rafmacalaba/datause-encoder-data huggingface dataset:
Rules Dataset, 2015 and Budina et al. (2012)), compared to only 10 in the 1990s.

Despite this increased popularity of fiscal rules, relatively little work has been undertaken on their functioning. We try to fill this gap by examining the effectiveness of different types of fiscal rules in promoting counter-cyclical fiscal policies. Moreover, we focus on the role of institutional quality and try to find whether there is a more optimal solution to promote counter-cyclical fiscal policy (i.e. which deals with the underlying political source of the problem). This analysis is motivated by Frankel et al. (2013), Bergman and Hutchison (2015) and Bergman et al. (2016) which argue that good and strong institutions have beneficial effects on public finance. In addition, we revisit the effect of exchange rate regimes on the cyclicality of fiscal policy and more interestingly, we try to find whether the effect of fiscal rules depends on the exchange rate regime in place.<sup>2</sup> In other words, we focus on the effect of rules conditional on the exchange rate regime in place (in line with a Mundell-Fleming approach). To the best of our knowledge, this issue has not yet been addressed in the literature.

The contribution of this paper to the literature is threefold. First, it contributes to the emerging literature on the impact of institutions and fiscal rules on the cyclicality of fiscal policy since it empirically addresses the issue of their complementarity or substitutability (Bergman and Hutchison (2015), Wyplosz (2005) and Wyplosz (2012)) in reducing pro-cyclicality and promoting fiscal discipline. Second, to the best of our knowledge, this is the first paper which examines the disciplining effect of fiscal rules, conditional on the exchange rate regimes. Third, it tackles the heterogeneity of fiscal rules since it examines the disciplining effect of different types of rules (expenditure rules, budget balance rules, debt rules and revenue rules).

We use a panel of 153 advanced, emerging and developing countries and employ a two-step econometric approach. We first compute the cyclicality coefficients of fiscal policy using the Local Gaussian-Weighted Ordinary Least Squares (LGWOLS): this allows us to obtain time-varying and country-specific coefficients. Second, we estimate how institutional quality, fiscal rules, exchange rate regimes and their interactions affect the predicted cyclicality. In addition to basic panel re-

> 2For example, the impact of fiscal rules in a monetary union has been tackled theoretically by Colciago et al. (2008).

3
ment in schooling. This is important evidence for government policy, as substitutability implies that public investment to improve the quality of rural schools is desirable on both equity and efficiency grounds. The evidence on the slopes of the investment equation suggests that better educated peers are complementary to financial investment in urban areas, but separable in the rural areas. While adult role models are substitutes of financial investment by parents in the urban sample, they are separable in the rural areas. Theory provides plausible explanations for these contrasting effects. Peer and role model effects weaken the impact of family background on educational attainment of children, and this is true in rural and urban samples. The peer and role model effects are not responsible for the convexity observed in the intergenerational mobility equation, indicating that the higher efficiency of educated parents in financial investment decisions might be important, as emphasized recently by Becker et al. (2018). The empirical results are robust to controlling for the heterogeneity in cognitive ability of children. The theoretical and empirical approaches developed in this paper are of wider interest, as they can be applied to other developing countries with household panel data.

# **References**

- Akresh, R., Halim, D., and Kleemans, M. (2018). Long-term and Intergenerational Effects of Education: Evidence from School Construction in Indonesia. NBER Working Papers 25265, National Bureau of Economic Research, Inc.

- Asher, S., Novosad, P., and Rafkin, C. (2018). Intergenerational Mobility in India: Estimates from New Methods and Administrative Data. Working paper, World Bank.

- Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among the “other 99 percent”. _Science_ , 344(6186):843–851.

- Azam, M. and Bhatt, V. (2015). Like Father, Like Son? Intergenerational Educational Mobility in India. _Demography_ , 52(6):1929–1959.

- Becker, G., Kominers, S. D., Murphy, K. M., and Spenkuch, J. L. (2015). A Theory of Intergenerational Mobility. MPRA Paper 66334, University Library of Munich, Germany.

- Becker, G. and Tomes, N. (1986). Human Capital and the Rise and Fall of Families. _Journal of Labor Economics_ , 4(3):1–39.

