rafmacalaba/datause-encoder-data huggingface dataset:
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- Stiglitz, J. E. 2000. “Capital Market Liberalization, Economic Growth, and Instability.” _World Development 28_ , 1075–86.

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Tobin, J. 1984. _On the Efficiency of Financial System_ . Lloyds Bank Review.

- Valickova, P., T. Havranek, and R. Horvath. 2016. “Financial Development and Economic Growth: A Meta-Analysis.” _Journal of Economic Surveys_ , 29 (3), 506-526.

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Zeugner, S. 2011. _Bayesian Model Averaging with BMS_ .

# A1. DESCRIPTION OF THE DATA SET

We use a commonly employed data set on the determinants of growth 50
25

# Table _5_ AD HOC INVESTMENT EQUATION AND THE EFFECT OF FINANCIAL REFORM (1984-1988)

|Coefficient||
|---|---|
|Y_|0.056|
||(2.03)|
|y2|0.0004|
||(6.40)|
|X3|0.008.|
||(3.34)|
||0.082|
||(1.82)|
|_4a|0.012|
||(0.14)|
|j41|-0.082|
||-(1.88)|
|y4la|0.034|
|_|~~~~~~(0.38)|
|WI|411.358|
|W2|10.340|
||_4_|
|ml|-9.570|
|M2|-0.049|
|Sargan/Hansen|49.339|
|_______________|____<br>3 7|

Instruments: gmm(I/K), gmrn(Y/K), gmm(CF/K), gimnmnB/K), _it/K,_ GDP by industry, 1 +i, all dated t-2. WI: Wald test for joint significance of all the regressors. W2: Wald test for joint significance of financial variables. Ml: first order serial correlation test; M2: second order serial correlation test, both distributed as N(0,1).

# V.- CONCLUSIONS

The estimation of Euler equations for the capital stock suggests that capital market imperfections have a differential effect across firms, both pre and post liberalization. In particular there is empirical support for a rising supply of funds schedule and rationing in the case of small (young) firms, but not in the case of large (old) firms. Our econometric results are not supportive of a substantial regime change after liberalization. Financial reform in Ecuador does not seem to have had much effect on the financial constraints faced by small firms when making investment decisions. The continuing presence of informational imperfections, and the decrease in resources
percent (France) to 4.1 percent (Russia) in 2030. If the carbon tax level is raised to US$250/tCO2, the economic costs in some countries, such as Russia, would be very high.

# **Table 3: Percentage change in GDP at various levels of carbon tax in Annex I countries in 2030 from their BAU cases**

|**Country/Regions**|**Ca**<br>|**rbon tax ra**<br>|**te (US$/tC**<br>|**O2)**<br>|
|---|---|---|---|---|
||**10**|**50**|**100**|**250**|
|**Australia and New Zealand**|-0.2%|-0.7%|-1.2%|-2.6%|
|**Canada**|-0.1%|-0.7%|-1.3%|-3.1%|
|**Germany**|0.0%|-0.2%|-0.4%|-1.1%|
|**Spain**|0.0%|-0.3%|-0.6%|-1.4%|
|**France**|0.0%|-0.1%|-0.2%|-0.6%|
|**UK**|0.0%|-0.2%|-0.4%|-1.0%|
|**Italy**|-0.1%|-0.3%|-0.6%|-1.4%|
|**Japan**|0.0%|-0.2%|-0.5%|-1.1%|
|**EFTA Countries & Rest of European**<br>**Union**|-0.1%|-0.4%|-0.9%|-2.1%|
|**Russia**|-0.7%|-4.1%|-8.2%|-18.5%|
|**United States**|-0.1%|-0.5%|-1.0%|-2.1%|
|**Non Annex I Europe & Central Asia**|0.0%|0.2%|0.3%|0.4%|
|**Argentina**|0.0%|-0.1%|-0.1%|-0.2%|
|**Brazil**|0.0%|0.0%|0.0%|-0.1%|
|**Rest of Latin America & Caribbean**|0.0%|-0.1%|-0.2%|-0.5%|
|**China**|0.0%|0.2%|0.3%|0.6%|
|**Indonesia**|0.0%|0.0%|0.1%|0.2%|
|**Rest of East Asia & Pacific**|0.0%|0.2%|0.3%|0.5%|
|**Malaysia**|0.0%|-0.1%|-0.2%|-0.5%|
|**Thailand**|0.0%|0.2%|0.3%|0.4%|
|**India**|0.1%|0.3%|0.4%|0.8%|
|**Rest of South Asia**|0.0%|0.1%|0.2%|0.4%|
|**Middle East & North Africa**|-0.2%|-0.9%|-1.6%|-2.9%|
|**South Africa**|0.0%|0.0%|0.0%|0.0%|
|**Rest of Sub-Saharan Africa**|-0.1%|-0.5%|-0.8%|-1.6%|

