rafmacalaba/datause-encoder-data huggingface dataset:
_Intertemporal Adjustment and Fiscal Policy in a Fixed Exchange Rate Regime_ along the stable path an increase in capital stock is followed by depreciation of the real exchange rate. This mechanism relies on the fact that capital increases as the foreign real interest rate falls. That is one of the reasons why if the interest rate parity holds the real exchange rate depreciates, other things being equal, and a given value of the real exchange rate in the next period (24). But, as we have already seen in the previous section, in a fixed exchange rate regime a tax increase triggers an increase in exports. This effect is also included in relation (38).

To strengthen this result, we experimented with numerical simulations.<sup>15</sup> These show that for relevant values of parameters, roots are positive. The derivative of the eigenvector slope with respect to the fiscal ratio is also positive, but its value is small with respect to the tax rate. As a consequence, a tax increase will raise the numerator of the above equation (38) and will push the ratio up.

In conclusion, with a fixed exchange rate regime, an active fiscal policy designed to reduce external debt and promote exports should gradually raise the tax rate until the desired export level is reached. Used in that way, fiscal policy may be an efficient way to promote macroeconomic stability. In the transition period it encourages convergence toward long-run equilibrium, while in the long run it modifies the distribution of exports and consumption, private and public.

> 15 A program written using MAPLE software is available on request.

21
# Growth, External Debt, and the Real Exchange Rate in Mexico

# by Sweder van Wijnbergen

# Table of Contents

|I.|Introduction|1|
|---|---|---|
|II.|Stability, Expansion and Collapse:<br>A Brief Review<br>of the Past|2|
|III.|Consistency of Fiscal Policy|4|
|IV.|Macroeconomic Stability, External Balance and Growth:<br>Can They Be Reconciled?|8|
||IV.1<br>A Quantitative Framework|8|
||IV.2<br>The Framework Applied:<br>Mexico's Outlook for Growth|18|
|V.|Conclusions|21|
|Refer|ences|23|
|Figur|es|24|

I thank Willem Buiter, Rudiger Dornbusch, Reuben Lamdany, Alejandro Reynoso, Marcelo Selowsky and my colleagues in the Mexico Department in the World Bank for helpful comments and discussions. I am moreover ndebtea to Sergio Pena for excellent assistance with data collection and estimation.
In order to use these estimates in the context of our static model, we cannot simply use the total costs computed above and compare those to a single year of annual gain. Indeed, the model is calibrated using yearly data (trade flows and GDP are annual) and hence total consumption levels found in our simulated results are comparable to one year of consumption.

One way to compare the cost and benefits of investing in transport infrastructure using such a static model could be to compare the one-time initial cost payment to the present discounted value of the benefits that will be felt from the investment onward. Let Gn be the total annual welfare gain for a country in terms of real consumption, Gn = In − In<sup>′</sup> , and Dn the one-time investment Pn Pn<sup>′</sup> cost. Assuming a constant discount rate r, we could compute the net gain as the difference between the net present value of all gains and the one-time initial cost: However, such an approach would assume that the whole cost of infrastructure is paid in full in the first year and the benefits are felt thereafter. In our model, this would imply setting the L annual lump sum tax for the household to zero (τn = 0) and assuming that investment occurs _before_ solving for the equilibrium. By doing this, however, we would not properly account for the interaction between the investment cost in the household budget constraint and the equilibrium allocation: since countries have different consumption baskets and sectoral distributions, it is important to be able to incorporate the investment cost within the annual equilibrium structure described above.

To take into account the costs of infrastructures in a way consistent with the static model and its annual equilibrium, we use an “annualized” cost which allows us to compare one year of household revenues to one “yearly equivalent” of the investment cost. To do so, we simply assume that the costs are paid through a perpetuity with interest rate r. The equivalent annuity for country L n, paid by the consumer as lump sum τn, is then computed as: Assuming an interest rate r of 2.5 percent, the total annual cost of the BRI would be around $9.2 billion. China, the country with the highest infrastructure costs, is assumed to sustain annual costs around $1.6 billion which would increase to $3.9 billion in the case it pays 30 percent of the total cost in other BRI countries in the form of equity investment. These country-specific L annualized costs τn are then included in the household’s budget constraint and in computation of 19
countries were withdrawn in single domains due to technical issues. Thus, the results cannot be simply compared across time to analyze achievement trends, and we propose a regression framework adjusting for time-invariant country characteristics and for country-specific time trends.