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measures than those hired before—either in the pooled model or in the country-specific models (Table 3). If anything, the estimates suggest that there is a positive relationship with being a postFPE hire. In the pooled model (Column 1 of Table 3) the effect of being a post-FPE hire is statistically significantly positive on the use of good pedagogical practices and negative on school absence (a pattern that is repeated in some of the individual countries such as Kenya, and Tanzania). Perhaps surprisingly, the only somewhat consistent result across countries from this analysis is that teachers hired post-FPE are less likely to have been absent on the day of the unannounced visit.

# 5. Discussion and conclusion

This analysis set out to explore the extent to which the effects of the rapid scale-up in the hiring of teachers associated with FPE reforms in Sub-Saharan Africa—and the potentially lower “quality” of those teachers, due to potentially lowering of standards required to rapidly recruit substantial numbers of teachers—are still detectable at the classroom level 5 to 16 years (depending on the country) after the reform. Administrative data confirm the rapid increase in the number of primary school teachers across these countries, with inflections at the time of FPE for several. In many of the countries, the years which current grade 4 teachers report as having been their first also reflect an inflection at the time of FPE.

Comparing the learning outcomes of students whose teachers were hired in the years before and after FPE suggests that there is, on average, a learning deficit for the latter—although, depending on the bandwidth used, the effects are not always statistically significantly different from 0. Pooled across all the countries in the study, the deficit is on the order of 0.2 standard deviation on the language test, and 0.1 standard deviation on the math test (test scores have mean 0 and standard deviation 1 across all test-taking students in these countries). Deficits in language are common across almost all the countries (Uganda is the exception), but in math the results are more varied, with strong deficits in Mozambique and Uganda, milder deficits in Madagascar and Togo, and a test-score advantage in Kenya and Tanzania.

15
# **Table 5: Causal Analysis: Estimates from MB-NIPW and Lewbel Estimators (Primary** **<u>Schooling as the Threshold for Cut-off)</u>**

|||**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|||**Parent’s**<br>|**Education and O**<br>|**ccupation**<br>|
||**Estimates**|**Non-farm, low**<br>**edu**|**Farm, high**<br>**edu**|**Non-farm, high**<br>**edu**|
|**Impact of F**<br>**Schooling**|**amily Background on Children's**<br>||||
|**_Daughters_**|||||
|**MB-**|Coefficient|**0.63**|**0.93**|**1.68**|
|**NIPW**|90% confidence interval|(0.18,1.02)|(0.62,1.23)|(1.32,1.97)|
||Coefficient|**0.57**|**0.98**|**1.34**|
|**Lewbel**|t statistics|2.87**|2.34**|5.59***|
||Breusch-Pagan heteroskedasticity test|191|315|185|
||Kleibergen-Paap rk Wald F statistic|427.52|120.11|324.05|
|**_Sons_**|||||
|**MB-**|Coefficient|**1.19**|**1.12**|**1.82**|
|**NIPW**|90% confidence interval|(0.17,1.57)|(0.77,1.34)|(1.57,2.11)|
||Coefficient|**0.89**|**0.59**|**1.49**|
|**Lbl**|t statistics|3.08***|2.21**|6.74***|
|**ewe**|Breusch-Pagan heteroskedasticity test|160.56|371.69|138.39|
||Kleibergen-Paap rk Wald F statistic|150.42|169.03|347.95|

|**Impact of F**<br>**Schooling**<br>**_Mothers_**<br>|**amily Background on Parent’s**<br> <br>||||
|---|---|---|---|---|
|**MB-**|Coefficient|**0.66**|**1.04**|**1.24**|
|**NIPW**|90% confidence interval|(0.22,1.08)|(0.82,1.22)|(0.91,1.92)|
||Coefficient|**0.49**|**0.81**|**0.85**|
|**Lewbel**|t statistics|1.62|4.25***|2.73**|
||Breusch-Pagan heteroskedasticity test|327.31|173.83|873.24|
||Kleibergen-Paap rk Wald F statistic|103.52|369.49|64.88|
|**_Fathers_**|||||
|**MB-**|Coefficient|**0.21**|**0.76**|**0.73**|
|**NIPW**|90% confidence interval|(-0.04,0.65)|(0.61,0.95)|(0.42,1.17)|
||Coefficient|**-0.1**|**0.13**|**0.61**|
|**Lewbel**|t statistics|-0.44|0.71|2.65**|
||Breusch-Pagan heteroskedasticity test|385.04|296.81|1537.28|
||Kleibergen-Paap rk Wald F statistic|153.91|371.53|109.98|

