rafmacalaba/datause-encoder-data huggingface dataset:
**Policy, Research, and Extemal AHairs**

# **Financial Policy and Systems**

This paper -a product of the Financia: Policy and Systems Division, Country Economics Department - is part of a larger effort in PRE to cxamine the c'fects of economic regulation on the financial sector. Copies are available free from the World Bank, 1818 H Streat NW, Washington DC 2(033. Please contact Wilai Pitayatonakam, room N9-(X)3, extension 37666 (83 pagcs with figurcs and tablcs).

To measure distorting taxes (cxplicit and implicit) on the financial sector of five African countries, Chamicy and Honohan view taxation morc broadly than is customary.

They go beyond thc currencv tax and reserve requirements to include the quasi-tax effects of' intcrest rate ceilings. Under this broader approach, thc true scalc of taxation on rinainciall intermcdiation is much higher than is commonly appreciated.

The level of tax has varied widely over timc and between countrics, but has bcen a significaint element in total governmient revenues, cspecially during fiscal crises induced by commoditv slumps. Between 1978 and 1988, this kindi of' tax accounited for an average 4 percent to 7 percent of GDP in three of thc less macrocconomically stable cconomics (Ghana, Nigeria, and Zambia) and close to 2 percent of G,DP in the two morc stable economics sampled _(C76tc_ d'lvoire and Kenya). Expressed as a percentage of M2, the tax rate has gone as high as _9()_ percent.

By any reckoning, the financial sector has been more heavily taxed than other sectors. Even cxcluding the currency tax, which does not directly affect the banking system, taxes collected avcraged the equivalent of several times the value added of' the banking system in the threc high-tax countrics. That is a serious disincentive to linancial intcnmediation and surely too high for cconomic crficicncy.

Dil'tcrent types of tax distort ')lhavior diffe'crnitly. In relatively Icss developed financial markets suchi as hie five studied, intciest rate ceilings may impose higher welf'are costs than explicit taxes or reserve requircmentis.

Surges in inillation in the five countries correlated witlh high taxes on thc financial sector. CGovernimes rcfomiing taxes on the finzacial system should consider building in mechanisms (adijusting administered interest rates, for example, or moving to explicit, such as valuc-add ed, taxes) to reduce the sensitivity ol' financial sector taxes to thc inflation rale.

The PRE Working Paper Series (isserninates the Finlings of ,ork **under** was; in the BiRnks Policsy Research, and External Affairs Complex. An objective of the serieS is to get these findings out Luickly, cven if presentations are less than full' polished. Thc findings. interpret:itions. and conclslions in thes papers (io not necessarilv represent ofricial Bank policV.

PIrodhced at thc IPRF Disseminat on (center
# **1 Introduction**

An increasing number of regional trade agreements (RTAs) have been signed during the last decades, with many of them covering provisions going beyond purely trade-related clauses. In particular, an increasing number of RTAs contains provisions regulating and easing the bureaucratic procedures to obtain a visa, and the cross-border movement of individuals. Such a reduction in the administrative cost of moving across countries, i.e. red tape for migration, may affect the optimal migration choice for potential migrants at origin. Conditional on other _push_ and _pull_ factors of migration – see Hatton (2005) and Mayda (2010) – the reduction in the administrative cost to obtain a visa is expected to affect the potential migrant’s choice about his/her optimal destination country, and hence may favor the settlement of migrants among an RTA’s signatory countries.

This paper benefits from the recent World Bank data on the content of RTAs and studies the effect of visa-related provisions in RTAs on the bilateral settlement of migrants. Using UN data on bilateral migration stocks in the period 1990-2020, we show that including visa provisions in RTAs stimulates bilateral migration stock by 5.9%. An Instrumental Variable (IV) approach addresses the endogeneity concerns and confirms the positive effect of visa provision on bilateral migration. Interestingly, we show a strong heterogeneous effect of visa provisions. This is magnified among countries having different income levels (i.e. North-South); and dampened in destinations with a strong anti-immigration sentiment of voters (i.e. destinations having a large share of votes for the extreme right party).

Visa provisions in RTAs may target specific types of individuals and regulate a wide spectrum of migration-related frictions. For example, visa provision may apply to a restricted set of professionals (i.e. specific job profiles), or to all individuals of RTAs’ member countries. Also, in some cases visa provisions grant the free mobility of individuals across signatory countries, or just simplify the administrative burden to obtain a visa or citizenship at destination (reduction in the bilateral migration cost). We benefit from the newly released World Bank database on the specific type of visa provision included in RTAs, and test the migration effect of specific types of visa provisions in RTAs.

