rafmacalaba/datause-encoder-data huggingface dataset:
Table 8: Piecemeal sensitivity analysis: Variation in Adult female and male wages and factor adjustments after “Full reform” (% change with respect to benchmark)

||P|arameter val|ue|Variation in|Adult female|and male wage|s(% change w|ith respecto to|benchmark)|F|actor adjustmen|ts(% change w|ith respecto t|o benchmark)||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Lower|Central|Uer|Low|er|Cen|tral|Up|per|Lo|wer|Cen|tral|U|pper|
|**Parameter**|||pp|Adult Females|Adult Males|Adult Females|Adult Males|Adult Females|Adult Males|Adult Females|Adult Males|Adult Females|Adult Males|Adult Females|<br>Adult Males|
|σ (va, bs)|0.5|1.25|2.00|1.80|2.30|2.00|2.78|2.36|3.64|0.82|0.95|1.11|1.35|1.66|2.15|
|σ (qi, qj)|2.00|3.00|4.00|4.98|6.90|2.00|2.78|1.62|2.20|2.45|2.87|1.11|1.35|0.89|1.14|
|σ (D, M)|2.00|4.00|6.00|1.93|2.23|2.00|2.78|2.11|3.26|0.70|0.91|1.11|1.35|1.48|1.75|
|σ (L,K)|0.7|1.00|1.3|1.87|2.85|2.00|2.78|2.09|2.73|1.06|1.30|1.11|1.35|1.14|1.39|
|σ (A1,...,An)|0.00|0.00|0.25|2.00|2.78|2.00|2.78|2.01|2.79|1.11|1.35|1.11|1.35|1.11|1.35|
|σ (D,E)|2.00|4.00|6.00|2.03|2.66|2.00|2.78|1.98|2.88|0.95|1.20|1.11|1.35|1.25|1.51|
|ε(di)|2.00|4.00|6.00|1.98|2.73|2.00|2.78|2.02|2.82|1.04|1.27|1.11|1.35|1.15|1.41|
|ε(fi)|2.00|4.00|6.00|1.06|1.39|2.00|2.78|2.78|3.86|0.67|0.77|1.11|1.35|1.42|1.78|
|θm(i)||ee table bel|w|1.97|2.81|2.00|2.78|2.07|2.71|1.15|1.41|1.11|1.35|1.04|1.26|
|θfdi(i)||||1.82|2.51|2.00|2.78|2.17|3.02|1.00|1.22|1.11|1.35|1.21|1.47|

Source: Author's estimates.

# **Parameter Definition of the parameter**

- σ (va, bs) Elasticity of substitution between value-added and business services σ (qi, qj) Elasticity of substitution between firm varieties in imperfectly competitive sectors σ (D, M) Armington elasticity of substitution between imports and domestic goods in CRTS sectors σ (L,K) Elasticity of substitution between primary factors of production in value added σ (A1,...,An) Elasticity of substitution in intermediate production between composite Armington aggregate goods σ (D,E) Elasticity of transformation (domestic output versus exports) ε (di) Elasticity of national service firm supply with respect to price of output ε (fi) Elasticity of multinational service firm supply with respect to price of output θm (i) Share of value added in multinational firms in sector I due to specialized primary factor imports in the benchmark equilibrium θfdi (i) Share of output of service sector i captured by multinationals firms in the benchmark equilibrium

|**Parameters values for:**||θfdi(i)|||θm (i)||
|---|---|---|---|---|---|---|
||Lower|Central|Upper|Lower|Central|Upper|
|Telecommunication|0.85|0.9|0.95|0.025|0.05|0.1|
|Insurance|0.2|0.3|0.4|0.025|0.05|0.1|
|Banking|0.3|0.4|0.5|0.025|0.05|0.1|
|Professional business services|0.2|0.3|0.4|0.025|0.05|0.1|
|Road transport|0.1|0.2|0.3|0.025|0.05|0.1|
|Railway transport|0.5|0.6|0.7|0.025|0.05|0.1|
|Water transport|0.7|0.8|0.9|0.025|0.05|0.1|
|Air transport|0.3|0.4|0.5|0.025|0.05|0.1|

32
<!-- Start of picture text --> Time series<br>Low pass filter High pass filter<br>S 1 D 1<br>Low pass filter High pass filter<br>S 2 D 2<br>Low pass filter` High pass filter<br>S 3 D 3<br>. . . . . .<br>Low pass filter` High pass filter<br>S J D J<br><!-- End of picture text --> Figure 3: Schematic representation of a wavelet multiresolution decomposition using Pyramid algorithm.

weighted averages of series vary from one averaging period to the next. The wavelet transform produces a series of wavelet coefficients that are associated with dyadic scales. These coefficients are proportional to the differences between the averages of the original data, and the scaling coefficients that are proportional to averages. The scaling coefficients capture the long term variations in the data. The discrete wavelet transform (DWT) effectively decorrelates even highly auto-correlated series, which can be perfectly reconstructed from the DWT coefficients making it an extremely useful technique for econometric analysis. The continuous wavelet transform is not considered in this paper due to small sample size of available time series.

