rafmacalaba/datause-encoder-data huggingface dataset:
The individual NTB effects for each RTA are summarized in Figure 3, Panel (a). On the x- axis, the 258 RTAs that entered into force between 2000 and 2015 are displayed and ordered by the size of the NTB effect, the y-axis shows the corresponding estimated NTB effects. For the significant agreements, 95% confidence intervals are included. As a robustness check, we estimate Equation (6) using the data starting in 1989 (only manufacturing and agriculture). Panel (b) plots the two sets of NTB coefficients against each other. If all of them were identical, they would perfectly align on the 45-degree line. Eyeballing already tells us that the two estimates are very similar. In fact, the correlation between the two distributions of the NTBcoefficients is 0.85 indicating that the results are robust to extending the period of observation. The comparison between the two sets of coefficients provides an additional interesting insight as it highlights the role of services trade: The NTB effects of the short panel, which includes services trade, are more often larger than the NTB effects of the long panel (only agriculture and manufacturing).

# **5.2 Predicting NTB Effects Using the Lasso Method**

Next, we ask whether we can find persistent patterns in the content of TAs that help predict the NTB effects—positive as well as negative ones. We would like to answer the question of whether there is a systematic difference in the content of the TAs that generate high NTB effects compared to those that have no or even a negative NTB effect. To do so, we need detailed information on the content of TAs. The World Bank has put together a new dataset that analyzes in detail 18 key policy areas for roughly 200 RTAs (Mattoo, Rocha, et al. 2020). For each agreement and policy area, the database provides granular information on the specific provisions covering stated objectives and substantive commitments, as well as aspects relating to transparency, procedures, and enforcement.<sup>13</sup> Mattoo, Rocha, et al. (2020) have coded almost 1,000 distinct characteristics of the content of trade agreements.<sup>14</sup> With this new and detailed data on the content of RTAs at hand we want to determine next if any specific combination of provisions is more prevalent in RTAs with a high NTB effect. To remove ourselves from the model selection problem, we use the Least Absolute Shrinkage and Selection Operator (LASSO) to determine the most important provisions.

Due to the high dimensionality of the covariates, OLS would surely overfit the data. LASSO offers a way out of this by regularizing the estimation process. More formally, we estimate the parameters by solving the problem

> 13 Breinlich et al. (2021) provide a good summary of the data.

> 14 We treat missing observations as not covered by the RTA and set it therefore to zero.

22
Cotton is a very important crop for the Francophone<sup>African</sup> (FPA) countries. As a percentage of agricultural<sup>export revenues,</sup> cotton export revenues ranged from<sup>6' for C8te d'Ivoire and</sup> Cameroon to 68% for Burkina Faso in 1990 (see Table<sup>1). As can be</sup> seen from this table, reliance on cotton<sup>exports has increased over</sup> time for the majority of FPA cotton producers.

The significant share of cotton in agricultural<sup>(and total)</sup> exports for many FPA countries, implies an exposure<sup>to the</sup> volatility in cotton prices. Moreover, by guaranteeing a fixed price to the farmers, FPA governments assume the<sup>risk from cotton</sup> price fluctuations.

Table 1: Francophone Africa: Cotton's Share<sup>of Agricultural Export</sup> Revenues in the Main Cotton Producing Countries

||1964-70|1971-80|1981-90|
|---|---|---|---|
||_<br>_|(%)|__|
|Benin|13|27|59|
|Burkina Faso|12|36|68<br>|
|Cameroon|8|4|6|
|Central African Rapublic|39|26|26|
|Chad|69|45|33|
|C8te d'Ivoire|1|3|6|
|Mali|13|37|25|
|Senegal|0|S|9|
|Togo|5|5|35|

Source: Calculated from data on FAQ trade tapes.<sup>1991.</sup> 'We would like to thank Jim Steel of REFCO for providing<sup>us</sup> with data and information. We would also like to thank Ronald Duncan, Vikram Nehru, and Luc De Wulf for valuable comments.
not only have implications on import duty but also on level of import tax , import VAT, and sales tax on domestically sold imported commodities; and the level of government transfer from the rest of the world.

