rafmacalaba/datause-encoder-data huggingface dataset:
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Brunnermeier, M. K. 2009. “Deciphering the Liquidity and Credit Crunch 2007-2008.” _Journal of Economic Perspectives_ 23 (1): 77–100.

Buera, F., J. Kaboski, and Y. Shin. 2011. “Finance and Development: A Tale of Two Sectors.” _American Economic Review_ 101 (5): 1964–2002.

Caballero, R. J., T. Hoshi, and A. K. Kashyap. 2008. “Zombie Lending and Depressed Restructuring in Japan.” _American Economic Review_ 98 (5): 1943–77.

De Jonghe, O., H. Dewachter, K. Mulier, S. Ongena, and G. Schepens. 2019. “Some Borrowers Are More Equal Than Others: Bank Funding Shocks and Credit Reallocation.” _Review of Finance_ 24 (1): 1–43.

Deaton, A. 1999. “Commodity Prices and Growth in Africa.” _Journal of Economic Perspectives_ 13 (3): 23–40.

Deaton, A., and R. Miller. 1996. “International Commodity Prices, Macroeconomic Performance, and Politics in Sub-Saharan Africa.” _Journal of African Economies_ 5 (3): 99–191.

Degryse, H., and S. Ongena. 2005. “Distance, Lending Relationships, and Competition.” _The Journal of Finance_ 60 (1): 231-266.

Dehn, J. 2000. “Commodity Price Uncertainty and Shocks: Implications for Economic Growth.” CSAE Working Paper Series 2000-10, Center for the Study of African Economies, University of Oxford.

Diamond, D. W. 1984. “Financial Intermediation and Delegated Monitoring.” _Review of Economic Studies_ 51 (3): 393–414.

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Gilje, E. P., E. Loutskina, and P. E. Strahan. 2016. “Exporting Liquidity: Branch Banking and Financial Integration.” _Journal of Finance_ 71(3): 1159–84.

21
Table 4: Lives and livelihoods - pandemic's aggregate eects on mortality and poverty (Current = early June 2020)

||(1)<br>LIC|(2)<br>LMIC|(3)<br>UMIC|(4)<br>HIC|(5)<br>World|
|---|---|---|---|---|---|
|Total number of Covid-19 d|eaths, in|million||||
|Current scenario|0.000773|0.0171|0.0883|0.290|0.397|
|No intervention, no saturation|1.015|9.396|14.82|7.387|32.66|
|No intervention, saturation|1.159|10.23|15.61|13.28|40.32|
|Total increase in lost-years,|in millio|n||||
|Current scenario|0.0113|0.221|1.204|2.893|4.330|
|No intervention, no saturation|14.73|121.4|195.9|73.75|406.4|
|No intervention, saturation|20.59|153.7|227.8|157.9|560.7|
|Total increase in poverty-ye|ars, in m|illion||||
|Distribution-neutral scenario||||||
|1.9 PPP-$ poverty line|13.74|47.12|6.780|0.235|68.22|
|WB classication poverty line|13.74|138.7|53.90|28.46|234.8|
|+3.6% in Gini scenario||||||
|1.9 PPP-$ poverty line|21.00|91.58|16.43|0.569|130.5|
|WB classication poverty line|21.00|196.4|106.5|44.44|368.3|

# 5 Concluding remarks

The Covid-19 pandemic has generated huge losses in well-being around the world, by increasing mortality, causing ill-health and suering, closing schools, etc. In combination with individual and policy responses, the pandemic has also generated a large global negative economic shock, with GDP declines currently expected to range from 4.8% on average in low-income countries to 8.9% on average in high-income countries. These marked economic contractions are causing substantial increases in poverty, reversing  at least temporarily a hitherto sustained trend of decline in global poverty.

In this paper we focus on the mortality and poverty costs of the pandemic. We propose a simple framework to conduct a welfare evaluation of those costs, relying on a comparison of the number of years lost to Covid-19 deaths (lost-years) with the number of additional person-years spent in poverty in 2020. The welfare weights associated with these two quantities are transparently captured by a single normative parameter _α_ , which denotes how much worse a lost-year is than a poverty-year in social welfare terms. We are agnostic about its value, imposing only that it is bounded below at one. Our approach is to compare it to empirical estimates of the ratio of poverty-years to lost-years in each

> available upon request).

