rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5: Firm Investment and Purpose of Mobile Money Use**

|**Dependent variable: Firm purchased fixed assets Y:1**<br>**N:0**||**Pro**|**bit**||
|---|---|---|---|---|
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Mobile money to pay employee Y:1 N:0(0 if not adopted)|0.506**||||
||(0.257)||||
|Mobile money to pay supplier Y:1 N:0(0 if not adopted)||0.787***|||
|||(0.164)|||
|Mobile money to pay utility bills Y:1 N:0(0 if not adopted)|||0.255||
||||(0.179)||
|Mobile money to receive payments from customers Y:1<br>N:0(0 if not adopted)||||0.612***|
|||||(0.148)|
|Log of size|0.233***|0.260***|0.231***|0.269***|
||(0.086)|(0.082)|(0.086)|(0.081)|
|Top manager experience in sector (years)|-0.001|-0.005|-0.002|-0.005|
||(0.007)|(0.007)|(0.007)|(0.007)|
|Log of sales per worker (outliers removed)|-0.021|0.004|-0.022|-0.009|
||(0.040)|(0.040)|(0.041)|(0.041)|
|Log of age of firm|-0.139|-0.125|-0.129|-0.072|
||(0.113)|(0.116)|(0.115)|(0.109)|
|Firm is part of a larger firm Y:1 N:0|-0.067|-0.093|-0.090|-0.039|
||(0.240)|(0.215)|(0.231)|(0.226)|
|Exports 10% or more of sales Y:1 N:0|-0.104|-0.112|-0.101|-0.077|
||(0.234)|(0.209)|(0.224)|(0.222)|
|Foreign ownership Y:1 N:0|0.276|0.334|0.268|0.290|
||(0.262)|(0.255)|(0.268)|(0.272)|
|Firm with unfavorable credit record Y:1 N:0|0.162|0.149|0.163|0.155|
||(0.166)|(0.157)|(0.166)|(0.159)|
|Establishment has a line of credit or loan at this time Y:1<br>N:0|0.224|0.173|0.210|0.183|
||(0.163)|(0.168)|(0.164)|(0.167)|
|Firms use email to interact with clients/suppliers Y:1 N:0|0.120|0.113|0.169|0.162|
||(0.183)|(0.181)|(0.188)|(0.188)|
|Establishment has checking or savings account at this time<br>Y:1 N:0|0.091|0.081|0.012|0.011|
||(0.271)|(0.230)|(0.267)|(0.246)|
|Log of mean distance - bank (km)|-0.778***|-0.743***|-0.793***|-0.884***|
||(0.212)|(0.216)|(0.215)|(0.208)|

24
# **Table 1: Predicting Ratification**

|||1988-2007<br>|<br>|
|---|---|---|---|
||CRC|OPAC|OPST|
|Average ratification rate in the|0.908|0.628|0.604|
|**region**|(1.95)|(1.29)|(1.24)|
|Common law legal origin|-0.287<br>(1.73)|-0.358<br>(2.06)*|-0.381<br>(2.20)*|
|GDP per capita (in constant 2000|-0.129|-0.137|-0.148|
|US dollars, log)|(2.38)*|(2.14)*|(2.42)*|
|DPT Immunization Rates (log)||-0.015<br>(0.05)||
|Measles Immunization Rates (log)|||0.068<br>(0.23)|
|Observations|606|551|549|

Absolute value of z statistics in parentheses * significant at 5%; ** significant at 1%

**Table 2: Correlations**

||Average CRC<br>ratification rate in<br>the**region**|Common law<br>legal origin|
|---|---|---|
|DPT Immunization<br>Rate (log)|0.079|0.069|
|Measles Immunization<br>Rate(log)|0.112|0.047|

