rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix Table 1. Means and Standard Deviation for the Variables in this Study**

||_Pool_|_ed_|_Rur_|_al_|_Ur_|_ban_|
|---|---|---|---|---|---|---|
|HeightforAgeZScore|-1.117|(1.511)|-1.604|(1.488)|-0.690|(1.398)|
|Male|_0.504_|_(0.500)_|0.495|_(0.500)_|0.512|_(0.500)_|
|Age 0-6 months|0.109|(0.312)|0.114|(0.318)|0.105|(0.307)|
|Age 7-12 months|0.097|(0.296)|0.104|(0.305)|0.091|(0.288)|
|Age 13-18 months|0.092|(0.289)|0.093|(0.291)|0.091|(0.288)|
|Age 19-24 months|0.090|(0.286)|0.081|(0.273)|0.097|(0.296)|
|Age 25-36 months|0.200|(0.400)|0.202|(0.402)|0.197|(0.398)|
|<br>Age 37-48 months|0.202|(0.402)|0.199|(0.400)|0.205|(0.404)|
|Age 49-60 months|0.210|(0.407)|0.206|(0.405)|0.213|(0.409)|
|Indigenous|0.082|(0.274)|0.161|(0.368)|0.012|(0.108)|
|Logarithm of income per capita|7.474|(0.556)|7.307|_(0.515)_|7.621|_(0.550)_|
|Female education in years|7.356|(3.948)|5.182|(3.104)|9.264|(3.610)|
|M4al**e**ducation in years|7.127|(3.986)|5.424|(3.344)|8.622|(3.904)|
|_Has neither_sanitation or water|0.339|(0.474)|0.603|(0.490)|0.108|(0.311)|
|supply|||||||
|Has sanitation or water supply|0.236|(0.424)|0.308|(0.462)|0.172|(0.378)|
|_Has both_sanitation and water|0.425|(0.494)|0.089|(0.285)|0.720|(0.449)|
|supply|||||||
|Urban|0.533|(0.499)|||||
|Non-self female education|7.469|(2.690)|5.345|(1.618)|9.333|(1.963)|
|Non-self average logarithm of<br>expenditures|7.649|(0.338)|7.486|(0.289)|7.792|(0.314)|
|Non-self neighborhood percentage<br>with_neither_type of infiastructure|0.320|(0.402)|0.580|(0.425)|0.092|(0.181)|
|Non-self neighborhood percentage<br>witi one type of infrastructure|0.246|(0.303)|0.329|(0.367)|0.173|(0.208)|
|Non-self neighborhood percentage<br>with both types of infrastructure|0.434|(0.426)|0.091|(0.231)|0.735|(0.316)|
|Number of observations|2,084||974||1,110||

# **References**

- Alderman, Harold. 2000. "Anthropometrics." In Grosh and Glewwe (eds.). _Designing Household Survey Questionnaires for Developing Countries: Lessons from Ten Years of LSMS Experience._ Oxford: Oxford University Press.

- Alderman, Harold and Marito Garcia. 1994. Food Security and Health Security: Explaining the Levels 6f Nutritional Status in Pakistan. _Economic Development and Cultural Change_ 42(3): 485-508.

- Alderman Harold, Simon Appleton, Lawrence Haddad, Lina Song and Yisehac Yohannes. 2000. Reducing Child Malnutrition: How Far Does Income Growth Take Us? World Bank. Processed.

- Barrera, Albino 1990. The Role of Maternal Schooling and its Interaction with Public Health Programs in Child Health Production. _Journal of Development Economics 32:_ 69-91.

Basu, K. and J. Foster. 1998. On Measuring Literacy. _Economic Journal_ 108:1733-1749.

_18_
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expenses from deposits. The wide spread measure includes total earnings assets as well as total interest bearing liabilities, and the explanatory variables are probably less able to explain a wider range of the banks’ interest generating and paying activities.

# **VI. Robustness Tests**

In this section, we briefly analyze whether the reported findings from above are robust to different estimation methods and also to the alternative wide spread definition that does include interest rate fees and charges. We employ the same samples and model specifications in the robust and MLS regressions as in tables 5 and 6, that is, the same exclusion criteria are adopted as well as the same variables used.

We use two alternative estimation techniques to control for the impact of outliers. Firstly, the robust estimation technique uses all observations available, but assigns different weights to avoid the impact of outliers (Beck, Cull and Jerome, 2005). Specifically, through an iterative process, observations are weighted based on the absolute value of their residuals, with observations with large residuals being assigned smaller weights. Secondly, the median least squares (MLS) estimator minimizes the median square of residuals rather than the average and thus reduces the effect of outliers. A look at Table 2, panel A, confirms that the median and mean differ quite a lot for many variables.

