rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1 illustrates the development of the average share of state ownership by bank assets. Specifically, the figure plots the average state ownership share for all countries, and separately for the groups of developing countries (and emerging markets), and high-income countries. The average state ownership share in each instance is the average of country averages for pertinent countries. During the last decades, the share of state ownership in developing countries has tended to decline, from 34.6% in 1999 to 19.4% in 2010. This decrease was especially pronounced during the years from 2007 to 2010. Average state ownership in highincome countries, instead, has increased somewhat from 7.9% in 1999 to 9.9% in 2010, with most of the increase occurring after 2007. Overall the state ownership share has been rather stable around 19%, although it declined from 17.5% in 2009 to 13.5% in 2010. These trend data suggest that government ownership of banks is likely to remain prevalent in future years. In the empirical work, as a robustness check we also consider a further breakdown of privately-owned banks into domestic and foreign banks.

Table 1 provides summary statistics for our sample of banks. The main variable of interest is the loans variable, which is the log of net loans in local currency and deflated using the national GDP deflator (see Table A2 in the Appendix for variable definitions and data sources). We consider several variables to represent the sources of bank funding: total liabilities, deposits, non-deposit liabilities, short-term funding, long-term liabilities, and equity. These variables are also constructed as the logs of amounts in local currency and deflated by the GDP deflator. To proxy for the cost of bank funding, we construct the net interest expense ratio as the log of one plus the bank‟s interest expenses over interest bearing liabilities net of the government T-Bill rate taken from the IMF International Financial Statistics database (IFS, 2012). As indices of the quality of lending, we consider the loan loss provision and non-performing loans variables.

The state bank variable is a dummy variable that equals one if a bank has a majority state ownership share. In our sample, 11.4% of bank-year observations concern state-owned banks. Privately owned banks can have domestic or foreign ownership. The domestic bank variable indicates majority private domestic ownership, while the foreign bank variable signals majority private foreign ownerships. Domestic and foreign banks constitute 52.8% and 35.8% of our observations.

6
# Contents

_Acknowledgme Fowword Guy 1ffermnn_

## 1 Introduction

<!-- Start of picture text --> 1<br><!-- End of picture text -->

- 2 The Economic Performance of Senegal, 1960-1987

_5_

- 2.1 Structure and Growth of the GDP, _5_

- 2.2 Structure and Growth of the Industial Sector, 8

- 3 Labor Market Institutions and the Industrial Relations System

13

- 3.1 Specific Institutional Aspects, 15

- 3.1.1 Hiring, 15

- 3.12 Employment Contacts, 16

- 3.1.3 Layoffs, 17 P

- 3.2 Wages, Bonuses, and Supplements, 17

- 4 Basic Demograpiic, Employment, and Wage Statistics 21

- 4.1 The Demographic Situation, 21

- 4.2 Labor Force, Employment, and Unemployment, 22

- 4.3 Industrial Employment in the Modern Sector, 24

- 4.3.1 Employment of Permanent Workers, 25

- 4.3.2 Employment of Temporary Workers, 28

- 4.3.3 National Composition of Employment, 31

- 4.4 Earnings, 34

- 4.4.1 Components of Total Compensation, 34

- 4.4.2 Evolution of Wages by Skill Groups, 38

- 4.4.3 Evolution of Total Compensation by National and Skil Groups, 40
**Figure 8:** Illustration of Counterfactual Experiment

<!-- Start of picture text --> (a) 1988–1996 period (b) 1996–2006 period<br>0.8 0.8<br>1996<br>0.75 1996 debt due to<br>0.7 decentralization<br>0.7 (18%)<br>2006 counterfactua<br>0.65<br>0.6<br>0.6<br>1996 counterfactual<br>0.5<br>debt due to 0.55<br>decentralization<br>(39%) 0.5<br>0.4 1988<br>2006<br>0.45<br>0.3 0.4<br>1986 1988 1990 1992 1994 1996 1998 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>Time Time<br>Change only (e,f,<br>) to 2006<br>) to 1996<br>Change only (e,f,<br>Change all parameters to<br>calibrated 1996 values<br>Change all parameters to<br>calibrated 2006 values<br>Total government debt Total government debt<br><!-- End of picture text --> Note: Counterfactual experiments change decentralization parameters ( _e_ , _f_ and _θ_ ) to the end year values, and keep all other parameters at their starting year values. The experiments illustrate the change in debt that is explained by decentralization.