# **4 Time and Frequency Decomposition**

This section introduces a simple application of the MODWT that is defined in Section A.1. We first use a synthetic data example in order to introduce the idea of wavelet scalogram. Then, we use actual time series data for GDP in levels and first differences to demonstrate the application in different countries. The wavelet techniques discussed here do not assume stationarity of the series. Therefore, first differencing is performed only for interpretation 9
Having obtained the coefficient vector, we can evaluate the marginal effect of financial sector growth accelerations on the conditional expectation of a _LARGE_ financial contraction: where the marginal contribution of each conditioning variable to the probability of _FIN.CONTRACTIONk,t=1_ is estimated at the sample average of government expenditures and currency crisis dummy set to 1. We use coefficient estimates of from probit specification (2) and logit specification (4) in Table 14 to calculate lower and upper bounds of the marginal effect of excess financial sector growth on the probability of a future financial contraction according to equation (10). The marginal effect based on the probit specification (2) in Table 14 is 0.038 while that based on the logit specification (4) is 0.043, indicating financial contractions have approximately 4 percent greater probability of occurrence if during the preceding 3 years the growth rate of the financial sector value added was double that of the average GDP growth over the same period.

# **5. Conclusion and future research**

Our empirical study validates the asymmetric nature of financial intermediation whereby financial contractions are associated with a large decline in the value added of key real sectors but financial expansions do not seem to have much effect. This asymmetric feature was pointed out previously by Philippon (2008) for the U.S., and we find that it applies more universally to a large number of developing and developed countries. This finding is important, as the U.S.’s position as the supplier of the key global currency allows it to buffer its exposure to financial contractions by the FED’s quantitative easing policies, _de facto_ supplying the key reserve currency elastically. Emerging markets and developing countries, which lack this capacity, would therefore benefit by paying more attention to the factors magnifying and mitigating their exposure to costly financial contractions.

For this latter group of countries, our finding that the adverse effects of abrupt financial contractions are magnified by financial openness and mitigated by international reserves has 21
# **Table** **<u>3</u>**

||**Go**<br>|**vernance networ**<br>|**ks: Bureau**|**crats**<br>||||
|---|---|---|---|---|---|---|---|
||**Individual**<br>**regression**|**Pradhan**<br>**characteristics**|**2SLS**|**Basti age**<br>**x**|**2SLS**|**Village**<br>**x**|**2SLS**|
|Assets (Rs. 10000s)|0.058|0.058|0.167|0.058|0.161|0.057|0.167|
||[0.014]**|[0.014]**|[0.086]+|[0.014]**|[0.085]+|[0.014]**|[0.079]*|
|Init. contact: close relative|-0.008|-0.008|-0.016|-0.006|-0.008|-0.011|-0.018|
||[0.029]|[0.028]|[0.042]|[0.029]|[0.043]|[0.029]|[0.040]|
|Init. contact: villager|0.035|0.035|-0.016|0.036|-0.012|-0.137|-0.147|
||[0.021]|[0.022]|[0.049]|[0.022]|[0.047]|[0.092]|[0.195]|
|Init. contact: other|0.071|0.071|0.044|0.072|0.052|0.070|0.046|
||[0.025]**|[0.026]*|[0.044]|[0.026]*|[0.043]|[0.028]*|[0.047]|
|Pradhan-shared identity||0.018|0.006|0.005|-0.014|0.049|0.034|
|||[0.023]|[0.039]|[0.067]|[0.118]|[0.028]+|<br>[0.060]|
|Pradhan's education||0.001|0.003|0.007|0.028|-0.004|-0.003|
|||[0.002]|[0.003]|[0.006]|[0.008]**|[0.002]+|<br>[0.004]|
|Pradhan- political affil.||-0.017|-0.066|-0.121|-0.314|-0.054|-0.139|
|||[0.025]|[0.065]|[0.060]+|[0.131]*|[0.028]+|<br>[0.138]|
|Pradhan's community yrs||-0.001|-0.002|0.003|0.011|-0.002|-0.001|
|||[0.001]|[0.003]|[0.005]|[0.006]+|[0.001]|[0.003]|
|Basti age x prdn shared<br>id.||||0.001<br>|0.002<br>|||
|||||[0.003]|[0.005]|||
|Basti age x prdn's educ.||||0.000|-0.001|||
|||||[0.000]|[0.000]**|||
|Basti age x pol. affil.||||0.004|0.011|||
|||||[0.002]+|[0.005]*|||
|Basti age x prdn's||||||||
|<br>res.(yrs)||||0.000|0.000|||
|||||[0.000]|[0.000]*|||
|Villager x prdn shared id.||||||-0.049|-0.044|
|||||||[0.040]|[0.061]|
|Villager x prdn's educ.||||||0.009|0.010|
|||||||[0.004]*|[0.006]|
|Villager x pol. affil.||||||0.082|0.107|
|||||||[0.073]|[0.194]|
|Villager x prdn's res.(yrs)||||||0.003|-0.002|
|||||||[0.002]|[0.003]|
|Household / basti controls|yes|yes|yes|yes|yes|yes|yes|
|Observations|780|780|474|780|474|780|474|
|Adjusted R-squared|0.06|0.06||0.05||0.06||
|F|16.2|25.44|0.097|108.63|0.515|54.79|0.067|
|Hansen's over-id J|||0.036||0.034||0.009|
|Prob > chi2|||0.849||0.854||0.923|

