rafmacalaba/datause-encoder-data huggingface dataset:
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|---|---|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|EARLY CHILDHOOD DEVELOPMENT|Rekha Menon|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Women's Health and Safe Motherhood Project|Teresa Ho|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Urban Health and Nutrition Project|Jayshree Balachander|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Transmission Grid Reinforcement Loan Project|Selina Wai SheungShum|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Communal Irrigation Development Project(02)|Syed Husain|
|Municipal Development Project(02)|Thomas L. Zearley|
|Municipal Development Project(02)|Thomas L. Zearley|
|Municipal Development Project(02)|Thomas L. Zearley|
Table 8: Disaster fatalities and aid from different donor countries

||Storms|Floods|Earthquakes|
|---|---|---|---|
|Ln_(FRENCH AIDj,t-1)_|0.789|0.136|-2.723***|
||(0.616)|(0.496)|(0.923)|
|Ln_(GERMAN AIDj,t-1)_|0.513|-0.566***|-1.038*|
||(0.437)|(0.215)|(0.562)|
|Ln_(JAPANESE AIDj,t-1)_|0.102|-0.429**|-1.236***|
||(0.148)|(0.170)|(0.231)|
|Ln_(UK AIDj,t-1)_|-0.538|-0.662**|0.375|
||(0.418)|(0.302)|(1.568)|
|Ln_(USA AIDj,t-1)_|-0.053|-0.690**|-0.957**|
||(0.272)|(0.271)|(0.441)|

_Notes:_ Negative binomial estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. Covariates from the baseline specification (equation (5)), continent dummies, time trend and constant term included in all specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively.

27
**<u>Table 2: Numbers of poor in millions</u>**

||Consumpt<br>1993 PPP<br>_Z_=$1.08|ion poverty<br>2005 PPP<br>_Z_=$1.25<br>(with<br>adjustment)|Income<br>1993 PPP<br>_Z_=$1.08|poverty<br>2005 PPP<br>_Z_=$1.25<br>(with<br>adjustment)|
|---|---|---|---|---|
|1981|638.0|838.9|623.4|816.2|
|1984|428.0|720.9|267.2|569.9|
|1987|314.0|587.9|235.0|499.1|
|1990|377.5|693.7|263.0|526.2|
|1993|336.1|648.3|248.9|472.9|
|1996|212.5|454.2|127.3|292.4|
|1999|223.5|448.9|100.6|224.0|
|2002|177.2|357.3|97.6|196.6|
|2004|128.6|286.8|75.4|153.7|
|2005|71.6|204.3|70.6|135.4|

<u>Note: Population figures used here are at the end of year from China’s Statistical</u> Yearbooks (NBS, 1982-2006). Poverty line in $’s per person per day.

16
8

# Capital Expenditures

Many public expenditures, including infrastructure projects, other capital spending, as wen as current expenditures for education, provide benefits to households for multiple years. Data on government capital expenditures are generally available by function for several years, but allocating the benefits of these capiti.. expenditures to households will generally involve mapping the present value of the multi-year benefits to households. In an expenditure-based framework, one would multiply the value of the capital stock by the sum of the opportunity cost of capital and depreciation as a proxy for capital services in each year. Of course, capital stock value data are typically not available in most countries. In practice, benefit incidence studies handle multiple period benefits in a variety of ways, and we reiterate several methods when we discuss specific studies.<sup>5</sup>

# Interest Payments

Several earlier studies incorrectly allocate government interest payments for bonds to households as governnent benefits. Interest payments should never be treated as transfer payments to households (Hammes and Wills, 1987), as the interest payments double-count benefits that have already been counted in the allocation of service flows from the capital expenditure. Some studies use interest payments as a proxy for services from capital expenditures, however, and avoid double-counting by not separately allocating service flows from capital expenditures.<sup>6</sup>

