rafmacalaba/datause-encoder-data huggingface dataset:
Figure 11: GDP of the West Bank & Gaza and Nighttime Lights (NTL), 2000-2010 <!-- Start of picture text --> 2000 2002 2004 2006 2008 2010<br>Year<br>ln(GDP) ln(NTL)<br>2.4<br>2.2<br>ln(output)<br>2<br>1.8<br><!-- End of picture text --> of natural-logs, to minimize this source of error.

DMSP-NTL appear to track West Bank Palestinian economic growth accurately for the period under study. To construct a NTL series for the West Bank as a whole, we sum the intercalibrated pixel values (i.e. the quantities of light observed) within 500m of West Bank Palestinian polygons. After taking natural logs, we plot the resulting time series for 2000-2010 in Figure 11, along with series of logged Palestinian GDP. The GDP data were obtained from the World Bank, which unfortunately does not disaggregate series separately for the West Bank and Gaza. The NTL series tracks the time-evolution of national GDP well. Note for example the marked decline in light before and during the early years of the Second Intifada, which commenced in late 2000. Note also the slump in both series from 2005 to 2007 resulting from the introduction of sanctions in response to the election of a majority-Hamas legislature. The subsequent recovery coincided with improvements in market access.

# _Joint evolution of Nighttime Lights and Market Access_

Figures 12 and 13 provide a preview of our regression results. Figure 12 plots the residual of the natural log of lights per capita against the residual of the natural log of market access, controlling for locality and year fixed effects, as well as proxies for local conflict, which will be discussed below. The positive slope suggests that higher market access is associated with more output (proxied by lights). Similarly, Figure 13 plots 24
variables are not available for majority of the countries in our sample with the frequency uaed for the estimation. Instead, the equltibrium raal exchange rate was calculated as a twelve period (quarter) moving average. This assumes that the actual real exchange rate observed during a span of three years roughly correspends to the equilibrium one. The difference between the calculated and the actual real exchange rate was used to proxy for the extent of disequilibrium of the real exchange rate at any point in time.

A 1n&-1n6,-1ne, We used a sample of twenty-one developing countries for which quarterly data are available. In turn, we divided the sample between countries having high premia (i.e. in e*zess of 35 percent) on sverage, and moderate premia (i.e. below 33 percent on average. There were thirteen countries in the high premium group, and seven experienced premia averaging between 10 and 35 percent. The rationale for this division is that we et,ect the relationship between the premium and macroeconomic variables would be clearer in high premium countries than in the countries with moderate premia. In addition, high premium countries were also subject to larger macroeconomic imbalances. For most countries estimation was for the sample period was 1970 to 1990 using quarterly data. All data are from the IFS tapes except the parallel exchange rate which are obtained from various issues of the World Currency Yearbook, Pick's Currency Yearbook and Currency Alert. Periods when the exchange rate was unified were excluded.' Dumaies were used for Bolivia, DUMMY1 for the period of the hyperinflation, DUMMY2 for the period following. In some cases, lagged values of the variables were used with or instead of the curren; values Lagged money was used in the case of Tanzania, 4 For Argentina, this means excluding the period from the last quarter of 1976 to the end of 1980. The period from the last quarter of 1977 to the second quarter of 1981 Was excluded from the regression for Chile (1970-1990). For Colombia, this meant excluding the period from the third quarter of 1977 to the second quarter of 1981 and for Ecuador it extended from the thirc! quarter of 1973 to the end of 1974. In the case of Venezuela, estimation was only for the period 1983 to 1989. For Mexico, the period was 1976 to 1987. Dual exchange arrangements were in place in these countries for only the stated periods.

9
Table 2: City Characteristics and the Instrumental Variable

||N. Obs|Distance to the nearest labor office (hours) *<br>Inspectorsper firm in the state|
|---|---|---|
||Method: OLS||
||(1)|(2)|
|Train Stations City (dummy)|5,242|-0.025<br>[0.020]|
|Access to Justice City|5,244|-0.037<br>[0.041]|
|Managerial Capacity City|5,243|-0.035<br>[0.041]|
|Political Concentration City|5,243|-0.002<br>[0.004]|
|Households Piped Water pc City|5,242|-0.014<br>[0.041]|
|Households Sanitation pc City|5,242|-0.001<br>[0.078]|
|Households Electricity pc City|5,242|-0.02<br>[0.011]*|
|Current Transfers from State to City|4,518|0.044<br>[0.063]|
|Homicide Rate City|5,242|-0.067<br>[0.074]|
|Log Population City|5,242|-0.039<br>[0.032]|
|Log GDP pc City|5,242|0.022<br>[0.025]|
|Share Agriculture in GDP City|5,228|0.002<br>[0.007]|
|Share Manufactiring in GDP City|5,242|-0.007<br>[0.008]|
|Share Services in GDP City|5,242|0.006<br>[0.007]|
|Share Informal Workers City (1980)|5,242|-0.004<br>[0.005]|
|Unemployment Rate City (1980)|5,242|0.002<br>[0.001]*|
|Theil Index City (1980)|5,242|0.008<br>[0.006]|
|Poverty Rate City (1980)|5,242|0.002<br>[0.004]|

