Within the study sites, the selection of villages was stratified so as to capture variation across some predefined (and partially correlated) gradients: remote vs. market-near areas, rich vs. poor in forest resources, indigenous vs. mixed populations, high vs. low population density, etc. Within villages, households were invariably selected by random sampling from (pre-existing or newly implemented) village census lists.

Second, after having collected the data, we also empirically tested for biases in the global PEN sample visà-vis the rural tropics with respect to two key variables: forest cover and population density were compared to province- and village-level developing-country controls (Angelsen et al. 2014: Annex). The PEN study areas match the full forest-cover range of controls, but somewhat overweight high forest-cover cases. Likewise, PEN does not cover cases with very high rural population density. Finally, it also excluded corporate forest frontiers dominated by largeholders. Hence, we can comfortably say that PEN is probably adequately representative of smallholder-dominated tropical and sub-tropical rural landscapes with moderate-to-good access to forest resources, and all but very high population densities. This implies that results should be trustworthy for the bulk of rural developing country settings, though they could differ for some of the under-represented locations.

A second concern is relevant when we bring in the climate: we are looking at the adaptive capacity of households through the lens of a cross-section. Our PEN sample includes rural households in 20 countries, with tropical as well as subtropical areas. While we have annual income and its disaggregation on four quarters, we do not have time series data, or at least several points of observation, to study households’ adaptation strategies in a longer perspective. We are thus estimating how households currently adapt their productive systems along a gradient of climate variables (temperature, precipitation, and their respective anomalies), within specific asset levels and contexts (at the country, village and household levels). We will below analyze these cross-sectional patterns from the angle of climate _variability_ , but will deliberately not refer to climate _change_ . We still hope that some of our conclusions can be given a time-relevant interpretation, and will in the closing section discuss under which circumstances and to what extent such an interpretation of the identified climate gradients may be adequate.

14
megacities.<sup>4</sup> The literature in urban and development economics points out that although a high degree of urban concentration might be useful in the early stages of development by conserving the economic infrastructure and enhancing information spillovers at precisely the point when infrastructure and information are at a premium, it results in a misallocation of resources at later stages of development.<sup>5</sup> Once a certain level of urban concentration is attained, economies of scale become exhausted, and mega-cities transform into sites that are excessively congested with high infrastructure costs. The consequences of this misallocation are both static and dynamic. For instance, Henderson (2003) provides evidence that supports the notion that excessive urban concentration has significant negative effects on productivity growth.

Given the importance of the consequences of excessive urban concentration, the natural question to explore is its causes. There is now have an extensive body of literature that argues that the observed levels of urban concentration arise from the nature of political institutions and the policy choices that follow (Ades and Glaeser 1995; Krugman and Livas 1996; Henderson and Becker 2000; Davis and Henderson 2003). One argument is that national governments may favor certain cities over others. The favorites may be capital cities (Mexico City, Seoul, London, or Paris) or the traditional seats of the elites (Istanbul or Sao Paulo). Such favoritism may take the form of underinvestment in provincial transport or telecommunications networks, restrictions in financial markets and transactions, preferential treatment of elites in favored cities in the allocation of licenses, quotas, production and trading rights, and the disproportionate provision of local public

> 4See, for instance, UN (1993) and the World Development Report (2000).

> 5See Williamson (1965) and Hansen (1990).
# **Regulation, Trade and Productivity in Romania: An Empirical Assessment**<sup>**1**</sup>

Donato De Rosa<sup>2</sup> (World Bank, <u>dderosa@worldbank.org)</u> Mariana Iootty<sup>3</sup> (World Bank, <u>miootty@worldbank.org)</u> Ana Florina Pirlea<sup>4</sup> (World Bank, <u>apirlea@worldbank.org)</u> Key words: cross country output convergence, aggregate productivity and regulation. JEL: O12, O47 Sector Board: Finance and Private Sector

> 1 The authors would like to thank Mamta Murthi, Peter Harrold, Francois Rantrua, Ismail Radwan, Pedro L. Rodriguez, Arabela Aprahamian  and Catalin Pauna  for useful comments. Views expressed in this paper are those of the authors and should not be held to represent those of the World Bank Group or its Executive Directors.

> 2 Senior Economist (FPDOK), corresponding author.

> 3 Economist (ECSSF2)

> 4 ETC (ECSF2)

1