This paper contributes to the strand of literature on the determinants of bilateral migration. Previous literature focused in particular on the role of origin- and destination-specific factors affecting bilateral migration, i.e. respectively _push_ and _pull_ factors. The average wage and the employment rate at destination have been shown to be key _pull_ factors (Hatton, 2005; Mayda, 2010; Grogger and Hanson, 2011; Ortega and Peri, 2013). Income dispersion and poverty are the 1
_page 36_ References.

Backus, D. (1993), 'Interpreting comovements in the trade balance and the terms of trade,' Journal of International Economics 34, p. 375-387.

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De Gregorio, J. (1992), 'Economic Growth in Latin America,' Journal of Development Economics 39, p. 59-84.

Etherington, D. & Yainshet A. (1988), 'The Impact of Income Terms of Trade for Coffee on Capital Goods hnports and Investment in Ethiopia,' Eastern Africa Economic Review 4, p. 48-52. Gavin, M. (1990), 'Structural Adjustment to a Terms of Trade Disturbance,' Journal of International Economics 28, p. 217-243.

Harberger, A.C. (1950), 'Currency Depreciation, Income, and the Balance of Trade,' Journal of Political Economy 58, p. 47-60.

Hsiao, C. (1986), _Analysis of Panel Data._ Laursen, S. & Metzler LA. (1950), 'Flexible Exchange Rates and the Theory of Employment,' Review of Economics and Statistics 32, p. 281-299.

Matsuyama, K (1988), 'Terms-of-Trade, Factor Intensities and the Current Account in a Life-Cycle Model,' Review of Economic Studies _55,_ p. 247-262.

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Figure 3: Validation with SICONFI data - commitment <!-- Start of picture text --> CE MG<br>2000<br>1500 4000<br>1000<br>2000<br>500<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PE<br>600<br>2000<br>400<br>1000<br>200<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PR RS<br>1500<br>1000<br>1000<br>500<br>500<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>SP % difference from SICONFI data<br>8000<br>6000<br>4000<br>2000<br>0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage deviation in the total amount of budget commitments, at the municipality-year level, between our dataset and SICONFI, the public finance dataset of the Brazilian Treasury. Values are positive whenever the total amount in our dataset, aggregated from individual commitments, is larger than that of SICONFI. See Table 1 for more details on our data coverage. We truncate observations at -25% to the left and at 25% to the right.

23
- 5 -

This framework allows us to further elucidate the question of whether income gains in low-income communities could reduce malnutrition (raise N), even when they only have a very marginal effect on nutrient intake at the household level. In their 1987 paper, Behrman and Deodalikar argue that raising nutrient intake of the poor cannot be achieved by increases in income, because empirically it has been found that even among very low income households (for example, in rural India in their study) the nutrient elasticity with respect to income may be close to zero. They state: "... the World Bank (1981)-type optimism about the nutrient improvemen **t** o be expected with ircome gains in commuilities such as the ones under examination (rural South India), seems fundamentally misleading," since "... increases in income in the present context will not result in substantial improvements in nutrient intakes." (p. 505).

And from this they suggest that developing country nutrition is unlikely to improve with income.

The questions before us are:

- (a) Is the level of the nutrient intake a good proxy for the health and nutrition benefits derived from food corsumption under circumstances other than the extreme case of famine?

- (b) Is the level of the various nutrients the relevant nutrition (N) variable, ano if not, how does this affect the relationship between changes in income and nutrition? and

- (c) Is the income elasticity of nutrient intake a relevant criterion to ass3ss the merits of education and other welfare programs aimed at improving nutrition?

As mentior.ed in the introduction, nutrition is one of the main determinants of health, and health is now regarded as a desirable end in itself, distinct from the general objective of improving economic welfare. How these two variables (N and H) respond when income gains occur among low-income households is then the central concern in this section.

From the production function of nutrit ion (N) defined above, we can derive the income elasticity of nutrition: eNy _a_ A + (NH **EHY** where N = level of nutrition H = level of health 3 See the Appendix for details.
higher interest rates and lower use of credit among the landless at baseline, landless households had the most to gain through participation in Jeevika. Consistent with this, we find that take-up of the program is more pronounced among these households, who were 11 percentage points more likely to belong to an SHG than landowning households in program areas (Panel B).

While the program appears to have had a slight negative impact (Rs. 860, p<0.1) on the total amount of outstanding household debt (column 3), we see a significant increase of close to Rs. 2,000 in the value of debt owed to SHGs (column 4), and an offsetting decrease in loans from informal sources of credit (Table 3, column 2).<sup>11</sup> Scaling the increase in SHG debt by programinduced SHG membership, this result implies that 96% of the lending capital initially transferred to groups through Jeevika had been extended as credit to households by the time of the follow-up survey.

