rafmacalaba/datause-encoder-data huggingface dataset:
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Piketty, Thomas (2014). _Capital in the Twenty First Century_ . Cambridge MA: Harvard University Press.

Prichard Wilson, Alex Cobham, Andrew Goodall (2014). The ICTD government revenue dataset. ICTD Working Paper 2014 19: 1–64.

Robinson, James A., Ragnar Torvik, and Thierry Verdier (2017). “The Political Economy of Public Income Volatility: With an Application to the Resource Curse.” _Journal of Public Economic_ 145: 243-252.

Rota-Graziosi, G., 2019, “The supermodularity of the tax competition game,” Journal of Mathematical Economics, 83, 25-35.

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13
- Feldman-Savelsberg, Pamela, Flavien Ndonko, and Bergis Schmidt-Ehry. 2000. “Sterilizing Vaccines or the Politics of the Womb: Retrospective Study of a Rumor in Cameroon.” _Medical Anthropology Quarterly_ 14 (2): 159–179.

- Jegede, Ayodele. 2007. “What Led to the Nigerian Boycott of the Polio Vaccination Campaign?” _PLOS Med_ 4 (3): e73.

- Jheeta, Mandip, and James Newell. 2008. “Childhood vaccination in Africa and Asia: the effects of parents knowledge and attitudes.” _Bulletin of the World Health Organization_ 86 (6): 419.

- Kaufmann, Judith, and Harley Feldbaum. 2009. “Diplomacy And The Polio Immunization Boycott In Northern Nigeria.” _Health Affairs_ 28 (4): 1091–1101.

- Middaugh, John. 1979. “Side effects of diphtheria-tetanus toxoid in adults.” _American Journal of Public Health_ 69 (3): 246–249.

- Morris, Saul, Rafael Flores, Pedro Olinto, and Juan Medina. 2004. “Monetary incentives in primary health care and effects on use and coverage of preventive health care interventions in rural Honduras: cluster randomised trial.” _Lancet_ 9450 (4): 2030–2037.

- Nichter, Mark. 1995. “Vaccinations in the third world: A consideration of community demand.” _Social Science and Medicine_ 41 (5): 617–632.

- Nyhan, Brendan, Jason Reifler, Sean Richey, and Gary Freed. 2014. “Effective messages in vaccine promotion: a randomized trial.” _Pediatrics_ 133 (4): 835–842.

- Ogunlesi, Tinuade. 2011. “Vaccines for women to prevent neonatal tetanus: RHL commentary.” _The WHO Reproductive Health Library_ .

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29
economies. This measure is consistent with the type of benefit spillovers specified in equation (1), where localization economies come from the absolute volume of other activity in the district. Own industry employment is calculated from employment statistics provided in the 1998-99 sampling frame of the ASI, which provides employment data on the universe of registered industrial establishments in India. The sample data used for the cost function estimation are drawn from this sampling frame.

# _Inter-Industry Linkages_

In addition to intra-industry externality effects, we also include a measure to evaluate the importance of inter-industry linkages in explaining firm level profitability, and thereby location decisions. The importance of inter-industry linkages as a major agglomerative force was first introduced by Marshall (1890, 1919). Venables (1996) recently demonstrated that agglomeration could occur through the combination of firm location decisions and buyer-supplier linkages even without high factor mobility. The presence of local suppliers can reduce transaction costs and therefore increase productivity. Inter-industry linkages can also serve as a channel for vital information transfers. Firms that are linked through stable buyer-supplier chains often exchange ideas on how to improve the quality of their products or on how to save production costs. It is such on-going interactions that make the dynamics of inter-industry externalities so vibrant. Therefore, if the performance of an industry is highly dependent upon the supply of high-quality intermediate goods (e.,g., automobile manufacturing), firms are likely to locate in regions with a strong presence of local suppliers. The presence of local supplier linkages makes buyer industries more efficient and reinforces the localization process.