**Table 5:** Counterfactual Experiment Effects of fiscal decentralization on debt levels

|||1988–19|96||1996–20|06|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Moments|1988|1996|Counterfactual<br>1996 (%)|1996|2006|Counterfactual<br>2006 (%)|
|Local debt|0.062|0.130|0.149 (129%)|0.130|0.122|0.103 (344%)|
|Central debt|0.337|0.614|0.385 (17%)|0.614|0.336|0.589 (9%)|
|Total government debt|0.399|0.744|0.534 (**39%**)|0.744|0.458|0.692 (**18%**)|

Note: Total government debt (also known as “general government debt”) is the sum of local and central government debt. Counterfactual experiments use revenue ( _e_ and _f_ ) and spending responsibility share ( _θ_ ) values from end year, and keep all other parameters at their starting year values. Numbers in **parentheses** show the percent of debt changes that is explained by decentralization. All debt levels are normalized by national GDP.

possible reason is that there was increased scrutiny over government borrowing during the later period in order to meet EU regulations, such as the Stability and Growth Pact. That made it harder for all levels of governments to borrow and could have weakened the effect of soft budget constraint.

For both episodes, fiscal decentralization explains a larger proportion of changes in local debt and a smaller portion for central debt. In an alternative calibration presented in section 4.3.2, we use a more elastic local interest rate schedule ( _ψb > ψd_ ), as motivated by anecdotal evidence. The more elastic local interest rate increases the cost of local relative to central borrowing. As such, fiscal decentralization explains larger changes in central government debt than in the baseline.

**Debt-neutral (balanced) fiscal decentralization.** Spending decentralization alone raises debt, so how much revenue decentralization is needed to make a decentralization reform debt-neutral? Figure 9 plots the relationship between revenue and spending decentralization such that the total gov-

31
4 credit soumes by rich and poor households, and finally (g) the lending pratices of different sources that affect household access.

# Credit is Imortant For Inaeasing Productivity in Agriculture

The importane of farm credit to agricultural productvity growth is well established in many countnes. Two recent studies in Pakistan corroborate these results. Sohail Malik, Mohammed Mushtaq and Manzoor Gill ("The Role of Institutional Credit in the Agricultural Development of Pakistan", Pakistan Development Review Winter 1991) estimate the relationship between the log of farm output and the log of institutional credit using household level data in the 1985 Rural Credit Survey. They estimate a coefficient value of 0.15 which is statistically significant at 99 percent level of confidence. Earlier, Habib Zubeiri ("Production Function, Institutional Credit and Agricultural Development in Pakistan", Pakistan Development Review Spring, 1989) using more aggregate data, had not found a statistically significant correlation between farm output and institutional credit. However, the relationship between credit and purchase of inputs such as seeds and fertilizer was found to be statistically significant. These inputs, in tur, are significantly correlated with productivity growth.

The rural credit survey carried out by IFPRI also provides direct evidence on the differences in input expenditure among farmers with and without access to rural credit Table 1.1 below shows that all farmers (regardless of asset size) who have access to nual credit spend more on farm inputs compared to farmers who do not have access to credit On average, input expenditure by farmers with access to credit is 37 percent higher than those without access.

|**Table 1.1.**In|ut ExDenditur|es<br>for Households w<br>(USS p<br>Ass<br>et|ith and withou<br>er hectare)<br>Qutile|t Access to Cre|dit by Asset Qui|ntiles|
|---|---|---|---|---|---|---|
|Household Type<br>.......................<br>.<br>......................|Lowest<br>........<br>.............................|Second<br>.....................................................|Third<br>..........................................|Fourth|Highest<br>.<br>.............................|Overall<br>....................................**.....**...........|
|Without credit|48.17|45.10|48.02|55.12|67.62|53.30|
||_(5.51)_|(3.65)|(3.13)|(3.64)|(4.57)|(1.80)|
|Withcredit|59.08|66.34|61.36|81.33|112.69|72.84|
||(3.05)|(8.23)|(6.19)|(8.69)|(13.76)|(3.45)|
|T-test|-1.72**|-2.70*|-2.12*|-3.29*|-3.76*|-45.43*|
|_No._of houslds|216|219|218|216|211|1,080|

Notes. Dollar expenditures per hetae are computed using $1 = Rs. 21.28 Figures in parenthesis are standard errors of respective means

* and ** indicate that the difference of means is staistically significant at the 5% and 10% level, respectively. Source: IEPRI, Pakistan Panel Survey, Round 13, 1990 cited in Von Braun, MaUik4 and Zeller (1993)
characteristics of the household head; employment status of working-age members; household size and dependency ratio; ownership of toilets, electricity, or farm plots; recipient of MASAF or education aid; rural-urban indicator; and distance to the closest town, while _μt_ are time fixed effects (season and year), _νd_ are district fixed effects, and _ict_ is the error term. Standard errors are clustered by districts.