The magnitude of first-order program effects differs by landholding status. The increase in SHG debt is higher among the landless than the landed by Rs. 390. In contrast, the negative impact of the program on new informal debt is twice as large among land-owning households (Table 3, column 3, panel B), reflecting lower baseline borrowing among the landless. The large decrease in informal borrowing among landholding households exceeds the expansion in loans taken through SHGs by these households, resulting in a marginally significant negative estimate of the program effect on overall indebtedness for this subsample (Table 2, column 3), and a significant decrease in overall borrowing over the past year (column 7). For landless households, on the other hand, the expansion in SHG borrowing exceeds the decline in informal borrowing, resulting in a positive (but not significant) point estimate on total new borrowing. The lack of an impact on total borrowing overall suggests that on average, the study population was not credit constrained prior to the intervention.

The substitution of credit source from informal moneylenders to Jeevika results in a marked decline in the average interest rate on households’ outstanding debt, and in the amount of debt for which the monthly rate is 4% per month or higher. On average, such “high cost” debt is Rs. 1,880

> 11 Informal sources include moneylenders, shopkeepers, neighbors, friends, relatives, chit funds, casual employers, casual employees and village temples. We exclude interest free loans, which likely reflect informal risk-sharing mechanisms rather than the informal credit market.
literacy. In addition, school inspectors visited schools to assess the quality of teaching and lesson preparation. Steps were taken to assure independence in data collection and minimize risk of collusion between and teachers.

We find that at midline, after one year of program implementation, there is a sizable and statistically significant impact on test scores (about 0.24 standard deviations, sd) for the in-kind treatment arm, and a much smaller and statistically insignificant impact for the recognition treatment arm (about 0.13 sd). At endline, after two years of program implementation, the impact of the in-kind rewards had fallen by about 30 percent in magnitude (to 0.16 sd) and is no longer statistically significantly different from zero. The impact of recognition rewards remains smaller (0.09 sd) and statistically insignificant . We also find that at midline the impacts are larger for girls than boys. We find no evidence of strategic behavior on the number of students who were assessed in each year, in average enrollment rates, or in changes in enrollment. We provide suggestive evidence that the reduction in impacts at endline is linked to the growing Ebola epidemic that Guinea experienced at this time.

Effects differ by teacher gender. The impact of in-kind rewards are very similar across genders, 0.20 sd and 0.18 for female and male teachers respectively. In contrast, the effects of the recognition rewards is 0.23 sd for female teachers, while it is 0.01 sd (and insignificantly different from zero) for male teachers. The findings further suggest that the positive effects of either scheme for female teachers benefit both female and male students. However, the positive effects of the in-kind treatment for male teachers benefit only female students. This is consistent with targeted action since female students have lower test scores at baseline.

Finally, data from the school inspections suggest an improvement in teaching preparation and practice. Teachers in the in-kind treatment arm made better use of resources in the classroom, were better prepared for the class, and implemented better teaching practices. Consistent with the results on student learning, the impacts on teaching practice are no longer statistically significant at endline.

Our paper adds to a growing literature with experimental evidence from teacher pay-forperformance interventions in low- and middle-income countries. This literature documents a range of findings: some studies find no impacts (Barrera-Osorio and Raju, 2017); others document positive impacts (Behrman et al., 2015; Cilliers et al., 2018; Filmer et al., 2020; Leaver et al., 2020; Loyalka et al., 2019; Muralidharan and Sundararaman, 2011); and others have shown impacts only in the presence of complementary resources (Gilligan et al., 2018; Mbiti et al., 2019). Last, one study finds impacts on the incentivized test, but no evidence of impacts on any other learning outcomes (Glewwe et al., 2010). In contrast, null and negative impacts have been typically been found in experiments in high-income settings 3
**Table 10. Real Adjusted**<sup>**1/**</sup> **Hourly Salary in Urban Areas (Constant 1994 pesos)**

|||**1988**|||**1994 **|||**1999**||
|---|---|---|---|---|---|---|---|---|---|
|**Type of Occupation **|**Mean **|**Median **|**S.D. **|**Mean **|**Median**|**S.D. **|**Mean**|**Median **|**S.D.**|
|**Primary Teacher in Public School**|12.6|10.4|9.2|19.1|18.9|7.4|14.8|14.1|10.1|
|**Primary Teacher in Private School**|12.4|11.1|8.7|20.1|16.4|16.1|9.9|8.7|5.9|
|**Lower-Secondary Teacher in Pub. School**|15.6|11.1|15.7|19.5|18.7|9.1|15.1|14.4|6.2|
|**Lower-Secondary Teacher in Priv. School**|11.7|10.6|6.0|21.1|17.3|15.8|14.8|13.0|8.4|
|Upper-Secondary Teacher in Pub. School|18.5|13.7|14.5|20.8|19.1|13.9|15.2|14.4|8.0|
|Upper-Secondary Teacher in Priv. School|23.3|9.2|44.8|19.8|17.0|10.2|16.7|14.6|12.4|
|University Teacher in Public School|21.0|19.3|11.7|25.5|23.4|15.7|24.3|18.6|31.1|
|University Teacher in Private School|14.4|13.3|9.7|25.5|23.1|16.8|20.6|15.2|14.6|
|Professionals with Intermediate Level in<br>Education not teaching|7.1|5.9|4.5|14.5|11.1|11.7|11.2|8.1|12.4|
|**Professionals with a University degree in**<br>**Education not teaching**|16.0|15.6|6.5|20.6|16.5|17.3|14.1|11.3|17.4|
|**The mixed-skilled group**|**7.9**|**5.2**|**16.3**|**10.5**|**5.9**|**104.1**|<br>**6.5**|**4.2**|**8.4**|
|The agricultural group|5.4|2.9|9.6|6.8|3.8|16.2|5.3|2.4|16.9|
|The low-skilledgroup|5.6|4.1|12.4|6.0|4.3|80.6|4.0|3.0|4.9|