Robust standard errors in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. The table reports the least squares estimates of the regression of each of the variables reported at the top of each row on the distance to the nearest labor office (hours) interacted with the log number of labor inspectors in the state. The controls are state dummies, distance to the nearest labor office, its square and interactions with state level variables, distance to the state capital city, its square and interactions with the number of inspectors per firm in the state and interactions with other state variables, city transportation costs, its square and interactions with the number of inspectors per firm in the state and interactions with other state variables, city altitude, city latitude and city longitude. Other state variables include average access to justice, political concentration, management quality in public administration and the GDP per capita in the state. City transportation cost is the transport cost between each city and the nearest capital city in 1995. We also include the log of total population, per capita income, average years of schooling and share of population in urban areas, in 1970, 1980, and 1991 (variables describ
The variable “ **_Administration_** ” in Table A.1.5 takes on three values; one if the administration of the fund is official, two if it is joint, and three if it is private. If the EDIS of a country is administered by the central bank, it is considered to have an official administration. Moreover, some privately administered institutions have limited authorities. For example, in Italy and Croatia certain decisions need to go through the central bank approval, hence the EDISs of these countries are considered to have a joint administration in the database.

Finally, the variable “ **_Membership_** ” in Table A.1.5 takes the value one if the membership to the fund is compulsory and zero if it is voluntary. Majority of the countries have compulsory membership, whereas only ten percent of them employ a voluntary system.<sup>9</sup>

# **3.4 Barth, Caprio, and Levine (2004) survey questions**

We also incorporate the deposit insurance related survey results from Barth, Caprio, and Levine (2004) database on banking regulation and supervision. All of the data is coded for empirical use and presented in three different panels in Table A.1.6.<sup>10</sup> The variables in this section and the way they are coded are as follows: 1) _Does the deposit insurance authority make the decision to intervene a bank?_ The answer “Yes” is coded with one and “No” with zero (panel A). 2) _Does the deposit insurance authority have the legal power to cancel or revoke deposit insurance for any participating bank?_ The answer “Yes” is coded with one and “No” with zero (panel A). 3) _As part of failure resolution, how many banks closed or merged in the last 5 years?_ The number of banks is reported (panel A). 4) _Were depositors wholly compensated (to the extent of legal protection) the last time a bank failed?_ The answer “Yes” is coded with one and “No” with zero (panel A). 5) _On average, how long does it take to pay depositors in full?_ The number of months is reported (panel B). 6) _What was the longest that depositors had to wait in the last 5 years?_ The number of months is reported (panel B). 7) _Were any deposits not explicitly covered by deposit insurance at the time of the failure compensated when the bank failed (excluding funds later paid out in liquidation_

> 9 The membership is voluntary in the following countries: Dominican Republic, Kazakhstan, Marshall Islands, Micronesia, Sri Lanka, Switzerland, and Taiwan.

> 10 The countries which did not provide answers for any of the survey questions are excluded from the Table. Please see Table A.1.6 (any panel) for a complete list of participants.

10
entry and activity regulations, and the role of foreign banks in affecting the competitive conditions of banking systems. Since the role of non-bank financial institutions in affecting the overall competition in the financial sector have received limited attention, we also study those.

Using bank-level data and applying an adapted version of the Panzar and Rosse (1987) methodology, we estimate the degree of competition in 50 countries' banking systems. We then relate our competitiveness measure to countries' structural and regulatory indicators. We find that systems with greater foreign bank entry, and lack of entry and activity restrictions have a higher competitiveness score. We find no evidence that banking system concentration negatively relates to competitiveness. Our findings confirm that contestability helps to determine effective competition, especially through allowing (foreign) bank entry and eliminating activity restrictions.

The paper proceeds as follows. Section 1 gives a review of related literature, both on the effects of competition in the financial sector as well as measuring competition in general and in the financial sector specifically. Section 2 discusses the methodology used to test for the degree of competition in the banking market of a particular country. Section 3 presents the data we use, the selection criteria we used for the sample we end up using, and the competitiveness measures. Section 4 relates the measure of competition to some structural and policy variables and presents the main empirical results. The section also reports several robustness tests. Section _5_ concludes.

