rafmacalaba/datause-encoder-data huggingface dataset:
- 19 -

**<u>Table 7 Spatial Autoregressive Model without Instruments</u>**

||<br>(1)||<br>(2)||<br>(3)||<br>(4)||(5)|(6)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|ln_RAI_|<br>0.046<br>(0.107)|<br>||||||||||
|ln_RDDEN_|||–0.132<br>(0.092)|<br>||||||||
|ln_GDRD_|||||0.055<br>(0.122)|<br>||||||
|ln_TCMK_|||||||–0.324<br>(0.091)|***<br>||||
|ln_TCPT_|||||||||–0.745<br> <br>(0.634)|||
|ln_MAI_|||||||||<br>|<br>0.653<br>(0.124)|***<br>|
|ln_N15_|0.806|***|0.853|***|0.808|***|0.749|***|0.789 ***|<br>0.718|<br>***|
||(0.069)||(0.072)||(0.070)||(0.068)||(0.066)|(0.067)||
|_POPDEN_|0.1680||0.2000||0.1660||0.2420||0.1880|0.1910||
||(0.1200)|<br>|<sup>(0.1360)</sup>|<br>|<sup>(0.1210)</sup>|<br>|<sup>(0.1120)</sup>|<br>|<sup>(0.1300)</sup><br>|<sup>(0.0969)</sup>||
|_INFO_|–0.004||–0.008||–0.004||0.001||0.000|0.005||
||(0.024)||(0.025)||(0.025)||(0.024)||(0.024)|(0.024)||
|_UNEMP_|<br>–0.001|<br>|<br>–0.003|<br>|<br>0.000|<br>|<br>–0.007|<br>|<br>0.003|<br>–0.003|<br>|
||(0.013)||(0.013)||(0.014)||(0.013)||(0.014)|(0.013)||
|_EDU_|<br>0.016|<br>|<br>0.007|<br>|<br>0.015|<br>|<br>0.002|<br>|<br>0.007|<br>–0.003|<br>|
||(0.091)||(0.090)||(0.091)||(0.089)||(0.091)|(0.089)||
|Constant|<br>–15.260|<br>***|<br>–14.120|<br>***|<br>–15.240|<br>***|<br>–14.200|<br>***|<br>–14.540 ***|<br>–13.140|<br>***|
||(4.831)||(4.744)||(4.908)||(4.644)||(4.750)|(4.667)||
|Observations|134||134||134||134||134|134||
|Number of||||||||||||
|region dummy||||||||||||
|<br>variables|5||5||5||5||5|5||
|Autoregressive c|oefficients|||||||||||
|λ|–0.357|**|–0.330|*|–0.358|**|–0.324|**|–0.370 **|–0.348|**|
||(0.160)||(0.181)||(0.160)||(0.159)||(0.147)|(0.157)||
|ρ|0.622||0.899||0.556||0.700||0.408|0.674||
||(1.111)||(1.060)||(1.151)||(1.118)||(1.126)|(1.148)||

Note: Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10%, 5%, and 1%, respectively.

One may wonder to examine whether the agglomeration variable _N15_ is also endogenous. More-lagged values are used to test this. The numbers of firms registered by 2013 and 2014 are denoted as _N13_ and _N14_ , respectively. It is less likely that past decisions directly affect a firm’s decision on where to locate. The results confirm robustness of the main estimation results shown above (table 8). Market accessibility is an important determinant of firm
# **1. Introduction**

Several studies that investigate the impact of armed conflict on the long-term development of a country find no significant effects (Davis and Weinstein, 2002; Miguel and Roland, forthcoming). By contrast, most studies that focus on the effects of conflict on the education and health of birth cohorts that were affected by a conflict find strong negative and lasting effects of armed conflicts (Bundervoet, Verwimp, and Akresh, 2009; Akresh, Verwimp and Bundervoet, forthcoming; Blattman and Annan, 2010; Akbulut-Yuksel 2009; Shemyakina, 2011; Valente, 2011). Other studies that examine the effect of conflict on education by gender find no significant negative impacts (Annan et al. 2009; Justino, Leone and Salardi 2010). Further, a small literature finds that veterans have significantly lower earnings than those who did not serve in the military (Angrist 1990; Angrist and Krueger 1994; Angrist 1998; Imbens and van der Klaauw 1995). Kondylis (2010), Menon and van der Meulen Rodgers (2011) and Galdo (2010) examine the impact of exposure to armed conflict on the labor market outcomes of the general population in the context of Bosnia-Herzegovina, Nepal and Peru respectively. Kondylis finds that displaced men and women are less likely to be employed as compared to those who did not move. Menon and van der Meulen Rodgers find that married women in conflict affected areas are more likely to participate in the labor market or become self-employed; which can be explained by “the added worker effect” where women increase their labor supply in response to the conflict related displacement, migration and deaths of men. Galdo finds that exposure to the armed conflict early on decreases one’s earnings later in life in Peru.