Source: Own calculations based on ENEU survey 1/ Following OECD, it is assumed that teachers work on an average of 41 weeks per year and non teachers work on an average of 49 weeks per year.

After using several definitions of teacher salaries and payments, it is clear that real salaries and real labor earnings for teachers in basic public education are significantly above other occupations and groups salaries.

# _Income Sources_

With respect to income sources, Table 11 shows the income source shares for primary teachers, lower secondary teachers and other occupational groups. From this table, one can see that salaries contribute close to 82 percent of the teachers’ total income. Notice that non-monetary income is the second highest income source, especially housing imputed rent and gifts (8.14 percent and 13.14 percent for primary teachers in public and private schools, respectively; 6.4 percent and 14.2 percent for lower secondary teachers in public and private schools, respectively). Financial income is also important for teachers; on average, its contribution to total income is about 5 percent<sup>_6_</sup> .

Yet half the income of _the mixed-skilled group_ and _the low-skilled group_ incomes comes from salary, while in _the agricultural group_ salary contributes just 22.8 percent. Furthermore, it is clear that _own business incomes_ are much more important to these occupational groups than to the teachers group.

> 6 Table 6 in the annex shows that teachers' incomes in upper secondary and tertiary levels are more evenly distributed across income categories.

17
education group now living in Lima is there a statistically significant effect of VTE on the likelihood of being employed as a manual worker. For this group, graduates of VTE are 7- to 9-times more likely to be in manual jobs, arnd the null hypothesis (that there is no relat.4on between VTE and manual work) cannot be rejected at the 5 percent level. For all of the other six worker groups, although graduates of VTE are more likely than graduates of general education to be working in manual jobs (up to 2-times more likely), in no case is this effect statistically significant at the the 10 percent level. One may conclude from this analysis that the relation in Peru between the type of educatioi, taken in school and the type of work that an individual performs later when employed is, in general, quite weak.

Table 10

# MWNAL WM= BY WUATh1N, MEMR, AND HIGESr I OF EO _WlCATIONATTK_

# Hi&est level of education attened

|||S|ecadary|||T|ertiary||
|---|---|---|---|---|---|---|---|---|
||_Nnber_of<br>munral<br>workers|Percen<br> <br>of<br>grow|t<br>Effect<br>of VIE<br>in logit re-<br>gressicn<br>0a|odds<br>ratio<br>(9)b|Umber of<br>mmwal<br>workers|Perce<br>of<br>group|nt<br>Effect<br>of VIE<br>in logit re-<br>gression<br>(pf|Odds<br>ratio<br>c (G)d|
|Netrpolitan<br>Lima|||||||||
|Wagse<br>eployee|s 221|51|0.504<br>(1.43)|1.7|19|11|2.223<br>(4.11)|++ 9.2|
|Self-employ|ed 96|51|0.505<br>(1.15)|1.7|29|31|1.982<br>(3.52)|++ 7.3|
|Other urban m|||||||||
|Wage_emlnplys_|135|55|0.514 (1.38)|1.7|16|24|0.693 (0.94)|2.0|
|Self-eployed|94|47|0.642 (1.47)|1.9|17|24|0.075 (0.11)|1.1|

Notes: t-staistis in parethess; two pluse (cm plus) = logit coefficient statistically sigfint at 5% (101) in a two-tailed test.

a,c Depaxit variable in the loot regressicns 1 if mral worker, 0 odtxeise. VIE variable: 1 if hi*wst education VIE, 0 otherise; p - lgit coefficient on this variable. Other independent varibles: PENffAL JO EXPERDI , YFRS IN PREENT JOB, and MIGMQr. b,d 9 - EXP(P), to be interpreted as follosJ: "An Individual hii took VIE is 0-thmes nore likely to be eployed as a nurual wrker than an individual who did not (given the other foar charmcteristics controlled for in the equation)." 25