18
the Iran-Iraq war between 1980 and 1988, the 1986 South Yemen civil war, the Gulf war in 1990 and 1991, and the Iraq war in 2003 following the United States invasion. The identifying assumption is that conflict occurrence in these countries constitute a strong predictor of return migration. The number of active armed conflicts is both country and year specific and comes from the Monadic Conflict Onset and Incidence data set of the Uppsala Conflict Data Project (UCDP).<sup>19</sup> When estimating the reduced form equation (2), equation (3), and equation (4), our regressions include the same vector of individual controls ( _Xi_ ). In these equations, _Xi_ additionally includes decades of migration dummies. In our CMP model, the errors terms _v_ , _ε_ , _ω_ , and _τ_ are allowed to be correlated through a multidimensional distribution.

# **4.2 Discussion on the Exclusion Restrictions**

In this section, we discuss issues pertaining to the validity of our empirical model presented in section 4.1. First, in section 4.2.1, we discuss the exclusion restriction of oil prices and present evidence supporting the orthogonality of oil prices to both illegality and return decisions. Second, in section 4.2.2, we discuss the evolution of Egypt’s diplomatic relations with other Arab countries and provide evidence that migration is not influenced by the bilateral relationship of countries. Third, in section 4.2.3, we discuss concerns related to the exclusion restriction of conflict occurrence and how we address them empirically.

## **4.2.1 The Exclusion Restriction of Oil Prices**

The exclusion restriction of oil prices is that oil prices affect wages in Egypt only through temporary migration decision. Since Egypt is a non-dependent oil economy, the exclusion restriction holds if oil prices at the age of 25 are not correlated with employment and wages in Egypt. To provide support to our identifying assumption, in Figure 1 in Panel A, we present oil prices against key aggregate economic indicators in Egypt including GDP annual growth rate, male labor force participation rate, male employment in agriculture (% of total male employment), male employment in industry (% of total male employment), and male employment in services (% of total male employment).<sup>20</sup> The top panel in Figure 1 provides support to our exclusion restriction

> 19 See the Data Appendix B.2 for a description of the Monadic Conflict Onset and Incidence data set from the Uppsala Conflict Data Project (UCDP).

> 20 Labor Force participation rate, employment in agriculture, industry, and services come from Key indicators of the Labor Market Database of the International Labor Organization. GDP growth rates come from the World Bank National accounts data files. The choice of the time period is dictated by data availability.

15
# WPS3620

## **Leadership and the Independent Regulator**

Mark A. Jamison Public Utility Research Center University of Florida

### Abstract

Being a utility regulator has perils because the independence of the regulator necessarily removes power from politicians, operators, and others. Furthermore, regulators are sometimes scapegoats for unpopular policies and unavoidably become involved in shaping the policies that they are supposed to implement. As a result of such frictions, regulators are sometimes removed from office or marginalized in some way. How can regulators not only survive in such an environment, but also thrive? This paper describes a leadership concept called adaptive leadership that regulators can use to help their countries adapt to new policies and changing situations, while allowing the regulator to stay in the game. The first leadership skill discussed is the ability to get on the balcony to see what is really going on with operators, politicians, consumers, and others. Once this perspective is obtained, then the regulator can engage stakeholders in an adaptive process in which people make necessary changes to traditions and expectations, while hanging on to the things that are truly important. Regulators can do this by bringing attention to problems that people want to ignore because they involve difficult trade-offs, providing certainty and stability when tensions become too high for work to be done, and keeping attention focused on the work and the issues.

