rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. Introduction**

In-service teacher training is an essential component of Ecuador’s teachers’ professional development strategy. However, available training interventions offered by the state do not necessarily respond to the teachers’ skills gaps. This occurs because available training programs do not systematically rely on diagnostics of the teachers’ needs. This paper proposes a roadmap for improving the design of in-service teacher training in Ecuador using available data from student and teacher assessments. Current teacher assessments in Ecuador have an active cognitive component capable of assessing the level of knowledge of teachers in the field they teach. They provide valuable information for determining the specific areas of knowledge that teachers have problems with. Coupling such information with available data on student academic achievement, the state could develop not only more pertinent in-service training programs, but also target interventions to teachers that serve the student populations with the greatest needs.

Many countries in Latin America and the Caribbean (LAC) face shortages of good teachers, which undermines the capacity of their systems to improve student achievement in the short, medium, and long term (Rivkin, Hanushek, and Kain 2005; Hanushek and Woessmann 2006; Figlio and Kenny 2007; Chetty, Friedman, and Rockoff 2014). Individuals who become teachers in LAC, most of whom are female, tend to be socioeconomically vulnerable and to underperform in university entrance exams compared to the average university student (Bruns and Luque 2014). Many structural factors explain this phenomenon, such as the lack of incentives to choose teaching as a profession and high turnover of teachers (Ingersoll 2004; Woessmann 2011; Muralidharan and Sundararaman 2011). Moreover, salaries for teachers in the LAC region tend to be substantially lower than in the average profession (Moura-Castro and Ioschpe 2007), which limits the capacity of the sector to attract the best professionals.

Despite remarkable progress in teachers’ educational attainment (at present, most teachers in the LAC region have completed a tertiary degree), graduates from the teaching profession enter classrooms with significant cognitive and pedagogical
# **8 Appendixes**

## **Appendix 1: Data Sources**

|**Data Type **|**Source**|
|---|---|
|Baseline population by age and sex, 2010|World Population Prospects|
|Baseline age-specific fertility rates, 2010-2100|World Population Prospects|
|Years of education by 5 year age-sex groups, 2010|Barro & Lee, 2013|
|Adult height by 5 year age-sex groups,|Liberia DHS, 2013|
|Labor force participation by 5 year age-sex groups, 2010|ILO|
|Sector- and gender-disaggregated earnings|HIES 2014|
|Hours devoted to domestic chores and housework|UN Women, 2019|
|Hours devoted to collecting firewood or fetching water|HIES 2014|
|Output, 2005|WDI|
|Output, 2010|WDI|
|Oil Output, 2010|WDI|
|Capital stock, 2010|WDI|
|Agricultural land, 2010|WDI|
|Proportion of GDP between sectors, 2010|Abid, 2016|
|Proportion of labor between sectors, 2010|ILO|

## **Appendix 2:  Departures from CKW 2017**

### **Non-Tradable Production**

It is assumed that there is no overlap between (1) hours devoted to domestic chores and housework and (2) hours devoted to collecting firewood (or charcoal) or fetching water.

The total daily female contribution through non-tradable production (NTPFt) is given by: Where POPft = female population aged 15-100 at time t hf1 = share involved in fetching water/firewood and housework and domestic chores ρf1f = hours devoted to fetching water/firewood by POPF1t × hF1 ρF1h = hours devoted to housework and domestic chores by POPF1t × hF1 W. infft = hourly wage at time t in the traditional sector.

### **Labor Allocation across Sectors by Gender**

Our gender-specific labor model requires that the modern sector and traditional sector wages, which endogenously adjust by gender G=
goods (Behar & Venables, 2011). For this reason, multilateral and donor organizations have sought to view “aid-for-trade” packages as a promising new developmental tool (Huchet-Bourdon, Lipchitz & Rousson, 2009).

Does such assistance work? Economists have studied the potential impact of transport and other costs on trade using gravity since at least Tinbergen (1962).<sup>1</sup> This paper makes a substantive contribution to our understanding of the importance of logistics for developing countries because we use a new World Bank index, which o¤ers a number of advantages. It draws on a wide range of criteria, has broad country coverage from a single source, and is based on detailed evaluations provided by logistics professionals (Arvis, Muster, Panzer, Ojala & Naula, 2007).

