rafmacalaba/data-use-mentions-extended huggingface dataset:
The full set of regressors is presented in appendix table A2. Lagged cognition is a significant predictor of math across the different specifications (i), (ii), and (iii). In addition, all four lagged readiness outcomes are significantly associated with literacy at school age in specification (i).

# IV. CONCLUSIONS

There are wealth gradients in child development that start early, change over time, and accumulate with age. Data from a longitudinal cohort of children living in Madagascar are used to document that, even in the context of a very low income country where more than 2/3 of the population lives below the poverty line, wealth gradients are substantial. The gradients are robust to the inclusion of a large set of explanatory variables, from lagged outcomes and early conditions to maternal education and endowments to the inclusion of parenting inputs.

Our four main findings are as follows. First, the SES gradients observed at preschool age are shown to persist into school age across all developmental domains tested. Substantial gradients in school readiness outcomes by household wealth are evident when the children were 3–4 years old; these gradients widen with increasing child age and flatten out by ages 9–10 for all domains. Second, the magnitude and evolution of the wealth gradient are found to vary by domain, widening earlier for receptive vocabulary and sustained attention (between ages three and six) and later for the cognitive composite and memory of phrases (between ages 7–8). As reported previously, when the children were 3–6 years, vocabulary and sustained attention demonstrate the largest gradients when these same children are 7–10 years after controlling for lagged outcomes, maternal endowments, and home environment. Third, an estimated 12–18% of the predicted wealth gap in school readiness at ages 7–10 can be accounted for by differences in home stimulation at the same age, even when controlling for early skills and initial endowments. Finally, there are also significant SES gaps in learning outcomes (math and
A further measure of gross fixed investment can be derived from the data included in the Main Economic Indicators of Industrial Enterprises. This publication gives the results of a monthly census of financial data of all industrial enterprises with sales of over a threshold value that changes over time. The available series includes the balance sheet data for the depreciated historic cost of fixed assets, the cumulated depreciation of the fixed assets and the original value of the assets. The latter series is the sum of the depreciated value of the assets and the cumulative deprecation of the assets. The net value of fixed assets cannot be used as a measure of the capital stock of industrial enterprises because the assets are valued at the historic acquisition cost, though assets acquired before 1998 were revalued to 1998 prices in that year. However, the difference between the original value of the assets at the end of two adjacent years represents the gross increase in fixed capital between the two years less the amount of fixed assets that were scrapped in that year. When assets are scrapped, as opposed to being fully depreciated, the depreciated value is removed from net assets, the original cost of the asset is removed from the original value series and a deduction is made from cumulated depreciation. The scrapping rate has been estimated by Holz (2006). He suggests that if the average asset life is 40 years (which appears reasonable given a depreciation rate of around 6% in the business sector), then the scrapping rate will be 0.2% per year. This scrapping rate has been used in the calculation of investment in the industrial sector using the following identity: Gross fixed capital formationt = Original value of assetst - original value assetst-1 + scrappingt

# _Sectoral estimates prior to 2002_

The analytical problem is how to combine the various sectoral investment series in way that generates a series that corresponds to the published overall data for gross fixed capital formation. The methodology adopted here differs for the periods before and after 2003, as definitions of sectors were changed in 2003 and more data on
is straightforward to calculate its parameters using input-output tables. Also, consumption shares give the consumer price index weights, _θ_<sup>_i_</sup> . We use Bureau of Economic Analysis input-output and consumption share tables to calculate the parameters of the labor demand equations<sup>8</sup> . The calculated parameters are reported in Table 5.