This is one of the first studies to explore the effect of armed conflict on the labor market outcomes of men and women who were of school age or just completed school when the conflict started. In particular, this study examines the effect of the 1992-1998 armed conflict in Tajikistan on the educational attainment and labor market experiences of the birth cohort that was of school age during the conflict and who lived in the more conflict affected areas. The study combines the 2003 and 2007 Tajik Living Standards Surveys data to address this question.

2
|**Availability**|||||||
|---|---|---|---|---|---|---|
|Amman|0.037|-0.017|-0.173|0.117**|0.063|-0.061|
||(0.04)|(0.08)|(0.13)|(0.06)|(0.10)|(0.19)|
|Beirut|0.064|0.039|0.148|-0.008|-0.033|0.263|
||(0.05)|(0.07)|(0.13)|(0.08)|(0.10)|(0.18)|
|Cairo|0.006|0.018|0.149*|0.038|0.038|0.205*|
||(0.05)|(0.06)|(0.09)|(0.05)|(0.06)|(0.12)|
|**Safety**|||||||
|Amman|-0.047|0.154|0.945**|-0.024|0.08|0.824|
||(0.16)|(0.25)|(0.42)|(0.21)|(0.34)|(0.63)|
|Beirut|-0.488*|-0.367|-0.607|-0.790**|-0.734|-1.539|
||(0.28)|(0.37)|(0.63)|(0.40)|(0.51)|(0.97)|
|Cairo|-0.761|-0.634|-0.448|-0.473|-0.544|-0.161|
||(0.69)|(0.73)|(1.25)|(0.81)|(0.86)|(1.54)|
|N|2961|1732|670|1701|999|345|
|R2|0.174|0.182|0.214|0.201|0.220|0.280|
|Adj. R2|0.148|0.139|0.102|0.159|0.153|0.096|

Notes: *, **, *** denote significance at the 10 percent, 5 percent and 1 percent levels respectively. Robust standard errors in parentheses. N: number of observations.

**Table 8: Marginal effect of public transport characteristics on employment probabilities, by city and gender.**

||**No i**|**ncome restric**|**tion**|**Lowe**|**r-income gr**|**oup**|
|---|---|---|---|---|---|---|
||(1a)|(2a)|(3a)|(1b)|(2b)|(3b)|
|**Sample restriction**|All|Birth +<br>Marriage|Birth|All|Birth +<br>Marriage|Birth|
|**Accessibility**|||||||
|Amman - Men|0.480***|0.466**|0.424*|0.505***|0.521**|0.601*|
||(0.10)|(0.19)|(0.23)|(0.13)|(0.25)|(0.35)|
|Amman - Women|-0.166|-0.368**|-0.276|-0.094|-0.315|-0.161|
||(0.11)|(0.18)|(0.38)|(0.14)|(0.23)|(0.52)|
|Beirut - Men|0.149|0.096|0.208|0.033|-0.198|-0.517|
||(0.13)|(0.16)|(0.23)|(0.21)|(0.30)|(0.38)|
|Beirut - Women|0.076|0.007|0.175|0.079|0.203|0.319|
||(0.12)|(0.15)|(0.22)|(0.15)|(0.21)|(0.32)|
|Cairo - Men|-0.092|-0.322|-0.416|-0.161|-0.226|-0.273|
||(0.28)|(0.32)|(0.46)|(0.33)|(0.37)|(0.55)|
|Cairo - Women|-0.141|-0.238|-0.101|0.01|-0.035|0.087|
||(0.31)|(0.35)|(0.51)|(0.37)|(0.41)|(0.60)|
|**Availability**|||||||
|Amman - Men|0.127***|0.153***|0.145*|0.109**|0.109|0.086|
||(0.03)|(0.06)|(0.08)|(0.04)|(0.08)|(0.11)|
|Amman - Women|-0.099***|0.01|-0.041|-0.026|0.132|0.163|
||(0.04)|(0.07)|(0.12)|(0.04)|(0.09)|(0.17)|
|Beirut - Men|0.015|-0.036|0.02|-0.05|-0.062|0.017|
||(0.05)|(0.07)|(0.08)|(0.09)|(0.12)|(0.15)|
|Beirut - Women|0.012|0.003|0.063|0.007|0.031|0.094|
||(0.04)|(0.05)|(0.09)|(0.06)|(0.07)|(0.13)|
|Cairo - Men|<br>-0.023|<br>-0.045|<br>-0.05|<br>-0.007|<br>-0.026|<br>-0.043|
||(0.03)|(0.04)|(0.05)|(0.04)|(0.04)|(0.06)|
|Cairo - Women|-0.012|-0.024|0.095|0.008|-0.012|0.102|
||(0.04)|(0.05)|(0.07)|(0.05)|(0.05)|(0.08)|