**_Notes:_**

**_1._** BM-NIPW=minimum-biased normalized inversed probability weighted estimator;

**_2._** The computation of MB-NIPW uses STATA program written by Millimet and Tachernis (2010);

**_3._** The MB-NIPW estimator uses boostrapping of 250 applications for computing the confidence interval.

**_4._** ***, **, and * Denotes statistical significance at 1%, 5%, and 10% level.
In the last two columns, we perform similar IV analysis with the WB MP dummy. Results from the 2SRI and GMM regressions point to a positive effect of internal forced displacement on that alternative indicator of multidimensional poverty. The second stage of the 2SRI regression returns a marginal effect of 0.071 as displayed in column (7), while the GMM model gives a marginal effect of 0.029 as we can see from column (8).<sup>22</sup>

**Table 6** . IV regression

|**Column**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|---|---|---|---|
|**VARIABLES**|**IDH**|**MP**<br>**dummy**|**MP**<br>**dummy**|**MP**<br>**dummy**|**monetary**<br>**dummy**|<br>**monetary**<br>**dummy**|**WB MP**<br>**dummy**|<br>**WB MP**<br>**dummy**|
||GLM|GLM|GMM|Logit|GLM|GMM|GLM|GMM|
|IBC|0.001***<br>(0.001)||||||||
|IDH||1.429***<br>(0.246)|0.030***<br>(0.014)|0.630***<br>(0.145)|0.385***<br>(0.112)|0.039**<br>(0.020)|0.854***<br>(0.205)|0.029**<br>(0.014)|
|Household in rural area<br>(origin)|0.383***<br>(0.118)||||||||
|Head of household age|0.001<br>(0.003)|0.003<br>(0.002)|0.001***<br>(0.001)|0.005**<br>(0.002)|-0.001<br>(0.002)|0.001<br>(0.001)|-0.006***<br>(0.001)|-0.001*<br>(0.001)|
|Dependency ratio|-0.003*<br>(0.002)|-0.001<br>(0.002)|-0.001<br>(0.001)|-0.001<br>(0.002)|0.007***<br>(0.002)|0.002***<br>(0.001)|0.015***<br>(0.001)|0.001***<br>(0.001)|
|Head of household<br>education|0.004<br>(0.015)|-0.655***<br>(0.081)|-0.009***<br>(0.001)|-0.387***<br>(0.029)|-0.153***<br>(0.011)|-0.022***<br>(0.001)|-1.691***<br>(0.088)|-0.019***<br>(0.001)|
|Head of household<br>married|-0.033<br>(0.386)|0.011<br>(0.158)|-0.003<br>(0.010)|-0.220<br>(0.144)|0.222***<br>(0.071)|0.036***<br>(0.012)|0.001<br>(0.089)|0.006<br>(0.010)|
|Head of household male|-0.316<br>(0.381)|0.131<br>(0.144)|-0.001<br>(0.010)|0.104<br>(0.146)|0.355***<br>(0.068)|0.034**<br>(0.013)|0.373***<br>(0.086)|0.004<br>(0.011)|
|Household in rural area|-0.086<br>(0.100)|1.215***<br>(0.086)|0.066***<br>(0.003)|1.429***<br>(0.070)|0.393***<br>(0.029)|0.088***<br>(0.005)|0.667***<br>(0.052)|0.070***<br>(0.004)|
|Public distribution|-0.493***|-0.103|-0.033***|-0.422|0.191|-0.014|-0.198|-0.046***|
|system|(0.125)|(0.297)|(0.011)|(0.281)|||(0.188)|(0.012)|

22 Again, for the monetary and WB MP dummies, we cannot attribute our positive and significant results in this section to our sample reduction. We ran logit regressions on the reduced sample for the two dummies separately and obtained significant marginal effect values of 0.019 and 0.006 respectively.

