rafmacalaba/datause-encoder-data huggingface dataset:
# **5 References**

Banerjee, A. V., Duflo, E. (2014). Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program. Review of Economic Studies, 81(2), 572-607.

Barrett, C. B., Christiaensen, L., Sheahan, M., Shimeles, A. (2017). On the Structural Transformation of Rural Africa. Journal of African Economies, 26(suppl1) _, i_ 11 _− i_ 35 _._ Barrett, C. B., Reardon, T., Webb, P. (2001). Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications. Food Policy, 26(4), 315-331.

Binswanger-Mkhize, H. P. (2013). The Stunted Structural Transformation of the Indian Economy: Agriculture, Manufacturing and the Rural Non-Farm Sector. Economic and Political Weekly, 48(26-27), 5-13.

Carleton, T. A., Hsiang, S. M. (2016). Social and Economic Impacts of Climate. Science, 353(6304), aad9837.

Chatterjee, U., Murgai, R., Rama, M. (2015). Job Opportunities along the Rural-Urban Gradation and Female Labor Force Participation in India. World Bank Policy Research Working Paper 7412.

de Janvry, A., Sadoulet, E. (2001). Income strategies among rural households in Mexico: The role of off-farm activities. World Development, 29(3), 467-480.

de Janvry, A., Sadoulet, E. (2019). Rural Development: From Vision to Action. World Bank Publications.

Ellis, F. (2000). Rural livelihoods and diversity in developing countries. Oxford University Press.

Foster, A. D., Rosenzweig, M. R. (2004). Agricultural productivity growth, rural economic diversity, and economic reforms: India, 1970–2000. Economic Development and Cultural Change, 52(3), 509-542.

Foster, A. D., Rosenzweig, M. R. (2022). Are There Too Many Farms in the World? Labor-Market Transaction Costs, Machine Capacities and Optimal Farm Size. Journal of Political Economy, 130(3), 636-680.

Gaur, K., Joshi, M. (2021). Gender and Rural Non-Farm Employment in Rajasthan: Patterns and Determinants. Economic and Political Weekly, 56(15), 45-52.

Haggblade, S., Hazell, P., Reardon, T. (2010). The rural non-farm economy: Prospects for growth and poverty reduction. World Development, 38(10), 1429-1441.

Hazell, P., Haggblade, S. (1993). Farm-nonfarm growth linkages and the welfare of the poor. Including the poor, 190-204.

Himanshu, Lanjouw, P., Stern, N. (2018). How Lives Change: Palanpur, India, and 33
These quantitative results hint at the mechanism of our measured impacts. Specifically, the behavior change in our study tracks well with the storyline that featured the main soap character, who herself depicted poor financial behavior before transforming her habits. This character was part of the show prior to the two-month financial storyline and remained part of the show afterwards. The non-result on knowledge of the NDMA could at least partly be explained by the fact that the NDMA messages were delivered not by the main character, but by a different character who was external to the main soap cast and who appeared in 2-3 episodes as an NDMA counselor, and then disappeared. Hence, the audience did not get the opportunity to establish or maintain a connection or following with this character.

In order to further assess the merits of this emotional connections channel, we conducted three qualitative focus group discussions with study participants three months after the final quantitative survey. In these meetings, the focus group leaders were unable to elicit recall of NDMA from the participants and only a few women seemed to remember, and they associated their memory more with the fact that the NDMA character was female rather than her representation of the NDMA. In contrast, all participants readily recalled the main soap character and aspects of the financial storyline delivered by her. Upon further inquiry, they attributed better recall to the fact that the main character was a mainstay in the soap opera, and was popular and likeable.

Overall, our findings suggest an important role for entertainment media as an accessible and important tool for policymakers to deliver carefully designed educational messages that resonate with the audience and can potentially influence financial knowledge and behavior. Further, our findings suggest that emotional connections and familiarity with media personalities certainly play a role in motivating knowledge and behavior change among viewers, and that harnessing such potential can be an important channel for achieving development impact.

This paper is organized as follows. Section 2 provides a brief literature review on financial literacy and mass media. Section 3 focuses on financial education in South Africa and explains the study context and the soap opera storyline. Section 4 outlines the research methodology and 5
Table 6: Effect of Average Temperature Difference on Firm Performance: Heterogeneity in GDP and Access to Finance