The main identification challenge in assessing the impact of shocks is endogeneity. When shocks are self-reported, one may argue that poor households with limited coping mechanisms are more likely to report them than the non-poor households, who tend to have good mitigation and coping strategies in place. The use of weather shock indicators derived by exogenously measured temperature and precipitation addresses this endogeneity problem. These indicators are aggregated at the TA level, the lowest administrative division in Malawi.<sup>8</sup> As shown in the annex, there is low correlation between self-reported weather shocks and exogenously measured shocks at the TA level.<sup>9</sup> This discrepancy may stem from the endogeneity of self-reporting and/or the difference in spatial coverage of the shock indicators, that is, TA average versus specific location of individual household.

Even when measured exogenously, the probability of occurrence of a shock is not exogenous to consumption per capita across households. Indeed, arid areas are more likely to be poor and hit by frequent droughts due to their geography. We use the fact that the timing of a shock is exogenous to welfare status conditional on the likelihood of its occurrence (Anttila-Hughes and Hsiang, 2013). We account for the probability distribution of weather events by including variables related to the TA-specific historical distribution of these shocks.

It is difficult to measure idiosyncratic shocks exogenously. In the annex, we present the correlation between self-reported shocks and household characteristics.<sup>10</sup> Households planting crops are more likely to report droughts or floods, and they are less likely to report food price shocks. These correlations can be explained by a real difference in exposure and/or by the fact that households report more on what matters most to them. We partially address this caveat of endogeneity by including a set of household and community observables that could correlate with differences in reporting.

> 8 This restricts the geographic fixed effects of the regression analysis at the district level, instead of the TA level.

> 9 See table A1 in the annex. Results are available upon request.

> 10 See Table 2. Results are available upon request.

12
**<u>Table A.21. The DID regressions of individual-level outcomes</u>**

|||||Dependen|t variables||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program|0.1239***|-0.0352***|-0.0445|-0.6460*|-0.2874**|-0.2743**|0.0274|0.5869*|
||(0.0441)|(0.0133)|(0.0272)|(0.3508)|(0.1449)|(0.1395)|(0.0524)|(0.3346)|
|(Poverty rate – 50)|-0.0086|0.0036*|0.0060*|0.0550|0.0134|0.0112|-0.0093*|-0.0058|
||(0.0057)|(0.0019)|(0.0032)|(0.0357)|(0.0198)|(0.0192)|(0.0052)|(0.0341)|
|Program * (Poverty rate – 50)|0.0088|-0.0036*|-0.0070**|-0.0446|-0.0096|-0.0073|0.0100*|0.0114|
||(0.0059)|(0.0019)|(0.0033)|(0.0392)|(0.0203)|(0.0196)|(0.0056)|(0.0395)|
|Program * Post-program period|-0.1399***|0.0532***|0.0542|0.8080***|0.4419***|0.4473***|0.0126|0.9449**|
||(0.0537)|(0.0143)|(0.0513)|(0.2988)|(0.1557)|(0.1481)|(0.0450)|(0.4628)|
|(Poverty rate – 50) * Post-program|0.0086|-0.0048***|-0.0063|-0.0592**|-0.0559**|-0.0532**|0.0092*|-0.0248|
|period|(0.0060)|(0.0017)|(0.0049)|(0.0299)|(0.0232)|(0.0225)|(0.0049)|(0.0493)|
|Program * (Poverty rate – 50) * Post-|-0.0039|0.0039**|0.0047|0.0168|0.0495**|0.0471**|-0.0131**|0.0303|
|program period|(0.0066)|(0.0017)|(0.0054)|(0.0335)|(0.0238)|(0.0230)|(0.0052)|(0.0527)|
|Age|0.0241***|0.0040***|0.0105***|0.1161***|-0.0070*|-0.0069*|0.1586***|0.3991***|
||(0.0013)|(0.0005)|(0.0006)|(0.0066)|(0.0039)|(0.0038)|(0.0094)|(0.0486)|
|Age squared|-0.0003***|-0.0000***|-0.0001***|-0.0015***|0.0003***|0.0002***|-0.0082***|-0.0205***|
||(0.0000)|(0.0000)|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0004)|(0.0022)|
|Male|-0.0641***|-0.0080***|0.0861***|0.9938***|-0.1551***|-0.1432***|0.0348***|0.0900***|
||(0.0075)|(0.0030)|(0.0050)|(0.0569)|(0.0276)|(0.0266)|(0.0103)|(0.0344)|
|Ethnic minorities (yes=1, Kinh=0)|0.1911***|-0.0729***|-0.0626***|-0.5110***|-0.3782***|-0.3585***|-0.0961***|1.0711***|
||(0.0273)|(0.0093)|(0.0174)|(0.1605)|(0.0769)|(0.0764)|(0.0193)|(0.1274)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.1470***|0.0470***|-0.0341|-0.1693|0.9745***|0.8798***|0.1858***|-1.6689***|
||(0.0503)|(0.0144)|(0.0287)|(0.2986)|(0.1553)|(0.1520)|(0.0641)|(0.3338)|
|Observations|128,446|128,446|128,446|128,446|86,980|86,980|50,645|50,645|
|R-squared|0.141|0.044|0.126|0.085|0.052|0.046|0.137|0.204|