22
Where _tarisr_ is the tariff rate on imports from region s shipped to region r. Note that for exports, there is a unique weight associated with any good exported from a particular region; but the import weights _impisr_ are bilateral by product.

# **5. Model Results**

In order to clearly examine the impact of model assumptions and stay close to the results in the literature, we begin with single sector and four-sector models and examine a ten percent reduction in iceberg trade costs. For our tariff policy scenarios, we employ a more realistic nine-sector model. All our models contain ten regions and an untruncated Pareto distribution in the Melitz model. To be consistent with the literature, we rebalance the GTAP data so that all final demand is consumption demand. For our welfare results, we report Hicksian equivalent variation as a percent of the benchmark value of real consumption.

## **5.1 Welfare Equivalence in the Model with One Sector, One Factor and No Intermediates**

We have two sets of simulations in table 2 with welfare equivalence between three market structures. First, we consider the simplest model of Arkolakis, Costinot and Rodriguez-Clare (2012), i.e., one-sector, one primary factor of production, no intermediate inputs, trade costs are iceberg, balanced trade, no labor-leisure choice, untruncated Pareto probability distributions of productivity. The counterfactual shock that we consider is a global uniform reduction in iceberg trade costs of ten percent. In column 2, we calculate the gains from trade using the formula of Arkolakis, Costinot and RodriguezClare (2012). In columns 3-5, we calculate the welfare change from the reduction in trade costs using our model. To be consistent with the model of Arkolakis _et al. (2012)_ , we rebalanced the data from the GTAP dataset such that all demand is final demand in table 2 (i.e., no intermediates), and there are zero trade balances in all regions of the model. Our simulations replicate the result of Arkolakis _et al._ (2012) that the alternative market structures generate identical welfare impacts. Further, the table shows that welfare can be derived from a very simple calculation based only on the domestic expenditure share trade response and the trade elasticity. These are precisely the points made by Arkolakis _et al. (2012)_ .

In columns 6-8, we allow the initial trade balances based on the data. Note that the domestic expenditure shares  _rr_ change as a result of the incorporation of the benchmark trade imbalances. In all the subsequent simulations in this paper, we assume trace imbalances based on the data and that the observed current-account imbalances are held fixed (in units of US consumption). Costinot and 18
The second type of tax related to immovable property, the recurrent property tax, is levied by ULBs. Also known as house tax in some states, the property tax is a direct tax levied annually or semi-annually on property value, and it forms the largest source of ULBs’ own revenue. Property taxes are grossly underestimated and undercollected, thus, reducing the ULBs’ revenues. Several factors are behind this: incomplete property records, policy inadequacy, and ineffective administration.

# Appendix 2: Legal framework for levying property-related taxes and charges in India

## Legal provisions for levying stamp duty and registration charges on transfer of immovable property

The Indian Stamp Act, 1899, confers the power of levying and collecting stamp duty and registration fees on state governments. Under this Act, documents pertaining to the transfer of property must be legitimized by paying the applicable stamp duty. Further, a registration fee may be charged by the state for providing the service of maintaining a registry of these documents.

Each state has the authority to enact its own laws for levying stamp duties, broadly following the contours of the central Stamp Act. Thus, the features of stamp duties remain similar across states but with variations in some specific characteristics.

This section provides an overview of a comprehensive review of the legal framework for levying stamp duty and registration fees provided for in the Stamp Acts of all 28 states and the Delhi Stamp Act. These Acts and their latest amendments have been sourced from the official websites of the state governments.

All of the state Stamp Acts clearly establish that the power to levy stamp duty and registration charges lies with the respective state government. No clear provision indicates how the revenue from stamp duties is to be shared with the ULBs. Where such arrangements exist, they are ad hoc and not codified in law. All the state governments and the government of Delhi can levy conveyance charges on any transfer of property instrument by order published in the official gazette. This charge is levied on the grantee, lessee, or purchaser of property, depending on the type of transfer instrument.

