rafmacalaba/datause-encoder-data huggingface dataset:
the required capacity threshold for local tax administration<sup>15</sup> ? How is it possible to reach citizens in remote regions?

# **5. Concluding remarks and areas for future research**

This paper demonstrates that without shaping a fiscal social contract through taxation of citizens in oil-rich economies, the outcome of public spending will probably remain low despite increased external pressure for transparency. The line of argument is straightforward: citizens are more likely to hold their governments accountable when they have to pay more taxes; and as a result, the governments have incentives to design and implement policies that improve the welfare of the population. (See, for example, More 2004.) However, in a poverty-ridden country, without prior redistribution of a small share to citizens, taxation is likely to remain impossible.

Some pilot initiatives should probably be undertaken first before expanding them to more countries or regions. Despite its complexity, this experiment should probably be launched because after decades of external pressure, it is probably time to acknowledge that donors’ pressure in these countries is not as efficient as expected. Most citizens in oil-rich economies do not have any link with their government. Even though not perfect (especially at the beginning), this experiment should create a link between citizens in some SSA countries and their government.

This proposal is not easy to implement but, in the field of oil-rich economies, there is not any initiative, such as EITI (Extractive Industries and Transparency Initiative) or the National Resource Charter<sup>16</sup> , which is easy to implement and they all depend on elites’ willingness to adhere to the process.

However, one of the most important advantages of this proposal (overt the other initiatives) would be to be aligned with some politicians’ incentives. Indeed, this

> 15 This is a concern raised by Ross (2007) regarding citizens funds. He points out that these schemes are complex to administer.

> 16 <u>http://www.naturalresourcecharter.org/.</u>

17
aspects of mental health and outlook.<sup>2</sup> Studies on the impact of accessing and using savings products also find positive effects. In particular, the literature has found that providing individuals access to savings instruments increases savings (Aportela, 1999, Ashraf et al., 2010<sup>3</sup> ), female empowerment (Ashraf, Karlan, and Yin, 2010), productive investment (Dupas and Robinson, 2009), and consumption (Dupas and Robinson, 2009 and Ashraf, Karlan, and Yin, 2010).

Using household-level survey data for El Salvador for the period 1995-2001, this study investigates the impact of remittances on financial inclusion. In particular, we focus on whether remittances promote the use of deposit accounts and credit by examining the impact of remittances on the likelihood that households have a deposit account, apply for loans, and receive loans from formal financial institutions.

There are several ways in which remittances could affect financial inclusion. First, remittances might increase the demand for savings instruments. The fixed costs of sending remittances make the flows lumpy, providing households with excess cash for some period of time. This might potentially increase their demands for deposit accounts, since financial institutions offer households a safe place to store this temporary excess cash. Second, remittances might increase household’s likelihood of obtaining a loan. Processing remittances flows provides financial institutions with information on the income of recipient households. This information might make financial institutions better willing and able to extend loans to otherwise opaque borrowers. On the other hand, since remittances might help relax households’ financing constraints, the demand for credit might fall as remittances increase.

To the best of our knowledge, there are only three papers that examine the direct link between remittances and the domestic financial system. Aggarwal, Demirgüç-Kunt, and Martínez Pería (2011) study the impact of remittances on the financial sector using balance of payments data on remittances and aggregate data on bank credit and deposit amounts for 109 developing countries over the period 1975-2007. They find strong evidence indicating that

> 2 Other studies that find that microcredit increases consumption or income include Pitt and Khandker (1998) and Khandker (2005). However, Roodman and Morduch (2009) are unable to reproduce those results.

> 3 Ashraf et al. (2010) show that giving migrants from El Salvador access to bank accounts that allow them to have control over the funds they remit results in an increase in savings.