25
<!-- Start of picture text --> 0.24<br>=<br>(O}3<br>% 0.22<br><<br>3<br>E<br>B 0.20<br>5<br>o<br>0.18<br>2011 2013 2014 2015 2016 2017 2018 2019<br>Year<br>If acommune has IDP HHs — 0 — 1<br><!-- End of picture text --> <!-- Start of picture text --> 0.20<br>ay<br>=<br>=<(0)<br>& 0.18<br>5<br>rom=<br>a<br>5<br>S o.16<br>0.14<br>2011 2013 2014 2015 2016 2017 2018 2019<br>Year<br>Ifa commune has IDP HHs — 0 — 1<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>3 0.90<br>fa<br>€<br>3<br>3<br>=FoO 0.85<br>2<br>g3<br>a<br>ao}<br>® 0.80<br>©<br>2<br>=—Ea<br>B 0.75<br>c<br>fo}<br>[o)<br>2011 2013 2014 2015 2016 2017 2018 2019<br>Year<br>If acommune has IDP HHs — 0 — 1<br><!-- End of picture text --> <!-- Start of picture text --> 0.6<br>Q<br>(0)o<br>e<br>oO305<br>a<br>3Q<br>§<br>¢<br>Ss<br>a<br>—<br>S)5 04<br>2011 2013 2014 2015 2016 2017 2018 2019<br>Year<br>If acommune has IDP HHs — 0 — 1<br><!-- End of picture text -->
such as cloud cover of ephemeral light sources.

In addition to night lights, we provide direct evidence of the impact of Fonden on local economic activity by investigating its impact on employment. The employment dataset is constructed from the labor force survey (LFS) ENOE. Specifically, we produce a quarter-year panel of employment at the municipal level for the 2005 to 2013 period. The LFS is produced by the Mexican statistical office INEGI. The survey samples 120,000 dwellings per quarter in both urban and rural municipalities. It has a focus and structure similar to that of the Current Population Survey, and at any given point in time, it provides information on roughly half of Mexico’s municipalities. As in the case of night lights, the outcome of interest is the log difference in employment between the quarter the disaster takes place and various quarters in the post-disaster period.

The national water commission (CONAGUA) provided us with three datasets: (i) Data on historical rainfall at the day-weather station level, this dataset spans the 1920 to 2015 period, and contains the universe of weather stations. (ii) The weather station-month level triggers for Fonden eligibility. (iii) The mapping between municipalities and representative weather stations. These datasets where merged with numeric weather station identifiers when available, and with string weather station identifiers when not. In spite of using natural language processing algorithms, we are still unable to fully match municipalities to all the possible weather stations used for verification. The resulting dataset is a municipal-month level panel that allows us to observe the rainfall mm to the Fonden heavy rainfall threshold. For events spanning more than one day the maximum was chosen.

Data on municipal level requests and approvals for disaster declarations were constructed from the archives of Mexico’s official diary. Data on municipal level Fonden expenditures, and days between disaster and authorization of funds are provided by the Ministry of Finance (MoF).

Given the introduction of the percentile 90 heavy rainfall rule in 2004 and the availability of log night lights, we are primarily interested in two samples. When working with annual composites, the set of relevant events will be those that occur between 2004 and 2011.<sup>4</sup> In this period we observe 3,083 municipal heavy rainfall requests. Of these, we have complete weather and Fonden threshold information for 1,745. As shown in table 1, using 2000 census data, we find no evidence of systematic differences between municipalities with missing and complete information. When we turn to monthly composites we are able to use the complete sample of heavy rainfall request which covers the period between 2004 and 2013. In this case we observe 5,652 municipal heavy rainfall requests and we have complete information for 2,825. Tables 2 and 3 present summary statistics of the key variables for each sample.

> 4Note that while annual night light data are available until 2012, we are interested in the change in night lights between the year the disaster takes place and the following year.

8
**Figure 2b: ENE Labor Income Data – Sample Size 100**

<!-- Start of picture text -->
-.5 0 .5<br>Difference in average income (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>6<br>4<br>Baseline<br>2<br>0<br>3<br>2<br>1<br>6 months later<br>0<br><!-- End of picture text -->

# **Figure 2c: ENE Labor Income Data – Sample size 300**

<!-- Start of picture text -->
-.4 -.2 0 .2 .4<br>Difference in average income (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>10<br>8<br>Baseline 64<br>2<br>0<br>5<br>4<br>3<br>2<br>6 months later<br>1<br>0<br><!-- End of picture text -->
- **Labor Productivity** .  A combination of poor education (both in terms of quality and overall coverage), deficient training, weak skills and a lack of entrepreneurial incentives puts Africa at a huge disadvantage compared to the rest of the world (figure 9). In addition to low investment in human capital, many African countries have difficulty retaining highly educated workers or attracting skilled expatriates.