30
Tables 9 and 10 report the regression results for the annual and monthly data respectively.

In Figure 9 we can appreciate the significant changes that have occurred over time in the demand for real Ml as measured by its velocity of circulationr. From 1960 through 1974 velocity remained approximately stable in the range of 6-7. In 1975 velocity starts an upward trend that does not show signs of stopping, taking it to a value of 33 in 1988. Unofficial data estimates put velocity around 50 as of the second half of 1989, after the hyperinflation of June-July of that year. The raise of 1975-76 may have been caused by the high price instability during those two years; we have reasons to believe, however, that a structural change took place in 1977, when interest rates were totally freed for the first time in decades and the public could invest in short term time deposits at market determined interest rates. This structural change is bound to reduce the real demand for Ml as the supply of alternative assets was now open. Our empirical results confirm this presumption as the value for the structural change dummy is of 0.67, meaning that there was a 95% (exp(.67)-1) upward shift in velocity due to the financial liberalization experience.

The estimates from the monthly data yield long run estimates of the semi-log elasticity (coefficient al corrected for the effect of lig'ged Ml) ranging between -2.83 and -3.01, implying a monthly revenue maximizing inflation rate between 33%35%. The regression using annual data yields a comparable estimate for al of -4.54, compatible with a maximum revenue rate of 21.9% per month./All of the above estimates of maximum revenue inflation rate shodld be subtracted the monthly real growth rate if any real growth is to be assumed./ Our estimates of the semi-log elasticity of demand for Ml fall within the range of other empirical studies. For example, Kiegel and Neumayer(1989), using monthly data for the period July 1982-March 1985, find values for al in the range -2.4 -3.8. Their estimates of al for the fixed exchange rate period of the Tabla Cambiaria (Jan.1979-January 1981), when there was a high degree of capital mobility, yields somehow higher estimates in the range of -4.9 -6.0 .

In order to estimate the possible range for **the** revenue from money creation we have decided to use the results from the regression using annual data since this allows for a direct estimate of velocity.

32
product offering and other business elements such as distribution or geographic specialization. As a result, more participants are able to occupy the market compared to smaller markets where the economies of scale can only be achieved by participating more generally in the whole market.

Market development measured through penetration appears most relevant for life markets and less so for non life markets. In the multiple regression, less developed markets have higher natural concentration levels after allowing for market dynamics – a result that is more consistent with expectations.

The pace of organic consolidation or liberalization is also suggested as being around 9 years given the resulting values for the adjustment parameter ‘ _d’_ . This gives an indication of the rate at which larger players will see their market share erode in a liberalizing case, and that business combinations can be expected to reach their ultimate outcome in the absence of other external encouragement in consolidating cases.

**<u>Table 5: Multiple Factor Regression Results</u>**

|Item|LifeInsura|nce Case|Non LifeInsu|rance Case|
|---|---|---|---|---|
||Without Market<br>Reversion|With Market<br>Reversion|Without Market<br>Reversion|With Market<br>Reversion|
|_Natural Level_|||||
|_n_<br>_H_|2265.|215|1531.4|20|
|_Market Size and Develo_|_pment Adjustment_||||
|_a_(constant)|1274.404|159.915|773.650|145.384|
|_b_(_ln(P):_Premium)|(242.713)|(43.634)|(136.999)|(28.240)|
|_c_(Insurance|56.912|34.685|6.520|1.629|
|Penetration)|||||
|_Market Reversion Adjus_|_tment_||||
|_d_(Adjustment toward<br>natural level)<br>||0.889||0.918|
|_R_<sup>_2_</sup>|45.25%|97.89%|45.34%|97.65%|
|No ofObservations|307|215|311|222|

Source: Staff Analysis

# **_Why Could Optimal Non Life Concentrations Be Lower Than Life Concentrations?_**

Economies of scale are more readily accessible for life insurers than non life insurers<sup>6</sup> . For example, where a life insurer adds a similar number of insurance policies of the same type then it can add them to the existing administrative systems and leave it at that – securing the benefit. For a non life insurer, the issue is more problematic. For example, each new policy cannot simply be added to the existing set without consideration of aggregation of risk and the need to realign reinsurance needs. For a life insurer, doubling

> 6 That is, when growing a business, the marginal addition of life business secures economies of scale with little additional consideration whereas, for non–life business, the addition may be able to be accommodated administratively but will immediately need to be considered in the context of physical or other risk aggregation challenges in the context, for example, of the reinsurance program.