Robust standard errors in brackets + significant at 10%; * significant at 5%; ** significant at 1% robust standard errors, clustered at the slum level 29
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# **Transport Connectivity, Medical Supplies, and People’s Health Care Access: Evidence from Madagascar**

Atsushi Iimi<sup>¶1</sup> , Voahirana Hanitriniala Rajoela<sup>2</sup>

- 1 Transport & Digital Development Global Practice, The World Bank Group 2 Health, Nutrition & Population Global Practice, The World Bank Group

Key words: rural roads; health care access; transport connectivity; three-stage least squares estimation.

JEL classification: C25, C26, I15, Q42, R40.

> ¶ Corresponding author.
strong message that privatization of state-owned enterprises, enterprise competition, and corporate governance related improvements (i.e., to name a few criteria covered by the EBRD enterprise restructuring index) are important in boosting commercial bank efficiency. This could obviously work through (i) improved efficiency of enterprises themselves (which then results in improved portfolio quality and revenue generation ability of banks), but also through (ii) intensified corporate governance pressures exerted by the newly emerging and stronger private sector on commercial banks.

Finally, there is some evidence that securities market and non-banik financial institutions development hinders the efficiency of banks. The results indicate that higher securities market capitalization reduces revenue-based efficiency, while more developed non-bank financial institutions hinder deposit taking. As unusual as these links may sound at first, they are not counter-intuitive. On the one hand, it is relatively straightforward to see that an opportunity to raise funds on the stock market would reduce the demand for bank loans by the best borrowers on the market. Less credits extended to the _cream de la cream_ would then manifest itself into lower _ex post_ returns and result in lower efficiency of banking operations. In that sense, this outcome supports the one on the competition-performance link made above. On the other hand, more developed non-bank financial institutions (i.e., pension funds, insurance companies, brokerage firms, etc.) would in all likelihood produce a greater demand for household savings, thereby reducing the amount of bank deposits available to the banks and eventually transaction services rendered by the banks.

# Chapter 5. Conclusion

This paper argues that to fully assess the efficiency of commercial bank operations it is necessary to model various types of functions performed by banks, and control for the inputs necessary to provide a certain level of utility to owners (i.e., profits) and depositors (i.e., services) while performing those functions. We therefore use Data Envelopment Analysis (DEA) to measure bank efficiency by stressing profit maximization and provision of transaction services as banks' primary objectives.

One of the contributions of this paper is that it demonstrates that DEA could be successfully applied to banking systems in transition countries. Despite certain concems about 22
- Charmarbagwala, R., Ranger, M., Waddington, H., White, H. (2004). The determinants of child health and nutrition: A meta analysis. OED Working Paper Series. Operations Evaluation Department, Washington, D.C.: World Bank.

- Chaudhury, N., Hammer, J., Kremer, M., Muralidharan, K. and Rogers, F. H. (2006). Missing in Action: Teacher and Health Worker Absence in Developing Countries. _Journal of Economic Perspectives_ , 20 (1), pp. 91-116.

