rafmacalaba/datause-encoder-data huggingface dataset:
scheme implemented here has the virtue of not only being simple but also surprisingly effective at times.

Our “naïve” scheme takes the following form. We first rank geographic areas by estimated poverty. If our interest was to gauge the impact of our scheme on the headcount rate, we would rank areas by the headcount. But as we wish, in this paper, to assess the impact on the squared poverty gap we rank by those estimates. We have an assessment of overall poverty in the country. We take our budget _S_ and divide it by the total number of poor persons in the country, _Np_ . Our budget divided by the total number of poor persons yields the transfer _a_ that will be distributed to each person. We select the poorest geographic area and transfer _a_ to all persons in that area. If the budget has not been exhausted in the first region, we move to the next poorest region and transfer _a_ to all persons in this second region. We continue until the budget is exhausted. In the marginal region - that in which the budget is exhausted - we do not transfer _a_ but transfer an equal share of whatever remains in the budget to the population of that last region. Note that this scheme does not guarantee some amount of transfer to all regions. The scheme also implies that households will be receiving differing amounts according to their overall size.<sup>12</sup>

# _Implementation_

Operationalizing our simulation exercises involves taking outputs from the micro-level estimation procedure described in Section II and subjecting these to the simulation procedures described above. To recap, in Section II we described a procedure whereby per-capita

> 12  Two alternatives that we have experimented with (but do not report here as results were not noticeably better) include a “naïve poverty-gap” scheme and a “naïve poverty share scheme”.  In the former we rank communities by estimated poverty.  We then cumulate the poverty gaps amongst the poor within each community.  We divide that total by the number of poor people in the region. We then transfer this amount to everyone in the community (poor and non-poor).  We carry on until the budget is exhausted, and estimate impact on aggregate poverty.  In the latter scheme we calculate community i’s contribution to total poverty.  We transfer that

16
To account for potential systematic non-response in the post-COVID-19 phone survey, we use the weights described in Section 2. By using these sampling weights, we can generate representative statistics under the assumption that data are “missing at random” conditional on the observables used in construction of weights (Wooldridge, 2007; Korinek et al., 2007). Access to PSNP and related unobserved factors are potentially correlated among households living in the same sampling unit, the kebele. To account for this, we cluster standard errors at the kebele level.

The impacts of the pandemic and the role of PSNP in mitigating them are likely to vary across households with varying socioeconomic characteristics. To uncover such potential differential impact of PSNP across various groups of households, we run our preferred specification in equation (1) for several dimensions of heterogeneity, including wealth quintiles and remoteness of location of residence.

# **5. Results**

We first discuss our results on the impact of the PSNP in protecting household food security before considering impacts on maternal and child diets.

# **_5.1. The role of PSNP in protecting household food security_**

Table 2 presents difference-in-difference estimates comparing the temporal evolution of food security of PSNP beneficiaries and non-beneficiaries under alternative definitions of PSNP participation and controlling for time invariant household characteristics. We report linear fixed effects regression results in the main text and present results from nonlinear models (fixed effects logit and fixed effects Poisson regression) in Appendix Table A5. We present results with and without sampling weights, although weighted regressions are our preferred specifications, to probe the robustness of our results. The first three columns of Table 2 report results for the food insecurity dummy variable and columns 4-6 show results for the continuous food gap measure. Columns 1, 3, 4 and 6 are based on weighted fixed effects specifications while columns 2 and 5 are based on unweighted fixed effects models. Panel A of Table 2 provides results using self-reported access to PSNP benefits; Panel B reports results using information on amount of transfers; and Panel C provides estimates based on aggregate indicator of access to PSNP at district level.

Three important findings emerge from the results in Table 2. First, the share of food insecure households increased by 11.7 percentage points in the six months prior to the June 2020 13
feasible (refer back to Table 1 to see the dramatic declines in the cost of air transport and international communications). But part of the answer clearly lies in policy choices of developing countries as well. China and India had almost totally closed economies, so their increased integration would not have been possible without policy steps in these countries to gradually liberalize trade and direct foreign investment.