# **4.2 Impacts on international trade**

The carbon tax introduced in Annex I countries to move forward in the direction of converging emission intensity would have large impacts on international trade (see Figure 8). Global trade would shrink by 0.5 percent in 2030 if the emission intensity gap between the Annex I and non-Annex I countries is reduced by 33 percent through a US$50/tCO2 carbon tax 16
# **Reaulation**

26. Regulation is ultimately a means to induce private enterprises to behave in a more socially optimal way than if they were left unregulated. The regulating agency is assumed to have the goals of the government and has the task of designing regulations which induce the enterprise to act as if it too were maximizing government objectives, rather than its own.46/ Regulation of this sort is often applied to private transport enterprises which, if left unregulated, would reduce services and charge higher prices in pursuit of higher profits. Regulation may, for example, require a transport enterprise to provide certain unprofitable services, maintain low prices for particular classes of consumers, or employ a specified number of workers. Regulated, privately-owned enterprises may encounter the same problems as public enterprises whose objectives are the same as those mandated by regulation.

27. Regulation may explicitly change the constrair,cs under which a firm operates and create distorted incentives in the use of resources. A well-known example of this phenomenon is the Averch-Johnson effect of rate-of-return regulation.47/ An increasing returns to scale firm, a natural monopoly, is allowed to earn no more than a specified rate of return on its captial. A profit-maximizing natural monopoly operating under this constraint overcapitalizes -- employs more capital than in the optimal (unconstrained) input mix. Rate-of-return regulation in conjunction with lump-sum subsidy payments can lead to overcapitalization beyond the Averch-Johnson results.48/ Rate of return regulation is 46/ John Vickers and George Yarrow, Privatization: An Economic Analysis, The MIT Press, Cambridge, 1988, p. 99-118 and Op Cit, Sappingtor and Stiglitz, p. 19. Regulatory agencies may also have their own goals which may be distinct from government's goals, 47/ Rate of return regulation allows a firm to earn a "normal' rateof-return on its capital. See H. Averch and L. Johnson, "Behavior of the firm under regulatory constraint," American Economic Review, Vol. 52, 1962, p. 1052-69.

48/ Op Cit, Kim and Spiegel, p. 105-119.

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Policy Research Working Paper 8441

# **Abstract**

This study explores the short-run transitions between poverty, vulnerability, and middle class, using synthetic panels constructed from multiple rounds of Colombia’s Integrated Household Survey (in Spanish Gran Encuesta Integrada de Hogares). The paper reports results from two approaches to define a vulnerability line: the first one employs a nonparametric and parsimonious model, while the second utilizes a fully parametric regression model with covariates. The estimation results suggest a range of between $8 to $13 per day per person in 2005 purchasing power parity dollars as the vulnerability line. Using an average daily vulnerability line of $10 per day per person, subsequent estimates on welfare dynamics suggest that, during the past decade, 20 percent of the Colombian population experienced downward mobility, and 24 percent experienced upward mobility. Furthermore, upward mobility increases with higher education levels and is lower for female-headed households.

This paper is a product of the Poverty and Equity Global Practice and the Development Data Goup. 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://www.worldbank.org/research. The authors may be contacted at solivieri@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
# Need to promote holistic ECD programs

Despite the challenges, Pakistan already has programs and platforms in place to effectively promote holistic ECD (Devercelli et al., 2022). Pilot interventions have shown that low-cost mental health interventions such as the Thinking Healthy Programme, delivered by community health workers, show modest benefits including reduced maternal depression and disability, improved knowledge about play and development, and improved breastfeeding practices (Rahman et al., 2012; Sikander et al., 2019). Community-based health interventions have been shown to promote newborn survival, improve children’s health, and reduce stunting (Lassi et al., 2016; Khan et al., 2019; Yousafzai et al., 2018). In addition, teaching stimulation and nurturing care in parenting programs delivered through community health services and family physicians has been shown to significantly benefit children’s development and care in Pakistan (Khan et al., 2019; Yousafzai et al., 2014).

Resources and attention directed toward families with children under age 3 who provide inadequate psychosocial stimulation would boost outcomes for this vulnerable population and would engender greater opportunities for healthy development and learning longitudinally. This population is likely to be larger than it appears in this study because the sample excludes the most disenfranchised families, those who lack the resources to have a cell phone and are therefore likely among the poorest households. Providing caregivers—including the more than one-tenth of the population lacking early stimulation knowledge and skills—with the support needed, for example, through parenting programs or health-based services, to responsively engage with their young children will facilitate disadvantaged children’s likelihood of reaching their developmental and productivity potential.