3
|Survey year dummy (1 if 2016) # Counties severely affected||||||
|---|---|---|---|---|---|
|by EVD (dummy)<br>0.077|-0.053|0.043|0.092|-0.055|0.053|
|(0.042)*|(0.078)|(0.044)|(0.041)**|(0.079)|(0.043)|
|Constant<br>10.609|9.422|10.948|10.609|9.422|10.949|
|(0.028)***|(0.066)***|(0.030)***|(0.028)***|(0.066)***|(0.030)***|
|Mean of dependent variable<br>5.160|0.426|0.550|0.448|0.230|0.366|
|Observations<br>5365|5365|5365|5365|5365|5365|
|Number of clusters<br>123|123|123|123|123|123|
|Fstat<br>39.942|51.174|90.213|36.386|47.367|82.968|
|Prob > Fstat<br>0.000|0.000|0.000|0.000|0.000|0.000|
|Adjusted R-squared<br>0.183|0.155|0.231|0.185|0.155|0.232|

_Source_ : Authors based on HIES 2014 and 2016. Note: Standard errors in parentheses clustered at the district level. *** p<0.01, ** p<0.05, * p<0.1. All models control for Enumeration Area fixed effects and observations are weighted to be representative of the population.

22
Becker Chris (2016), “Vulnerabilities of Isolated Small Island States in the Pacific” in _Resilience and Growth in the Small States of the Pacific_ (eds) Hoe Ee Khor, Roger P. Kronenberg, and Patrizia Tumbarello, International Monetary Fund.

Bharadwaj, Ritu, Tom Mitchell and N Karthikeyan (2023), “Protecting against sovereign debt defaults under growing climate impacts: Role for parametric insurance”, IIED, London. http://pubs.iied.org/21426IIED.

Burns Andrew, Charl Jooste and Gregor Schwerhoff (2021), “Macroeconomic Modelling of Managing Hurricane Damage in the Caribbean: The Case of Jamaica”, Policy Research Working Paper 9505, the World Bank.

Cabezon, Ezequiel, Leni Hunter, Patrizia Tumbarello, Kazuaki Washimi, and Yiqun Wu (2016), “Strengthening Macro-fiscal Resilience to Natural Disasters and Climate Change in Small states in the Pacific” in _Resilience and Growth in the Small States of the Pacific_ (eds) Hoe Ee Khor, Roger P. Kronenberg, and Patrizia Tumbarello, International Monetary Fund Cevik, Serhan (2022), “Waiting for Godot? The Case for Climate Change Adaptation and Mitigation in Small Island States”, Working Paper WP/22/179, International Monetary Fund.

Doan, Miki Khanh, Ruth Hill, Stephane Hallegatte, Paul Corral, Ben Brunckhorst, Minh Nguyen, Samuel Freije-Rodriguez and Esther Naikal (2023), “Counting People Exposed to, Vulnerable to, or at High Risk from Climate Shocks: A Methodology”, Policy Research Working Paper, 10619, World Bank.

Fresnillo, Iolanda and Illaria Crotti (2022), _Riders on the storm: How debt and climate change are threatening the future of small island developing states_ , EURODAD (European Network on Debt and Development).

Gall, Melanie (2015). “The suitability of disaster loss databases to measure loss and damage from climate change”, _International Journal of Global Warming_ , vol 8, no 2, pp170-190.

Guerson, Alejandro (2020), “Government Insurance Against Natural Disasters: An Application to the ECCU”, Working Paper WP/20/266, International Monetary Fund.

Hallegatte, Stephane, Julie Rosenberg, Jun Rentschler Claire Nicolas and Charles Fox (2019), “Strengthening New Infrastructure Assets. A Cost-Benefit Analysis,” Policy Research Working Paper 8896, World Bank, Washington, DC.

Hallegatte, Stephane, Jun Rentschler and Brian Walsh (2018), _Building Back Better: Achieving resilience through stronger, faster and more inclusive post disaster reconstruction_ , World Bank.

Heal, Geoffrey and Antony Miller (2013), “Uncertainty and Decision in Climate Change Economics”, Working Paper 18929, National Bureau of Economic Research.