# **B. Time Trend in the Structural Composition of the Ethiopian Economy**

**Table A.1. Sectoral composition of GDP in Ethiopia**

||**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**1999/**<br>**00**|**2000/**<br>**01**|**2001/**<br>**02**|**2002/**<br>**03**|**2003/**<br>**04**|**2004/**<br>**05**|**2005/**<br>**06**|**2006/**<br>**07**|**2007/**<br>**08**|**2008/**<br>**09**|
|**Agri-**<br>**culture**|55.3|56.4|54.5|49.8|52.1|52.6|52.3|51.2|49.5|47.8|
|**Industry**|9.7|9.5|10.2|11.0|11.0|10.7|10.6|10.4|10.3|10.2|
|**Services**|37.0|36.3|36.9|39.9|38.0|38.0|38.6|39.8|41.6|43.1|
|**Total**|100.5|100.5|100.2|100.3|100.3|100.3|100.5|100.5|100.7|100.7|
|**Less:**<br>**FISIM**|0.6|0.6|0.4|0.4|0.5|0.5|0.6|0.6|0.7|0.7|
|**GVA**<br>**at**<br>**Constant**<br>**Basic**<br>**Prices**|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|100.0|
|**Sector**|**20**<br>**20**|**02**<br>**09/10**|**2003**<br>**2010/11**|**2004**<br>**2011/**|**12**<br>**20**<br>**20**|**05**<br>**12/13**|**2006**<br>**2013/14**|**2007**<br>**2014**|**/15**<br>**2**<br>**2**|**008**<br>**015/16**|
|**Agriculture**|**46**|**.5**|44.7|43.1|42|.0|40.2|38.7|3|6.7|
|**Industry**|**10**|**.3**|10.5|11.5|13|.0|13.8|15.0|1|6.7|
|**Service**|**44**|**.1**|45.5|45.9|45|.5|46.6|47.0|4|7.3|
|**Less: FISIM**|0.|7|0.7|0.6|0.|5|0.6|0.6|0|.7|
|**GVA**|**at**<br>10|0|100|100|10|0|100|100|1|00|
|**Constant B**<br>**Prices**|**asic**||||||||||

_Source: NPC (2016)_

# **C. Carbon Content Analysis (Input Output Multiplier Approach)**

A version of input-output multiplier analysis, a variant of Leontief (1970), that recognizes the fact that activities produce multiple products and products are produced by multiple activities is introduced in Arndt, _et al_ . (2013). We follow this approach to estimate the carbon content of activities and products in the Ethiopian economy. Suppose we define matrix (X) that consists of N activities and K commodities. Suppose we also have an (N+K x N+K) matrix A that consists of NxN zeros in its upper left quadrant, NxK coefficients in its upper right quadrant that signify the proportion of domestic supply of the commodity by the activity, aa (NN+ KK) xx 1 KxN matrix of coefficients in its lower left quadrant that describe the proportion of the commodity that is used to produce final output of an activity. Suppose we also define the final demand of the product of an activity and commodity by an (N+Kx1) matrix F where the first N elements represent the amount of the activity that is self-consumed. The rest, i.e. K, represents the amount of the commodity that is consumed by households, government, saved, or exported to the rest of the world.

26
response efforts and a rapid recovery (that is, the countercyclical role of fiscal policy for economic resilience), but fiscal policy is also crucial in pre-disaster planning, as it can incentivize investments in long-term resilience, transfer risks, and provide the fiscal space for debt-financed crisis response. Next, we highlight the link between climate risks and fiscal policy empirically, aiming to quantify the impact of climate shocks on output given existing fiscal policies. Using data from the EM-DAT database for the number of people killed or affected by natural disasters, our analysis indicates that economies with constrained fiscal space, proxied by high government debt, procyclical policies, and running persistent deficits, experience more pronounced negative effects from natural disasters, indicating a reduced ability to respond effectively.