Unlike the estimation step, we need to define all aggregate shocks for the simulations. We assume that there is an unexpected shock-therapy trade liberalization in the metal manufacturing sector which decreases the metal output price 50%<sup>9</sup> . As we discussed previously, it is practically impossible to model the exact liberalization process in the metal sector as the trade policy has been changing dynamically. Our simulation exercise is an illustrative counterfactual. We normalize initial prices to one. After the metal sector trade shock, prices in the tradable sectors adjust endogenously over the transition, while non-tradable sectors’ output prices stay constant. Thanks to uniqueness of the equilibrium, and the concavity of relevant equations, the simulations are straightforward: After we guess initial values for _Vt_<sup>_i,s_</sup> and _L_<sup>_i,s_</sup> _t_ for _t_ = 1 _,_ 2 _, .., T_ , we calculate _w_<sup>_i,s_</sup> , then implied _Vt_<sup>_i,s_</sup> and _L_<sup>_i,s_</sup> _t_ by the wage flows using equations presented in the _Model_ section. Using an arbitrarily weighted average of the initial and implied values and labor allocations, we update the guessed values. We repeat this procedure recursively until we reach a fixed point and make sure that the economy reaches free trade steady state before time _T_ . We omit the details because the simulations are easy to replicate.

We consider two different specifications. Simulation I uses the restrictions we imposed to estimate the model with CPS (i.
784|
|Panel C: Placebo Test|||||||
|Sample:|||||||
|Fake Treated: Age in 2007|16-22|21-24|21-24|25-30|25-30|25-30|
|Not Exposed: Age in 2007|23-26|25-30|25-30|31-35|31-35|31-35|
|Fake Treated _×_ Treated Industries|0.003|0.002|0.015|0.012|0.016|0.121|
||(0.006)|(0.005)|(0.042)|(0.018)|(0.019)|(0.078)|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: The table presents the heterogeneous effects by exposure to the sanctions. Panel A presents estimated coefficients of _T reat×cohort_ ( _γ_ 1 in equation (6)) using observations from years 2013 and 2019. The dependent variable for attending any level of education is an indicator for whether an individual ever enrolls in a specified education level (including students and graduates). The dependent variable for completing high school and college is an indicator for whether an individual graduated. Panel B presents estimated coefficients of _T reat × cohort_ ( _γ_ 1 in equation (6). Panel C shows the results of a placebo test considering fake treatment cohorts. Heteroskedasticity-consistent standard errors accounting for clustering at the province and industry level in parentheses. p-values are calculated using wild bootstrap randomization inference (WBRI). Treated: oil and gas, energy supply industries. Synthetic control: information (0.148), education (0.169), and health (0.683) industries.<sup>_∗_</sup> Significant at 10% level;<sup>_∗∗_</sup> significant at 5% level;<sup>_∗∗∗_</sup> significant at 1% level.
the scope of the current paper, being cognizant of the potential impact of using the unit context model on small area estimates is important.

First, the bias that has been reported in the literature is relative to an assumed gold-standard unit (household) level model and the availability of up-to-date household-level census microdata. It is our view that if recent census data are available, the census-EBP method should be preferred. We argue, however, that in the absence of recent census data, the use of geospatial covariates may constitute a valid alternative for providing up-to-date small area estimates until data from the next census becomes available. Second, we have observed that the extent of bias in the unit context model depends on the method used to account for sample weights. In this paper, weights are incorporated following Guadarrama et al. (2018). This weighting procedure was implemented in a way that adjusts the estimates of the regression coefficients and random effects to account for sample weights but does not account for weights when estimating the variance components.<sup>6</sup> As shown below, this can cause significant differences in small area estimates which are larger for models with lower predictive power, which is typically the case with unit context models. In Section 4 we explore both weighted and unweighted versions of the unit context model to assess how this impacts the estimates. Third, noting that aggregation is unavoidable due to the way geospatial data are processed, it is worth mentioning that the geographic level at which geospatial covariates are processed and linked to survey data (grid size) impacts the estimation. Because of this and because geospatial covariates can only act as proxies for the kind of variables typically used to model income (or consumption), it is reasonable to assume that the unit context model may show lower levels of predictive power and higher uncertainty than the unit (household) level model. However, because the estimators of interest are aggregations of individual-level predictions, it is not obvious that the lower predictive power and higher uncertainty will substantially
A8, Appendix A).