PISA data also include detailed characteristics of students and their schools. We use information on gender, socioeconomic status, and immigrant background to explain differences in achievement scores, but also to control for possible changes in student samples across time. The PISA measure of socio-economic status (ESCS) is an index measuring student family economic, social, and cultural status. We use scores that were equated to 2015 to make them comparable across cycles. Descriptive statistics for these variables are presented in Annex Table A1. In the regressions, we use student-level variables as typical control variables but also country averages for every cycle to adjust for sample and population changes over time.

Finally, we use information from students and principals to estimate length of school closures. In 2022, students and principals were asked for how long their schools were closed due to COVID-19. We recoded their responses to the number of weeks of school closures (see Annex Table A1). We also merge PISA data with information from UNESCO (2023b) on the total number of weeks schools were closed fully or partially. Since the pandemic began, UNESCO monitored school closures and modalities for delivery around the globe daily. Schools were considered fully closed in case of “government-mandated closures of educational institutions affecting most or all of the student population.” On the other hand, schools were considered partially open when: (a) open in certain regions and closed in others; and/or (b) open for some grades, levels, or age groups and closed for others; and/or (c) open with reduced in-person class time, combined with distance learning (UNESCO 2023c).

Descriptive statistics for the four measures of the length of school closures caused by COVID-19 are provided in Table 1, while Annex Table A2 provides exact values on each 6
**Table 3. Description of the variable used and descriptive statistics, Kagera region, 1991-1994**

||||**Stan**<br>**dard**|
|---|---|---|---|
|**Variables**|**Definitions of Variables**|**Mean**|**Deviation**|
||**Community variables**|||
|Partage or feeding posts|= 1 if there is Partage and/or feeding post in the<br>village|0.120|0.325|
|Urban|=1 if the child lives in a rural area|0.163|0.370|
|Health worker|=1 if there is a health worker in the village|0.818|0.386|
|Distance to health center|<br>Distance to the nearest health center in km|3.131|3.495|
|Motoroad|=1 if there is a motor road in the village|0.954|0.210|
|Road impassable|<br>=1 if the road is often impassable|0.483|0.500|
|<br>Ratio of children vaccinated|<br>Ratio of 0 to 3 year old children in the community<br>who have been vaccinated|0.712|0.212|
||**Individual and household characteristics**|||
|Height for age z-score|Height for age z score statistics|-1.643|1.388|
|Weight for age z-score|Weight for age z score statistics|-1.293|1.221|
|Aged 12_23 months|=1 if Child’s age id between 12 and 23 months|0.171|0.377|
|<br>Aged 24_35 months|<br>=1 if Child’s age is between 24 and 35 months|0.169|0.375|
|Aged 36_47 months|=1 if Child’s age is between 36 and 47 months|0.186|0.389|
|Aged48or more months|=1 if Child’s age is 48 months or more|0.308|0.462|
|Born in rainy season, aged < 2|Born in a rainy season and aged less than 2|0.251|0.434|
|<br>Born in rainy season, aged > 2|<br>Born in a rainy season and aged more than 2|0.508|0.500|
|Farm income/total income|Farm income as fraction of total household income|0.687|0.245|
|Log per capita consumption|Log of per capita consumption|10.483|0.609|
|Father dead|=1 if Father dead|0.125|0.331|
|Mother dead|=1 if Mother dead|0.041|0.197|
|Male|=1 if male|0.523|0.500|
|Household size|Number of household’s member|8.992|4.315|
|Father’s height|Father’s height (cm)|168.835|5.387|
|<br>Mother’s height|<br>Mother’s height (cm)|158.522|5.790|
|<br>Father’s height missing|<br>=1 if Father’s height missing|0.380|0.485|
|<br>Mother’s height missing|<br>=1 if Mother’s height missing|0.149|0.357|
|Father’s year of education|Father’s year of education|6.158|2.031|
|<br>Mother’syear of education|<br>Mother’syear of education|5.191|2.681|
||**Instruments**|||
|Mean log per capita consumption|Mean log per capita consumption in the village|10.528|0.344|
|Crop Disaster|<br>=1 if crop damage was the main disaster in the past<br>ten years in the village|0.080|0.268|
|Drought disaster|<br>=1 if drought was the main disaster in the past ten<br>years in a village|0.356|0.479|
|Daily Market in Community|<br>=1 if there is a daily market where the child resides|0.616|0.486|
|<br>AIDS main adults’ death cause|<br>=1 if AIDS is the main cause of adults’ death in the<br>village|0.480|0.500|
|Mean male height|<br>Average male height of adults aged 25-32|169.284|3.985|
|Mean female height|<br>Average female height of adults aged 25-32|158.09|2.851|
|<br>Good roof in the house|<br>=1 if the child lives in a house with good roof|0.638|0.481|
|Mean (log) per capita income in|Log of per capita income|||
|the village||10.224|0.724|