15
**Putting together household consumption and production from the previous section, it is clear that lower income households are on average much larger net buyers of maize relative to their incomes.** To the extent that households grow their own maize, they are insulated from the effects of price increases, and may even benefit from them. So to understand total welfare impacts we work with net consumption (consumption less production). Figure 12 shows mean net purchases of maize as a percentage of expenditure, across income deciles. Only 8 percent of households are net sellers of maize, as compared to 17 percent with net purchases of zero and 75 percent with positive net purchases.  From the figure we can see that those at the poorer end of the distribution are far more affected than the wealthy.  The net budget share of those in the poorest decile households is estimated to be 6.5 times that of the wealthiest decile.

**Figure 12:** **_Net_ expenditure share on maize**

_Source: KIHBS (2005/06), author calculations._

**Without accounting for change in behavior, poorer households will benefit to a greater extent from the lower prices that can be gained from better-functioning markets.** If we ignore behavior change for a moment, a change in the price of maize will affect households in direct proportion to their budget share on this item.  The solid line in the left hand panel of Figure 13 illustrates the impact of a 20 percent decrease in maize prices across income levels – the y-axis measures the gain in income that would have equivalent effect on a household’s welfare to this price cut.  This figure illustrates precisely the same concept that we covered with respect to sugar.

**Altering current policy to reduce maize prices by 20 percent would have approximately the same effect as a real income increase of 1.2 percent on average, with much greater gains falling on the poor.** When prices fall, on average we would expect people to purchase more of that product (but probably not so much that their total expenditure on the item increases).  At the same time we would usually expect an average decrease in the production of maize by some suppliers (since the profits to be earned are lower).  As in the case of sugar,

26
> i For ease of exposition throughout the text, we interchangeably refer to workers or production workers.

ii Firm-level data often do not contain detailed information on the characteristics of individual workers, which may be a bigger drawback than not having information on firm characteristics.

iii For example, Grindling and Terrell (2005) look at a 12 year panel in Costa Rica, Lemos (2009) looks at Brazil, while Maloney and Nuñez-Mendez (2004) look at a number of Latin American countries, focusing on Colombia. All find evidence consistent with wage compression, i.e. that minimum wages serve to raise wages relatively more at the lower end of the wage distribution. Maloney and NuñezMendez (2004) show kernel distributions of wages for eight Latin American countries, which exhibit a bunching of firms paying just above the minimum wage – while there is not full compliance, there is certainly a significant share of firms that find the minimum wages binding. In addition, these papers also find impacts of higher minimum wages across the wage distribution, consistent not only with lighthouse effects at wages above the level of minimum wages, but also in the informal sector.

> iv Existing studies typically find that impacts on employment from minimum wages are small on average, but heterogeneous across firms. Using province level-aggregates, Rama (2001) finds that that a doubling of the minimum wage caused wage employment to fall by less than 5 per cent. However, small firms reduced employment more than large firms. Similar conclusions are reached by Alatas and Cameron (2008). Harrison and Scorse (2010) employ a difference in difference approach and regress employment growth between 1990 and 1996 on the change in minimum wages (for those firms for whom it is likely to be binding) and find that a doubling of the minimum wage would reduce employment by approximately 12.3 per cent in firms which were affected by such minimum wage increases (that is, who were paying wages in 1990 that were below the 1996 minimum wage).

46
reduction program and safety net would ensue from the strengthening of institutional structures and policies, including: 1) national norms for identifying the poor consistently across regions; 2) survey and other instruments with which to consistently measure and monitor local needs and program performance; 3) integration and coordination between sub-programs with welldefined and universal rules for implementation at the local level; 4) welfare maximizing redistribution of resources across space so that everyone is treated equally regardless of where they reside; and 5) increased resources and attention to helping households and communities deal with covariate risk.

The government's new program, the Hunger Eradication and Poverty Reduction Program (HEPR), does not signify a new policy focus. It subsumes most existing programs, and is primarily an attempt to coordinate policy efforts and resources to increase the safetynet's cost-effectiveness and performance. The success of the HEPR depends in large part on it's success in transforming the institutional structures and processes which have guided transfer and safety net provision in the past. In particular, progress in the areas listed above could lead to significant improvement in social protection for Vietnam's poor and vulnerable.