# Rent-Seeking **and Side Payments**

In both developed and developing countries, benefits can be shifted and dissipated through what has become known as "rent-seeking," whereby individuals or groups of individuals attempt to influence government behavior (Rowley, Tollison, and Tullock, 1988, and Mohammed and Whalley, 1984). It appears reasonable to subtract rent-seeking expenditures by individuals from the cost of the govenmment good or service to more closely approximate th- net benefit to the individual. Data on side-payments is generally not available, but researchers should be mindfiu that side-payments may reduce the direct benefits of the program.
**Table A2. Summary statistics, estimation sample, 1997-2005**

||1997|1998|1999|2000|2001|2002|2003|2004|2005|
|---|---|---|---|---|---|---|---|---|---|
|sales|5.57|5.45|5.18|4.97|5.04|7.36|6.91|9.86|9.35|
||(0.51)|(0.44)|(0.45)|(0.46)|(0.50)|(0.67)|(0.61)|(0.79)|(0.70)|
|number of output categories|4.51|4.52|4.51|4.53|4.47|5.93|6.04|3.89|3.82|
||(0.07)|(0.07)|(0.06)|(0.06)|(0.06)|(0.17)|(0.17)|(0.09)|(0.09)|
|export share of sales|0.21|0.21|0.21|0.20|0.19|0.14|0.14|0.24|0.24|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|
|share of exports to richer countries|0.79|0.80|0.81|0.80|0.79|0.76|0.75|0.79|0.78|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|number of destination countries|7.71|7.53|7.53|7.53|7.50|7.32|7.15|8.85|8.94|
||(0.15)|(0.14)|(0.14)|(0.14)|(0.15)|(0.33)|(0.30)|(0.27)|(0.27)|
|number of export categories|10.03|9.89|9.88|9.86|10.02|9.76|9.39|13.04|13.77|
||(0.28)|(0.26)|(0.26)|(0.25)|(0.27)|(0.72)|(0.61)|(0.56)|(0.59)|
|avg. income of destination|11.62|11.63|11.60|11.48|11.41|10.73|10.69|11.46|11.36|
||(0.05)|(0.05)|(0.05)|(0.05)|(0.04)|(0.07)|(0.06)|(0.07)|(0.07)|
|purchases|3.24|3.09|2.91|2.87|2.92|4.19|3.70|5.49|5.12|
||(0.43)|(0.35)|(0.36)|(0.43)|(0.42)|(0.37)|(0.32)|(0.55)|(0.47)|
|number of input categories|7.70|8.12|8.44|8.78|8.90|7.05|7.18|7.75|7.87|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.14)|(0.14)|(0.15)|(0.15)|
|import share of purchases|0.20|0.20|0.20|0.20|0.19|0.18|0.18|0.28|0.27|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|
|share of imports from richer countries|0.90|0.89|0.90|0.90|0.88|0.87|0.88|0.87|0.87|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|(0.01)|
|number of source countries|5.63|5.65|5.65|5.58|5.56|5.34|5.21|6.78|6.83|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.08)|(0.18)|(0.17)|(0.14)|(0.14)|
|number of import categories|21.74|21.70|21.94|22.00|21.61|20.06|20.60|29.41|31.04|
||(0.65)|(0.64)|(0.61)|(0.59)|(0.59)|(1.19)|(1.51)|(1.28)|(1.33)|
|average income of source|10.87|10.88|10.89|10.81|10.68|10.55|10.51|11.17|11.07|
||(0.03)|(0.03)|(0.03)|(0.03)|(0.03)|(0.06)|(0.05)|(0.06)|(0.06)|
|fraction exporter|0.49|0.49|0.49|0.47|0.45|0.45|0.46|0.59|0.60|
|fraction importer|0.50|0.52|0.51|0.49|0.48|0.45|0.45|0.60|0.59|
|N (frms)|6585|6873|7194|7713|7994|2046|2160|2455|2639|

Notes: Table reports averages across firms, weighting firms equally. Average income of sales destination and purchase sources include the home market as possible destination or source, as described in Section 3 of the text. Sales and purchases are in millions of 2002 Euros, avg. incomes of destination and source in thousands of 2002 Euros.
their use after the pandemic.