9
2000 US$, corrected for purchasing power (economic development), the log of initial poverty, the average GDP deflator (average inflation), the log of the average fraction of the population in rural areas, the log of the average fraction of the population employed in the agricultural sector, and the log of the average value of trade (exports and imports) as a fraction of GDP.

We also take into account trade in food which may affect undernourishment and agricultural productivity. Specifically, in our panel regressions, we control for the yearly total food production per person in kilograms. We also control for the effects of international food trade, using the net food flow that leaves the country yearly, i.e., food export minus food import, expressed in kilograms per person. For both total food production and trade data, we use the data as provided by FAOSTAT of the Food and Agriculture Organization of the United Nations, averaged for the following time periods: 1979-81, 1990-92, 1993-95, 1995-97 and 2001-03. Finally, to investigate more general effects, we calculate a local producer price index based on yearly data from 1991-2001. The price index is the weighted-average price of the following main food categories: wheat, rice, maize, oats, barley, sheep meat, chicken meat, and pig meat. The price of each category is weighted by its share in total production of all categories in the particular country. The prices are in US dollars and corrected for “green” purchasing power parity. This PPP is calculated by FAO using a basket of agricultural products and related producer prices. To retain consistency, we make the periods for the price index as close as possible to the periods defined by the FAO. Specifically, we average the data over the following periods: 1991-92, 1993-95, 1995-97, and 2001.

# <u>Correlations</u>

Panel B in Table 2 shows the correlations among the most important variables. All variables are significantly correlated with each other, with the expected sign. Importantly, the correlations show that higher levels of GDP per capita and private credit to GDP are associated with lower poverty and undernourishment and higher agricultural productivity and cereal yields. Panel C shows the correlations between our several agricultural productivity measures. All are positively correlated, but to different degrees, with the 12
Policy Research Working Paper 6976

# **Abstract**

Firms very rarely cut nominal wages, even in the face of considerable negative economic shocks. This paper uses a unique survey of fourteen European countries to ask firms directly about the incidence of wage cuts and to assess the relevance of a range of potential reasons for why the firms avoid cutting wages. The paper examines how firm characteristics and collective bargaining institutions affect the relevance of each of the common explanations put forward for the infrequency of wage cuts. Concerns about the retention of productive staff and a lowering of morale and effort were reported as key reasons for downward wage rigidity across all countries and firm types. Restrictions created by collective bargaining were found to be an important consideration for firms in Western European (EU-15) countries but were one of the lowest ranked obstacles in the new EU member states in Central and Eastern Europe.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean 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 jmessina@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
the adult population. Second, most of the latent factors associated with child labor and changes in household composition should be captured by controlling for household composition. The set of cases where changes in household composition associated with changes in child labor vary with rice prices continuously is much smaller. In addition, these shifts in household composition are likely of second order to the large income and substitution effects associated with rice price increases observed in Edmonds and Pavcnik (2003a). Even in cases where households receive dramatic increases in income, changes in household composition are relatively minor and require extremely large data sets to identify (see Edmonds, Mammen, and Miller (2002) for discussion). In the present case, regressions of all of the various household composition measures used in this study on rice prices and the other controls described in (6) reject the hypothesis that there are changes in household composition with rice price increases in every case except for the number of persons in the household above the age of 65. Thus, endogenous household composition represents a potential problem in interpreting the results of the next section, but the available evidence does not suggest this problem is of first-order concern.

# **5. Results**

This section examines in the VLSS data how the relationship between child labor and rice prices varies with household and sibling composition. A number of studies have examined the relationship between various measures of child labor and rice price changes in this dataset (e.g. Edmonds and Pavcnik 2003a, 2003b, and 2003c), so the focus of the present discussion is purely on how the relationship varies based on who resides with children 6-15. The basic finding is that household composition attributes that cause children to work more given rice prices are associated with larger declines in child labor. Two interesting exceptions to this general finding are in the data. First, the presence of younger children (0-5) and younger siblings are associated 27
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based identification approach due to Kelin and Vella (2009a). For the analysis of household borrowing from informal sources, we take advantage of panel data and implement a fixed effect difference-indifference approach and combine it with alternative matching and propensity score reweighting estimators.

