rafmacalaba/datause-encoder-data huggingface dataset:
Table A.2.1 Studies on advanced economies (continued)

|Authors|Questions|Economies<br>(sample period<br>and frequency)|Methodology|Results|
|---|---|---|---|---|
|Kumar et al.<br>(2015)|Are firms’ inflation<br>expectations well<br>anchored?|New Zealand<br>(2014Q3-2014Q4)|<sup>Quantitative survey of cross-section of firms</sup>|Firm managers display little anchoring of inflation<br>expectations, despite 25 years of inflation targeting by the<br>Reserve Bank of New Zealand. Managers are generally<br>poorly informed about recent inflation dynamics. Their<br>forecasts of future inflation reflect high levels of<br>uncertainty and are volatile at both short- and long-run<br>horizons.|
|Johnson<br>(2003)|Does inflation targeting<br>affect long-term inflation<br>expectations?|Australia,<br>Canada, New<br>Zealand, Sweden,<br>and the United<br>Kingdom (1990s,<br>monthly)|<br>Comparison of forecasts of actual inflation<br>with predicted forecasts, which are derived b<br>multiplying the estimated coefficients of<br>Phillips curves using the same independent<br>variables before and after the target<br>announcement|y<br>Inflation targeting stabilizes long-term inflation<br>expectations.|
|Strohsal,<br>Melnick, and<br>Nautz (2016)|<br>Did the global financial<br>crisis affect inflation<br>expectations?|United States<br>(2004-14,<br>monthly)|Regression of deviations of long-term inflatio<br>expectations from the inflation target on<br>observed inflation or news-driven short-term<br>inflation expectations using a model with<br>time-varying parameters|n<br>Inflation expectations in the United States became<br>partially de-anchored during the global financial crisis,<br>but this de-anchoring was temporary.|
|Strohsal and<br>Winkelmann<br>(2015)|Are long-term inflation<br>expectations well<br>anchored?|Euro Area,<br>Sweden, United<br>Kingdom, and<br>United States<br>(2004-11, daily)|Estimation of a market-perceived inflation<br>target using an exponential smooth transition<br>autoregressive (ESTAR) model|<br>Expectations appear to be best anchored in the Euro<br>Area, followed by the United States, Sweden, and the<br>United Kingdom. In most of these countries, the degree of<br>anchoring did not change during the global financial<br>crisis.|

30
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**Figure 1: Timeline of the EHCVM Survey and Covid-19 response**

<!-- Start of picture text --> January 2020:<br>Household<br>survey begins<br><!-- End of picture text --> <!-- Start of picture text --> 11 March 2020 :<br>Covid-19<br>Declared Global<br>Pandemic<br><!-- End of picture text --> <!-- Start of picture text --> 16 March 2020 :<br>President<br>addresses<br>nation on Covid-<br>19<br><!-- End of picture text --> <!-- Start of picture text --> 23 March 2020 :<br>Interisland<br>travel and<br>international<br>flights<br>suspended<br><!-- End of picture text --> <!-- Start of picture text --> 3rd April 2020 :<br>National plan<br>announced<br><!-- End of picture text --> <!-- Start of picture text --> 1st May 2020 :<br>First Covid-19<br>case reported<br><!-- End of picture text --> <!-- Start of picture text --> September<br>2020 :<br>Household<br>Survey data<br>collection ends<br><!-- End of picture text --> The empirical analysis of the Covid-19 outbreak’s impact on household welfare was undertaken using the 2020 Harmonized Survey on Living Conditions of Households (EHCVM) for the Comoros. The survey was conducted by the National Institute of Statistics and Economic and Demographic Studies and the World Bank and was collected between January and September 2020.<sup>5</sup> Figure 1 provides the timeline of the survey and the relevant Covid- 19 intervention policies in the Comoros. Due to its timing, the survey provides informative data pre-and postCovid-19 lockdown on household socio-economic status and characteristics. The survey was conducted across the four islands that make up the Union of Comoros and was therefore representative nationally as well as of the four (4) regional locations (Moroni, rest of Ngazidja, Ndzuwani and Mwali). We use the lockdown announcement date as a natural treatment or cutoff date for identifying households surveyed pre- and post-Covid-19 lockdown measure. The sample distribution of interviews covered before and after the Covid-19 lockdown in the Comoros is provided in Table A1 in the appendix. A total of 11,712 individuals belonging to 2,150 households were interviewed before the national lockdown. The sample for the main regions in the Comoros, Ngazidja and Ndzuwani, accounted for 39% and 42%,

> 5 The survey included a few households interviewed in November 2018 and January 2019 and were excluded from this analysis. The country was struck by Cyclone Kenneth in April 2019. We exclude households prior to this episode to avoid conflating the impact of the cyclone with that of Covid-19.