20
Another option recently used by Calderón, Moral-Benito and Servén (2014) in a panel timeseries setting is to establish the existence of a single cointegrating relation between infrastructure, aggregate output, and other production inputs, which can then be interpreted as the aggregate production function. If infrastructure and the other productive inputs do not react systematically to temporary deviations from the long-run relation, its parameters can be estimated by conventional single-equation methods (even if the parameters characterizing the short-run dynamics cannot). Formal exogeneity tests confirm that this is the case,<sup>7</sup> and the estimation places the long-run elasticity of output with respect to a synthetic index of infrastructure in the range 0.08-0.10.

A third alternative is to use an instrumental variable approach, ideally featuring external instruments for infrastructure. In this vein, Calderón and Servén (2003, 2004) employ demographic variables as instruments, alone or in combination with internal instruments, in a GMM panel framework. Roller and Waverman (2001) follow a similar approach.

Recent microeconomic studies concerned with the impact of public infrastructure interventions have used randomized control trials (RCTs) to establish causality. This approach enables researchers to assess whether any changes observed in the population are due to the public infrastructure program, exogenous factors, or unmeasured individual effects. RCTs isolate the impact of interventions by randomly assigning individuals to treatment and control groups. In this vein, several studies have examined the impact of improved water and sanitation on health outcomes (Capuno et al. 2011, Andrés et al. 2014). A limitation of this approach, however, is that the findings may depend on the specific context and time frame in which the experiment is conducted, so evidence of a successful policy intervention might not be relevant to other cities, regions or countries.

Lastly, _heterogeneity_ is also a major concern. The contribution of infrastructure to development outcomes may vary across countries and over time for multiple reasons —starting with the heterogeneous quality of infrastructure assets and services themselves. However, few studies take into account the quality dimension, in large part due to data limitations. Hulten (1996) finds that differences in the effective use of infrastructure resources explain one-quarter of the growth differential between Africa and East Asia, and more than 40 percent of the growth differential

> 7 In contrast, Canning and Pedroni (2008) find the opposite result in a similar setting, although their framework excludes non-infrastructure production inputs.

19
Figure 2.2 b. Terms of Trade Shocks (Percent of GDP) <!-- Start of picture text --> 2.5 -<br>"ArgentUna -Brazil<br>2.0 - -- ,-Colombia Peru<br>1.5- /<br>1.0<br>0.5 -<br>0.0<br>-0.5-<br>-1.0<br>-1.5<br>10<br>OArgentina -- Chile<br>8 - Ecuador +Mexico<br>6 - _ - Venezuela, RB<br>4-<br>2-<br>-2 -2- > \ 1 9 H \ ; < < _ o 7 ~~~~~1999<br>-4,<br>-6<br>-8 c<br><!-- End of picture text -->