||LIC +|LMIC|U|MC||HIC|
|---|---|---|---|---|---|---|
||Low|High|Low|High|Low|High|
|Log(Revenue)|-0.295|-0.229|0.009|-0.035|0.253|0.122|
||(0.078)|(0.086)|(0.049)|(0.052)|(0.072)|(0.073)|
|Log(Rev. per worker)|-0.171|-0.138|0.032|-0.023|0.196|0.069|
||(0.047)|(0.05)|(0.029)|(0.029)|(0.042)|(0.04)|
|Invest (binary)|-0.002|-0.006|-0.001|-0.005|-0.001|-0.004|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.003)|
|Log(Investment)|-0.13|-0.141|-0.023|-0.116|0.063|-0.096|
||(0.134)|(0.136)|(0.08)|(0.078)|(0.107)|(0.1)|
|Log(Avg. wage)|-0.068|-0.125|0.024|-0.048|0.099|0.014|
||(0.038)|(0.041)|(0.024)|(0.025)|(0.036)|(0.035)|
|Log(Capacity Util)|-0.013|0.051|-0.019|0.028|-0.024|0.009|
||(0.019)|(0.021)|(0.012)|(0.012)|(0.017)|(0.016)|
|Log(Energy Intensity)|0.082|0.037|0.027|0.047|-0.018|0.054|
||(0.054)|(0.058)|(0.038)|(0.039)|(0.058)|(0.056)|
|Innovation|-0.013|-0.031|-0.004|-0.017|0.003|-0.006|
||(0.013)|(0.014)|(0.008)|(0.009)|(0.012)|(0.012)|

Notes: Each row represents a different regression of a given firm performance variable on a variation of the model specified in equation 1, where we model heterogeneity by GDP and an index of access to finance. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the access to finance index and columns labeled “high” evaluate the estimated model at the 90th percentile of the access to finance index within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

41
since investment profits would be determined after taking into account the net investment performance and longevity experience of each pension company.

These considerations imply that there would be limited room for engaging in price competition. Initial quotations compiled by ZUS would probably show little dispersion. Pension companies would attract retiring workers on the basis of their performance as asset managers and eventually on their success in managing longevity risk.

## **Compensation mechanism for the use of unisex mortality tables**

A compensation mechanism was provided to cope with the adverse effects of the compulsory use of unisex mortality tables. Pension companies would be required to calculate their technical reserves both on gender-specific and on unisex mortality tables. A government agency would compute the factor that is needed to equate the total reserves under each calculation. Pension companies for which the reserves under gender-specific mortality were higher than the reserves calculated with unisex tables multiplied by the specified factor would receive a compensating transfer through the government agency from pension companies with the opposite result.

# **3.2 Developments in Hungary**

The 1997 pension law allowed retiring workers to choose between annuities and lump sum withdrawals during the first 15 years after the reform. Life annuities were mandated after 2013 with retirees allowed to choose from four types of annuities: a single life annuity; a joint life annuity; a fixed term joint annuity, covering the spouse or other beneficiary for a fixed period, combined with a single life annuity for the primary beneficiary; and a single life annuity combined with a fixed term annuity covering the spouse or other beneficiary for a fixed period after the death of the main beneficiary (Impavido and Rocha 2006).

All annuities were to be fixed real annuities, indexed to the average of price and wage inflation (the so-called Swiss indexation formula that was also used for first pillar pensions). Providers were compelled to use unisex mortality tables without any provision for a mechanism to compensate for greater exposure to female pensioners. Providers were to include life insurance companies and pension funds. The latter needed to comply with specific conditions, including a minimum number of members and maintenance of adequate reserves.

## **Deficiencies of regulatory framework**

This regulatory framework suffered from several deficiencies. The main deficiency concerned the compulsory use of the Swiss indexation formula for linking annuity payments to inflation. One problem with this was the lack of instruments allowing providers to hedge this risk. Another was that initial annuity payments would be much smaller with this rule than with products indexed to price inflation.

43
Policy Research Working Paper 8291

# **Abstract**

This paper identifies and estimates the impact of firm entry and exit on plant-level productivity in Ethiopia as part of a selection mechanism that might be driving aggregate productivity growth in cities. Specifically, the paper investigates how firms’ entry and exit contribute to the pace of factor reallocation and total factor productivity growth within industries—and whether these processes occur in higher numbers and rates in larger cities. The analysis is carried out using establishment census data from Ethiopia that cover the period from year 2000 to 2010. Importantly, these data include information on plants’ physical outputs and their prices, which allows distinguishing between revenue-based measures of total factor productivity (TFPR) and those based on physical productivity (TFPQ). The analysis reveals that these two measures generate very different results under imperfect competition, suggesting that physical productivity measures (TFPQ) are better suited to examining firm dynamics when local producers have some degree of market power. In addition, the findings show that less productive (higher cost) firms are more likely to exit than their more productive (lower cost) rivals—but the analysis controls for producers’ transport costs. This is consistent with the probability of firm exit being higher when transport costs are lower.