# 1. **LITERATURE REVIEW**

There are several, related strands of literature. We highlight some findings of the growing literature on the definition and effects of competition in the financial sector, and then review the 3
MONGOLIA'S INFORMAL SECTOR Contents

**iii**

# Table of Contents

|**Abstract.**<br>**............**<br>**i**|
|---|
|**Preface .............**<br>**ii**|
|**Table of Contents...........**<br>iii|
|**Tables ..............**<br>**v**|
|**Figures.**<br>**v**|
|Boxes**.............**<br>**v**|
|**1.**Overview**.1**<br>What Does It Mean to be "Informal" in Mongolia?<sup>.</sup><br>2|
|Sources of Information.<br>6|
|Outline of the Report.<br>6|
|**2. Observations**on**the Size of the Informal Sector.**<br>**8**|
|Employment in the Informal Sector.<br>8|
|Background on Employment Statistics in Mongolia.<br>8|
|<br>Trends in Employment in Ulaanbaatar's Informal Sector.<br>9|
|The Current Situation: Employment in Ulaanbaatar's Informal Sector .12|
|Aggregate Household Income in the Informal Sector .........<br>13|
|Trends in Informal Income .13|
|The Current Situation: Aggregate Informal Income in Ulaanbaatar .14|
|<br>Trends and Current Size of the Informal Sector Implied by Monetary Aggregates .14|
|Summary -<br>The Current Size of the Informal Sector in Mongolia .15|
|3.**Why So Big, So Fast?**<br>17|
|A Response to Crisis -<br>Supplying Labor to the Informal Sector .17|
|Rural-Urban Migration -<br>Supplying Informal Labor .18|
|The Market's Reallocation of Society's Resources-<br>A Demand for the Services Informals<br>Provide .19|
|Easy Entry for Informals, Difficult Entry for Formals .19|
|There's No Escaping Bureaucracy, but Informals Face Less of It .19|
|<br>Informals Make Their Own Labor Arrangements .20|
|Access to Premises? Not a Problem for Informals .22|
|Small Scale Means Independence from Formal Banking .22|
|Taxation of the Informal Sector-<br>Rich Informals Win, Poor Informals Lose .<br>22|
**Establishment-Level Productivity Measurement** Total factor productivity is measured as the log of sales minus the logs of labor, capital, and materials, each multiplied by their output elasticities in the production function. We estimate production functions in each case using the control function approach with refinements by Ackerberg et al. (2015) in the longitudinal establishment panel compiled from the Economic Census by Busso, Fentanes T ́ellez and Levy Algazi (2019).<sup>17</sup> Specifically, for each treatment and control industry we estimate a distinct Cobb-Douglas production function for sales as a function of labor, capital, and intermediate inputs. The factor shares are not restricted to sum to one, and indeed are consistent with decreasing, constant, and increasing returns to scale across different specifications. To account for potential omitted variable bias when estimating the production function, materials are included in a control function to stand for the component of productivity that is unobserved to the econometrician, but was forecast by the firm when it made its decision to fix capital assets in the previous period, as in Levinsohn and Petrin (2003). In the control function we also include the log of the firm’s specific wage rate, which is set equal to zero if the firm has no remuneration; a dummy for whether the firm has no remuneration; and a dummy indicating whether the antitrust decision has occurred. These choices follow the advice of De Loecker and Syverson (2021) to control for factors that could affect firms’ input decisions or the competitive environment. The estimation also includes a selection function to account for potential attrition bias as in Olley and Pakes (1996), though in practice this made little difference to the results, nor did including investment instead of materials in the control function.

Distinct production functions are estimated for each treatment industry and all control industries in each case. When estimating the production function for control industries, fixed effects for each six-digit control industry are included in the control function. Total factor revenue productivity (TFPR) for each establishment _k_ in case _j_ in each year is measured by subtracting from (the log of) sales, (the log of) employment, (the log of) fixed capital assets, and (the log of) materials consumption where the three inputs are multiplied by their output elasticities in a Cobb-Douglas production function (with unrestricted returns to scale).

**Effects of Antitrust on Revenue Productivity and Measured Misallocation** Table 7 reports the results of the meta regression in Equation (2) where outcomes are changes in alternative measures of productivity and misallocation. In Column (1) the outcome is the annualized change in the average of the log of TFPR _jk_ within an industry, where the average is weighted by the market (sales) share of firm _j_ . The effect of sanctions here is

> 17A match is not available between firms in the Economic Censuses of calendar years 1994 and 1999. Therefore the production function estimation only makes use of the censuses between 2003 and 2018.