**Source:** World Development Indicators - World Bank.
<!-- Start of picture text --> 2,000<br>1,800<br>1,600<br>21,400<br>2<br>mo<br>o<br>2 1,200<br>Qa<br>”n<br>ez<br>5 1,000<br>a<br>5<br>¢ 800<br>c<br>o<br>3<br>(C) 600<br>400<br>200<br>Oo (ac SSS fs SS<br>2019 2020 2021 2022<br>om P33-P66 — Countries Without Fiscal Rule (P50) —- Countries With Fiscal Rule (P50)<br><!-- End of picture text -->
- 15 -

managerial problems has increased from 30-35 percent of the total to more than 60 percent during the period FY80-FY88. It is reasonable to conclude from this evidence that the achievement of ID objectives in Bank projects has become progressively more difficult in recent years. Among the reasons for the deteriorating ID performance reported in ARIS are limited institutional capacities exacerbated by the difficult macroeconomic conditions in borrower countries, the growing complexity of project design and the adverse impact of the surfeit of donor activities in some countries.

More detailed and systematic evidence on ID performance in Bank projects is available from the Annual Reviews of Project Performance Results of the Operations Evaluation Department (OED), which unlike the ARIS data, provides information on projects completed and audited a few years ago and hence sheds light on the trends in ID performance going farther back into the past. OED reviews have evaluated ID performance in projects for over 10 years and have categorized them into those with "substantial," "negligible" and "partial" ID achievements based on evaluations by OED sta.'f of the extent to which the ID objectives of projects have actually been ar.h:te-,ei on the ground. While this clearly reflects the use of judgment by staff a-,d consultants, it is the type of evaluation in which OED has built up consic±rable expertise over the years, using data generated through extensive field visits and discussions with diverse groups of interested persons including borrower representatives and Bank operational staff. In spite of their limitations, the OED time series on ID performance in projects is the best and most comprehensive evidence we have on the subject.

Of the 1,250 projects with ID components over the periods CY78-CY87, OED estimates that 36 percent made substantial progress towards the achievement of their ID objectives while 19 percent showed negligible achievement.22 Nearly 45 percent of the projects were reported to have partially achieved their ID objectives. Among the sectors, industry (48%), public utilities (42%), and DFC (40%) had the highest substantial ID achievement ratings. Among the sectors with the highest negligible ratings were agriculture (25%) and population/health (23%). The latter, being a relatively new sector, had very few completed projects (See Table 5).

A regional comparison of these ratings showed that Africa had the highest negligible achievement rating (26%), a not surprising finding given the general state of institutional weaknesses in that region. ID achievement was substantial in Asia in 43 percent of the cases and rnegligible in 11 percent. However, ID efforts in Asia focused on incremental improvements in established institutions, and fewer Asian projects had ID objectives. Substantial ID achievement in EMENA and LAC were 32 percent and 29 percent respectively. Their negligible achievement ratings were 18 percent and 16 percent respectively.

22 Eaves, op. cit.
<!-- Start of picture text --> = 70<br>= Ranges:<br>oS :-->: ee MAX<br>7 woe | 80%<br>— medimedian tfi<br>— i<br>— i i é<br>Sf min oc scene v =<br>SS4] 50 pre; aa = INDCs@ TM<br>nn gS<br>S ey<br>a 40 Fae<br>3<br>P= @ Pre-INDC scenarios (High Cancun pledge scenarios) until 2030<br>rd with const. policy thereafter (n = 31; Ampere HST P3 in IPCC ARS scenario database)<br>¥ © Min/max of conditional & unconditional INDC ranges, globally aggregated<br>oS vw Reductions below reference scenarios due to INDCs (median)<br>30<br>2000 2005 2010 2015 2020 2025 2030<br><!-- End of picture text -->
fuel savings. Of course, the amount of _injera_ batter is standardized (see Gebreegzhiaber et al., 2015 for additional details) and households may typically cook more or less than in the CCT. Total fuelwood savings estimates derived wholly from CCTs are therefore best viewed as indicative.

To adjust for such differences in cooking amounts and therefore get better estimates of total fuelwood savings, we incorporate _duration_ and _frequency_ of cooking events using electronic stove use monitors. One possibility for deriving stove use frequency information is to interview households as was done, for example, by Dresen et al. (2014) and Bluffstone (1998). However, with such survey data there is a very important tendency to overstate improved stove use frequency (e.g. see Thomas et al. forthcoming) and total fuelwood savings.