- Chin, A. (2005). Can redistributing teachers across schools raise educational attainment? Evidence from Operation Blackboard in India. _Journal of Development Economics_ , 78 (2), pp. 384-405.

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- Clemens, M. A., Kenny, C. J. and Moss, T. J. (2007). The Trouble with the MDGs: Confronting Expectations of Aid and Development Success. _World Development_ , 35 (5), pp. 735-751.

- Coady, D. P., Parker, S.W. (2004). Cost-effectiveness Analysis of Demand- and Supply-side Education Interventions: the Case of PROGRESA in Mexico. _Review of Development Economics_ , 8 (3), pp. 440-451.

- Deininger, K., Mpuga, P. (2005). Economic and Welfare Impact of the Abolition of Health User Fees: Evidence from Uganda. _Journal of African Economies_ , 14(1), pp. 55-91.

- Drèze, J., Kingdon, G. G. (2001). School Participation in Rural India. _Review of Development Economics_ , 5 (1), pp. 1-24.

- Duflo, E. (2001). Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment. _American Economic Review_ , 91 (4), pp. 795-814.

- Duflo, E., Rema, H. (2005). Monitoring Works: Getting Teachers to Come to School. NBER Working Paper, 11880, National Bureau of Economic Research, Cambridge.

- Ensor, T. and Cooper, S. (2004). Overcoming Barriers to Health service access: Influencing the Demand side. _Health Policy and Planning_ , 19 (2), pp. 69-79.

- Fafchamps, M., Minten, B. (2007). Public Service Provision, User Fees and Political Turmoil. _Journal of African Economies_ , 16 (3), pp. 485-518.

- Fay, M., Leipziger, D., Wodon, Q., Yepes, T. (2005). Achieving child-health-related Millennium Development Goals: The role of infrastructure. _World Development_ , 33 (8), pp. 1267-1284.

- Filmer, D. (2003). Determinants of Health and Education Outcomes. Background Note for the WDR 2004. The World Bank. Mimeo.

- Filmer, D., Hammer, J. S., Pritchett, L. H., (2000). Weak Links in the Chain: A Diagnosis of Health Policy in Poor Countries. _World Bank Research Observer_ , 15 (2), pp. 199-224.

- Filmer, D., Pritchett, L. (1999). The impact of public spending on health: Does money matter? _Social Science and Medicine_ , 49 (10), pp. 1309–1323.

- Filmer, D., Schady, N. (2008). Getting Girls into School: Evidence from a Scholarship Program in Cambodia. _Economic Development and Cultural Change_ , 56, pp. 581-617.

- Fiszbein, A., Schady, N. (2009). Conditional Cash Transfers. World Bank Policy Research Report, Washington D.C.

29
the presence of village chiefs, communal authorities and program staff. They led to the selection of 100 clusters (169 villages) to participate in the cash transfer program. A control group of 52 clusters (85 villages) was also drawn for inclusion in the evaluation sample. The lotteries were held separately in each commune, and further stratified between sedentary and nomadic clusters of villages.<sup>11</sup> After the public lotteries, a detailed listing of households was implemented in all sample villages. A random sample of 30 households was drawn from the listing in each cluster to be included in the baseline survey.<sup>12</sup> The baseline survey was collected between April and June 2012. The sample of 4,330 households included 1,469 households in control villages and 2,861 households in treatment villages. The baseline survey included a questionnaire based on the 2011 Niger LSMS-ISA national household survey, which contained detailed modules on consumption, food security, and economic activities, among other topics.

Following the baseline survey, a registry census collected basic information about all households in treatment villages. The census data were used to calculate a proxy-means test (PMT) score for each household. The households with the lowest PMT scores were selected as beneficiaries for the cash transfer program. On average, 40 percent of households in program areas were selected. Premand and Schnitzer (2020) provide additional details on the implementation of the PMT targeting procedure and analyze targeting efficiency and legitimacy compared to alternative methods in a subsequent phase of the program (in different communes and using a different experiment).

Importantly, in our case, the registry census to generate PMT scores could not be collected in control villages. For that reason, the baseline survey was designed to collect information to recalculate PMT scores for each sample household (i.e. both in the treatment and control groups). As such, it was originally planned that the PMT threshold could be re-applied based on the estimated PMT scores calculated from the baseline survey. This would have allowed to make

> 11 Additional details on the RCT design are provided in Premand and Barry (2020).

> 12 Approximately 20% of households with high self-reported income were excluded from the sampling frame as they were considered ineligible for the cash transfers. The use of such an exclusion filter was shown not to worsen targeting performance (Premand and Schnitzer, 2020).