<!-- Start of picture text --> (a) By BPS round (b) By income groups<br>60 100<br>47<br>80<br>41<br>40<br>60<br>28<br>40<br>20<br>20<br>0 0<br>Developing countries High income<br>1 2 3<br>Survey Round Started Increased Use but didn't increase<br>Cumulative predicted probability<br>Prob. of starting or increasing use of digital<br><!-- End of picture text --> Note: (a) Result from 24 countries with three rounds of data. Aggregate indicator might differ from figure 2 which includes all available data. (b) BPS countries includes all 4 categories. For WBES data, previous rounds are used to identify firms that used digital platforms but not increased use.

Figure 3: Likelihood of starting or increasing the use of digital technology

# **4. The digital divide**

Even before COVID-19 hit there was growing evidence of a digital divide in firms (see Bach et al. (2013)). Results show differences in digital adoption across firm sector, size or manager characteristics that over time have persisted. Different channels such as access to finance, access to human capital, or differences in management practices have been hypothesized as mechanisms that might prevent this gap from closing. When lock-downs and customers’ behavior changed due to the pandemic, many of those channels were exacerbated. A report from the European Commission shows that about a third of jobs in the EU could be done remotely, and that the COVID-19 shock expanded remote work to younger workers in "blue-collar" occupations. Nevertheless, its expansion and persistence depends on improving firm digital capabilities. Vo et al. (2022) show that firms with higher digital infrastructure before COVID-19 were able to perform better during the crisis. Furthermore, the digital divide can be higher in developing countries where a large percentage of firms face access restrictions to infrastructure, skills, and networks (Ndulu et al. (2022)).

This sections analyzes the heterogeneity in the uptake of digital technologies across sectors, firm-size categories, and business function purpose, in response to the COVID-19 pandemic.

## **4.1**

The increase in digitalization after the pandemic shock amplified existing differences by sectors. The firms with the highest likelihood of starting or increasing the use of digital technologies, with more than 60%, were firms in knowledge intensive sectors (ICT and financial services). This is not entirely surprising given that these services are the most amenable to home-based work (Dingel 8
<!-- Start of picture text --> Plc,Planning, and Research<br><!-- End of picture text -->

# **Trade Policy|**

Should the United States increase taxes and tariffs in the energy sector to reduce its federal deficit?

welfare. Each dollar raised through the excise tax on petroleum products would come at a loss of only one cent in welfare.

The authors used a twelve-sector general equilibrium model to estimate the fiscal effects, and the effects on welfare and employment, of

* A 25 percent import tax on imported crude oil.

* A 15 percent excise tax on petroleum products.

* A combination of the two.

T.ae excise tax would be the most efficient instrument for raising revenues.

The 25 percent import tariff would raise $7.3 billion in government revenues, while the 15 percent excise tax on petroleum products would raise $35 billion in government revenues.

Moreover, each dollar raised through a tariff on imports would come at a loss of 25 cents in Not only v ould an import tariff on crude oil cause much dislocation (an estimated 153,000 workers would have to relocate), but it would pose trade policy problems.

A combination of excise taxes, subsidies, and import ta'iffs would be the least costly way (in terms of welfare) to raise $20 billion in government revenues. Taxing both sectors minimizes distortion-induced resource movements. The welfare cost of raising $20 billion is least when domestic petroleum production is subsidized by the combination of an import tariff and a small subsidy to counteract the distortion resulting from the tax on oil and gas, an input of the petroleum sector.

The optimal tax structure would involve a tariff and a small subsidy on petroleum products to counteract the distortion induced by a tax on oil - the most important input for petroleum products.

This paper is a product of the Trade Policy Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Karla Cabana, room N8-065, extension 61539.

The PPR Working PapePr Series disseminates the ftdings of work under way in the Bankts Policy. Planning, and Research Complex. An objective of the series is to get these findings out quickly. even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily Tepresent of ficial policy of the Bank.