We avoid self-reported use and fuelwood savings by measuring the total number of actual cooking events per week and the total time households spend cooking using electronic stove use monitors discussed in detail in the following section. These devices measure the surface temperature of the Mirt stoves every 10 minutes allowing credible measurement of cooking event frequency. As cooking _injera_ typically takes about one hour, this logging interval is short enough that we believe we can make reasonable estimates (albeit with some nonsystematic estimation error) of household cooking duration and incorporate that information into our fuelwood savings estimates.

This feature also allows us to explicitly adjust – using measurements – for a variety of unobservable factors that affect cooking time. For example, bringing in measured cooking duration allows us to explicitly incorporate unobservables such as particularly hungry family members and cooks who are less attentive to their cooking fires. Both these factors – largely unobservable – increase baseline fuelwood use and therefore the savings from the Mirt stove.

# _2.2. Effects of Behavioral Interventions_

The literature suggests that the terms under which technologies are provided are important. Miller and Mobarak (2014) investigate the promotion of improved stoves in Bangladesh through social networks. They find a positive effect from learning through social networks, but a negative effect if a local leader promotes the stove (for similar findings see BenYishay and Mobarak, 2014; Munshi, 2004). Interestingly, Cohen and Dupas (2010) find that subjects who pay for treated mosquito bed nets use them more frequently than those who received them for free.

Studies have shown tendencies toward decreased use over time going back to Jones (1988). As Shankar et al. (2014) emphasize, the goal of promoting “improved” cookstoves should be on

**7 |** P a g e
they are not significantly different from those for larger firms.

Table 4 presents the estimates of Equation (2) for SPS and TBT provisions on mutual recognition (MR) (column (1)), harmonization (columns (2) and (4)), and transparency (columns (3) and (5)).<sup>25</sup> For SPS, the type of integration through MR versus harmonization provisions is an important factor behind the increase in smaller firms’ exports of agricultural and agro-food products.<sup>26</sup> Column (2) shows a boost in exports of agricultural and agro-food products for smaller firms selling to destination markets with which their country harmonizes SPS measures of 44 percent, compared to close to 20 percent for larger firms selling in those markets.<sup>27</sup> Chile, Colombia, and Peru have no TBT MR provisions in their PTAs and only one of their PTAs includes TBT harmonization provisions. Hence, the results in column (4) of Table 4 should be taken with caution as the identification reflects only the EFTA-Peru PTA of 2011.<sup>28</sup> Transparency provisions in the areas of SPS and TBT seem to also be important drivers behind the relatively larger increase of smaller firms’ exports compared to larger firms’ exports, as seen in columns (3) and (5) of Table 4. Specifically, the estimates in column (3) imply that, upon entry into force of a PTA with the maximum number of provisions for the transparency of SPS regulations (like the CPTPP or the Pacific Alliance), the agricultural and agro-food exports of smaller firms grow by 85 percent whereas those of larger firms do not increase significantly.<sup>29</sup> This finding is consistent with the idea that transparency provisions can reduce the level of uncertainty related to SPS and TBT measures, which can affect disproportionally small firms (Ghosal and Loungani, 2000; Ghosal and Ye, 2015) particularly in developing countries (Bloom, 2014).

> 25 Since none of the PTAs signed by Chile, Colombia, or Peru during our sample period include MR of TBT, the specification with _Prov_ being MR of TBT cannot be estimated.

> 26However, the effect of SPS harmonization provisions is very difficult to disentangle from the effect of SPS MR provisions, as all the PTAs including some MR provisions also include some harmonization provisions. 27The t-test indicates that the coefficients across smaller and larger size categories are significantly different (at a 95% confidence level).

> 28Moreover, the t-test shows the coefficients across smaller and larger size categories are not significantly different (at a 95% confidence level).

> 29The t-test indicates that the coefficients across smaller and larger size categories are significantly different (at a 95% confidence level).

17