Stamp duties can be charged at an ad valorem rate, a fixed rate, or a mixture of both. In the majority of states, stamp duty is charged as an ad valorem rate, i.e., as a percentage of the market value of the property or of the transaction value of the property, whichever is higher.

The minimum value at which a property must be registered is assigned and published by the stamp duty and registration department of each state every year and is known by different names in different states, e.g., circle rate, benchmark value, ready reckoner rate, etc. It is a state-defined proxy for market value (differentiated by property type, e.g., commercial, residential, etc.) in the specific region in which the 35
# **Table 4. Estimates using a polynomial function of rainfall**

||Dependent<br>|variable: Share o<br>|f votes for incumb<br>|ent in 2006<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Below Cutoff|7.69|7.434|7.674|10.136|
||(1.000)***|(1.087)***|(1.443)***|(1.650)***|
|Rain Deviation|0.03|0.015|0.028|0.143|
||(0.018)*|(0.041)|(0.068)|(0.077)*|
|(Rain Deviation)^2||0.000|0.000|-0.007|
|||(0.000)|(0.001)|(0.003)***|
|(Rain Deviation)^3|||0.000|0.000|
||||(0.000)|(0.000)***|
|(Rain Deviation)^4||||0.000<br>(0.000)***|
|Constant|7.255|6.754|7.216|13.666|
||(9.071)|(9.252)|(9.515)|(9.647)|
|Observations|1038|1038|1038|1038|
|R-squared|0.82|0.82|0.82|0.82|
|State controls|Yes|Yes|Yes|Yes|
|Controls at electoral section level|Yes|Yes|Yes|Yes|
|Controls at municipal level|Yes|Yes|Yes|Yes|
|Mean of dependent variable|45.37|45.37|45.37|45.37|

Robust standard errors in parentheses State controls are dummy variables for each state. Controls at the electoral sections include altitude, distance from the weather station, distance to the nearest river and distance to the "cabecera". Controls at the municipal level include municipal income per capita for the year 2000.

* significant at 10%; ** significant at 5%; *** significant at 1%

Page **26** of **29**
**Figure A.9: Comparison between Live and Video Observations**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3 Live<br>2<br>Video<br>1<br>0<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

# **A.5 Standard Deviation of Scores**

Finally, we calculated the standard deviation of the scores across all dimensions, and compared the results with those from the U.S. comparator studies (figure A.10).

The larger standard deviation could be introduced through two channels. First, it is possible that the raters were more willing to give a wider range of scores for a similar set of classrooms. Second, and more likely, is that the range of teachers observed in Guangdong was greater than in the U.S. studies. While on average these classrooms were quite similar in many regards, the range of quality among the Chinese teachers was greater. Given the greater differences in economic context and teacher training, this seems a possible explanation.

31
|Karonga<br>|161|54%<br>|138|46%<br>|299|100%|
|---|---|---|---|---|---|---|
|Rumphi<br>|122|22%<br>|423|78%<br>|545|100%|
|Kasungu<br>|485|38%<br>|780|62%<br>|1,265|100%|
|Nkhotakota<br>|119|24%<br>|382|76%<br>|501|100%|
|Ntchisi<br>|499|51%<br>|473|49%<br>|972|100%|
|Dowa<br>|481|56%<br>|377|44%<br>|858|100%|
|Lilongwe Rural<br>|1,203|29%<br>|2,996|71%<br>|4,199|100%|
|Ntcheu<br>|404|57%<br>|308|43%<br>|712|100%|
|Chiradzulu<br>|528|77%<br>|161|23%<br>|689|100%|
|Blantyre Rural<br>|725|74%<br>|249|26%<br>|974|100%|
|**Total***|**4,727**|**43%**|**6,287**|**57%**|**11,014**|**100%**|

Notes: (*) the total in the second panel corresponds to the total number of villages in matched GVNs only.