# **_D2. Projected impacts under future climate change_**

We next provide projections of the effects of future temperature on poverty to better understand potential effects under different scenarios. To do this, we combine the model estimates in Tables 1 and 2 with data on simulated weather conditions at the subnational level taken from the recent released Coupled Model Intercomparison Project Phase 6 (CMIP6). Following Burke and Emerick (2016) and Kalkuhl and Wenz (2020), we generate temperature projections as follows. First, we use annual

> 16 For simplicity, we assume land degradation to be constant, but it could play a role in the poverty and environment nexus (Barbier and Hochard, 2018). Temperature may also affect poverty via different channels such as civil conflicts and labor productivity (for a review, see Burke _et al._ , 2015 and <mark>Somanathan</mark> _<mark>et al.</mark>_ <mark>, 2021).</mark>

> 17 The data is available at https://hub.worldpop.org/.

80
spatial dimension in its model specifications, which gives birth to the Spatial Computable General Equilibrium (SCGE) model. SCGE models are able to account for price differentiations between regions and specify transport costs as one of the determinants of trade flows between regions. Examples of this modeling approach can be found in Bachmann (2017), who models the impacts of free trade agreements on transport costs and trade flows from and into Canada. Johansen and Hansen (2016) proposed a model to analyze the impact of transport infrastructure development programs on wider economic impacts in Norway. Lee et al. (2013) studied the impact of a carbon tax on the global economy by focusing on containerized commodities using an SCGE modeling approach from the Global Trade Analysis Project-Environment (GTAP-E). The model encompasses 129 countries and 29 sectors and is calibrated on the 2007 base year of the GTAP version 8 database. Sheng et al. (2018) studied the impact of a global carbon tax applied to international bunker emissions on several economic indicators using an SCGE model. The economic indicators studied include import and export volumes, GDP growth, and real GDP of countries and the countries’ market share of their export commodities.<sup>6</sup> Shahrokhi Shahraki and Bachmann (2018) provided a comprehensive review of SCGE models that have been applied to the domain of transportation. According to their study, the representation of transportation networks in SCGE models could influence the accuracy of model results, especially when the analysis requires a high level of disaggregation. Furthermore, Tavasszy et al. (2002) also noted the pitfalls of the applications of SCGE models in transport, which include oversimplification in the specification of transport costs of sectoral productions. Specifically, aggregate statistics on the share of transport costs in product values are commonly used in CGE models while,

> 6 SCGE models do not produce GDP as its direct output, but rather a variation of a welfare indicator that can be translated into a GDP prediction with additional assumptions.

17
use these probabilities to assess mechanisms that impact treaties, thereby allowing institutions to develop mitigation to extreme events. The probability of non-compliance of a treaty obligation can help policy makers identify the relative risk and economic impacts. In addition, the estimated hydrological variability and flow probabilities provided by the BWI can be used to forewarn of future extreme events, thus minimizing their vulnerability and stress during periods of low or high water levels. Finally, we believe that the BWI provides appropriate and more accurate water variability data, compared to other predictive variables such 22
closer to the city core restrict access to economic opportunities and services among low-income households.

# **2. Data and background**

## **2.1 Data**

The analysis of this paper relies primarily on a recently collected household survey in Kinshasa. Conducted by the INS of the DRC under the World Bank’s statistical operation in the country, it is an integrated survey, consisting of two surveys, targeting individuals and households. The first part of the survey includes sociodemographic characteristics, education, health, living condition, employment, urban agriculture and non-agricultural household entrepreneurship. The second component is the household consumption survey.

The Kinshasa Household Survey was collected in two 18-day phases between mid-October and the end of November 2018 on a sample of 2,592 households. Given the outdated last population census—which was collected in 1984—the sampling method for the Kinshasa Household Survey was based on high-resolution satellite imagery. A two-stage stratified sampling design with equal allocation of first-degree EAs was used for the survey. Kinshasa had been divided into two clusters: urban and rural. The 23 communes/districts of the city were considered as urban and some areas of Maluku, as part of the survey, were considered rural.