World Bank Policy Research Working Paper 3620, June 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Contact details 205 Matherly Hall PO Box 117142 University of Florida Gainesville, Florida 326711-7142 USA Tel: +1.352.392.6148 Fax: +1.352.392.7796 Email: jamisoma@ufl.edu
58

# Table III.7 (continued)

|||Source of Con|traceptivesa||
|---|---|---|---|---|
|Country<br>Location<br>Education|Government<br>(Percent)|Commercialb<br>(Percent)|<br>NGO<br>(Percent)|OtherC<br> <br>(Percent)|
|Thailand (1984)|||||
|Urban<br>None|76.1|23.9|0.0|0.0|
|<Primary|71.4|26.8|0.0|1.8|
|Primary Comp.|55.2|37.9|3.4|3.4|
|Secondary +|56.7|37.5|1.4|4.3|
|Rural<br>None|77.5|19.7|0.5|2.3|
|<Primary|81.9|16.2|0.7|1.2|
|Primary Comp.|68.7|26.5|1.2|3.6|
|Secondary +|74.1|25.0|0.0|0.9|

# Source: CPS; DHS

- a. Sample is currently married women.

- b. Includes private physicians, hospitals, pharmacies, and any other private, non-NGO.

- C. Unspecified source, may encompass NGOs when private, nonprofits are not a category, and may include commercial where it is not a separate category. Other also includes don't knov and not stated.

**nP: Yabl.** 11.7
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51 The questionnaire asked about the main cooking fuel and about the expenditure on LPG during the previous month. LPG was by far the most commonly used fuel, followed by firewood and natural gas (Figure A2.5). As expected, virtually no households purchased both LPG and natural gas.

Figure A2.5: Main source of energy for cooking across all households in Mexico <!-- Start of picture text --> LPG 80<br>firewood 12<br>natural gas 6.4<br>electricity 0.5<br>coal/charcoal 0.2<br>other 0.4<br>0 20 40 60 80 100<br>Percentage of households<br><!-- End of picture text --> _Source:_ Authors’ calculations.

LPG is sold in 10-, 15,- 20-, 30- and 45-kg cylinders to residential consumers, with the last three being the most common sizes. Cylinder sales accounted for about half of LPG sold to residential consumers in 2007 (Mexico, Ministry of Energy 2008). Instead of cylinders, many middle- and high-income households have large tanks—typically 300 liters in capacity (about 165 kg)— installed on rooftops, which are refilled by LPG delivery trucks. Apartment complexes can also have large tanks serving multiple households. Prices are independent of cylinder and tank sizes.

The relatively large sizes of cylinders and tanks commonly used in Mexico appear to have resulted in many LPG-using households reporting zero expenditure in the previous month: 80 percent of households reported that LPG was their primary cooking fuel, but only 60 percent had purchased it in the previous month. The 4.8 million households that cited LPG as their main cooking fuel but had not purchased it presumably represents those who had bought large quantities of LPG but not in the month preceding the survey. Because of this large discrepancy, data on LPG as the primary cooking fuel are used in the selection equation. The quantities of LPG purchased were estimated for the survey period from the expenditure information using national average LPG prices (published by the energy ministry) for the month prior to that in which the survey was administered to the various households.

Quantities of LPG purchased in Mexico were markedly higher than in other countries. Household consumption was slightly larger in urban than in rural areas (Table A2.21). In each  quintile, household size was smaller in rural than in urban areas by as much as 20 percent in  the top quintile making rural per capita consumption of LPG larger.
The derivation of markups follows De Loecker and Eeckhout [2017] and Traina [2018] where the markup is simply defined as the ratio of price for the output good over marginal cost: where _θit_ is output elasticity of the variable input, and<sup>_P_</sup> _P_<sup>_<u>it</u>_</sup> _it_<sup>_<u>Q</u>_</sup> _Vit_<sup>_<u>it</u>_istherevenueshareofthevariable</sup> input (or simply _SALE/OPEX_ ). To estimate this, we consider 3-digit NAICS industry-specific Cobb-Douglas production functions with variable inputs and capital.<sup>14</sup> Thus, for a given industry (3-digit NAICS): where _SALEit_ is Log (sales deflated by GDP deflator), _OPEX_ is the variable input and is measured as Log(Operating Expenses deflated by GDP deflator), _Kit−_ 1 is Log(Capital Stock), and _ωit_ is Log Productivity which is assumed to follow an AR(1) process _ωit_ = _ρωit−_ 1 + _ξit_ . The parameter _ξit_ is the innovation to the firm’s productivity process. We use the perpetual inventory method to construct measures of capital stock. We first initialize the capital stock using the first available entry of gross PPE and then iterate forward on capital using the accumulation equation: where ∆ _Iit_ is net investment computed using changes to PPENT and deflated by the investment goods deflator.<sup>15</sup> The coefficient _βv_ represents the output elasticity of the variable input OPEX.