The analysis presented below is based entirely on the final sample of matched villages. Therefore, it is important to check whether there is any systematic selection bias in the sample. Table 3 shows the comparison of census means in the matched and unmatched villages. It shows that in most of the features, the difference in means between matched and unmatched villages is very small and not statistically significant. However, we observe that matched villages have a higher share of households with more educated adults. Also, households in matched villages have a lower child dependency ratio and slightly higher elderly dependency ratio. Finally, a higher share of households in matched villages have better roof quality in their houses.

**Table 3.** Comparison of census means in matched and unmatched villages

||**Mean**|**Difference**<br> **(matched-unmatched)**|**P-value**<br>**(Difference)**|
|---|---|---|---|
|Highest educated man: primary education|0.27|0.01|0.16|
|Highest educated man: secondary education|0.11|0.01|0.05|
|Highest educated man: tertiary education|0.02|0.00|0.22|
|Highest educated woman: primary education|0.26|0.02|0.09|
|Highest educated woman: secondary education|0.06|0.01|0.03|
|Highest educated woman: tertiary education|0.01|0.00|0.30|
|Share of households with literate house. head|0.73|0.02|0.09|
|Household size|4.87|(0.16)|0.30|
|Overcrowding|1.91|(0.10)|0.31|
|Elderly dependency ratio|0.08|0.01|0.00|
|Children dependency ratio|0.94|(0.03)|0.09|
|Firewood for cooking|0.90|(0.00)|0.66|
|Access to pipe water|0.06|0.00|0.92|
|Access to flush toilet|0.01|0.00|0.24|
|Share of HH that own a house|0.91|0.00|0.81|
|Share of HH with improved walls|0.86|0.04|0.21|
|Share of HH with improved roof|0.38|0.07|0.04|

9
**<u>│</u> 29**

A snapshot of differences in the ratio of woman-owned or managed foreign multinationals relative to domestic firms highlights the lower numbers of women in managerial positions (see Fig. 12). Firm scale, skills mismatches, and the nationality of managers often expatriated could explain these patterns. Although in countries where high positive differences are observed local conditions seem to be an important driver: Malaysia and Indonesia are good examples of high differences in female managerial responsibilities.

Overall, operating with global labor standards in countries where women do not have the same opportunities stands to make a difference for the local populations. Yet while the direction of this effect on gender inequality is undisputed, its magnitude would inevitably vary (Shu, Zhu and Zhang, 2007; Aguayo-Tellez, 2012; Braunstein and Brenner, 2007; Glick and Roubaud, 2006; Siegmann, 2007).

Domestic supply linkages The strength of linkages between foreign and domestic firms is often shaped by the motivation for foreign investment. In particular, market-seeking FDI is more likely to result in linkages with local firms than efficiency-seeking, natural resource-seeking, or strategic asset-seeking ‐ FDI (Farole and Winkler, 2014; Amendolagine et al., 2013; Sánchez Martín, de Pinies and Antoine, 2015). More generally, what multinationals do – i.e., their industrial activities – largely explains the extent of linkages with local suppliers. The food, wood and furniture, automobile and auto parts industries are structurally more dependent on foreign inputs ‐ (Sánchez Martín, de Pinies and Antoine, 2015). By contrast, in the apparel industry, the presence of rules-of-origin requirements limits their development (Farole and Winkler, 2014). Services may be the most promising sector for local firms to develop and benefit from linkages with FDI activities due to the prevalence of market-seeking motivation and their high labor intensity.

The Enterprise Surveys confirm that domestic linkages of foreign multinationals tend on average to be less strong than those of domestic firms, with only very few exceptions (see Fig. 12). Part of this variation could be due to industrial activities where multinational firms are concentrated. Yet, in countries with large market size, such as China, India, Nigeria, and Indonesia, both multinationals and domestic firms exhibit strong local sourcing. This is due to the availability of local inputs for a variety of activities and also because of the intensity of market-seeking FDI in addition to other types. A number of countries in South East Asia upgrading their GVC participation exhibit very high foreign input use, as well as Vietnam and Poland.