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### 2. Predicting probability of severe COVID-19

<mark>To calculate the probability of severe COVID-19, we apply the odds ratios estimated in Ma et al to the proportion with the corresponding probability factor/comorbidity at the district level. lpl l(p p) = lpl(</mark> _<mark>2</mark>_ <mark>.</mark> _<mark>6</mark>_ <mark>)p p + lpl(</mark> _<mark>1</mark>_ <mark>.</mark> _<mark>7</mark>_ <mark>)p p + lpl(</mark> _<mark>2</mark>_ <mark>.</mark> _<mark>6</mark>_ <mark>)p</mark> _<mark>50</mark>_ <mark>+</mark> _<mark>intercept where psc = probability of severe COVID-19, pco = proportion with a comorbidity, psm =</mark>_ _<mark>where psc = probability of severe COVID-19, pco = proportion with a comorbidity, psm = proportion that smoke, p50 = proportion over 50.</mark>_ <mark>In order to apply this equation, we need the intercept value specific to Zimbabwe. This was set so that the mean district level probability of severe COVID was equal to that expected on the basis of the age-breakdown of the population. To estimate this expected probability of severe COVID, we applied the Imperial College London (ICL) clinical study age-specific estimates of probability of severe disease to the age breakdown data from the Zimbabwe census from 2012. This generated a mean probability of severe disease across the population in Zimbabwe of 2.9%. After testing a range of different intercepts, an intercept of -4.2 was chosen as this produced a mean district level probability of severe disease of 2.9%.</mark>

### 3. Generating counterfactual scenarios

We produce two counterfactual scenarios for probability of severe COVID-19. These are based on predictions of prevalence of respiratory illness under different levels of handwashing risk, which affect the probability of having a comorbidity and in turn the final probability score. The first scenario uses a handwashing risk of 0.25 throughout the country, equivalent to the lowest level of handwashing risk (i.e. the highest level of handwashing access) currently observed at the district level. This is a realistic scenario where it is assumed that all the districts can improve their handwashing access levels _at least_ to the level of the best performing district. The second scenario, which shows an ideal case, uses a handwashing risk of 0, equivalent to assuming that _every_ household has access to a hand washing facility, soap and water.

# Results

## Handwashing Risk Estimates

Figure 1 shows the cluster level handwashing risk and the district level estimates. There is considerable variation across Zimbabwe, with the lowest cluster level handwashing risk of 0.036 and highest of 0.923. At the district level, hand washing risk varied from 0.25 in Bulawayo to 0.69 in Mberengwa.
# **Two Heads Are Better Than One:**

**Agricultural Production and Investment in Côte d’Ivoire**

## **Aletheia Donald, Markus Goldstein and Léa Rouanet**<sup>*****</sup>

**Keywords** : agricultural investment, gender, efficiency, intra-household allocation, <mark>Côte d'Ivoire</mark> **JEL Classification** : Q12, J16, J24, O12

> * Donald: World Bank, <u>adonald@worldbank.org; Goldstein: World Bank, mgoldstein@worldbank.org; Rouanet: World Bank, lrouanet@worldbank.org. This paper is a product of the Gender Innovation Lab, Office of the Chief Economist, Africa Region. We</u> are grateful to Ervin Dervišević, Ezechiel Djallo, Dede Houeto, Tigist Ketema and Ambika Sharma for excellent research assistance. We thank Kathleen Beegle, Eliana Carranza, Annemie Maertens, Christopher Udry, Tavneet Suri and seminar participants at CSAE (University of Oxford), the Sorbonne, Trinity College Dublin, ICDE, NEUDC and PacDev for valuable comments on an earlier draft. We gratefully acknowledge funding from the World Bank Group’s Umbrella Facility for Gender Equality. 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.
# **1. Introduction**