While the HEPR concept offers the potential for significant improvements in the safety net, the Government of Vietnam faces a number of difficult challenges. The very principles on which the current highly decentralized, community-based assistance and safety net system is built are threatened by the emerging market economy. In particular, increasing mobility, without which the market system cannot function, dictates a thorough rethinking of the safety net's foundations. Household mobility renders community level identification and targeting of the poor less effective and is likely to make the mobilization of community resources for 35
# **Public Finance, Security, and Development: A Framework and an Application to Afghanistan**

William Byrd and Stéphane Guimbert<sup>1</sup>

> 1 The analysis in this paper was initiated as part of the World Bank’s Public Finance Management Review for Afghanistan (World Bank 2005b).  Nicole Ball, Peter Middlebrook, and Christopher Ward contributed to Volume V of that report (“Improving Public Financial Management in the Security Sector”).  See Manthri (2008) and Byrd et al (2008) for recent analyses of fiscal aspects of the security sector in Afghanistan.  Very useful comments by Nicole Ball on an earlier draft of this paper are gratefully acknowledged.  However, responsibility for any errors or inaccuracies, as well as for the views, findings, and conclusions expressed in this paper, rests solely with the authors.
R Core Team. (2013). R: A language and environment for statistical computing. R Found. Stat. Comput. Vienna, Austria.

Savage, N. (2023, 04 27). Synthetic data could be better than real data. doi:10.1038/d41586023-01445-8 Schneider, J., Zabel, F., & Mauser, W. (2022). Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Nature Scientific Data, 9(527). doi:10.1038/s41597-022-01632-8 Schneider, K., Fanzo, J., Haddad, L., Herrero, M., Moncayo, J., Herforth, A., et al. (2023). The state of food systems worldwide in the countdown to 2030. Nature Food, 4, 1090-1110. doi:10.1038/s43016-023-00885-9 Senthilnathan, S. (2019). Usefulness of Correlation Analysis. doi:10.2139/ssrn.3416918 Snedecor, G., & Cochran, W. (1989). Statistical Methods (8th ed.). Ames: Iowa State University Press.

Solano-Hermosilla, G., Adewopo, J., Gorrín-González, C., Micale, F., Arbia, G., & Nardelli, V. (2021). FPCA-II. Food Price Crowdsourcing Africa-expansion. Sevilla, Spain: European Commission, Joint Research Centre (EC-JRC) [Dataset]. Retrieved from <u>http://data.europa.eu/89h/f3bc86b0-be5f-4441-8370-c2ccb739029e</u> Solano-Hermosilla, G., Barreiro-Hurle, J., Adewopo, J., & Gorrin-Gonzalez, C. (2022). Increasing engagement in price crowdsourcing initiatives: Using nudges in Nigeria. World Development, 152(105818). doi:10.1016/j.worlddev.2022.105818 Subbaraman, B., & Goodwin, E. (2017). FAO AMIS Nigeria. Final Report. Contract Report, Knoema Corporation, Agricultural Data Exchange, Kaduna.

Taylor, R. (1990). Interpretation of the correlation coefficient: A Basic Review. JDMS, 1, 3539. doi:10.1177/875647939000600106 Thomas, A. (2023). Food Insecurity in Nigeria: Food Supply Matters. IMF Country Report No 2023/094 and Special Issue SIP/2023/018. 27p. IMF Country Report, International Monetary - Fund (IMF). Retrieved 01 02, 2024, from https://www.imf.org/ <u>/media/Files/Publications/Selected-Issues-Papers/2023/English/SIPEA20230</u> Tukey, J. (1977). Exploratory Data Analysis; Addison-wesley series in behavioral sciencequantitative methods. Reading, MA, USA: Addison-Wesley.

UNFPA. (2023). World Population Dashboard Nigeria. United Nations. Retrieved 01 03, 2024, - from United Nations Population Fund Dashboard: https://www.unfpa.org/data/world <u>population/NG</u> Wang, D., Andrée, B.P.J., Chamorro, A., & Girouard, S. (2022). Transitions into and out of food insecurity: A probabilistic approach with panel data evidence from 15 countries. World Development, 159. doi:10.1016/j.worlddev.2022.106035 Waterlander, W. E., Jiang, Y., Nghiem, N., Eyles, H., Wilson, N., Cleghorn, C., et al. (2019). The effect of food price changes on consumer purchases: a randomised experiment. The Lancet Public Health, 4(8), e394-e405.

19