Two criteria were used to stratify the city (a) the building density of EAs and (b) typology of Kinshasa areas. The stratification of EAs according to the density criterion was carried out as follows: 1) Low density; fewer than 10 buildings per hectare, 2) Medium density; from 10 to 20 buildings per hectare, and 3) High density; more than 20 buildings per hectare. Precarious neighborhoods are those not suitable for habitation. In the survey, precarious areas are defined based on their characteristics of dwellings and geographic and environmental features.

- **Precarious areas.** This is a neighborhood whose dwellings are built by the occupants on land acquired in undeveloped areas, on farmland and market gardens, or on uneven ground (exposure to erosion and flooding)
This macro picture is also supported by micro data. The Moroccan High Commission for the Plan (HCP) estimates that household consumption in real terms has increased at an average rate of 2.8 percent a year between 1999 and 2007 as compared to -1.8 percent for the previous decade. The same institution estimates that the poverty rate has declined at an annual rate of -7.3 percent as compared to a rate of +2.8 percent for the previous decade.<sup>6</sup> Comparing the 2001 and 2007 household consumption surveys shows that the poverty rate has declined from 15.3% to 8.9% and this has resulted in an effective reduction in the number of the poor from 3.4 million in 2001 to 2.7 million in 2007. In sum, one could argue that growth over the past decade has been pro-poor as well as pro-rural areas vis-à-vis the previous decade (Figure 4).

**Figure 4 – Official Poverty Rates Estimates 1991-2007**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>Poverty rate (%)<br>8<br>Number of poor (m.)<br>6<br>4<br>2<br>0<br>1991 1994 1999 2001 2004 2007<br><!-- End of picture text --> Source: HCP (2012). 1994 and 2004 are population census estimates made with poverty mapping techniques.

This change in fortunes for the poor can be traced back to a combination of public policies that have been implemented over the years. Starting from the mid-1990s, the government has launched ambitious macroeconomic reforms coupled with stabilization measures. The macroeconomic policies include regulatory and institutional improvements to attract foreign investments, price liberalizations, acceleration of the privatization process, better competition laws, a better framework for SME development and a progressive opening of
- 76 -

institutional effectiveness in changing economies. It seems reasonable to think, moreover, that it has been achieved through the combined influence of the other eight characteristics.

5.19 It is important not to over-generalize from this kind of analysis. Nevertheless, it does illustrate how these developing countries have developed training systems which have the characteristics necessary to adjust to changing economies. Significantly, these systems involve a variety of training modes, including secondary vocational schools that are often comparatively successful.

5.20 We know very little, of course, about the economic effectiveness of the different kinds of training institutions in these systems, although the Korea data demonstrate at least the possibility of good external efficiency for secondary vocational schools, and there is stronger evidence for the external efficiency of nonformal training in several regions. Post-secondary institutions have been reported to have high placement rates for graduates (Malaysia, Korea, Jordan, Algeria). Teacher training institutions have been able to place gradutates in teaching posts where salaries and conditions of work have been attractive.

5.21 Secondary vocational schools in middle income countries have been more effective than those in low income countries, particularly those in Africa. The explanation lies in the fact that many of the effective schools have acquired characteristics -- such as linkages with enterprises, incentives to attract and retain qualified instructors and students, good 'feedback" systems -- which are most often thought characteristic of nonformal modes. They exhibit, in short, what Dougherty (1988) has identified in his review of the literature as the "convergence of modes." Thus they may no longer be "public vocational secondary schools" in the stereo-typical sense, but rather be evolving towards a new institutional form. Further -- investigation into this hypothesis, of course, is needed particularly to see the extent to which these institutions have been freed of the more constricting features of traditional secondary education.