<!-- Start of picture text --> Figure 3. Average unweighted tariff rates by<br>region<br>Percent<br>1980-85<br>1986-90<br>60<br>1991-95<br>1996-98<br>40<br>20<br>0<br>South Latin America East Asia Sub- Middle East Europe and Industrialized<br>Asia and the and the Saharan and North Central economies<br>Caribbean Pacific Africa Africa Asia<br><!-- End of picture text --> _Source:_ Martin (2001) Some measure of this policy trend can be seen in average import tariff rates for the developing world. Average tariffs have declined sharply in South Asia, Latin America, and East Asia, while in Africa and the Middle East there has been much less tariff-cutting (Figure 3). These reported average tariffs, however, only capture a small amount of what is happening with trade policy. Often the most pernicious impediments are non-tariff barriers: quotas, licensing schemes, restrictions on purchasing foreign exchange for imports. In China’s case, reducing these non-tariff impediments starting in 1979 led to a dramatic surge in trade (Figure 4). In 1978 external trade was monopolized by a single government ministry. (The phrase “free trade,” 9
Policy Research Working Paper 9057

# **Abstract**

This paper collects meta data on transport corridor projects financed by the Asian Development Bank, Japan International Cooperation Agency, and World Bank and links them to one important wider economic benefit—local economic activity. The meta data cover 47 projects in 16 countries, with appraisal dates between 1991 and 2007. First, the paper reviews the variation in project design and implementation—including the local initial conditions, complementary non-transport interventions, and private sector involvement. Second, using the difference-in-differences methodology, the paper links this variation to a measure of local economic activity—the geocoded intensity of nighttime lights. The effect of the supported corridor projects on local economic activity could be very heterogenous and significantly depend on certain initial conditions and project characteristics. The latter could include locations with access to the sea, as well as projects with a strong theory of change and better engagement of the private sector.

This paper is a product of the Office of the Chief Economist, South Asia Region, the Transport Global Practice, and the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at malam5@worldbank.org, mdappe@worldbank.org, and mmelecky@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
percent for each surviving child under 21 years of age. In the early phase of the new pension system, most pensioners were disability and survivor pensioners and there were few old age retirees. This was expected because most retiring workers in the early 1980s preferred to stay in the old system. The share of disability and survivor pensioners has recently stabilized at around one-third of the total.<sup>21</sup>

**<u>Table 6: Breakdown of the Stock of Pensioners, by Type of Retirement, 1990-2007</u>**

|**Year**|**Total**|**Normal**<br>Number|**Old Age**<br>% of Total|**Early R**<br>Number|**etirement**<br>% of Total|**Disability**<br>Number|**+ Survivors**<br>% of Total|
|---|---|---|---|---|---|---|---|
|1985|7,609|2,647|34.8%|-|0.0%|4,962|65.2%|
|1990|57,119|23,876|41.8%|5,790|10.1%|27,453|48.1%|
|1995|190,400|55,591|29.2%|69,537|36.5%|65,272|34.3%|
|2000|343,965|93,152|27.1%|132,221|38.4%|118,592|34.5%|
|2004|540,342|134,207|24.8%|221,201|40.9%|184,916|34.2%|
|2005|574,011|148,096|25.8%|229,033|39.9%|196,882|34.3%|
|2006|606,342|163,223|26.9%|233,384|38.5%|209,735|34.6%|
|2007|642,064|179,146|27.9%|239,790|37.3%|223,128|34.8%|

Source: SP

**<u>Table 7: Average Retirement Age by the Type of Retirement, 1988-2007</u>**

||1988-1990|1991-1995|1996-2000|2001-2003|2004-2007|
|---|---|---|---|---|---|
|Old Age|65|65|65|64|64|
|Men|67|67|67|66|66|
|Women|63|62|62|62|62|
|Early Retirement|58|57|56|55|54|
|Men|58|57|56|56|56|
|Women|56|55|54|53|53|