Produced at the PPR Dissemination Center
Policy Research Working Paper 6660

# **Abstract**

This paper sheds light on the impact of improving employment and education conditions on poverty and social exclusion indicators. More specifically, it answers the following question: Will achieving the Europe 2020 national targets on employment and education lead countries to achieve the Europe 2020 poverty and social exclusion target with no other policy interventions? The paper presents a simple partial equilibrium model that is flexible enough to be implemented in a number of different settings and uses widely available household survey data. The simulation model analyzes poverty and social exclusion outcomes in response to changes in education completion rates and employment rates. The model is applied to ten of the European Union’s new Member States—Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia—and the model’s performance is evaluated through a validation exercise. The Europe 2020 national employment targets are ambitious in many of the new Member States, given historical employment patterns in the countries. Especially in light of the slow and uncertain recovery, labor markets remain weak and employment rates in 2020 could fall short of rates targeted by national policy makers. In this eventuality, the poverty and social exclusion goals may not be reached in many of the new Member States without additional policy measures.

This paper is a product of the Human Development and Poverty Reduction and Economic Management Sector Units, Europe and Central Asia Region. 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 majwad@worldbank.org and ksimler@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
cashew nuts in Mozambique (McMillan et al. 2003). Lacking the necessary household surveys before and after the reforms, it is hard to know how rural poverty has been affected by these reforms. With household surveys straddling the reform period during which the marketing board was eliminated, however, Madagascar’s vanilla reform is an exception.

Madagascar's vanilla policy since independence is in many ways typical of a pattern common to large parts of SSA, having gone through three successive phases. Throughout the colonial period, the vanilla market had been marred by price instability, low farmgate prices, and collusion among traders. Right after independence, the Government of President Tsiranana intervened in a bid to bring more stability and equity in the distribution of the gains from the vanilla trade (sound economic arguments could also be invoked in favor of public intervention in the market). A vanilla stabilization fund was created, prices were guaranteed, and a cartel was formed in 1962 with the Comores and the Réunion, with the objective of taking advantage of the region's market power over the world's second most expensive spice. Government intervention initially brought about positive results, with exports up by a factor of five in the 15 years following independence (Blarel and Dolinsky, 1995).

However things turned for the worse in the mid-1970s after the Socialist revolution of President Didier Ratsiraka. Policy capture, rent-seeking, inefficiency, and corruption were the hallmarks of this era, in Madagascar as in so many other SSA countries at the time. Export taxation became confiscatory, with farmers receiving less than 8% of vanilla’s FOB price. On the world market, the cartel’s overpricing policy encouraged Indonesia's successful entry. By the early 1990s, the golden-egg goose had been all but killed, and in 1995 the government finally adopted the reforms long advocated by donors: elimination of the stabilization fund, phasing out of the export tax, and limitation of public intervention to coordination and non-intrusive forms of quality control. The ten years since reform have featured renewed turbulence on the market and changes in the distribution of the gains from trade, which this paper purports to study.