Figure 9: Value Added per Worker in Medium-Sized Firm (in US$) <!-- Start of picture text --> 20000<br>16165<br>16000 14198 15238<br>12167<br>12000 9303 11037<br>7093<br>8000<br>4413<br>3321<br>4000 1773<br>0<br>ZambiaUganda Nigeria Bolivia KenyaTanzaniaNicaragua China IndiaMorocco<br><!-- End of picture text --> <u>Source: Eifert, Gelb and Ramachandran, “Business Environment and Comparative Advantage in</u> Africa: Evidence from the Investment Climate Data,” _Center for Global Development_ , Working Paper 56, 2005

- **Flexibility** .  Firms need to be responsive to the buyer’s ever-changing needs and be able to deliver accordingly (be it small or large orders) as well as in a timely manner. Apart from South Africa and Mauritius, which produce high value-added products, most Sub-Saharan African countries’ manufactured exports are basic products that are vulnerable to lower-cost Asian production in general.<sup>19</sup> It is hard to pin down a specific indicator that can measure flexibility. One possible way is through surveys with the buyers, asking them for a detailed assessment of their producer’s performance. Flexibility could be broken down into “coping with small orders” and “coping with changes in large orders.” In general, Africa performs rather poor compared to its competitors from Asia and Latin America.<sup>20</sup>

- **Product Quality.** A good indicator of product quality is the “defect rate” or the percentage of items returned by the buyer. Unfortunately, reliable data on these indicators are also scarce and rather incoherent. However, from the detailed value chain studies, we find that the defect rate in the Kenyan apparel industry is over

> 19 Harry Broadman (ed.), _From Disintegration to Reintegration: Eastern Europe and the Former Soviet Union in International Trade_ , Chapter 7: Linkages between FDI and Trade Flows and Gereffi (2005)

> 20 Hubert Schmitz and Peter Knorringa, “Learning from Global Buyers,” _IDS, University of Sussex_ , 2001

10
# **Institutional structure**

A regulated competitive market is envisaged. Life insurance companies and private pension funds will be allowed to compete in offering both types of products. However, fixed real annuities will also be offered by the state agency, which will also act as the default provider. The reserves backing fixed real annuities will be invested in inflationprotected bonds. Switching of annuity provider will not be allowed.

The private pension funds and life insurance companies will be required to maintain adequate levels of capital reserves, appropriate mathematical reserves, and adjustment reserves. The latter will be used to manage the investment and longevity risks. It will be funded by a 2.5 percent fee on annuity premiums (accumulated account balances). The private pension funds that currently operate as mutual benefit societies will be required to convert into joint-stock companies with adequate levels of capital and reserves. The standing of this provision has been referred for examination by the Constitutional Court.

# **Calculation of initial payments and profit sharing rule**

Strict rules will be applied to the initial calculation of annuity payments. For both inflation-protected and ‘guarantee and bonus’ annuities, this will be based on unisex mortality tables issued by the Central Statistical Office, an operating fee that cannot exceed 5 percent of the annuity premium and a zero percent technical interest rate.

The fixed real annuities will be increased annually by the previous year’s rate of inflation. The ‘guarantee and bonus’ annuities will receive a bonus that will be set once a year and will depend on the investment performance and mortality experience of each provider. Providers will have to distribute 95 percent of annual profits. Negative bonuses will not be allowed since the annuities will provide minimum guaranteed benefits.

Annuity contracts will use a centralized quotation system to be organized by the regulatory authority. Providers will be required to report the annuity factor, their gross and net investment returns, their asset management and administrative fees and the size of the annual bonus promised to annuitants.

# **Prudential regulations and government guarantees**

Annuity providers will be required to build technical reserves by using gender-based mortality tables and market rates of interest.<sup>32</sup> Mortality results will be shared among providers to offset the impact of the compulsory use of unisex mortality tables in pricing

> 32 Although redesign plans in Poland and Hungary broadly pointed in the same direction, a significant difference in pricing and reserving policies should be underscored.  In Poland, pension companies would be free to set their own pricing policies but would be required to use a very low rate of interest for reserving purposes. In contrast, in Hungary, annuity providers would be compelled to use a prescribed very low interest rate for calculating initial annuity payments and for the calculation of reserves. The implications of this difference are briefly discussed in section 3.5 below.

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