Note: This table reports DID regression of individual-level outcomes. The impact of the program is measured by variable ‘Program * Post-program period’. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

71
countries and Japan. The topic of details of the distinct characteristics of credit cycles in MENA will remain for the future papers to be looked into employing deeper analysis. We start by explaining our estimated credit cycles following the details of the data we use. Then we show our analysis of credit cycles in a few Western countries and Japan. Next, we compare the credit cycles in the MENA oilimporter and exporter countries with the cycles in Western economies and Japan. We use Saudi Arabia as the representative of the oil-exporting countries, the Arab Republic of Egypt for non-oil exporting countries and the United States to represent Western countries.

# **II.   EMPIRICAL APPROACH**

## **A.   Defining the Credit Cycle**

The notion of credit cycles refers to recurring patterns over time in the growth rate of credit. The idea is that credit growth accelerates and decelerates in regular intervals that alternate over time. We estimate the cyclical components for the real credit growth using the frequency based ChristianoFitzgerald<sup>2</sup> band-pass filters (BP) similar to Aikman, et al. (2014) and Drehmann et al (2012). The BP filter is a standard filter that decomposes time series into frequency bands. The normalized frequency is the inverse of the unit of time of our data set, which is in our case one year. Applying BP to the data let us focus on variation within a particular frequency range. According to the spectral representation theorem, any time series within a broad class can be decomposed into different frequency components. The theory also supplies a tool for extracting those components: the ideal band pass filter, which is a linear transformation of the data that leaves intact the components of the data within a specified band of frequencies and eliminates all other components. The adjective ideal on this filter reflects an important practical limitation. Literally, application of the ideal band pass filter requires infinite data. Some sort of approximation is required.

On the reasonable, and commonly used, assumption that the growth rates of macroeconomic series are stationary, the filter implies a zero trend (or drift). To facilitate comparison with the

> 2 <mark>Christiano, Lawrence J., and Terry J. Fitzgerald. "The band pass filter."</mark> _<mark>international economic review</mark>_ <mark>44, no. 2</mark>

> <mark>(2003): 435-465.</mark>

4
# **Nutrition, Religion, and Widowhood in Nigeria**

## Annamaria Milazzo and Dominique van de Walle<sup>1</sup>

_Development Research Group, World Bank_

**Keywords:** Nigeria, Widowhood, Nutrition, Body Mass Index, Religion **JEL classifications:** O15; J12; J16; I31.

> 1 The authors are grateful to Caitlin Brown, Sylvie Lambert, Martin Ravallion and Paola Villar for useful comments and to the World Bank’s Research Support Budget for funding support. These are the views of the authors and need not reflect those of the World Bank and its affiliated organizations. Corresponding author: Dominique van de Walle, 1818 H St. NW, Washington, DC, 20433; dvandewalle@worldbank.org
|**Table 8: Details on Types of Externa**|**l Financing **<br>Sample<br>Size|**Deals Made**<br>Treatment<br>Group|Control<br>Group|P‐value|
|---|---|---|---|---|
|_Who was deal made with?_|||||
|Sold Firm|221|0.017|0.038|0.547|
|Deal with Other Business Owner|221|0.165|0.179|0.385|
|Deal with Angel Investor|221|0.096|0.104|0.804|
|Deal with Crowdfunding|221|0.043|0.019|0.164|
|Deal with Accelerator|221|0.087|0.038|0.096|
|Deal with VC Fund|221|0.096|0.113|0.967|
|Deal with Government Fund|221|0.070|0.075|0.706|
|_What type of deal was made?_|||||
|Equity‐Share|221|0.209|0.236|0.765|
|Licensing Deal|221|0.043|0.009|0.113|
|Royalty Deal|221|0.035|0.057|0.737|
|Convertible Note Deal|221|0.026|0.019|0.868|
|Other Deal|221|0.043|0.057|0.826|
|_Says financing is main challenge_<br>Notes: Data are for firms that answ<br>approximately two years post‐inter<br>deals, and so numbers given are pr<br>deal type. Says financing is main ch<br>viewed as the main challenge the b|204<br>ered the full<br>vention. So<br>oportion of f<br>allenge is an<br>usiness face|0.239<br>survey in the<br>me firms made<br>irms which m<br>indicator of w<br>s in its ability t|0.411<br>second foll<br>multiple<br>ade at least<br>hether fina<br>o grow.|0.002<br>ow‐up,<br>one of this<br>ncing is|