Herndon, Thomas, Michael Ash and Robert Pollin (2014), “Does high public det consistently stifle economic growth? A critique of Reinhart and Rogoff”, _Cambridge Journal of Economics_ , vol 38, no 2, pp257-279.

International Monetary Fund (2021A), “Fiscal Policies to Address Climate Change in Asia and the Pacific”, IMF Departmental Paper, No. 21/207, Washington DC.

29
# **C. Infrastructure Investment Expenditures and Infrastructure Improvement**

The final objective of the paper is to link infrastructure investment expenditures with economic growth in Egypt. To accomplish this objective, we need the estimation of the growth effect of improvements in the infrastructure indices, obtained in section IV above. In addition, we need information concerning the connection between infrastructure investment expenditures and improvement in the infrastructure indices for Egypt. This is the goal of this subsection. For this purpose, we use historical Egyptian data to answer the question: how much improvement in an infrastructure index is obtained for given expenditure in the corresponding area of infrastructure?

**Table 6.  Electricity Expenditure and Improvement**

## **Electricity**

_Estimation Method: Quantile regression_

||**Chang**|**Dependent v**<br>**e in Electricity In**|**ariable:**<br>**frastructure Ind**|**ex**|
|---|---|---|---|---|
||**[1]**|**[2]**|**[3]**|**[4]**|
|Ratio of expenditure to labor force|0.006 ***||||
|(expenditure on electricity per 100,000 workers)|[5.00]||||
|Ratio of expenditure to labor force||0.051 ***|||
|(expenditure on electricity per 100,000 workers, in logs)||[5.04]|||
|Ratio of expenditure to GDP|||0.005 ***||
|(expenditure on electricity / 1,000 GDP)|||[6.84]||
|Ratio of expenditure to GDP||||0.079 ***|
|(expenditure on electricity / 1,000 GDP, in logs)||||[8.02]|
|Constant|-0.056 ***|-0.094 ***|-0.084 ***|-0.206 ***|
||[3.57]|[3.89]|[5.32]|[7.39]|
|Observations|34|34|34|34|
|R-squared|0.37|0.32|0.41|0.37|

### _Notes:_

_The dependent variable is smoothed by using the Hodrik Prescott filter. All the expenditure variables are the moving average of expenditures in the last three years. Numbers in brackets are the corresponding t-statistics._ _* significant at 10%; ** significant at 5%; *** significant at 1%_ We run a series of regressions for which the dependent variable is the change in a given infrastructure index and the explanatory variable is the corresponding investment 40
in discrete choice model, we assume that _ε_ is distributed iid extreme value type I with location parameter _−νγ_ , scale parameter _ν_ , and cdf _F_ ( _ε_ ) = exp ( _−_ exp ( _−ε/ν − γ_ )), where _E_ ( _ε_ ) = 0, _V ar_ ( _ε_ ) = _π_<sup>2</sup> _ν_<sup>2</sup> _/_ 6 and _γ_ is the Euler’s constant. This assumption allows us to have an analytical <u>1</u> solution for the option value, which becomes Ω<sup>_i_</sup> _t_<sup>=</sup><sup>_ν_log�</sup> _k_<sup>exp</sup> �� _EtVt_<sup>_k_</sup> +1<sup>_−EtV_</sup> _t_<sup>_i_</sup> +1<sup>_−C_</sup> _t_<sup>_k_</sup> � _ν_ �.<sup>6</sup> This analytical solution simplifies the Bellman equation and makes it tractable.<sup>7</sup> We can now derive the employment allocations implied by the model. Let _m_<sup>_ij_</sup> _t_<sup>betheratioof</sup> agents who switch from sector _i_ to sector _j_ . This can be interpreted as gross flows from _i_ to _j_ , or the probability of choosing _j_ conditional on _i_ . The total number of agents moving from _i_ to _j_ is equal to _yt_<sup>_ij_=</sup><sup>_L_</sup> _t_<sup>_imij_</sup> _t_<sup>,where</sup><sup>_Li_</sup> _t_<sup>isthenumberofagentswhoarein</sup><sup>_i_attime</sup><sup>_t_.Undertheextreme</sup> value distributional assumption, the gross flow _m_<sup>_ij_</sup> _t_<sup>canbewrittenas</sup> Finally, the allocation of labor between sectors is given by: There are four key pieces in the model: the common mobility costs, _C_ , the variance of the idiosyncratic mobility costs, _ν_ , the compensating differentials, _η_<sup>_i_</sup> , and the wage differentials. At each time period, workers compare the costs and benefits of changing employment sector. The benefits are given by the utility differentials, that is the wage differentials net of the compensating differentials. Workers move when the utility differentials are larger than the mobility costs, inclusive of the idiosyncratic benefits. In the end, given the utility differentials, the flow of workers across sectors depends on _C_ and _ν_ , which are the parameters that we want to estimate.