Tables 7 and 8 present the panel estimations based on the robust estimation technique. Most of the main previous findings hold which can be viewed as a very strong result given the usual data problems surrounding bank balance sheet and income statement data from developing countries especially in Africa. To focus on a few results, there is now some evidence that banks with higher liquidity holdings charge lower narrow and wider spreads in 2004 and 2005. The interaction term of liquidity with the year dummy 2005 is significantly negative for both the narrow and wider spreads which provides additional support for the hypothesis that banks subject to increased liquidity holdings might have been for looking for lending outlets and lowered spreads. A small caveat is that unlike the sizable economic effect of overhead costs on spreads, the economic magnitude of liquidity is rather small.

19
as well as standard measures related to participation in the labor market: occupation, hours worked, employment conditions, search and earnings. Importantly, the ENEU is constructed as a rotating panel, where individuals are surveyed every quarter for a total of five quarters.<sup>16</sup> Worker earnings include overall earnings in the individual’s principal occupation from fixed salary payments, hourly or daily wages, piece-meal work, commissions, tips and self employment earnings. The ENEU, in its modern form, has employed a consistent survey instrument from 1987 to 2004; it is thus one of very few long-running surveys with a panel dimension in the developing world. In our study, we are able to use this 18 year span comprising a total of 72 quarters of data.<sup>17</sup> We note that while the ENEU survey records employment information on all members of the household above 12 years old, for younger workers employment is generally transient and time is often divided among schooling, unpaid support to the household and paid work. Similarly, much later in life, work again becomes more transient. In our analysis, we focus on individuals between the ages 20 and 65.

# V. Results

As discussed in the previous section, our estimation methodology proceeds in two steps. As in Carroll and Samwick (1997), we first use individual data to estimate a Mincer earnings regression. In a second step, the residuals from the Mincer regression are used to estimate income mobility parameters using (4’) and (6’). Table 2 reports the estimates from the first stage earnings regression using the ENEU data described in the preceding section. Our estimates are consistent with earlier findings in the literature. Specifically, earnings increase, but at a decreasing rate, with education. Further, earnings increase with potential experience (age) up until the age of 44 after which they decrease again. Males appear to earn 31 percent more than women, conditional on the other covariates.<sup>18</sup>

> 16In each round of the rotating panel, the questionnaire records absent members, adds any new members who have joined the household, and records any changes in schooling that have taken place. If none of the original group of household members is found to be living in the dwelling unit in the follow-up survey, the household is recorded as a new household. The interviewers do not track households that move, so they leave the panel. Rates of attrition are comparable to other developing countries (See Antman and McKenzie, 2007).

> 17Since 2004, the ENEU has been replaced by the Encuesta Nacional de Ocupacion y Empleo (ENOE, Survey of Occupation and Employment) in 2005. Unfortunately, however, the ENOE instrument differs from ENEU in important ways that make it impossible to match the surveys with confidence.

> 18For robustness we have also run alternate earnings specifications, allowing for both more and less

10
- 23 -

MS(t) is defined above, EKS(t) is the survey median of the expected change in money stock from the previous announcement in week t-l to the present, and K is the money stock level. The anticipated component of the money supply is calculated as: AM(t) - [EMS(t)/M(t-l)I*l0O.

38. The announced percentage change in the UR (AUR) is calculated as - ([AUR(t) AUR(t-l)]/AUR(t-l))*lO0, where AUR(t) is the announced level of unemployment in period t; and the expected percentage change in the - unemployment rate (EUR) is calculated as follows: ([EUR(t) AUR(t-l)]/AUR(t1))*100, where EiJR(t) is the market median survey of the unemployment rate level for period t. 39. For other announced variables used in this study (PPI, CPI, IP), since both the announced and expected figures are themselves in terms of percentage changes, the unexpected percentage changes are calculated as the differences between the announced and survey expectations figures.

40. The two remaining independent variables are: the unexpected changes in daily interest rates (IR) and exchange rates (ER). The interest rate chosen is the three-month U.S. Treasury bill rate. It is he daily average as reported by the U.S. Treasury. The exchange rate is defined as the London noon quotation of the number of SDR per U.S. dollar as reported by the Bank of England. 1/ An increase in that number corresponds to an appreciation of the dollar. Both of these rates were obtained from International Monetary Fund 1/ This exchange rate is chosen for two important reasons. First, in terms of timing, investors in the U.S. have access to it in the morning. Second, the SDR/U.S. dollar rate sunmarizes the movements of a basket of important international currencies vis-a-vis the U.S. dollar.
# **2.  A numerical example**

In this section we explicitly solve the model for a set of values of the exogenous parameters,<sup>15</sup> and for the transition probability function π ( _x_ ) = (1- _x_ )<sup>2</sup> . Figure 4 illustrates the partitioning of the space ( _x,_ θ _)_ by the switch lines. Below the _economic switch line_ (Area I), agents build value in state _N_ ; above it (Areas II and III), they strip assets in state _N._ The economic switch line is downward sloping because increasing opposition to legal reform decreases the expected lifetime return to building value.