Implications of these differences for domestic economy are vast, as this is one of the main channels for productivity spillovers between foreign firms and their domestic suppliers (Javorcik, 2004).
# WPS4256

## **Nigeria’s Growth Record: Dutch Disease or Debt Overhang?**

Nina Budina Gaobo Pang Sweder van Wijnbergen World Bank World Bank University of Amsterdam <u>nbudina@worldbank.org</u> gpang@worldbank.org and Tinbergen Institute s.j.g.vanwijnbergen@uva.nl

### **Abstract**

Nigeria's oil boom has not brought an end to perennial stagnation in the non-oil economy. Is this the unavoidable consequence of the resource boom or have misguided policies contributed? This paper indicates that the extreme volatility of expenditure rather than Dutch Disease effects is behind the disappointing non-oil growth record. Fiscal policies failed to smooth highly volatile oil income; on the contrary government expenditure was more volatile than oil income. The authors provide econometric evidence showing that volatility of expenditure was increased by debt overhang problems. Moreover, they also find evidence of voracity effects that exacerbated expenditure volatility prior to 1984.

### World Bank Policy Research Working Paper 4256, June 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1
# **Fertility and Parental Labor-Force Participation: New Evidence from a Developing Country in the Balkans**<sup>_∗_</sup>

Iva Trako<sup>_†_</sup>

**JEL Classification** : J13, J22, C26 **Keywords** : fertility, parental labor-force participation, instrumental variables

> _∗_ I would like to thank and recognize my advisor Karen Macours for her special support and guidance throughout the elaboration of this paper. Helpful comments and advice from Ekaterina Zhuravskaya, Oliver Vanden Eynde, Gustavo Bobonis, John Giles, Marc Gurgand, Andrew Clark, Jamele Rigolini, Iv ́an Torre and Martin Rossi are also gratefully acknowledged. I also benefited from comments by two annoymous referees and seminar participants at PSE, Royal Economic Society Conference 2016, GRETHA 2016 and the Spring Meeting of Young Economists 2016. The views expressed in this paper are those of the author and do not necessarily represent those of Paris School of Economics. I am responsible for all remaining errors.

> _†_ Paris School of Economics and The World Bank, `iva.trako@psemail.eu`
labor inputs from the data. This procedure results in the simulated moments ̃ _g_ 1 _− g_ ̃3 and ̃ _g_ 5 _− g_ ̃6.

The next step calculates the equilibrium allocation of labor, given an aggregate labor supply of _Lt_ , and ICT capital stocks of _K_<sup> ̃</sup> _c,r,t_ and _K_<sup> ̃</sup> _c,nr,t_ . This yields simulated values for routine and nonroutine labor, _L_<sup> ̃</sup> _r,t_ and _L_<sup> ̃</sup> _n,t_ . These simulated values are then used for calculating the simulated moments ̃ _g_ 7 and ̃ _g_ 4.

Note that though the information contained in routine and non-routine labor income shares is the same as the information contained in routine and non-routine employment shares, the procedure utilizes different equilibrium conditions to target income and employment shares. In particular, the moments relating to income shares are computed taking routine and non-routine employment as given, while the moments relating to employment shares require the optimality of the allocation of labor across routine and non-routine occupations.

Since the model is highly non-linear, our objective function has many local minima. To obtain a global minimum, we use a Newton-type convergence algorithm, starting from over 10,000 randomly drawn initial conditions.

43
44 the model. Hence, the LSMS data do not support<sup>changes in neonatal</sup> mortality during 1970-86 either in the overall sample or in socioeconomic groups considered in this analysis.

During the period, urban nonpoor (Urban2) and families with mothers with at least a secondary education (Meduc3) experienced significantly lower postneonatal mortality than their counterparts. But the data support<sup>the finding that</sup> children of urban nonpoor families born during the economic crisis-adjustment period (Time3*Urban2) experienced significantly higher postneonatal mortality than their counterparts, other things being equal. Similarly, children born to families with virtually no savings (Time3*Savingsl) experienced heavier postneonatal mortality after controlling for other variables. These last results are consistent with patterns suggested in the descriptive analysis.

The occurrence of neonatal death or postneonatal death is negatively correlated with being born among the rural poor between 1982 and 1986 (Time3*Rurall), after controlling for the effects of other variables. These results, though<sup>weak,</sup> signal improvements in child health among the rural poor during the adjustment period.