> 6See Artuç (2012) for the derivation.

> 7See McFadden (1973)

6
The rest of the paper is structured as follows. Section II discusses related literature. The data and methodology are presented in sections III and IV, respectively. Section V presents the results, which include the extension of the analysis by Henderson et al. (2012) to South Asia and the prediction of economic activity at the district level. Section VI sheds new light on recent economic episodes and section VII concludes.

# **II. LITERATURE**

Donaldson and Storeygard (2016) provide a great overview of the different applications of nightlight data analysis in economics. The discussion herein is restricted to the literature that is very closely related to the present work. Building on early work on the relationship between nightlights and GDP (Croft 1978, Elvidge et al. 1997, Sutton and Costanza 2002, Sutton et al. 2007, and Ghosh et al. 2009), Henderson et al. (2012) introduce a comprehensive framework to employ nightlights to adjust GDP measured in national accounts. Chen and Nordhaus (2011) use a similar framework and find that nightlights are also useful in evaluating growth, especially when national accounts are of poor quality or no information is available from national accounts. Nightlights are also used to assess the overall size of economies (e.g. Clark et al. 2017).

The high degree of correlation between nightlight intensity and GDP also holds at the subnational level (Doll et al. 2006, Bhandari and Roychowdhury 2011). This insight has been exploited to generate a range of subnational economic indicators, which are not readily available otherwise (Ebener et al. 2005; Ghosh et al. 2010b; Sutton et al. 2007; Bundervoet et al. 2015). For instance, Ghosh et al. (2010a) focus on the size of the informal economy. By comparing economic activity as captured by nightlight data with official GDP estimates, they conclude that India’s 3
balancing tests we conduct to assess the validity of our identification as well as several robustness tests, provide comfort regarding the consistency of our estimates.

Despite the small magnitude of the effects estimated, the key result of this paper highlights the importance of the integration process in offsetting potential adverse effect of immigrant concentration in the classroom. That negative spillovers are small and short-lived suggests that putting in place integration programs for recently arrived migrant students, either in schools or in the host society more broadly, could be sufficient to offset those effects, with a particular focus on host language acquisition. This could be of particular help in schools where native and immigrant children disproportionally come from disadvantaged families. Because of the similarities shared by the migration context in the Netherlands with other countries, particularly the predominance of migrants with low socio-economic backgrounds, we believe our findings are of relevance beyond the Dutch context.

# **References**

Alicia Adsera and Mariola Pytlikova. The Role of Language in Shaping International Migration. _Economic Journal_ , 125(586):F49–F81, 2015.

Andreas Ammermueller and Jarn-Steffen Pischke. Peer Effects in European Primary Schools: Evidence from the Progress in International Reading Literacy Study. _Journal of Labor Economics_ , 27(3):315–348, July 2009.