The critical value θ _p_ ( _x;_ λ _)_ is a horizontal line that divides Area II from Area III. Above the line, agents’ stake in the maintenance of a legal regime that does not interfere with their theft leads them to oppose legal reform. Below the line, that stake is sufficiently small, since the ability to strip is sufficiently small, that agents demand legal reform for the sake of the improvement in the returns to building value.

Area III is of particular interest because the argument was made in Russia in the 1990s that individuals who steal may nonetheless support the establishment of the rule of law. The parameter λ —the fraction of current stripping returns reappropriated under the rule of law –influences the size of Area III. If λ falls, Area III expands for two reasons— see Figure 5. First, more agents strip assets since the expected return to asset stripping rises (the economic switch line shifts down). Second, fewer asset-strippers oppose legal reform because they forgo a smaller fraction of what they have “stolen” in that period in the transition to the rule of law (the political switch line shifts up).

In the limiting case, λ = 0: “stolen” assets are fully protected. In this limiting

> 15 The parameter values are _f =_ 0.05, _IL_ = 0.01, _IN_ = 0.0475, _g_ ~ = 1.05, ~ _z_ = 0.9, λ = 0.3, and δ = 0.945. The excel program is available at www.econ.worldbank.org/staff/khoff .

19
Rising energy prices also explains the convergence in energy intensity. Industrial electricity prices on average increased by 12 percent during 1998-2008 in ECA. Higher energy prices cause energy intensity to fall by encouraging the adoption of energy saving technologies—especially in sectors where energy constitutes a significant portion of their total costs. This includes the introduction of more energy efficient machines, production processes or materials. For example, producing clinker from raw material is the main energy-consuming process in a cement factory. Using other synthetic material to substitute for clinker can significantly reduce energy use in cement production.

Industry sector normally consists of a small number of large players with similar manufacturing processes. It is therefore the part of the economy best organized to become more energy efficient. Although industry in ECA has become much more efficient over the past decade, the potential for reducing energy use and associated CO2 emissions is high. The region produces 5 percent of manufacturing value added in the world, but consumes 9 percent of energy. Many ECA countries still use several times more energy per unit of output than their Western partners, as shown in Figure 3-a. The energy savings potential estimated for three energy-intensive industrial sectors (iron and steel, cement, and pulp and paper) in ECA amounts to 50 percent of current energy use (Deichmann and Zhang, 2013). Given the old vintage of capital stock in ECA, upgrading to energy efficient technologies are likely to be cost-effective if undertaken as part of the natural cycle of plant replacement. This offers an excellent opportunity for new technology deployment to have an impact on energy savings in the near to medium term.

Empirical results in this paper provide several policy implications for improving manufacturing energy efficiency in the region: First, one obvious strategy to improve energy productivity is to target energy efficiency efforts on industries with high energy intensity, high total energy use, and high economic growth. Industries undergoing growth in production are less capital restricted and may also be more receptive to efforts to improve their competitive edge through increased management of energy costs.

Second, despite recent progress in energy price reforms, subsidies still persist in many countries in ECA. The effectiveness of continued energy price reforms in the region will largely determine the potential to save energy.

Finally, the lack of energy-use data is one of the stumbling blocks to developing rational energy efficiency strategies and policies. Important data gaps remain, especially in Central Asian countries. For example, more than 80 percent of the industrial energy use is reported as “nonspecified” in Kazakhstan, Kyrgyz, Turkmenistan and Uzbekistan. Collection of good quality, timely and detailed data should be a priority in these countries in order to provide the basis for rational policy making.

13
**<u>Table 4: Productivity gains from land rental</u>**

||**Total**|**Ren**|**ting to**|
|---|---|---|---|
||**Sample**|**Relative**|**Non-relative**|
|**Tenants’ assessment**||||
|Profit before transfer (Y/mu)|317.65|||
|Profit after transfer (Y/mu)|584.74|||
|Productivity gain through rental (Y/mu)|267.09|||
|Productivity gain through rental (%)|84.08|||
|of which to tenant (%)|65.33|||
|of which to owner (%)|34.67|||
|**Land owners’ assessment**||||
|Profit generated by owner before transfer (Y/mu)|340.93|326.25|347.60|
|Profit after transfer (Y/mu)|623.9|512.08|685.99|
|Productivity increase through rental (Y/mu)|282.97|185.83|339.39|
|Productivity gain through rental (%)|83.00|56.96|97.35|
|of which to tenant (%)|65.46|69.03|65.88|
|of which to owner (%)|34.47|30.97|34.12|
|**Actual & perceived constraints to rental**||||
|Tenants rationed in the past (%)|39.02|||
|Tenants thinking RLCL improved market functioning|81.34|||
|Owners rationed in the past (%)|12.24|||
|Owners thinking RLCL improved market functioning|81.44|||
|Tenants having doubts about future (%)|44.54|||
|Owners having doubts about future (%)|22.98|||
|No. of observations|1149|140|311|