Source: SP

**-** **<u>Table 8: Average Monthly Pensions by Type (in UF), 1990 2007, Period Averages</u>**

||**Old Age**|**Early Retirement**|**Disability**|**Survivorship**|**Total**|
|---|---|---|---|---|---|
|1990-94|5.6|9.5|7.4|2.7|5.7|
|1995-99|6.1|9.8|7.7|3.2|6.8|
|2000-04|6.8|10.2|8.2|3.7|7.4|
|2005-07|7.7|10.7|9.1|4.7|8.2|

Source: SP

# **3. The Menu and Performance of Retirement Products**

## **3.1 The Menu of Retirement Products**

Retiring workers can take a partial lump-sum payment subject to strict conditions and can also choose among 3 basic retirement products: a phased withdrawal (PW), an annuity and a temporary withdrawal (TW) combined with a deferred annuity. This section provides a brief

> 21Although disability and survivor pensions play a critical role in any pension system, they involve specific issues that require a separate examination and which are beyond the scope of this report.  The focus of this report is on old age and early retirement.

12
The remainder of Table 3 presents results for adults. Columns 4 through 6 show that there are no meaningful links between a cook’s reported symptoms and either assigned treatment status or stove use. Columns 7 and 8 report effects for summary measures of the activities of daily living outcomes assessing the likelihood of experiencing any specific difficulties and the number of reported difficulties. The estimated effects are small in magnitude and not statistically different from zero. The null physical functioning results hold using ordered probit models to examine the gradient of difficulties for each outcome and when considering binary outcomes for severe levels of limitations rather than severe or moderate difficulty used in Table 3. As with children, the null findings hold after dividing the adult cooks by age, by region, and by wealth quantile.

Taken as a whole, the results suggest a nuanced pattern of significant gains in early-life growth for young children, yet no noticeable impacts for either adult cooks or older children. Understanding the physiological mechanisms facilitating growth effects alongside null results on other health domains for older individuals remains a key component in this research program. We next present evidence examining the link between pollution measurements and health outcomes.

# **6. Mechanisms – Pollution Concentrations and Health Outcomes**

As a step toward explaining the high rates of _Mirt_ use, positive child growth effects, and the absence of other health effects, we examine the relationship between indoor air quality measures and key health outcomes. Indoor exposure to pollutants from the combustion of solid fuels was measured with pump and filter kits and light scattering meters in a random subset of 201 households: 98 treatments and 103 controls. More details on the data collection process and treatment effects of the intervention on air pollution levels are provided in Bluffstone et al. (2018).

The recorded levels of particulate matter in the treatment and control households are consistent with measurements in similar environments (Chen et al., 2016; Dutta et al., 2007) and well above WHO standards. Mean (median) concentrations in treatment and control households are 1,162 (464) and 1,297 (525) μg/m<sup>3</sup> for average 24-hour exposure. Twenty-four-hour maximum concentration levels are an order of magnitude higher. For comparison, the WHO’s current recommended 24-hour safety standard is 25 μg/m<sup>3</sup> (WHO, 2006b).

Here, we make use of pollution readings to descriptively assess the possibility of pollutionhealth gradients. For each of our key outcomes we estimate regressions of an individual’s health marker against the natural log of their household’s particulate concentration. As the shape of these 19
# **D.2 Alternative Samples and Specifications**

The results shown throughout Appendix Section D.2 re-estimate the output shown in the main body of the paper using a number of alternative cohort ranges, specifications, and samples. Each panel is described below, and the results in the following tables display the panels in a consistent order.

## **Panel A – Baseline**

For reference, Panel A reproduces the results used in the paper. These estimates use a sample of all Ethiopian women born between 1970 and 1988. All estimates in this section include a cubic in age, and birth year and district fixed effects. The baseline estimates use the FPE instrument, _Izy_<sup>_F P E_</sup> , starting age data from the 2007 census, and a district specific linear time trend.