The slow growth of Indian manufacturing is a concern for many observers of the Indian economy, and India’s manufacturers have long performed below their potential. Although the country’s manufacturing exports are growing, its manufacturing sector generates just 16 percent of India’s GDP, much less than the 55 percent from services. India’s manufacturing sector has been overshadowed by China’s rapid development, and many economists and policy makers argue that a robust manufacturing sector is a key requirement for boosting India’s growth rate from 5% up to 10%. For example, Amirapu and Subramanian (2014) worry about a “premature de-industrialization” that is occurring with respect to formal sector work, a worrisome sign if India’s manufacturing sector is to be an engine for future development. Revitalizing manufacturing growth can help drive substantial poverty reduction and improved standards of living (e.g., Anand, Tulin and Kuman 2014).

Since its liberalization, India has undertaken many trade reforms to increase its global integration, and the country has invested in domestic infrastructure projects to improve its regional connectivity. These trade reforms have impacted many parts of the economy (e.g., Topalova 2010, Goldberg et al. 2010), and they seem to have held special importance for informal firms. Nataraj (2011) finds that tariff declines increase productivity for small informal firms. Thomas (2013) similarly notes that manufacturing employment growth for the informal sector appears to be in traded goods; he also describes the overall disconnect between output and employment growth for India.

This paper documents and analyzes two intriguing and under-appreciated facts about India’s manufacturing growth. The first fact, which closely relates to the observations of Thomas (2013), is that much of the manufacturing sector’s employment growth that has occurred since 1989 has come in the form of informal establishments in tradable sectors<sup>1</sup> . As described in greater detail below, we define tradable industries at a three-digit industry level using high export and import ratios to gross output levels. While it may not be surprising that manufacturing

> 1 As we discuss in greater detail in Section 2, the informal sector data comes from the National Sample Survey Unorganized manufacturing survey. Establishments in the unorganized sector in India are unregistered, do not pay taxes, and are generally outside the purview of the state, which closely parallels common definitions of the informal sector in other countries. Hence, we interchangeably refer to this sector as both unorganized and informal.

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## Figure 27

<!-- Start of picture text --> Generalized Impulse Response(s) to one S.E. shock in the equation for<br>LGOVSSTP<br>0.04<br>0.02<br>0.00<br>-0.02 LKSTPUBPC<br>-0.04<br>-0.06<br>-0.08<br>0 1 2 3 4 5 6 7 8 9 10<br>Horizon<br><!-- End of picture text -->

# **_V. Conclusions and Policy Implications_**

During the past decade, the successful episodes of Brazilian stabilization coincide with those when fiscal policy was flexible to increase primary surpluses, while crises emerge when there is little flexibility to adjust this fiscal variable to external shocks. For instance, the 1998-1999 episodes show the importance of the primary balance as a signaling tool in a world of imperfect information. In contrast to the 1998-1999 stabilization, fiscal policy was unresponsive to shocks in 2002, causing concerns of fiscal policy sustainability. Compounded by electoral uncertainty, the situation ended in the 2002 debt crisis Brazilian fiscal adjustment has been of mixed quality. On one hand, most of the adjustment has been revenue-based and cutting capital expenditures. In the early 1990s, the tax burden was 25% of GDP while in 2005 it reached 37%. On the other hand, the expenditure composition shows the rising trend in social security and assistance transfers.

Our findings show that Brazilian fiscal policy is procyclical in the short run: output expansions are associated with smaller primary balances, while output contractions with higher ones. In the long run, however, the evidence shows that fiscal policy is countercyclical, that is a 1% increase in output is associated with a higher primary balance of 0.2% of GDP.