Joshua D. Angrist and Victor Lavy. Using Maimonides’ Rule to Estimate the Effect of Class Size 41
27

**<u>Table 5 (Cont’d)</u>**

||||||_M_|_obility Categ_|_ory_|||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Regressors_|_(-5)_|_(-4)_|_(-3)_|_(-2)_|_(-1)_|_(0)_|_(+1)_|_(+2)_|_(+3)_|_(+4)_|_(+5)_|
|_Household Asset Position_||||||||||||
|Economic Status in 1990|0.001***|0.000***|0.001***|0.002***|0.003***|0.002***|-0.003***|-0.001***|-0.001***|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.000)|
|Single Family Dwelling in 1990∆|-0.001|-0.001|-0.002|-0.004|-0.008|-0.003*|0.007|0.003|0.003|0.003*|0.003*|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.005)|(0.002)|(0.004)|(0.002)|(0.002)|(0.002)|(0.002)|
|_Household Access to Non-Labor Income_||||||||||||
|Public Transfers∆|0.003***|0.002***|0.006***|0.009***|0.020***|0.010***|-0.018***|-0.009***|-0.009***|-0.008***|-0.008***|
||(0.001)|(0.000)|(0.001)|(0.002)|(0.004)|(0.002)|(0.003)|(0.002)|(0.002)|(0.001)|(0.002)|
|Non-Farm Real Estate Earnings∆|0.004***|0.003***|0.007***|0.010***|0.022***|0.005***|-0.019***|-0.009***|-0.009***|-0.008***|-0.008***|
||(0.001)|(0.001)|(0.001)|(0.002)|(0.004)|(0.001)|(0.003)|(0.002)|(0.001)|(0.001)|(0.001)|
|_Household Social Capital_||||||||||||
|Social Capital Index (PCA)|-0.000|-0.000|-0.001|-0.001|-0.002|-0.001|0.002|0.001|0.001|0.001|0.001|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|_Location_||||||||||||
|Coastal Urban∆|-0.001|-0.001|-0.002|-0.003|-0.007|-0.004|0.006|0.003|0.003|0.003|0.003|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.004)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|Coastal Rural∆|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|0.000|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.003)|(0.007)|(0.003)|(0.003)|(0.003)|(0.003)|
|Central Urban∆|0.002|0.001|0.004|0.005|0.012|0.004***|-0.010*|-0.005*|-0.005*|-0.004*|-0.004*|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.001)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Central Rural∆|-0.002**|-0.002**|-0.004**|-0.007**|-0.015**|-0.010*|0.014**|0.007**|0.007**|0.006**|0.007**|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.006)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|Mountain Urban∆|0.001|0.001|0.002|0.003|0.007|0.003|-0.006|-0.003|-0.003|-0.003|-0.003|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.002)|(0.006)|(0.003)|(0.003)|(0.002)|(0.002)|
|Mountain Rural∆|-0.000|-0.000|-0.000|-0.000|-0.001|-0.000|0.001|0.000|0.000|0.000|0.000|
||(0.001)|(0.001)|(0.002)|(0.003)|(0.007)|(0.003)|(0.006)|(0.003)|(0.003)|(0.003)|(0.003)|
|**Clusters**||||||455||||||
|**Observations**||||||9,396||||||
|**Log-Likelihood**||||||-13,995||||||
|**Adjusted R2**||||||0.088||||||

**Notes for Tables 5 and 6:** Marginal effects are computed at the multivariate point of means; Robust standard errors, adjusted for intra-cluster correlation, are in parentheses; */**/*** indicate significance at the 10/5/1 percent level, respectively; ∆ indicates a dummy variable; The default region is Tirana.
sample bias. The source of data on India is the 1999 Rural Economic and Demographic Survey done by the National Council for Applied Economic Research, and the data on Bangladesh comes from the 1996 Matlab Health and Socioeconomic Survey (MHSS). The Bangladesh survey collected information on household head and spouse’s all children (including from past marriages) irrespective of their residency status from 4538 households in Matlab thana of Chandpur district.<sup>16</sup> The India survey also collected information on all of household head’s children from current marriage but not non-coresident mothers of children from earlier marriage(s). We utilize these information to create data sets containing education and other personal characteristics of parents and children. Both of these surveys focus on rural areas in respective countries. An advantage of rural samples is that the bias from censoring due to possible non-completion of younger children may not be as important, because only few go on to have more than middle school (or high school) education. The children who go for more than high school education (10 years of schooling in Bangladesh and India) are also the children who leave the village household, because the “colleges” (for grades 11 and 12) and universities (for three-four year undergraduate, and graduate study) are not located in villages.