## **Panels B to E – Alternative Cohort Ranges**

These panels include two expanded samples, 1968 to 1992 (Panel B) and 1969 to 1989 (Panel C), and two more restrictive ranges. In Panel D, one cohort from each end of the baseline sample is removed, yielding a range from 1971 to 1987. This sample no longer includes any fully post-reform cohorts. The data are restricted to 1972 in Panel E, the final fully pre-FPE cohort. Removing additional cohorts on the later end of the range would remove significant and necessary identifying variation; therefore, the 1987 cohort remains the cutoff on the upper end of the range in Panel E.

## **Panel F – Matched 1984 Start Ages**

Intensity measures are constructed using starting age information from the 1984 census. While the prereform timing of these data are ideal, the administrative boundaries are not consistent between the 1984 and post-1991 periods. Therefore, while there is starting age information contained in the 1984 census, the level two administrative information does not match with the zones used in the study. To adjust the 1984 data to the 1994 geographical boundaries, shapefiles from each time period provided by Minnesota Population Center and the Ethiopian Central Statistical Agency (2017) are overlaid, and new start values for the post1991 boundaries are calculated as the weighted averages of the start age value from the 1984 area and the portion of the post-1991 zone that is made up of that 1984 area. Unfortunately, this requires an unrealistic assumption of a consistent distribution of population within geographic area, and introduces a significant amount of measurement error into the start age calculations.

A.44
Policy Research Working Paper 9608

# **Abstract**

The majority of services trade is currently transacted under the terms of preferential trade agreements (PTAs) with increasingly ambitious provisions on crossborder trade and investment in services. This paper exploits novel and detailed information on the architecture and depth of services PTAs (the World Bank Deep Trade Agreements 2.0 database) to study which provisions, or policy configurations, characterise an effective agreement. The richness of policy information is crucial for being able to identify those aspects that matter most, namely an agreement’s structure, its rules of origin for firms and natural persons, and provisions that ensure accountability. Ambitious provisions in these areas are associated with 15–65 percent higher bilateral trade, driven by regulation-intensive services. Services PTAs also lead to an increase in services value added sourced from PTA partners, through provisions that facilitate the exchange of capital and people. This finding sheds light on how services PTAs can affect the configuration of value chain trade.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at I.Borchert@sussex.ac.uk and M.Di-Ubaldo@sussex.ac.uk.

_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
control, and fabrication (only available for firms in manufacturing). The list of technologies for each business function includes: (i) specialized software and ERP for Business Administration; (ii) specialized software and ERP for Production Planning; (iii) non-integrated and integrated Supplier Relation Management (SRM) for Supply Chain Management; (iv) Customer Relationship Management software (CRM) and Big data Analytics or Machine learning algorithms for Marketing; (v) computer numerical controlled machine, robots, and advanced manufacturing for Fabrication; (vi) online sales and electronic orders integrated to specialized supply chain management systems for Sales Methods; (vii) online or electronic payment through a bank wire and online payment through platform for Payment Methods; and, (viii) statistical process control with software monitoring and data management and automated systems for inspection for Quality Control. For instance, in the case of business administration, we have information on whether firms adopted specialized software or ERP and, more importantly, the date on which the firm adopted it. Using the years of adoption, we create an indicator for each business function equal to 1 from the year the firm adopted a given advanced technology and 0 in the previous years.

We then combine firms’ information on the year of adoption of sophisticated technologies with firms’ export status from the data available in Brazil’s Ministry of Trade. Thus, for each firm in our dataset, we have information on the year it started to export and the year it adopted a sophisticated technology in each business function. We also merge this data with the employer-employee census, including firm-level information on size and average wage. The resulting longitudinal dataset from 1994 to 2020 allows us to use a quasi-experimental design (difference-in-differences estimator) to explore the effect of entering export markets on adopting advanced technologies. In essence, we aim to compare the adoption rates of treated firms over the short and medium run with the adoption that would have occurred if they had not started to export.