We then use the World Bank's macro-fiscal model (MFMod) to study the effects of natural disasters on economic resilience and fiscal resilience. MFMod is one of the World Bank’s main forecasting tools and provides the basis for tailored country analysis. Its primary purpose is to generate countryspecific forecasts of the main macroeconomic and fiscal variables and to assess the potential effects of policy changes. Importantly, the MFMod framework permits the evaluation of policy (and climate-related disaster) impacts alongside traditional economic indicators like economic growth, fiscal sustainability, price inflation, and stability of the external account. Hallegatte, Jooste, and McIsaac (2024) use MFMod to study macroeconomic consequence of natural disasters with a focus on monetary policy. Harnessing the detailed representation of the national accounts in MFMod, we focus on the fiscal aspects of natural disasters. Thus, we are combining macroeconomic modeling with the “financing gap” analysis of disaster research (Mechler et al. 2006).

We model the impact of a climate shock on a small open economy with strong reliance on tourism and vulnerability to climate events. We simulate the effect of a 5 and 10 percent destruction shock to the country’s existing capital stock, emulating the effects of a natural disaster. We explore several ways of funding reconstruction efforts (debt-financing, redirection of existing current and capital expenditure, or increases in revenue) and we test for the presence of threshold effects by doubling the size of the disaster shock. This leads to more adverse GDP effects within five years and persistently disproportionately higher effects in the long run. The effects vary across modes of funding reconstruction efforts. In the absence of reconstruction efforts, we find a doubling in GDP loss throughout the simulation period. Hence, the presence of threshold effects is due to the government’s (in)ability to absorb the larger climate shock. To test for risk compounding, we repeat this exercise in the presence of other macroeconomic shocks and find no evidence that this creates a different response of the economy to the climate shock.

2
of weak instruments using the critical values reported in Stock and Yogo (2005).<sup>8</sup> In addition, the variables are also found to be orthogonal to the error process of financial inclusion on the basis of the Hansen J statistic for over-identifying restrictions (column (3) for each of the countries). Thus, the instrument set is deemed valid for the purpose of the current exercise. Finally, given these findings, we can test for whether international remittances is an endogenous variable in the first place. This is essentially a postmortem type of test to see if we needed to instrument for international remittances in the first place. The test is based on C-statistic and follows a chi-squared distribution under the null hypothesis that the endogenous regressor can be treated as exogenous and, therefore, no need for instrumenting in the first place. In almost all the cases, except marginally so for Senegal, the test does not reject the null hypothesis of exogeneity of the international remittances variable. This means that endogeneity is not a serious issue in the first place and the baseline regression results are generally valid.

Table 6 presents the estimation results of the second stage regressions. For each country, column (1) provides estimation results, using the weighted per capita income of the migrant destination as the instrument; column (2) shows results of the regressions using employment status of the international migrant as instrument, while column (3) provides the results using the two instruments jointly. In all the regressions, standard errors are clustered at the regional level and regional fixed effects are controlled for. As can be seen, for Kenya, international remittances enters with positive and significant coefficient in the three specifications. Interestingly, the size of the coefficient is essentially similar to the baseline regressions. The results for the other control variables are also identical to the estimates in the baseline regression. For Uganda as

> 8 The critical value for the 10% (15%) maximal IV size is 16.38 for one instrument. When remittances is instrumented by the two instruments, the corresponding values are 19.93(11.56). The test statistics reported in the table are higher than the critical values of the 10% maximal IV size, except in the first column for Senegal, which is significant at 15%, indicating that the instruments used are not weak enough to distort the results of the IV regression.