The empirical evidence on the effects of MFIs on moneylender interest rates based on an exceptionally large cross section data set with almost 800 villages shows that moneylender interest rates increase in response to MFI penetration into the village credit markets. The effect is however heterogeneous; at low levels of MFI coverage, there does not seem to be any perceptible impact, and the effect is strong for the villages in the top quartile of coverage. The evidence based on the panel data demonstrates clearly that a household’s propensity to borrow from informal sources declines significantly once it becomes a member of an MFI, and that the total volume of credit from informal sources (and formal banks) also decrease substantially in both absolute and relative terms. The evidence on the declining importance of informal sources in rural credit markets along with higher informal interest rates contradicts some of the widely held perceptions among contending camps of practitioners. While our results do not support the view of MFI proponents that MFI competition reduces informal interest rates, the evidence also rejects the claim by the critics that MFIs cause increased reliance on informal loans among their borrowers due, for example, to rigid repayment schedules and indivisibility of investment projects. When taken together, the evidence on interest rates and household borrowing is more consistent with cream skimming by MFIs and fixed costs in informal lending by moneylenders.

# **REFERENCES**

Aleem, I (1993) “Imperfect Information, Screening, and the Costs of Informal Lending: A Study of a Rural Credit Market in Pakistan” in _The Economics of Rural Organizations: Theory, Practice, and Policy_ , edited by Karla Hoff, Avishay Braverman, and Joseph E. Stiglitz , Oxford University Press.

Alexander-Tedeschi, G and D. Karlan (2009), “Cross-sectional Impact Analysis: Bias from Drop-Outs”, FAI, Yale University.

Armendáriz, B, and J. Morduch (2010) The Economics of Microfinance, Second Edition, The MIT Press, Cambridge, Mass.

Banerjee, A (2003), “Contracting Constraints, Credit Markets, and Economic Development”, Chapter in “Advances in Economics and Econometrics: Theory and Applications, Eighth World Congress”, Volume III Edited by Mathias Dewatripont, Lars Peter Hansen and Stephen J. Turnovsky Bell, Clive (1990) “Interactions between Institutional and Informal Credit Agencies in Rural India” The World Bank Economic Review, vol. 4, No. 3: pp. 297-327 27
- Dupas, Pascaline and Jonathan Robinson. 2013. “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya.” _American Economic Journal: Applied Economics_ 5(1): 163-192.

- Fujiki, Hiroshi, and Kiyotaka Nakashima. 2019. “Cash Usage Trends in Japan: Evidence Using Aggregate and Household Survey Data.” Tokyo Center for Economic Research (TCER) Working Paper Series, E-131, 1-42. https://doi.org/10.2139/ssrn.3351429

- Gentilini, Ugo, Mohamed Almenfi, Ian Orton, and Pamela Dale. 2020. “Social Protection and Jobs Responses to COVID-19: A Real-Time Review of Country Measures.” Washington, DC: World Bank. <u>https://socialprotection.org/discover/publications/social-protection-and-jobsresponses-covid-19-real-time-review-country.</u>

- Gutierrez, Eva, and Sandeep Singh. 2013. “What Regulatory Frameworks Are More Conducive to Mobile Banking? Empirical Evidence from Findex Data.” World Bank Policy Research Working Paper No. 6652, Washington, DC.

- Imbens, Guido W. 2021. “Statistical Significance, p-Values, and the Reporting of Uncertainty.” _Journal of Economic Perspectives_ 35(3): 157-174.

- Jack, William, Adam Ray, and Tavneet Suri. 2014. “Risk Sharing and Transaction costs: Evidence from Kenya’s Kenya’s Mobile Money Revolution.” _American Economic Review, Papers and Proceedings_ 103(3): 356-361.