41
# **Annex Table 2: Average restrictiveness towards movement of natural persons by STRI measure (2017)**

|**Country**|**Temporary**<br>**licensing**<br>**system in**<br>**place**|**At least one**<br>**engineer**<br>**must be**<br>**licensed for**<br>**issuing**<br>**constrn**<br>**permits**|**Domicile**<br>**required**<br>**for**<br>**Licence**<br>**to**<br>**practice**|**Foreign**<br>**constrn**<br>**engineers**<br>**required**<br>**to**<br>**practice**<br>**locally**<br>**for at**<br>**least 1**<br>**year**|**Foreign**<br>**constn**<br>**engineers**<br>**required**<br>**to take**<br>**local**<br>**exam**|**Foreign**<br>**profes’nals**<br>**required**<br>**to practice**<br>**locally for**<br>**at least 1**<br>**year**|**Foreign**<br>**profes’nals**<br>**required**<br>**to take**<br>**local exam**|**Foreign**<br>**providers**<br>**reqd. to**<br>**completely**<br>**re-do**<br>**university**<br>**degree,**<br>**practice**<br>**and exam**<br>**in the**<br>**domestic**<br>**country**|**Labour**<br>**market**<br>**tests:**<br>**CSSs **|**Labour**<br>**market**<br>**tests:**<br>**ISSs **|**Labour**<br>**market**<br>**tests:**<br>**ICTs**|<br>**Laws/**<br>**regulations**<br>**establish**<br>**process for**<br>**recognizing**<br>**qualifications**<br>**gained**<br>**abroad**|<br>**Laws/**<br>**regulations**<br>**establish**<br>**process for**<br>**recognizing**<br>**qualifications**<br>**in engng.**<br>**gained**<br>**abroad**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|AUS|0.5|0|0|0|0|0.5|0.25|0|1|1|1|<br>0.57|1|
|AUT|0|1|0.5|0|1|0.25|1|0|1|1|1|<br>0.38|<br>0|
|BEL|0.25|0|0.33|0|0|0.25|0.5|0|1|1|1|<br>0.36|<br>0|
|BRA|0.5|1|0.67|0|1|0|0.5|0|1|1|1|<br>0.21|<br>0|
|CAN|0.5|0|0.17|1|1|0.75|1|0|1|1|0|<br>0.43|<br>0|
|CHE|0.5|0|0.17|0|0|0|0|0.33|1|1|1|0.46|<br>0|
|CHL|0.5|0|0.17|0|1|0|0.25|0|0|0|0|<br>0.21|<br>0|
|CHN|1|1|0|0|1|0|0.5|0|1|1|1|<br>0.43|<br>0|
|COL|0.5|0|0.33|0|0|0.25|0|0|0|0|0|<br>0.21|<br>0|
|CRI|0.5|1|0.33|0|0|0|0|0|0|1|0|<br>0.00|<br>0|
|CZE|0.25|1|0.33|0|1|0.5|0.75|0|1|0|0|<br>0.23|<br>0|
|DEU|0|1|0.33|0|0|0.25|0.25|0|1|1|0|<br>0.57|<br>1|
|DNK|0.75|1|0.17|0|0|0.25|0.25|0.17|1|1|1|<br>0.50|<br>1|
|ESP|0|1|0.17|0|0|0|0|0|0|0|1|<br>0.43|<br>0|
|EST|0.5|1|0|1|0|0|0|0.5|1|1|1|<br>0.64|<br>1|
|FIN|0.75|1|0|0|0|0.25|0.25|0|1|1|1|<br>0.57|<br>0|
|FRA|0.75|0|0|0|0|0.25|0.5|0.17|1|1|1|<br>0.43|<br>0|
|GBR|0.5|0|0|0|0|0.5|0.5|0.17|0.89|0.89|1|<br>0.57|0|
|GRC|0|1|0.5|0|1|0|0.5|0.17|1|0|1|<br>0.21|<br>0|
|HUN|0|0|0.33|0|0|0.5|0.25|0|1|1|1|<br>0.38|<br>0|

27