# Implications for data collection

Better and more data are needed through national and provincial surveys to explore the nexus between ECD services and children’s cognitive and social-emotional development. At present, household surveys such as PSLM, MICS and National Nutrition Survey, along with administrative data on service uptake, allow for analyses of some ECD outcomes, such as nutritional status and ECE enrollment. However, these surveys do not lend themselves to a comprehensive analysis of holistic ECD that focuses on cognitive and social-emotional development for children across the birth to age 6 span. The current survey constitutes a first attempt to gather and analyze this information—albeit in an operating environment restricted by the pandemic. Comprehensive and regular survey data tracking children’s development and school readiness, based on ECE experience, socioeconomic status, location, gender, and other factors, can provide insights into which interventions work and for whom. Monitoring of inequities is crucial to assess if the most 32
their goods and services, and charge them a market fee to sell in the market. The average market in our study has 22 firms surveyed in it, with 75 percent in retail trade and 25 percent in services.<sup>3</sup>

# **2.2 Randomization Procedure**

Individuals were then assigned to treatment and control for the business training intervention in a two-stage process, diagrammed in Figure 1.

First, **Markets** were assigned to treatment (have some individuals in them invited to training) or control (no one in the market would be invited to training) status. Randomization was done within 35 strata defined by geographical region (within county) and the number of women surveyed in the market. The need to ensure sufficient numbers for training meant that more than half the markets were allocated to treatment, with 93 markets assigned to treatment and 64 to control.

Then within each market, **Individuals** were assigned to treatment (be invited to training) or control (not be invited to training) within treated markets by forming four strata, based on quartiles of weekly profits from the census (<=450, 451-800, 801-1500, 1501-4000), and then assigning half the individuals within each stratum to training. This resulted in 1,172 of the 2,160 individuals in treated markets being assigned to treatment, and 988 to control.<sup>4</sup> For the follow-up mentoring intervention, we dropped markets with 15 or fewer firms in the study, since these would have too few firms to make mentoring cost-effective. This left 73 of the 93 treated markets. We randomly assigned 37 of these (within the original randomization strata) to mentoring, and the remaining 36 to be training only. Then within the mentoring markets, all those individuals assigned to training were assigned to be offered mentoring services, although in practice only those businesses still surviving were actually offered these services. We then compare the mentoring and training-only markets to the 44 markets in the control group with 16 or more firms.

# **2.3 Characteristics of the Sample and Verification of Random Assignment**

Table 1 provides some key characteristics of the women selected in our sample and their firms by treatment assignment (Appendix Table 1 provides a similar table by assignment to mentoring).

> 3 In addition, markets typically have a market day once a week, where outside vendors come to sell goods such as clothing, plastic housewares, and shoes. These vendors who are only in the market one day a week are not included in our study.

> 4 When the number of firms within a stratum was odd, the additional unit was also randomly allocated to training.

5
the plots in order for fertilizer to be profitable when maize is valued with either farm gate price or harvest season price.

# _Profitability of Subsidized Fertilizer vs. Cash Transfer Equivalent for Smallholders_

Using the average NUE of 11.89, average nitrogen price of MK 348.15 per kg, and the average farm gate and lean season maize price of MK 23.72 and MK 38.69 per kg respectively, we compared the cash amount of fertilizer subsidy with how much the average farmer will gain from using subsidized fertilizer in maize production. The results indicate that at all rates of fertilizer subsidy (0 – 100%), unless farmers are able to store their produce and sell in the lean season, the average farmer would be MK 66.16 (US$ 0.18) per kg of subsidized nitrogen betteroff with the cash equivalent of the subsidy than participating in the subsidy program if maize is valued at the farm gate (figure 6).<sup>5</sup> If farmers are able to store maize and sell in the lean season however, they will be MK 111.70 (US$ 0.30) per kg of subsidized nitrogen better off with the subsidized inputs than with the cash amount of the subsidy. The estimates should be interpreted as lower bounds because we could not account for the opportunity cost and the other operational costs associated with maize production in the estimation, both of which will make the cash equivalent of the subsidy more favorable than the subsidized inputs to the beneficiary smallholders. Hence, although a fertilizer subsidy increases the profitability of fertilizer use, farmers would, on average, be better off with

> 5 As an example, for a 90% fertilizer subsidy rate, the cash equivalent of the subsidy would be MK 313.34 (i.e. 90% of MK 348.15, the price of nitrogen). If a farmer participated in the fertilizer subsidy program and uses the subsidized fertilizer to produce maize, he or she would gain a net income of MK 247.18 [i.e. (11.89*23.72) – 34.82]. Where 11.89 is the NUE, 23.72 is the farm gate price of maize, and 34.82 is the price (10%) that the farmer would pay for a kilogram of subsidized nitrogen. Given the cash equivalent of MK 313.34 and the net gain of using subsidized fertilizer to produce maize of MK 247.18, the amount by which the farmer would be better-off with the cash equivalent instead of subsidized nitrogen is given MK 66.16 (i.e. MK 313.34 - MK 247.18). This is true for all rates (0 to 100%) of fertilizer subsidy. An excel sheet showing the calculation at all rates of the subsidy (0 to 100%) is available upon request.