Vanilla in Madagascar is a particularly interesting case to study the hurdles that reforms must overcome to be successful. The characteristics of the vanilla market and of vanilla preparation suggest significant externalities and market failures (e.g. asymmetric quality information) that justify intervention of the type that was initially set up. So if opportunistic behavior could be controlled, cooperation among agents involved in the value chain leading to export would be optimal 3
|**Table 5. Consumption and the choice of migration destination**|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Consumption**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|
|Average consumption expenditures (log)|0.140|0.635|0.232|1.007|||||||
|Log consumption differential due to education and high caste|||0.566***|6.937|||||||
|Combined average and differential|||||0.457***|4.264|||0.227|0.997|
|Relative log consumption controlling for education and ethnicity|||||||5.609***|7.143|3.548|1.584|
|**Prices and amenities**|||||||||||
|Log of rice price|-1.929**|-2.100|-2.016**|-2.340|-2.083**|-2.378|-1.920**|-2.154|-2.015**|-2.340|
|Housing price premium (log)|0.186***|2.887|0.188***|2.900|0.195***|3.196|0.180***|2.871|0.187***|2.865|
|Time travel to nearest paved road|-0.881***|-9.904|-0.850***|-9.393|-0.790***|-8.492|-0.920***|-8.932|-0.854***|-9.412|
|Time travel to nearest bank|0.139|0.635|0.092|0.422|0.096|0.452|0.101|0.442|0.093|0.425|
|Elevation in meters|-0.674***|-2.752|-0.693***|-2.957|-0.777***|-3.524|-0.607***|-2.949|-0.687***|-2.921|
|**Population**|||||||||||
|Population density|0.783***|5.504|0.878***|6.290|0.842***|6.239|0.887***|6.489|0.878***|6.292|
|Log(population)|0.334*|1.866|0.265|1.529|0.252|1.412|0.295*|1.719|0.266|1.536|
|Ethnicity similarity index|1.719***|7.155|1.719***|7.230|1.742***|7.138|1.703***|7.177|1.723***|7.244|
|Language similarity index|1.485***|10.484|1.589***|11.198|1.548***|10.771|1.608***|11.181|1.592***|11.210|
|Religion similarity index|-0.578|-1.373|-0.529|-1.267|-0.508|-1.249|-0.552|-1.311|-0.526|-1.261|
|**Distance**|||||||||||
|Distance above 100 Km|-0.811***|-2.601|-0.826***|-2.627|-0.796**|-2.506|-0.851***|-2.733|-0.827***|-2.632|
|Log-Likelihood|-57,09|6.58|-56,77|6.16|-56,79|4.57|-56,78|5.89|-56,77|3.81|
|Number of observations|1,076,|556|1,072,|804|1,072,|804|1,072,|804|1,072,|804|
|Pseudo R2|0.15|5|0.15|7|0.15|6|0.15|7|0.15|7|

The estimator is Fixed Effect Conditional Logit. Standard errors are corrected for clustering across district of origin. *** p<0.01, ** p<0.05, * p<0.1
# 2 **Transaction data from the Trucking portal**

Our main source of data is the confidential transaction-level data on trucking from a logistics company we call JDL. The data set covers 487,406 transactions that took place on its platform between January 2017 and February 2021. These transactions hauled about 8.8 million metric tons of freight. The platform was open to truckers and their customers, and a bidding process was followed leading to the ultimate transaction. Each transaction recorded its origin and destination at administrative/census code level 4 (town/village level), the types of goods transported, truck characteristics including its length, capacity, and type, and most importantly transport charge paid by the customer and weight carried by each truck. Though JDL maintains a fleet of trucks, the platform attracted many other truckers and customers beyond its own customer base. All transactions were facilitated by JDL for a small fee. About 5 percent of the transactions in the data set involve JDL’s own trucks. The main analysis of the paper is based on transactions that took place before the COVID-19 lockdown on March 24, 2020. After light cleaning, we have about 470,000 transactions in the data set during this pre-COVID-19 period. The data during and after lockdown due to COVID-19 are utilized to track changes in transport costs during the pandemic period. The COVID-19 period data consists of 7,344 observations. We complemented these transactions data with distance, travel time, and crow fly distance using google distance API. This data set is further augmented with night light data from the VIIRS (Visible Infrared Imaging Radiometer Suite) which improved upon the data from the DMSP satellites.<sup>8</sup> The trips in the data set cover nearly all corners of India ensuring very wide geographical coverage. About 31 percent of trips either originate or end in 7 main Indian cities highlighting the importance of economic density in trade flows.<sup>9</sup> Though the amount of freight hauled in this data set constitutes a small

> 8 In 2011, through a partnership with NOAA and the Department of Defense, NASA launched the Suomi NPP satellite and in 2018 the NOAA-20 satellite. Both satellites carry the VIIRS instrument, which also collects NTL emissions and continues the long-term NTL data record. The VIIRS DNB improves upon the older DMSP OLS with higher spatial and radiometric resolution: VIIRS has a spatial resolution of 375 and 750 meters (depending on the band), daily temporal resolution, and more complete global coverage and higher quality data.

> 9 These cities are Delhi, Mumbai, Bangalore, Chennai, Hyderabad, Ahmedabad and Kolkata.

7