This paper also makes a methodological contribution to the estimation and interpretation of gravity models and hence our understanding of the importance of transport costs for trade. We develop a new gravity model, which shows that standard approaches would produce an almost three-fold exaggeration of the typical impact of such factors for developing countries.

We uncover this dramatic exaggeration because our novel gravity modelling approach simultaneously accounts for two issues, namely multilateral resistance ...rm heterogeneity. Regarding the ...rst issue, Anderson & van Wincoop (2003) show that it is not just bilateral trade costs, but those costs relative to multilateral trade costs, captured by price indices, that are relevant for predicting bilateral trade ‡ows. In particular, imports by country i from country j, Mij, are an increasing function f (�) of, inter alia, bilateral trade costs tij relative to the product of the two countries’price indices Pi � Pj, such that Anderson & van Wincoop call the price indices multilateral resistance because they work to aggregate trade costs across the two countries’multiple trading partners. Omitting controls for multilateral resistance (MR) can lead to biased coe¢ cient estimates. More importantly, it can lead to grossly
Perotti, R. (2004). Public Investment: Another (different) Look. Mimeo, Università Bocconi.

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Puig, J. (2014). Multiplicador del Gasto Público en Argentina. _Económica_ 60, 188‐210.

- Qiang, C. Z. (2010). Broadband infrastructure investment in stimulus packages: Relevance for developing countries. _Info (Cambridge, England), 12_ (2), 41. doi:10.1108/14636691011027175

- Ramey, V. A. (2011). Identifying government spending shocks: It's all in the timing. _The Quarterly Journal of Economics, 126_ (1), 1-50.

- Ramey, V. A. (2019). Ten years after the financial crisis: What have we learned from the renaissance in fiscal research? _Journal of Economic Perspectives_ , Vol. 33, No. 2. 89-114.

- <mark>Ramey, V. A. & Zubairy, S. (2018). Government spending multipliers in good times and in bad: Evidence from U.S. historical data.</mark> _<mark>Journal of Political Economy</mark>_ <mark>, vol. 126, pp. 850– 901.</mark>

- Richarson, D. (1998). Asian Financial Crisis. Current Issues Brief 23 1997-98. https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parliamentary_Library/Publi cations_Archive/CIB/CIB9798/98cib23 (Last accessed in February 4, 2021).

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economics at the University of Cape Town, School</u> of Economics (E-mail: jfedderk@commerce.uct.ac.za). The paper builds on and extends the earlier related work by Bogetić and Fedderke (2005 a,b). Helpful discussions with Ioannis Kessides, Anton Eberhard, Antonio Estache, Paul Collier, Alan Gelb, Ritva Reinikka, Cecilia Briceno-Garmendia and Luiz Maurer, and research assistance by Andrew Hill are gratefully acknowledged.

1 For example, Easterly and Serven (2004) show that about one-fifth of Latin American growth underperformance relative to East Asia was directly related to underinvestment in infrastructure. Also shown was that sub-Saharan Africa's poor growth performance was in part related to underinvestment in electricity and telecom infrastructure (Esfahani and Ramirez 2003). It is estimated that if Africa had enjoyed Korea's quantity and quality of infrastructure, it would have raised its annual growth per capita by about 1 percentage point (Eustache 2005). The recent World Development Report (World Bank 2004) on investment climate documents that about half of Sub-Saharan African entrepreneurs cite inadequate power supply as a major constraint to their growth, well ahead of some other constraints such as corruption. Also, Reinikka and Svensson (1999, 2002) find, using a firm-level data, that inadequate provision of public capital (especially infrastructure) reduces private investments.

2
equality of means in column 8 show only one difference out of 9 (number of no-extension passes used) is statistically significant at the 5% level.

We conclude that there is no evidence of substantial differential selection on unobservables, at least in pass use. Below we will show similar null results for default rates. We thus pool across initial offers and focus henceforth on the effect of the contract, comparing borrowers of the standard loan and flexible loan (irrespective of the initial offer).