11
# Appendix C: Sources for health spending benefit incidence analyses

Appendix Table C-I: Relative value of the benefits (subsidies) of public spending on health by household income or consumption per capita quintile.

|||Relative value of be|nefits (qI norm<br>|alized to 1)<br>||||
|---|---|---|---|---|---|---|---|
|Country|Year||Poorest|q2|q3|q4|Richest|
|Brazil|1990|Public spending on health|1|2.25|3.75|3.13|2.50|
|Bulgaria|1995|All health<br>Primary facilities|1<br>1|1.30<br>1.09|1.64<br>1.33|2.04<br>1.54|1.99<br>1.32|
|||Hospitals|I|1.45|1.86|2.40|2.48|
|Colombia*|1992|Public health system|1|1.03|0.78|0.77|0.68|
|||ISS (Institute Soc. Sec)|1|2.59|2.24|_1.55_|1.24|
|Ghana|1992|All health|1|1.33|1.61|1.84|2.84|
|||Primary facilities|1|1.64|1.82|2.21|2.97|
|||Hospital outpatients|1|_1.15_|1.33|1.45|2.67|
|l__________|________|Hospital inpatients|I|1.34|1.91|2.17|3.00|
|Honduras*|1992|FHIS (Soc. Inv. Fund)<br>Hospitals|1<br>1||0.71<br>0.47||.357<br>2.84|
|l_________||Primaryhealth|1<br>_||0.89||.778|
|Indonesia|1987|Total|_|1.22|1.56|2.25|2.42|
|||Hospital|1|1.40|2.10|3.14|4.27|
|||Public healthcenter|1|1.07|1.14|1.32|.949|
|Kenya|1992|Total|1|1.20|1.57|1.51|1.70|
|(nural)||Hospitals|1|1.26|1.72|1.71|1.99|
|||Health centers|1|.950|.967|.692|_.550_|
|||Dispensaries|1|1.67|1.33|1.11|1.11|
|Madagascar|1993/94|Public health care|1|1.53|1.12|2.05|2.44|
|Mongolia|1995|Health subsidy|I|1.11|1.06|1.09|1.34|
|Vietnam|1993|All|1|1.34|1.78|1.87|2.46|
|||Commune health centers|1|1.50|1.30|1.00|.500|
|||Hospital outpatients|1|1.48|1.67|2.42|4.18|
||________|Hospital inpatients|I|1.28|1.89|1.69|1.84|

Notes: * Persons are assigned to quintile on the basis of the ranking of households (i.e. the number of individuals in each quintile is non-constant). Brazil: groups defned relative to the minimum rage; the groupings are <1/4, 1/4-1/2, 'h-I, 1-2,>2 times the minimum wage. The population distribution across these groups (in percent) is 11,17,23,22,27.

Honduras: categories are poorest, intermediate, least poor whose distribution is 31,19,50 percent of households Nicaragua: categories are extreme poor, poor, non-poor, whose population distribution is 19.4, 30.9, 49.7 percent. See Appendix 3 for sources.

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# Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach

Tito Yepes, Justin Pierce, and Vivien Foster

## **Acknowledgments**

This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. Financing for AICD is provided by a multi-donor trust fund to which the main contributors are the Department for International Development (United Kingdom), the Public Private Infrastructure Advisory Facility, Agence Française de Développement, and the European Commission. Inquiries concerning the availability of datasets should be directed to vfoster@worldbank.org.

The authors thank Cesar Calderón for generously providing some of the data used in our analysis of the interplay of infrastructure and growth.
<!-- Start of picture text --> Benin Burkina Faso Cameroon Chad<br>Côte d ’ Ivoire Kenya Malawi Mali<br>Mozambique Nigeria Senegal Tanzania<br>Togo Uganda Zambia Zimbabwe<br>1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020<br>Year<br> Monopsony Regulation<br> Low Compet. Strong Compet.<br>1000<br>500<br>0<br>1000<br>500<br>0<br>1000<br>500<br>0<br>Production (FAO, 000 Tons)<br>1000<br>500<br>0<br><!-- End of picture text --> Figure 2: Production and market organisation in African cotton sectors after the independances
middle and upper-middle income countries, the formal Engel slope falls, contributing to the decrease in progressivity.