9
<mark>measuring monthly total consumption and monthly food consumption, using retrospective questions with varying recall periods depending on the item considered (from seven days to one year). The consumption aggregate is compiled by aggregating the different food and non-food items, including</mark> expenditures on education, health, durable assets, and rent. <mark>In the framework of this study, we consider two welfare indicators from the PDM data set. The first is the household total consumption expenditure per capita per month, and the second is the household food consumption per capita per month.</mark><sup>5</sup> For poverty imputation purposes, three data sets are constructed from the ProGres, Targeting, and PDM data sets. The first, which we refer to as “ProGres 2,” is obtained by appending the ProGres data to the end of the PDM data. As the ProGres and PDM data share only demographic variables, ProGres 2 contains demographic variables for all observations, although only the observations from the PDM data have consumption expenditure. The second set of data constructed, “Targeting 2,” comes from appending the Targeting data to the end of the PDM data. Therefore, Targeting 2 contains demographic variables, asset and animal ownership, and coping strategies variables as well as consumption data. The last set of data, “ProGres Targeting,” is obtained by first merging the ProGres and Targeting data (matching 72% of the observations) and then appending these data to the end of PDM data. This set of data is the more complete data set in terms of variables. <mark>The motivation of the construction of these three sets of data is to check whether the different sources of data as well as different sets of variables generate different poverty figures, consequently determining the set that best predicts poverty. To ensure comparability across the three data sets, we restrict the analysis to 16 of the 19 refugee sites, because the PDM data cover only 16 sites. Consequently,</mark> this study covers only refugees from the Central African Republic and Sudan, and not the Nigerian refugees, and all the estimates provided in the paper are not representative of all the refugees living in the country.

> 5 The aim of the paper is not to measure consumption accurately or estimate nationally representative poverty figures for refugees in Chad. The purpose of the paper is only to test the cross-survey imputation methodology using a sample of refugee data. In this respect, our only concern is that the poverty predictions are close to the poverty rates calculated with the consumption data. Whether our consumption data are accurate or not, this is less relevant for us. We should expect the cross-survey methodology to produce even better results if the quality of the consumption aggregate improves.

9
per year (1995-2002 and 2011-2018). On the other hand, the change in the employment rate has been negative during most of the 1995-2018 period, with the exception of the 2003-2010 subperiod.

# 2.2 Sectoral labor productivity growth

In the last decades, agriculture was the only major sector that had signicant and sustained labor productivity growth (Table 1).<sup>4</sup> Whereas the industry sector had negative annual growth (-0.4 percent)<sup>5</sup> in 1996-2020, services ́ productivity was stagnant (0.1 percent per year) throughout the period. Since services account for more than 70 percent of total employment in Brazil, the lack of productivity growth in this sector contributed signicantly to the sluggish aggregate productivity growth. The subperiod 2003-2010 was characterized by substantial productivity growth in several service activities, such as retail and nancial intermediation. However, this turned out to be an exception, since services had negative productivity growth both in 1996-2002 and 2011-2020.

Table 1: Sectoral Labor Productivity Growth, Brazil 1996-2020 <u>( percent per year)</u>

|Years|1996-2002|2003-2010|2011-2020|1996-2020|
|---|---|---|---|---|
|Agriculture|5.3|5.7|6.2|5.8|
|Mining|6.3|2.2|2.5|3.5|
|Manufacturing|-3.7|0.3|0.1|-0.9|
|Construction|-2.8|0.6|-1.4|-1.2|
|Utilities|3.7|2.4|3.2|3.1|
|Retail|-2.5|2/1|-0.2|-0.1|
|Transportation|-1.2|0.5|-2.4|-1.1|
|Information Services|-3.1|-1.7|1.7|-0.7|
|Financial Intermediation|0.6|5.0|0.3|1.9|
|Other Services|-1.2|0.4|-1.0|-0.6|
|Real Estate|2.3|-0.5|0.8|0.8|
|Government Services|0.7|-0.2|-1.4|-0.4|
|Agriculture|5.3|5.7|6.2|5.8|
|Industry|-2.6|0.5|0.5|-0.4|
|Services|-0.5|1.1|-0.4|0.1|

Source: Observatório da Produtividade Regis Bonelli at FGV IBRE.

> 4Labor productivity is measure as value added per worker. The mining sector also experienced sustained growth in the last decades but it accounts for a much smaller proportion of value added and employment.

> 5The industry sector includes mining, manufacturing, utilities and construction. Manufacturing had negative productivity growth during 1996-2020 (-0.9 percent per year).

7