This lack of selection refutes the idea that there are many profitable entrepreneurs who reject the standard loan but would accept the flexible loan.<sup>8</sup> This result contrasts, however, with the finding of Barboni and Agarwal (2023) that individuals who accept a flexible loan are more financially sophisticated and have considerably more income volatility. Why is there no selection in our case? Data from the lender phone survey of clients indicate that lack of information cannot be an explanation. Panel A of Appendix Table 7 reports almost all flexible credit clients (98%) understood the use of passes. Unlike Barboni and Agarwal (2023) that required a month-long lag between communication and actual use of the pass, our lender’s passes could be used immediately and thus borrowers maybe were more subject to temptation or procrastination in repaying the loan. In addition, unlike most other studies that introduce flexibility, our sample consisted exclusively of new clients who were perhaps less financially disciplined or had on average weaker internalized repayment norms (both because they have not had as much experience learning to repay loans, and because the lending process has not yet filtered out borrowers who are predilected to default).

In addition, the limited use of passes at the start of the loan is not consistent with the idea that flexible credit clients want to use the product to make larger initial investments. Instead, clients might be reacting to business opportunities as they arise or to unexpected negative shocks to business or household finances.

Appendix Figure 2 shows pass use
in broad money supply,<br>due to the increase in the CRR. The acceleration is evident even after controlling for GDP<br>growth in the regressions in Table 2.<br>**Table 2: Money Supply during Capital Flow Surge**<br>**(Quarterly data from 2000-2007)**<br>0<br>1997-98<br>1998-99<br>1999-00<br>2000-01<br>2001-02<br>2002-03<br>2003-04<br>2004-05<br>2005-06<br>2006-07<br>2007-08<br>2008-09<br>2009-10<br>2010-11<br>2011-12<br>2012-13<br>2013-14<br>2001-02<br>2002-03<br>2003-04<br>2004-05<br>2005-06<br>2006-07<br>2007-08<br>2008-09<br>2009-10<br>2010-11<br>2011-12<br>2012-13<br>2013-14|
|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>% growth in<br>% growth in<br>% growth|
|Dependent<br>Variables<br>% growth in<br>reserve<br>money<br><br>% growth<br>in reserve<br>money<br><br>reserve money<br>supply in<br>circulation with<br> <br>reserve<br>money supply<br>in circulation<br> <br>in broad<br>money<br>supply<br><br>% growth<br>in broad<br>money<br>|
|supply<br>supply<br>public<br>withpublic<br>(M3)<br>supply (M3)|
|Surge dummy =1
5 recipient countries<sup>2</sup> , empirical studies still lag behind and have focused mostly on growth, inequality and poverty, leaving issues of macroeconomic stability largely uninvestigated.

Relying on the idea that remittances could relax financial constraints and favor investment, few scholars have run cross-country growth regressions. The results are mixed: Faini (2002, 2004) finds a positive, but not very robust, relationship between growth and remittances, while Chami, Fullenkamp and Jahjah (2003) find the opposite; using instrumental variables to account for the potential endogeneity of remittances, IMF (2005) finds no statistically significant relationship. The mechanism through which remittances can positively affect growth can be better detected in micro-econometric studies based on household-level data. On the basis of recent and quite accurate evidence, surveyed by Lopez Cordova and Olmedo (2005a), the positive impact of remittances on education and entrepreneurship at the household-level is at this stage widely acknowledged<sup>3</sup> .

Remittances could instead finance basic consumption needs. Along these lines, crosscountry evidence by Adams and Page (2003), Adams (2004) and IMF (2005) shows that indeed remittances have a clear-cut poverty-reducing effect. Similar results are found by Esquivel and Huerta-Pineda (2005) and Lopez Cordova (2005) using a Mexican household survey and a crosssection of Mexican municipalities, respectively. In the same spirit, remittances help improve health conditions (Amuedo-Dorantes and Pozo, 2004a; Hildebrandt and McKenzie, 2004; Dureya et al., 2005; Lopez Cordova, 2005).