- Jack, William, and Tavneet Suri. 2011 “Mobile Money: The Economics of M-PESA.” NBER Working Paper No. 16721, National Bureau of Economic Research, Cambridge, MA.

- Jack, William, and Tavneet Suri. 2014. “Risk Sharing and Transactions Costs: Evidence from Kenya’s Mobile Money Revolution.” _American Economic Review_ 104(1): 183–223.

- Jack, William, and Tavneet Suri. 2016. “The Long-run Poverty and Gender Impacts of Mobile Money.” _Science_ 354(6317): 1288-1292.

Jonker, Nicole, Carin van der Cruijsen, Michiel Bijlsma, and Wilko Bolt. 2022. “Pandemic Payment Patterns.” _Journal of Banking and Finance_ 143: 106593. <u>https://doi.org/10.1016/j.jbankfin.2022.106593</u>

- Karlan, Dean, Jake Kendall, Rebecca Mann, Rohini Pande, Tavneet Suri, and Jonathan Zinman. 2016. “Research and Impacts of Digital Financial Services.” National Bureau of Economic Research Working Paper 22633. Cambridge, MA.

Koulayev, Sergei, Marc Rysman, Scott Schuh, and Joanna Stavins. 2016. “Explaining Adoption and Use of Payment Instruments by US Consumers.” _Rand Journal of Economics_ 47(2): 293-325.

29
market interest rates<sup>8</sup> . This implies that increases in social security contribution rate will be matched with decreases in individual saving rates.

Finally, the steady state level of capital per unit of effective labor in the FF case is: <!-- Start of picture text --> kf V 1 1~( -a() a (28)<br>if L,h(2++ p) ( + ( cn) )]<br><!-- End of picture text -->

## **_2.3 Comparing both Systems under the Steady State_**

In order to obtain a meaningful comparison between both systems, we assign realistic values to the model's parameters in order to find numerical solutions. In particular, v' t; use values that resemble the case of the Mexican economy. **Table 1 Parameters in Base** Case **Scenario**

|**Parameter**|**Value**|
|---|---|
|Capital's share in production|**a=**.5|
|Discount rate|p=.03|
|Population<br>growth rate|n=.02|
|Social Securty tax rate|-cs=.115|

# **Calibration**

The share of capital in total income is represented by a. From the Mexican National Accounts, we know that this value has fluctuated around 0.5 during the last 20 years. (See Arrau 1990). In the empirical literature, we find a great variety of estirmates for the discount rate, p. (Auerbach and Kotlikoff [1987]; Hansen and Singleton [19,83]; Haussman [1979] or Hubbard et. al. [1995]). Here we choose p= 0.03 which is consistent with many such studies. The population in Mexico is expected to grow at an approximate 2% rate for the next 30 years<sup>9</sup> . And the value of the Social Security contribution rate that we choose is the one that will apply with the reform. Finally, f and y are, respectively, the fraction of the labor force that is skilled (rich agents) and the fraction of the total stock of human capital that they own. Because of the lack of empirical estimates for these variables, we use several different possible values for them. Recall that we are not talking about the percentage of the population that is poor; we are talking about the percentage of the labor force that is at the bottom part of the wage spectrum and does not have access to the financial system.

> 8 Although this is not a very realistic assumption, we use it to show in a more clear way the effects of elimination of liquidity constraints.

> 9 Source: US Bureau of the Census.