This paper is a joint product of the Finance, Competitiveness and Innovation (FCI) Global Practice Group and Africa 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 tmengistae@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
# **Survey Responses broken down by type of system**

|||**Yes**|||**No**|||**Partiall**|**y**|
|---|---|---|---|---|---|---|---|---|---|
||Public<br>DMS|In-<br>house|Comm.|Public<br>DMS|In-<br>house|Comm.|Public<br>DMS|In-<br>house|Comm.|
|**Does the system accommodate your transaction flow**<br>**(e.g. recording, payment, reporting)?**|14|7|7|0|0|0|2|2|0|
|**Please assess the capacity of your DMS to process the**<br>**below debt instrumentspertaining toyour needs**||||||||||
|Bills|13|9|6|2|0|0|1|0|1|
|Bonds(Bullet)|15|9|6|0|0|0|1|0|1|
|Bonds(Amortizing)|15|9|6|0|0|0|1|0|1|
|Variable Interest Rate Products|13|8|6|1|0|0|1|1|1|
|Inflation-linked Instruments|9|7|2|3|0|2|2|2|2|
|Project finance loans|15|8|3|0|0|1|1|0|2|
|Guarantees|14|5|2|0|3|3|2|1|1|
|On-lent Debt|15|5|3|0|4|2|1|0|1|
|**Receivables(from on-lending, defaultedguarantees)**|12|5|0|2|4|2|2|0|3|
|Fees|13|8|3|1|1|0|2|0|3|
|Interest Rate Swaps|6|6|3|5|1|2|3|2|2|
|Cross CurrencySwaps|4|5|3|5|0|2|4|4|2|
|**Doesyour system:**||||||||||
|Record transactions not yet committed (e.g. loan<br>pipeline)?|9|8|4|7|1|3|0|0|0|
|Integrateyour auctionplatform to the debt database?|5|4|2|10|5|5|0|0|0|
|Interface with external sources such as electronic<br>trading platforms to record a transaction?|1|4|4|15|5|3|0|0|0|
|Integrate with market data providers (e.g. Bloomberg,<br>Reuters,Central Bank,Eurostat,etc.)?|1|5|4|15|4|3|0|0|0|
|Check whether a transaction violates your compliance<br>and limit requirements (e.g. debt strategy guidelines or<br>budget limitations)?|3|3|3|13|6|4|0|0|0|

33
While agricultural reforms in industrial countries are indeed likely to benefit large and diffuse groups of taxpayers and consumers, they will hurt some of the farm sector – with the impact concentrated on some of the most powerful and well-organized interest groups in that sector. By contrast, farmers in developing countries — the potential beneficiaries of reform — have little or no influence in the political process, while their urban counterparts have some interest in maintaining the status quo.

The political economy of trade policy has long recognized the greater effectiveness of concentrated lobbies – see, for example, Winters (1987) or Anderson (1995) on agriculture – and 70 years ago Schattschneider (1936) recognized that one needs to evaluate such concentration at a fine level of disaggregation. Thus, in this paper we argue that the interesting issue in agricultural reform is not the potential global welfare gains, although these can be substantial (Anderson, Martin and van der Mensbrugghe, 2006), but rather, the _distribution_ of the benefits and costs of reform across households in rich and poor countries. Given the ambition of this exercise, we can consider only the United States among rich countries, and 15 developing countries for which we can assemble household survey data on income sources on a relatively uniform basis.

We are very interested in the impacts of agricultural reforms previously deemed possible under the DDA, but to help advance the policy-making process – currently at a stalemate — we devote considerably attention to those reforms not currently under consideration. Notably, we consider greater agricultural liberalization by developing countries, which turns out to be pro-poor – and some compensation mechanisms which might reduce rich country opposition to agricultural trade reforms.

2
the availability of credit to firms or the vulnerability to financial crisis).

Third, it may be insightful to analyze the dynamics of the connection between the financial sector and the real economy for additional countries, and study how connection was related to performance during the Great Recession. In this sense, it would be interesting also to address “normative” questions, such as the effects of connectedness on welfare.

Finally, and especially for policy purposes, it would be important to go beyond the aggregate perspective we take in this paper and use balance sheet data on single financial institutions to analyze the impact of their connection with the real economy on a range of performance measures. This could also help improve the regulation and monitoring of financial institutions. We plan to pursue these avenues in our future research.

22
Further income information is contained in the data in the form of a discrete choice variable, which takes value one when an individual reported that income is insufficient and there are many difficulties to cover basic needs. More detailed income data that, for instance, includes information on household assets, savings, expected future income or income-of-kind would be likely to further sharpen the precision of the estimation results, but is not available for this sample.