_Source:_ Own calculation based on NBS/World Bank survey 19
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sample - often fewer than ten - medium and large enterprises are pooled together when calculating averages. The dependent variables are averaged over all enterprises of that type (e.g., small enterprises) in each country. These average scores are then regressed on the banking sector and the macroeconomic variables.<sup>3</sup> 1 Despite the low number of observations, the results are broadly similar to those from the enterprise level analysis, although significance levels tend to be lower (Table X). Both small and medium-sized and large enterprises rated access to longterm loans as lesser constraints in countries with higher levels of foreign bank penetration. In addition, medium and large enterprises rated interest rates as lesser constraints in countries with higher levels of foreign bank penetration and tended to finance greater proportions of investment through bank loans. Although the coefficients on foreign bank penetration in the regressions for interest rates and percent of investment financed through bank loans have similar signs for small enterprises, they are statistically insignificant. The coefficients on the other macro and banking sector control variables generally have the same signs as in the enterprise-level regressions, although they are mostly statistically insignificant.

# V. CONCLUSION

Policymakers in developing countries are often concerned that even if increased penetration by foreign banks improves sector efficiency, sector stability and competition, it might have some harmful side effects. In particular, it has been suggested that foreign entry might result in less credit to some sectors of the economy, particularly small and medium-sized enterprises. Combining responses from a survey of over 4,000 enterprises in 38 developing and transition economies with data on the degree of foreign bank penetration in those countries, this paper investigates the impact of foreign bank entry on enterprises' access to credit.

20
# **Notes**

1 Baldwin (2011) proposes a political economy study which disentangles the theoretical mechanisms through which a unilateral liberalization affects developing countries.

> 2 Berthou and Fontagn ́e (2009) also explore how firms adjust their product mix and exported value as a consequence of a reduction of trade costs.

> 3 Our sample includes: China, India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand.

> 4 Several robustness tests demonstrate that our results are not driven by the countries included in the comparison group.

> 5 Multi-product firms’ models include Allanson and Montagna (2005), Baldwin and Gu (2009), Feenstra and Ma (2008), Eckel and Neary (2009), Nocke and Yeaple (2008), Bernard et al. (2009), Mayer et al. (2009), Arkolakis and Mundler (2008) and Dhingra (2009).

> 6 Her findings point out that Thai tariffs cut has a negative effect on process and product innovation among exporters, while it has a positive effect on product innovation of less export-oriented firms.

> 7 India, Indonesia, Japan, Korea, Philippines, Pakistan, Thailand and Singapore.

> 8 For industrial goods the average bound tariff level will go down to 8.9 percent with a range from 0 to 47 percent.

> 9 There is a lot of heterogeneity across other Asian destinations. For example, the average MFN tariff applied by Korea, Japan and Singapore is not changing over time, while Indonesia’s and Philippines’s average tariff falls by 3 p.p. and Pakistan and Thailand by around 1 p.p. and India’s MFN tariff falls more than 10 p.p.

> 10Other Asian destinations includes: India, Indonesia, Korea, Japan, Philippines, Singapore, Pakistan and Thailand.

> 11This figure is based on BACI dataset and French customs dataset (1999-2005). To make the figure clearer the outliers observations are excluded. Similar figures for the alternative competition measures are available upon request.

> 12 Export information is collected in the following way: exports outside EU are reported if firms’ annual trade value exceeds 1,000 Euros or a weight of a ton.

> 13 The source of MFN applied tariffs is TRAINS: http://unctad-trains.org.

14The BACI dataset is provided by the CEPII and constructed based on COMTRADE dataset from the UN. This dataset provides bilateral trade flows at the 6-digit product level (Gaulier and Zignago, 2010). From BACI dataset we take information on import flows in each of our destination country. BACI is downloadable from http://www.cepii.fr/anglaisgraph/bdd/baci.htm.

15We thank two anonymous referees for suggesting these two alternative measures of foreign competition (ii) and (iii).

16The assumption behind this measure of extensive margin competition is that equal HS6 varieties imported from different countries are perfect substitutes. This idea has been recently discussed by the trade literature on quality (e.g. Khandelwal, 34