30
**Figure 2. Schematic of urban informality, by firm**

**More Informal**

|||**Less informal**|
|---|---|---|
|**Low**<br>**educated**<br>**Self-**<br>**employed**|**High educated**<br>**Self-employed**|**Employers**|

Source: authors’ rendering The IES and GMD samples are not strictly comparable since they differ in several respects. First, the country samples are different. We use data from 26 countries in the worker analysis and from three countries in the firm analysis. Second, all the firms surveyed in the IES are not registered, while in the worker analysis we do not have (nor wish to use) a registration variable, so we use the job category and education level of the respondent to proxy informal firm owners. Third, the IES sample frame is restricted to a few cities that represent the main urban centers while GMD is based on the urban sample from nationally representative household surveys. The IES only has information on manufacturing and service firms, whereas the GMD covers urban agriculture and other sectors as well. Finally, the IES includes firm level variables and aims at analyzing firm level characteristics. In contrast, the GMD is derived from household surveys and therefore focuses on householdrelated variables, with very limited information on firms.

# **3. Methodology**

We use three main methodologies to describe informal sector workers and enterprises in urban SSA. First, we use simple, unconditional summary statistics to profile the distribution of worker characteristics, firm, and urban variables within employment or enterprise type.<sup>31</sup> Second, we use Linear Probability Models to estimate conditional correlates to better understand which worker, firm-characteristics, and urban variables are disproportionately represented in each employment or firm type, while controlling for other observed characteristics. Finally, we estimate the conditional correlation between characteristics of the urban space and the spatial distribution of employment types.

# **3.1 Conditional Correlates of Workers and Informal Firms**

For the worker analysis, we estimate a series of linear probability models where the dependent variables ( i ) are indicators of employment type _g_ (g=unpaid worker, low- gg YY

> 31 For workers (firms), we use the sample weights, meaning that the sample is representative of all workers (firms) across the sample countries. In other words, each country is not given equal weight. Instead, each worker (firm) is given equal weight, regardless of its country of origin.

10
(10) where _M_ (0 _,_ 1) is the migration status of heads of households _i_ , _RA_ is the observed risk aversion of heads of households, _X_ is a vector of household and individual caracteristics and _η_ is the error term. If the predictions of the QM model for risk averse and risk tolereant individuals match the risk preferences observed among stayers and leavers, we consider our model successfully tested. Note that we expect _RAi_ to be independent of _ηi_ because many of the known correlates of _RAi_ are observed and present in _Xi_ .

The fourth and final step is to test for a possible confounding factor. As already discussed, one possible confounding factor of our results is that conflict could increase risk-tolerance as found by Voors et al. [2012] for Burundi and by Callen et al. [2014] for Afghanistan. If this is the case, results could be explained solely by the fact that stayers and leavers are exposed to conflict for different lenghts of time. To test for this confounding factor we carry out a Difference-in-Differences (DiD) test. We consider risk-aversion as an outcome, conflict as treatment and migration as a control and interact conflict and migration to obtain the DiD estimator as follows: Where _RAi_ is risk-aversion, _Ci_ is a dummy variable taking value 1 if the individual has experienced conflict and 0 otherwise; _Mi_ is a dummy variable taking value 1 if the individual never migrated over the period taken into account and 0 otherwise, _Xi_ is a matrix of individual’s socio-economic characteristics, and _π_ is the error term.

# **4 Data and context**

The paper uses household and conflict data from Nigeria, the largest country in Africa in terms of population and GDP and also the largest country in terms of IDPs in spite of relatively low migration flows. Estimates of external migration are gross and point to relatively low stocks. It is estimated that in 2010 the stock of immigrants was about 1.1 million people with the quasi totality originating in other African countries whereas the stock of emigrants was estimated at just about over one million with most of these emigrants found in the Sudan, United States and United Kingdom (IOM [2009]). More significant is internal migration with an estimated 6-7 percentage of the population living in areas other than those where they were born but still a low figure when compared to countries with high internal migration.

In this migration context, forced migration is a very significant factor and most of this migration can be reconduced to one conflict: The Boko Haram conflict. The current number of IDPs is estimated at over 2.2 million, a very large number when compared to external migration and even to internal migration. The quasi-totality of these IDPs derived from the conflict between the Nigerian state and the Boko Haram insurgents in the North 10
Private of African areas management protected improves wildlife and tourism outcomes but with concerns in security conflict regions Sean Denny<sup>1*</sup> , Gabriel Englander<sup>2*</sup> and Patrick Hunnicutt<sup>3</sup> 1Bren School of Environmental Science and Management, University of California, Santa Barbara, USA.

> 2Development Research Group, The World Bank, USA.

> 3Environmental Science and Policy, Chapman University, USA.

*Corresponding author(s). E-mail(s): smdenny@bren.ucsb.edu; aenglander@worldbank.org; Contributing authors: phunnicutt@chapman.edu;

**Keywords:** Protected areas, Private sector management, Wildlife conservation, Tourism, Armed conflict

> **JEL Classification:** Q57 , O13 , Q28 , Q26