In Panel B of Figure 6, we study progressivity of the de jure food exemption in the unrealistic setting with perfect enforcement and no informal consumption (scenario #2). The de jure food exemption is substantially less progressive than the de facto informal sector exemption (scenario #1) in the poorest countries, while both scenarios achieve similar levels of progressivity in upper-middle income countries. This is because the non-food budget share is much larger than the formal budget share in the poorest countries (making formal consumption a much better tag for household income than nonfood and much less than formal shares over consumption), grows budget development (Figure 4). Finally, by comparing Panel C (scenario #3) to Panel A (scenario #1), we find that de jure exemptions have no effect on progressivity in the poorest countries once the de facto exemption of the informal sector is taken into account. It has a positive impact however in upper-income-countries. This is because the formal food Engel curve is roughly flat in poor countries, and downward sloping among the richest countries in our sample (Figure 4).

# **4.3 Extensions and Robustness**

**Pass-through of taxes in the informal sector** Our analysis thus far assumes zero passthrough of taxes to prices of informal varieties. This assumption may not hold, for several reasons. First, if the consumption tax is a VAT, informal retailers may pay taxes on their inputs from formal suppliers. Second, competition between formal and informal retailers could lead informal retailers to tax increases to We relax pass through prices. this assumption by allowing some pass-through of taxes to informal prices. In Appendix C.6, we show that under a VAT system the pass-through of taxes to informal prices is equal to the share of formal input costs in informal retailers’ total input costs. Using the Mexican census data described above we find that this share is on average 10% among all informal retailers.<sup>29</sup>

Table A6 summarizes our progressivity results under the assumption of a 10% pass-

> 29In the 2013 Census, 85% of retailers are informal by not reporting any payment on VAT. Among informal retailers, only 8% report paying VAT on inputs, which applies on average to 40% of their intermediate input purchases. The informal retailers that report positive VAT on inputs account for 25% of all informal sales. Combined, this leads to our estimate of 10%.

18
observable characteristics among surviving firms. Although a part of the training was intended to encourage women to expand into other lines of business, in the round 5 data we find only a small and statistically insignificant impact of the training or training plus grant on change in product lines, introduction of new products or services, or starting a second business since baseline.<sup>15</sup> Given these findings, we proceed to analyze business outcomes for the group of surviving firms.

Table 4 examines whether the improvements in business practices following the training, along with the cash grants, lead to improvements in business outcomes. We examine the impact of the two treatments on monthly profits (panel A), monthly sales (panel B), and capital stock (including inventories and raw materials but excluding land and buildings, panel C). Panel D examines changes in hours worked in the business by the owner. For each outcome we first show the results using all follow-up rounds pooled together to achieve greatest power, and then examine the effects round-by-round. Profits, sales and capital stock are all noisy variables, and so in addition to showing the impact on the raw levels in column (1), we use two measures which are less susceptible to outliers. The first, in column (2), involves truncating the data at the 99<sup>th</sup> percentile of reported levels. The second, in column (3) takes the log of the outcome of interest. In panel A, we report both the ITT and the TOT for profits. For sales, capital stock, and hours we just report the ITT estimates. Approximate TOT estimates can be obtained by dividing the ITT by the proportion of firms attending some training (0.70). Finally, Figures 4A and 4B show the cumulative distribution functions of profits by treatment status for round 2 and round 5 respectively. These allow us to better assess in which parts of the distribution effects occur.

The results show that business training alone does not improve profits, sales, or capital stock of current firm owners, or change the number of hours the owners spend working in their businesses. The CDF for profits of the training only group lies almost entirely on top of that of the control group, and the point estimates of the impact on profit are actually negative in most survey rounds. We do not find firms assigned to training only using significantly more, or significantly less, capital stock, with the point estimates small relative to the mean. The fact that profit does not increase suggests the owners are not substituting skills for capital. Instead, they appear to be using the same amount of capital to generate the same level of sales and profits.

> 15 The point estimates are 5.7-6.7 percentage point increases in the likelihood of changing or adding product lines relative to a control group mean of 16.9 percent. An F-test that the two treatment effects are jointly zero has p-value 0.22, and neither is individually significant at standard significance levels.

18