The analyses presented in this paper support the contention that Brazil may benefit from increased public spending in the area of economic infrastructure **.** The econometric analysis using historical data from Brazil indicates positive and strong growth effects of public physical capital stock and public investments. The analysis points out clearly the negative effects of increasing taxation on economic growth **.** Thus, it is not advisable for Brazil to pursue its need for increasing public investments via expansionary fiscal policy that results in a higher tax burden than the current level. Instead, a long-run solution to recovering an adequate level of public investments must be sought in reallocation of public spending within the fixed overall fiscal envelope. This means the need to re-examine the composition of the current expenditures, including those allocated to the social sectors that today consume a lion’s share of Brazil’s public expenditures.
# **Table 2: Determinants of Union Membership (Logit)**

|**Variable**|**Marginal Effect (%)**|
|---|---|
|**Education level**(residual=**tio**education)||
|Primary|25.30*<br>(7.4)|
|Secondary Incomplete|39.52*<br>(11.1)|
|Secondary Complete|54.08t<br>(14.7)|
|Tertiary|54.63::<br>(14.9)|
|Experience|2.09*<br>(14.7)|
|Experience squared|-0.03*<br>(10.0)|
|Northern State Residence|-4.55*<br>(3.9)|
|Male|-3.50_*_<br>(3.5)|
|_Municipio_indigenous percent|0.10<br>(1.7)|
|Constant|-3.69|
|N|9,939|
|Model Chi-square|758.35|
|Mean of Dependent Variable|0.297|

Source: INEGI (1989). Note: Numbers in parenthesis are t-values. t indicate **s** ignificant at the 99% level.

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within schools, devising a system to mitigate pressures for schools to select advantaged students, working with risk averse teaching staffs, and promoting professionalism in private schools are all necessary to make ambitious voucher programs effective. As experiences in those developing countries that have voucher or quasi-voucher programs suggest, that is easier said than done.

# Rleferenees

- The word "processed" describes informally reproduced works that may not be commonly available through library systems.

- Aedo, C., and 0. Larrafiaga. 1994. "Sistemas de Entrega de los Servicios Sociales: La Experiencia Chilena." In C. Aedo and 0. Larrafiaga, eds., _Sistema de Entrega de los Servicios Sociales: Una Agenda Para la Reforma_ (pp. 33-74). Washington, D.C.: Banco Interamericano de Desarrollo.

- Angrist, J.D., E. Bettinger, E. Bloom, E. King, and M. Kremer. 2001. "Vouchers for Private Schooling in Colombia: Evidence from a Randomized Natural Experiment." Working Paper 8343. National Bureau of Economic Research, Cambridge, Mass.

- Ball, S., and C. Vincent. 1998. "'I Heard it on the Grapevine': 'Hot' Knowledge and School Choice." _British Journal of Sociology of Education_ 19(3): 377-400.

- Bradley, S., and J. Taylor. 2002. "The Effect of the Quasi-Market on the Efficiency-Equity Trade-off in the Secondary School Sector." _Bulletin of Economic Research_ 54(3): 295314.

- Calder6n, A. 1996. "Voucher Programs for Secondary Schools: The Colombian Experience." Report 16232. Human Capital Development Working Paper. Washington, DC: World Bank.

- Comer, J. 1989. _A Conversation between James Comer and Ronald Edmonds: Fundamentals of Effective School Improvement._ Dubuque, IA: Kendall/Hunt.

- Carnoy, M., and McEwan, P. 2002. "Does Privatization Improve Education? The Case of Chile's National Voucher Plan." In D. Plank and G. Sykes, G., eds. _Choosing Choice: School Choice Policies in International Perspective._ New York: Teachers' College Press (in press).

- Camoy, M. 1998. "National Voucher Plans in Chile and Sweden: Did Privatization Reforms Make for Better Education?" _Comparative Education Review_ 42(3):309-337.

- Cox, D., and E. Jimenez. 1991. "The Relative Effectiveness of Private and Public Schools: Evidence from Two Developing Countries." _Journal of Development Economics_ (34):99121.

- Daun, H. 2003. "Markets Forces and Decentralization in Sweden: Impetus for School Development or Threat to Comprehensiveness and Equity?" In D. Plank and G. Sykes, G., eds. _Choosing Choice: School Choice Policies in International Perspective._ New York: Teachers' College Press (in press).

- Espinola, V. 1995. "The Decentralization of the Educational System in Chile." CIDE Working Paper.