# C.onstraints on Effective Systems Evolution

5.22 In implementation terms, at least, investments in the development of
level, and _FinStructit_ is the measure of financial structure, capturing the relative importance of banks vis-`a-vis capital markets or the relative importance of foreign banks vis-`a-vis domestic banks. _ExtFin_ measures the industry’s external financial dependence. _FinDevit_ is a measure of financial development while _Dit_ and _Dst_ are country-year and industry-year fixed effects, respectively. Country-year fixed effects capture time-varying country characteristics, including endowment differences that can drive trade patterns and country-specific macroeconomic shocks that may affect domestic production and firm exports. In turn, the industry-year fixed effects absorb shocks to industries that are common across countries, including world demand or supply shocks for specific industries. Finally, _εist_ denotes the error term which captures some unobserved factors that may affect export activities, but that we expect to be uncorrelated with the explanatory variables in (1).

> 6 We use the June 2017 version of the database. For an overview of the data please refer to the 2013 Global Financial Development Report and Cih ́ak et al. (2012).

7
|169.51|222.21|260.91|
|... real input value ($/ha)|94.88|94.04|81.83|124.50|82.42|97.36|77.08|87.12|115.63|134.75|
|Net revenue (US$/ha)|345.9|363.5|311.5|417.8|216.6|496.0|294.2|379.6|345.3|368.0|
|Sold anything in market|0.77|0.78|0.76|0.75|0.80|0.71|0.58|0.80|0.85|0.89|
|... if yes, % of value|0.57|0.57|0.57|0.58|0.59|0.50|0.69|0.55|0.57|0.51|
|Cash flow (US$/ha)|-25.6|-30.3|-18.9|-36.2|-6.6|-38.3|89.1|8.1|-99.5|-188.6|
|% w. negative cash flow|0.46|0.43|0.46|0.51|0.47|0.51|0.40|0.43|0.50|0.56|
|No. ofobs. (farms)|1,714|852|213|367|89|192|413|632|419|250|

_Source:_ Own computation from MAPF/WB/EU small producer survey _Note:_ Cash flow is defined as the value of output sold minus cost of purchased inputs 25
for the likely poverty and inequality consequences of the Canal expansion.

The paper is organized as follows. The next section discusses the data and presents some background information on Panama and the Canal shock. Section 3 summarizes the model framework, while section 4 discusses the macro and micro results of our simulations. Section 5 offers concluding remarks.

# **2. Background**

## **_2.1. Panama before the Canal Expansion_**

Panama has been often characterized as a dual economy, consisting of a dynamic, high-wage export-oriented segment and a rigid, low-earning domestic-oriented segment.<sup>1</sup> Service sectors dominate Panama‘s economy, accounting for 77 percent of total value

> 1 The data used in this exercise come from an updated 2003 Social Accounting Matrix (SAM) for Panama as well as two Encuesta de Condiciones de Vida (ECV) Panama household surveys for 1997 and 2003. The SAM has been constructed specifically for the purposes of this paper, with particular attention devoted to the identification of labor and capital remuneration in both formal and informal activities (Annex 1). Furthermore, considerable efforts have been devoted to improving consistency between macro (SAM) and micro (survey) data, although a full reconciliation of the two data sources remains beyond the scope of this paper. The SAM data is summarized in the table presented in Annex 1, which shows the structure of final demand and value added at the level of SAM accounts.

2
, unpaid, and marginal activities may be underreported in the Current Population Survey (CPS), among others because people may not think of their activity as work, respondents were asked in a debriefing study to categorize different hypothetical situations as “work”, “job”, “business”, and so on. While the majority were able to classify consistent with CPS definitions, large minorities gave incorrect answers for each vignette. Thirty eight percent of the respondents for instance the advice to policy makers. Nevertheless this is still the dominant approach in the analysis of education and related policies, in particular in low income countries, and it is therefore useful to analyze their sensitivity to survey methods.

6