In a typical difference-in-differences setting, we are confronted with two time periods: no firm is treated in the first period, and a group is treated in the second. Nevertheless, in our setting, in addition to multiple periods, firms enter exporting markets at different times, thus creating variation in the treatment timing. Traditionally, the response to this challenge is by estimating a model that includes dummies for cross-sectional units ( _αi_ ) and time periods ( _αt_ ) and a treatment dummy ( _Dit_ ). For example, the basic event study model would be: where _yit_ is the outcome of interest. Nevertheless, under the presence of time-varying treatment effects, the difference-in-differences estimator has been found to be biased (Goodman10
# **<u>Table A5: Financial Integration and the Sensitivity of Consumption to Income (1994-2011)</u>**

# **_<u>All Countries</u>_**

||**Chinn-Ito**<br>**[1]**|**Quinn**<br>**[2]**|**Chinn-Ito**<br>**[3]**|**Quinn**<br>**[4]**|**Chinn-Ito**<br>**[5]**|**Quinn**<br>**[6]**|
|---|---|---|---|---|---|---|
|Output|1.186<br>[0.006]***|1.428<br>[0.016]***|1.188<br>[0.007]***|1.451<br>[0.017]***|1.119<br>[0.007]***|1.341<br>[0.017]***|
|Output × _De Jure_|**-0.529**<br>[0.009]***|**-0.707**<br>[0.031]***|**-0.637**<br>[0.016]***|**-0.756**<br>[0.032]***|**-0.460**<br>[0.020]***|**-0.550**<br>[0.031]***|
|World Interest Rate×<br>_De Jure_|||0.029<br>[0.001]***|0.027<br>[0.002]***|||
|Country Specific|||||0.024|0.024|
|<br>Interest Rate×_De Jure_|||||[0.001]***|[0.001]***|
|Observations|1,580|1,392|1,492|1,340|1,492|1,340|
|Countries|88|85|88|85|88|85|
|**_Advanced Economie_**<br>Output|**_s_**<br>2.248<br>[0.184]***|1.454<br>[0.851]*|3.210<br>[0.180]***|1.993<br>[0.625]***|3.266<br>[0.216]***|1.944<br>[0.430]***|
|Output × _De Jure_|**-1.605**<br>[0.182]***|-0.825<br>[0.853]|**-2.710**<br>[0.170]***|**-1.476**<br>[0.643]**|**-2.773**<br>[0.199]***|**-1.382**<br>[0.455]***|
|World Interest Rate×<br>_De Jure_|||0.178<br>[0.051]***|0.010<br>[0.043]|||
|Country Specific|||||0.121|-0.021|
|Interest Rate×_De Jure_|||||[0.030]***|[0.045]|
|Observations|394|392|372|371|372|371|
|Countries|22|22|22|22|22|22|
|**_Developing Econom_**<br>Output|**_ies_**<br>1.145<br>[0.013]***|1.341<br>[0.052]***|1.103<br>[0.021]***|1.269<br>[0.036]***|1.045<br>[0.018]***|1.205<br>[0.041]***|
|Output × _De Jure_|**-0.453**<br>[0.026]***|**-0.556**<br>[0.088]***|**-0.453**<br>[0.030]***|**-0.406**<br>[0.060]***|**-0.289**<br>[0.041]***|**-0.298**<br>[0.066]***|
|World Interest Rate×_De_<br>_Jure_|||0.067<br>[0.004]***|0.059<br>[0.005]***|||
|Country Specific Interest<br>Rate×_De Jure_|||||0.040<br>[0.003]***|0.038<br>[0.004]***|
|Observations<br>Countries|1,186<br>66|1,000<br>63|1,120<br>66|969<br>63|1,120<br>66|969<br>63|

Note: Dependent variable is consumption growth. GMM Dynamic Panel Estimates. Lags 2-4 of consumption growth, all right hand side variables and lagged saving are used as instruments. De Jure refers to financial integration measures of Chinn-Ito and Quinn. In columns [3]-[6], interest rate is also included as a control. Sample period 1960-2011 or as dictated by availability of data.

25