10
TABLE **1**

# SUMMARY OF MAJOR ENVIRONMENTAL COSTS IN **MEXICO ()**

|Probles<br>-|Potentialeffects<br>production/health|Method/formula|Annual costs<br>(tUSS billions)|Suitt-in<br>assumptfons|
|---|---|---|---|---|
|SOIL<br>EROSION|Loss of agricultural<br>output|Average productivity<br>lossAoutput<br>(soybeans, maize,<br>sorghunmandwheat)|1.00|Application<br>of<br>projected tendenicy<br>losses in U.S. to<br>perpetuity.|
||Particulates:<br>Morbid-<br>ity (respiratory<br>restricted<br>activity<br>days- RRAD)|ARRAD<br>*****0.0114 x<br>baseline RRAD x (conc.<br>fine particulates<br>-<br>legislationstandard)|0.36|Day Lost=USS32.<br>Conc.FP=119pg/m3.<br>FP Standardx5OAg/m3.<br>Baseline RRAD=3.|
||Particulates:<br>Mortality<br>(MR)|_AXR_**=**1.69/million<br>x<br>concent. suspended<br>particles|0.48|Conc.sa298gg/m3.<br>1 StatisticaL<br>lifez<br>US$75,000.|
||Ozone: Morbidity|ARRAD<br>abaseline x<br>adult population x exp<br>_(6.88<br>x Aozone) - 11|0.10|50% productivity<br>loss<br>other assumptionsas<br>above.|
|HEALTH<br>EFFECTS<br>FROM<br>AIR POLLUTION<br>(MEXICO CITY ONLY)|Lead: Children's<br>treatment for high<br>blood lead levels<br>(BLL)|Population affected x<br>average estimated<br>treatment costs|0.06<br>_|Screening if>251Lg/dl.<br>EDTA test if>35g&g/dL.<br>1X require chelation.<br>Hospital cost = 1/15<br>_ _ _ _ _ _ofU.S. cost.|
||Lead: Children's<br>compensatory education|Population affected x<br>education costs|0.02|20% of those>40gg/dl.<br>Average education<br>expenditureper<br>child|
||Lead: Hypertension in<br>aduLts|Population affected x<br>average estimated<br>treatment costs|0.01|A11tg/m3 lead in aira<br>A3Ag/dl in blood.<br>1%BLLaAO.8%prob.<br>of hypertension.<br>Lead conc.air**=**1.4<br>jLg/m3.<br>Average BLL 15Ag/dl.|
||Lead: Myocardial<br>infarctions|As above|0.04|60% occur in<br>hypertense.<br>Costs=1/2 as in U.S.|
|EXCESS<br>USE OF<br>UNDERGROUND<br>WATEP<br>DUE TOPOOR<br>PRIC *|Subsidies to supply<br>water to Mexico City|(Marg.cost - charge) x<br>consumption|1.00|Average price PS2900.<br>Marginal Cost PS4500.<br>Average consumption -<br>1.8 billion<br>m3.|
|(not social costs)|Subsidies to<br>irrigation<br>_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>_ _<br>_ _ _ _ _<br>_ _ _ _<br>_ _ _ _ _|Asabove<br>_ _ _ _<br>_ _ _ _ _<br>_ _ _ _ _<br>_ _|0.16<br>_<br>_ _ _ _ _<br>_ _ _ _|Implicit<br>subsidy of<br>US$82/ha/year<br>to 2<br>m illion<br>_h<br>a.|
||Morbidity|Incidence x average<br>treatment costs|0.03|Children and elderly<br>require treatment,<br>50%ORT,20%lab<br>analysis|
|DIARRHEAL<br>DISEASES<br>FROM<br>WATER<br>AND SOLID<br> <br>||||.<br>Adults require treat-<br>ment and 20% ORT.|
|WASTE<br>POLLUTION,<br>LACK<br>OF SANITATION<br>AND<br>FOODSTUFF<br>POISONING|Mortality:<br>Scenario 1<br>- current situation|No.of lives x life<br>expectancy x value|3.60|1 Statistical<br>lifex<br>USS75,000.|
|l_______________________|Mortality:<br>Scenario 2<br>- with oral rehydrat.<br>therapy(ORT)|Incidence x average<br>treatment costs|0.00<br>(USs450,000)|Treatment *US$3,<br> ORTaUSS1 + 12<br>adninistration.|

(*) - In all cases, the rate of discount is 5%.

2