To estimate the above, we follow the literature and adopt a control function approach to address endogeneity concerns due to the potential simultaneity between unobserved productivity shocks and the demand for inputs. If the demand for an input increases with productivity shocks, that input’s demand function can be inverted and the unobserved productivity shocks can be derived as a function of observables. In the first stage, we remove idiosyncratic errors from the production

> 14In our current implementation we do not apply the Olley and Pakes [1996] correction. The effect of that correction is somewhat controversial, as discussed by Ackerberg et al. [2015], Ackerberg [2016], Gandhi et al. [2017], and Frank and Yang and footnote 12 in De Loecker and Eeckhout [2017]. As shown by Yasar et al. [2008], the correction is small on this data set. All our results are materially the same if we were to apply the correction

> 15GDP deflator is given by line 1 of NIPA Table 1.1.9 and the Non-residential fixed investment good deflator is given by line 9 of Table 1.1.9.

14
parallel trends, the corresponding figure for deforestation outcomes is less conclusive. Therefore, it is prudent to adjust for potential differential time trends in order to alleviate concerns regarding the methodology’s validity.

The findings consistently show that proximity to the most populated camps after the Rohingya’s arrival led to increased night light density and deforestation, though the impact is more pronounced for night light density. Specifically, column (2) in Panel A indicates that a one-unit increase in the standard deviation of SID results in a 1.7 percent increase in night light density and a marginal 0.02 percent increase in deforestation. An additional one-unit standard deviation in SID implies that the Euclidean distance between the coordinates decreases by 0.20 units (1/3.172), which is approximately equivalent to a reduction in distance of 30 km or 18.6 miles. Hence, the estimates suggest that when proximity of a grid to the refugee camps increases by 30 km (18.6 miles), night light density increases by 1.7 percent and deforestation increases by 0.02 percent. Considering that a one pp change in night light density corresponds to a 0.28 pp change in GDP (Henderson, Storeygard and Weil 2012), grid areas 30 km closer to the camps had approximately 0.5 percent higher GDP (1.7×0.28 = 0.47) than grids farther away. These results remain robust after restricting the sample to grids that are either closest to or farthest from the camps, ensuring a cleaner control group. The detailed results can be found in Table C.1.

The validity of these findings is further corroborated by Table 4, which demonstrates that closer proximity to the camps after the Rohingya’s arrival corresponds to a decrease in dense-open forest and an increase in areas used for grass and crops. More precisely, Panel A shows that a one-unit increase in the standard deviation of SID leads to a 0.7 percent reduction in dense-open forest coverage, while areas for grass and crops increase by 0.7 percent and 0.4 percent, respectively. In summary, the data reveals complex effects of hosting displaced populations. The increases in night light density, however, are much greater than the increases in deforestation or the decreases in forest-covered land.

# **V IMPACTS OF REFUGEE CAMPS ON INDIVIDUAL-LEVEL OUTCOMES**

The impacts of proximity to the refugee camps cannot be estimated using a differencein-differences methodology since the two waves of data were collected in 2019 and 2023, 19
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In keeping with the literature on migrant selection, we have thus far treated education as exogenous when modeling selectivity into migration. However, there are number of reasons to believe that migration prospects can in turn affect education levels acquired. Most first-time migrants from Mexico travel to the U.S. without documentation, including 91% of first-time migrants in the ENADID sample. Kossoudji and Cobb-Clark (2002) argue that there is no return on schooling in the first job for unauthorized workers, and using a sample of individuals becoming legalized during an amnesty program, find that finishing high school or having posthigh school education only pays off in terms of wages about four years after legalization. As a result, as demonstrated by Chiquiar and Hanson (2005), the return to schooling is higher in Mexico than it is for Mexican migrants in the United States. As a result, the prospect of future education may actually lower the incentive to invest in education.