25
finds similar significant differences in the total number of sessions attended. These differences in attendance rates provide valuable insight on the mechanism behind differential treatment effects, as we discuss next in Section 5.

# **5. Results and Discussion**

Tables 4-9 present the ITT regression analysis, progressing from financial attitudes and beliefs to financial behaviors. Appendix Tables 1-6 present the corresponding TOT results.

First, Table 4 analyzes financial attitudes. The household survey asked two questions on attitudes, adapted from Carpena et al (2017), that present hypothetical financial situations to respondents and ask them to select the best option out of a menu of potential responses. For instance, the first question presents a scenario where a family is trying to decide how to pay for their young child’s future schooling costs, and saving up to pay school fees is the preferred option.

Column (1) presents the results for the composite score and columns (2) and (3) present analysis for individual questions. We find a 1.8 percentage point improvement in financial attitudes for respondents in T1 over the control group, statistically significant at the 5 percent level. The coefficient for T2 is lower in magnitude and not statistically significant, although the two coefficients (T1 vs T2) are not statistically distinguishable from each other. The corresponding estimates for attending financial education are shown in the TOT regressions in Appendix Table 1. The estimates are scaled higher for attendance, with take-up in T1 resulting in a 8.8 percentage point improvement in financial attitudes over the control group. The coefficient for T2 is about half the magnitude and not statistically significant.

Table 5 examines financial rules of thumb where we do detect statistically significant differences across the two treatment groups. Rules of thumb refer to simple, intuitive statements that refer to good financial decision-making, for example separating business and household accounts, or never borrowing beyond one’s own means for repayment. Column (1) of the table presents results for the composite score across individual questions and finds that the score on financial rules of thumb improves by 1 percentage points in T1, and the effect is very close to zero in T2.

13
# **Table 4:  Simulation Results**

||True model|||Using on|ly Formal variable|s||
|---|---|---|---|---|---|---|---|
|Run|Coef|ficients<br>Sta|ndard Error t Sta|t<br>Coe|fficients Standard|Error|t Stat|
||1 TotalN|0.043|0.353|0.120 TFN|0.024|0.349|<br>0.070|
||2 TotalN|0.692|0.488|1.416 TFN|0.665|0.484|<br>1.373|
||3 TotalN|1.467|0.248|5.926 TFN|1.371|0.282|<br>4.868|
||4 TotalN|1.180|0.309|3.820 TFN|1.050|0.341|<br>3.082|
||5 TotalN|0.286|0.456|0.626 TFN|0.125|0.457|<br>0.274|
||6 TotalN|0.246|0.437|0.563 TFN|0.421|0.419|<br>1.004|
||7 TotalN|1.391|0.368|3.783 TFN|1.441|0.338|<br>4.270|
||8 TotalN|1.563|0.458|3.410 TFN|1.480|0.471|<br>3.143|
||9 TotalN|1.464|0.344|4.250 TFN|1.491|0.321|<br>4.645|
||10 TotalN|0.811|0.500|1.622 TFN|0.680|0.510|<br>1.333|
||11 TotalN|1.153|0.410|2.813 TFN|0.767|0.478|<br>1.606|
||12 TotalN|0.792|0.366|2.162 TFN|0.876|0.341|<br>2.567|
|Average||0.924|0.395|2.543|0.866|0.399|<br>2.353|
|Note:|TotalN|=(TF +TI)/6.|Mean = 2.29|TFN= TF|/3. Mean = 2.46|||

The key results from the illustrative simulations in Table 4 are:

- (i) The coefficient (slope) estimates from using only the Formal measures of quality are about 5-10% lower than from using Formal and Informal measures;

- (ii) The estimated standard errors from using only the Formal measures of quality are about 1-3% higher than from using Formal and Informal measures.

- (iii) Estimated t-values are more often lower than higher from using only the Formal measures of quality but, on average, the difference was a reduction in estimated t-values of around 7%.

The illustrative simulation results from this data set would not justify abandoning the idea of carrying out empirical work with only the formal variables in place. This is encouraging in itself and supportive of the work that we report in our companion paper, which uses the DPS data on regulatory governance – which does not have information on regulatory practice.

We have, however, noted that the Stern-Holder data set contains relatively little data on regulatory practice, even in its informal measures – much less than the recent Prayas Report on Indian State Electricity Boards. Hence, it remains a priority to collect more and better information on regulatory practice and its development _both_ in its own right _and_ to test whether the results from the simulation analysis based on the Stern-Holder data set generalise to up-to-date and better data sets.

32