Our estimation sample consists of household head and spouse, and their children, including those from other marriages in the case of Bangladesh. For the empirical analysis, we use alternative samples defined by different age ranges for the children. Our main results are based on a sample of children aged 13-60 years. To test the sensitivity of our conclusions with respect to the specific age cutoffs, we estimate the IGRC and IGC for a number of alternative age ranges; 16-60, 20-69 and 13-50 years.

Table A.1 reports the summary statistics of the relevant variables for both the Bangladesh and India data sets for our main estimation sample (children in the age range 13-60 years). Several interesting observations and patterns are noticeable in our data sets. The average 16The MHSS 1996 is a collaborative effort of RAND, the Harvard School of Public Health, the University of Pennsylvania, the University of Colorado at Boulder, Brown University, Mitra and Associates and the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B).

8
Aggregation of the average treatment effects across cohorts and survey years indicates no evidence that DSM affects wheat yield. Given that DSM has not led to increases in seed purchases for wheat (Tables 5 and 6; Figures 1 and 2), the lack of yield effects can also be interpreted as evidence that DSM did not impact other attributes of the wheat seed system, such as availability and quality, that could have affected crop yield levels.<sup>5</sup>

**Table 6. The impact of DSM on maize and wheat yields**

|**Mize**||||**Wheat**||||
|---|---|---|---|---|---|---|---|
|**Cohort**|**ATT(g)**|**Survey**<br>**year**|**ATT(t)**|**Cohort**|**ATT(g)**|**Survey**<br>**year**|**ATT(t)**|
|2013|-0.042<br>(0.123)|2016|-0.049<br>(0.135)|2013||2016|-0.098<br>(0.182)|
|2014|-0.069|2019|0.246*|2014|-0.117|2019|0.167|
||(0.156)||(0.082)||(0.196)||(0.152)|
|2015|0.163|||2015|-0.063|||
||(0.128)||||(0.139)|||
|2016|0.305*|||2016|0.064|||
||(0.119)||||(0.223)|||
|2017|-0.006|||2017|0.323|||
||(0.100)||||(0.240)|||
|2018|0.377*|||2018|0.390|||
||(0.111)||||(0.195)|||
|2019|0.263<br>(0.142)|||2019|0.147<br>(0.387)|||
|ATT|0.183*||0.098|ATT|0.141||0.034|
||(0.073)||(0.078)||(0.124)||(0.107)|

_Source_ : Authors’ calculation based on the ACC 2012, 2016, and 2019 survey data. _Note_ : P-value for a pre-test of parallel trends assumption is 0.682 for maize and 0.0 for wheat. Comparison group: “never treated.” Anticipation periods: 0. Estimation method: Doubly Robust. Standard errors are reported in parenthesis. * _p <_ 0.10.

Figure 3 provides a summary of the ATTs on maize and wheat yields based on event-study (dynamic) aggregation, showing results consistent with those from cohort-based aggregation. The average treatment effect of DSM on maize yields, based on event-study aggregation, shows a 15 percent increase in maize yield. Figure 3 further indicates that improvements in maize yield occurred during the year DSM was introduced and the following year. For wheat, however, the

> 5 Our dataset is not well-suited for analyzing seed quality and availability attributes within the seed system, as questions regarding availability and quality in the two survey rounds were limited to the year preceding each survey.

20