This effect is likely to be largest for individuals with a migrant parent, who are themselves then much more likely to migrate. Moreover, although the migrant parent can provide remittances which can ease in credit constraints restricting schooling, the absence of a parent can also have a detrimental effect on schooling. In McKenzie and Rapoport (2006b) we estimate that the net effect of having a migrant parent is to lower the level of schooling attained by 16 to 18 year old males. Using 2SLS we estimate the size of the effect to be a 1.3 year reduction in years of schooling attained. We also carry out estimation using a censored ordered probit (COP), which accounts for the fact that some 16 to 18 year olds are still completing their education and so have censored schooling levels, and for the fact that distribution of years of schooling tends to clump at 6, 9 and 12 years, representing finishing different levels of schooling. This results in estimates of the extent to which having a migrant parent changes the probability of attaining different levels of schooling. The net effect of these is approximately a 1.8 year reduction in predicted years of schooling.

To investigate the robustness of our results to endogenous education formation, we use the point estimates described to adjust schooling levels. Unfortunately the ENADID only allows identification of migrant parents for individuals still living with their parents, preventing us knowing whether many individuals in their late twenties, thirties and forties had migrant parents. Therefore to make an extreme assumption to examine the sensitivity of our results, we will assume that all migrants had migrant 17
# Wealth Accounting Analysis

## **BEYOND GDP GROWTH: WEALTH ACCOUNTING AND NATIONAL SAVINGS**

**Macroeconomic analysis often focuses on GDP growth as the primary measure of economic development; however, GDP does not tell the whole story, and a number of other indicators - although harder to quantify - can be equally crucial in explaining socioeconomic gains or losses.** This is especially true for countries with a large endowment of extractive resources. GDP and similar measures describe the value of national production or, from a different perspective, national income, but they do not account for the basis of that production—the contribution of different types of capital assets or changes in the composition of those assets over time, including the depletion of renewable and nonrenewable resources or damage to the local ecosystem as well as the accumulation of reproducible physical and human capital. In resource-rich countries these dynamics have crucial implications for long-term growth, and national-income indicators are not capable of evaluating them.<sup>19</sup>

**In this context wealth accounting can be used to quantify the different types of capital—produced, intangible and natural—that drive economic growth.** The following analysis attempts to identify patterns in capital formation and depletion in Mauritania and reveal their implications for the country’s long-term development. The methodology, known as wealth accounting, is designed to determine the value of renewable natural resources (agricultural land, forests, fisheries, etc.), nonrenewable resources (minerals, hydrocarbons, etc.), produced capital (public infrastructure, private capital stocks, etc.), and human capital (education, training, workforce skills, etc.), as well as intangible capital that is not easily valuated in monetary terms (public institutions, the administrative and governance systems, private and civil-society organizations, etc.), but which nevertheless has a major impact on long-term growth potential.

**Among the most useful measures available for gauging a change in a country’s underlying stock of productive capital is Adjusted Net Savings (ANS) calculated as a percentage of Gross National Income (GNI) per capita.** ANS is derived from the standard national accounting measure of gross savings, but includes four key adjustments: (i) the depreciation of fixed capital is deducted in order to obtain net national savings; (ii) the depletion of natural resources is deducted to reflect changes in asset values associated with their exploitation

> 19 Hamilton, K. and E. Ley (2010) “Measuring National Income and Growth in Resource-Rich, Income-Poor Countries,” _Economic Premise_ n.28 Washington DC: The World Bank.

8