- Filer, R., and D. Munich. 2003. "Public Support for Private Schools in Post-Communist Central Europe: Czech and Hungarian Experiences." In D. Plank and G. Sykes, G., eds. _Choosing_

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|Received remittances|16.5%|32.8%|16.3%|***|
|---|---|---|---|---|
|Able to save in the past month|72.7%|79.7%|7.0%||
|Owns a bank account|70.0%|65.4%|-4.6%||
|Can borrow money from someone if|||||
|needed|62.4%|78.4%|16.0%|***|

# **Appendix 9 – The use of Coarsened Exact Matching and k-to-k matching to estimate the impact of vulnerability on resilience and recovery**

According to Iacus et. al. (2012), the goal of matching techniques is to study the “pure” effect of a treatment intervention (in our case losing more than a certain threshold of annual expenditures) on a phenomenon we want to explain (here, the ability of households to recover from the flood in less than two years).

Matching is an extremely useful method when the original sample suffers from imbalance between the treatment group and the control group. There are two aspects of imbalance: (i) the treatment group is small in comparison to the control group; and (ii) potential explanatory variables of the phenomenon we want to explain have very different distributions within the treatment and the control groups. It has been proven that sample imbalance often leads to biased and unreliable results when doing statistical inference. More specifically, it induces model dependency which can be defined as obtaining substantially different results in inference estimates through small changes in a model’s specification.

Matching helps to limit model dependency by rebalancing the sample. There are numerous matching techniques to strike balance within a sample amongst which the most popular are propensity score matching, matching based on distances, and coarsened exact matching. Following Iacus et. al. who have reviewed the properties and efficiencies of these different matching techniques, we choose to adopt CEM to our data in order to obtain better balance in our sample.

The process of matching consists in finding the most similar control observation for each treated observation. This is done by designing segments (or grids) in the sample based on variables that are thought to be associated with the treatment. Once the entire sample is segmented, the matching process prunes all the control observations which are in segments with no treatment observations (i.e., they do not share common characteristics with at least one treated observation). Segments with at least one treatment and one control observation are kept and form a matched subsample of the data. We can then perform standard causal inference on this subsample to estimate the “pure” treatment effect which we have detailed in the body of the report.

We report the results obtained when proceeding to CEM after detailing those obtained with a more standard procedure, i.e. logistic regressions of recovery on a set of household characteristics without matching data. As such we can compare regression results with and without the use of CEM. Table 28 displays the results for a logistic regression of recovery on losing more than 5% of annual expenditure and additional control variables. We find that without matching, the coefficient associated to the binary variable of losing more than the 5% threshold is not significant.

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# **8.  Conclusion**

The _World Bank Group Entrepreneurship Survey 2007_ provides a new set of indicators to study the relationship between business creation, the investment climate, and economic development. Preliminary findings suggest that a higher level of entrepreneurship significantly relates to greater economic development, formal sector participation, and better governance. For instance, countries with lower barriers to entry and less corruption generally see higher percentages of firm registrations and entry. Consistent with the findings of Brander et al. (1998) for the Canadian economy, we find that in the 84 countries included in our analysis, entrepreneurship, measured both in terms of new registrations and entry rates, is also positively correlated with economic growth. This might suggest that countries that facilitate entrepreneurship see commensurate increases in overall economic growth and an expansion of the formal sector. Alternatively, it might be the case that periods of economic expansion encourage optimism and entrepreneurship; for instance, individuals might be willing to leave their job security to start a business if they are more confident they could find another job if their business fails. We hope to continue collecting data on firm creation over time, which will allow us to better understand how the private sector behaves over business and financial cycles. The current data limitations prevent us from observing the evolution of new entrants over time in order to asses their longevity and their growth. Furthermore, entrepreneurship indicators can be used to complement other World Bank group indicators – such as the _Doing Business_ indicators – in the development of policy recommendations to promote private sector development and growth.

In addition, the data collected could become the base for further studies in business ecology. For instance, the distribution of businesses per sector could be used for a deeper research paper aimed to answer questions such as which kind of businesses are easier to incorporate in challenging business environments, which sectors are interdependent on one another, and which ones contribute more to the countries economic development.

Moreover, the process of collecting data has become a valuable tool for the diagnosis of the business environments. For instance, direct contact with business registries in more than a 125 countries helps us to better understand the difficulties that entrepreneurs face when incorporating a business, as well as the impact of the institutional and technological framework of registries in the ease of starting a business.

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