The questionnaire also includes a range of demographic variables, as well as geographic dummy variables to capture potential local area characteristics. Table A.1 lists the variables that were used in the estimations presented in this paper. In developing countries, those unemployed who are in a position of being able to formally claim unemployment benefits, are often relatively privileged. As a result, the variable _Not employed_ was chosen in the estimations to better capture distress commonly associated with unemployment in the sense of not being formally employed. The discrete variable _Maracaibo_ takes value one when the respondent lives in Maracaibo, Venezuela’s second largest city, where most of the country’s oil production takes place.

The political environment in Venezuela is polarized, and at the time of the survey used in this paper many Venezuelans tended to either strongly support or oppose the government of President Hugo Chávez. To account for this politicized context, a dummy variable for approval of the President and a variable for political participation are included in the model. In order to distinguish between ordinary students and individuals who are students in one of the government’s recently introduced education programs for the poor, the binary variable _Student above 30_ was defined to take value one when a respondent indicated to be a student and is above 30 years of age.

When employing the sample of 400 Venezuelans in multivariate regression analyses, missing observations decrease the sample size in spite of the replacement of missing income observations by employment category averages. Even in parsimonious model specifications the sample size drops to below 300. To avoid this decrease in sample size, modified zero-order regressions are estimated (Greene, 2003), and for this purpose three 12
13 overstates the true coverage rate due to double–counting of insured individuals (those working for more than one employer) at the onset of the mandatory regime. The numbers since the mid-1990s are therefore more reliable. They convey a relatively high and stable coverage rate for male workers, as well as a slightly increasing coverage for women.

**Figure 8  Workers Covered by an Occupational Pension Plan since 1970**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>Total Men Women<br>1970 1972 1974 1976 1978 1982 1984 1987 1989 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Source: Own Calculations The main goal of the second pillar is to maintain the pre-retirement living standard together with the benefits stemming from the first pillar. The target replacement rate differs between companies, but it is usually in the range of 50-70 percent of pre-retirement labor income (before taxes and social security contributions), including benefits from the first pillar. Most pension funds specify their target replacement rate as a fraction of the insured wage (see below). Apart from retirement income, the second pillar also provides insurance for _disability_ and _survivors_ of insured workers during the accumulation period (see section 2.5 for additional information).

As already mentioned, employers organize occupational pension plans, by either having an autonomous pension fund or a contract with an insurer. As a consequence of this (and also for historical reasons), the system is highly fragmented. In 2002, there were more than 8,000 funds, but this number has decreased rapidly since then (c.f. Section 5.1). The consolidation process was due to many small firms outsourcing the organization of the second pillar, as well as many joint initiatives of groups of firms.

When occupational pension plans were mandated, almost all schemes were operated as defined-benefit schemes (DB). At that time the accumulated pension capital was not fully portable. However, changes in regulation have mandated the full portability of retirement savings and have caused a conversion of schemes so that now more than 85 percent have become defined contribution schemes (DC).
where U<sup>i</sup> jt<sup>, j = 0, 1, 2, 3 is the utility of each of the three WSS alternatives. U i</sup> 0t<sup>is the utility of the opt-out</sup> choice, which in a simple model is just γ 0, an alternative-specific constant for the opt-out choice. The utility of WSS Alternative A is U 1t<sup>iand the utility of Alternative B is U i</sup> 2t<sup>.</sup> Stochastic utility maximization asserts that individual i will choose alternative j from among the full set of available alternatives K if, and only if, alternative j provides a higher overall level of utility than all other alternatives in the choice set.<sup>3</sup> Assuming the disturbance term follows a Type I extreme-value error structure, the probability that alternative j will be selected from choice set t is the standard conditionallogit expression: where C<sup>i</sup> t<sup>is the selected alternative in each of 3 choice sets and Vi</sup> jt<sup>is the determinate part of the utility of</sup> alternative j.<sup>4</sup> The probability that an alternative will be selected is the ratio of the exponentiated utility that alternative provides, relative to the exponentiated sum of the utilities that each alternative in the choice set provides. Individual characteristics do not vary among choices, and thus must be interacted with WSS attributes or alternative-specific constants.

The conditional logit model specified by Eqs. (4.2) and (4.3) is estimated using maximum-likelihood. That is, given the characteristics of the alternatives in the choice sets presented to the respondents, the

> 3 Mathematically, individual i will choose alternative j from among the set of alternatives K, if U<sup>i</sup> jt<sup>> U i</sup> kt<sup>for all j in K, j ≠ k</sup> substituting for U<sup>i</sup> jt<sup>from Eq. (4.1), and rearranging terms we have</sup>

> V<sup>i</sup> jt<sup>– V i</sup> kt<sup>> e i</sup> kt<sup>– ei</sup> jt<sup>.</sup>

> 4The basic exposition of the properties of this model can be found in McFadden (1981).

8