The first attempt to link remittances and volatility appears in IMF (2005), which finds lower volatility of aggregate output, consumption and investment in countries with larger remittance inflows. Amuedo-Dorantes and Pozo (2004b) find that remittances can cause an appreciation of the real exchange rate; as a result of this loss of price competitiveness, export flows might decrease (Lopez Cordova and Olmedo, 2005b).

> 2 Docquier and Rapoport (2005) provide a comprehensive and clear survey
Cameroon due to the tariff reduction on rest of world imports. Given that Cameroon and its partners start from the common external tariff of UDEAC and move to the common external tariff of CEMAC, we assume that all partners are cutting their external tariffs by about the same percent as Cameroon (Cameroon is cutting its overall tariff from 20.9 to 10.7 percent). On balance, we assume that Cameroon will realize a net increase in its demand by 11 percent, i.e, the preferential reduction of partner country tariffs by 22 percent is eroded by about 50 percent due to partner country tariff reduction against imports from the rest of the world. The actual extent of the erosion of the 22 percent tariff reduction against Cameroon's region is dependent on elasticity of substitution possibilities in the CEMAC markets.

# **Choice of elasticities**

Table 4 presents the values chosen for the elasticities in the model. Elasticities of substitution between capital and labor are based on estimates from Caddy (1976). For composite final demand, we employ the Linear Expenditure System" demand functions. The own price elasticities may be derived from estimates of the "minimum consumption requirements," which were obtained from the poverty assessment report of the World Bank (1995). For industry and services, these values are consistent with the estimates of Lluch, Powell and Williams (1977). The elasticities of substitultion between imported and domestic goods in final consumption were based on estimates in Shiells, Stem and Deardorff (1986) and adjusted to reflect the limited possibilities for substitution between imports and domestic goods that are usually characteristic of a developing economy like Cameroon. Elasticities of substitution between imports from the region and imports from the rest of the world were interpolated from Faini (1994). The same values were chosen for intermediate goods. Elasticities of transformation between domestic supply and exports were also based on

-18-
and allows users to choose between original I/O tables that reflect either direct intermediate input requirements alone, or that take into account both direct and indirect requirements.<sup>6</sup> Moreover, it allows users to deviate from the perfect homogeneity assumption between imports and domestic inputs, underlying most studies on tariff‐related input costs available to date.<sup>7</sup> Assuming perfect homogeneity between imported and domestic varieties has two implications. First, domestic input producers are likely to compete with an infinitely elastic supply of imports and will therefore be unable to pass‐on higher tariff associated costs to upstream producers. Only then would calculating upstream tariffs on the basis of direct inputs alone appear to be the appropriate assumption. Second, the pass‐ through from changes in tariffs to changes in input costs is likely to be close to 100 percent. By allowing for differentiation between the domestic and imported varieties of the same product, UTAS thus allows considering scenarios where the pass‐through effect of input tariffs on production costs is incomplete. With this choice, UTAS therefore greatly increases the range of modeling scenarios that can be considered compared to previous applications.

# 3) Analytical framework

UTAS is an Excel‐based tool that allows policy makers to simulate the impact of tariffs on production costs across sectors in an economy and perform comparative static analysis for different tariff structures on the basis of a simple partial equilibrium framework.<sup>8</sup> It combines I/O data with detailed product‐level tariff data to establish average output tariff rates for each sector specified in the I/O table. To do this, UTAS draws on a series of pre‐set concordance tables that establish how product‐ level tariff schedules match with more aggregate sector‐level data of I/O tables. For each I/O sector, the tool then calculates an upstream tariff ( UT ) reflecting the total incidence of all intermediate input tariffs on sector i ’s production costs as follows:
these dummy-variables and interaction terms together in the same regression.

# 7 Heterogeneity of e¤ects across products and countries

We now examine the extent to which the impacts of rainfall shocks on agricultural tari¤s are heterogeneous across products and countries. Table 9 presents the results from regressions in which the rainfall variable is interacted with dummy variables for each of the agricultural products included in the UN statistics division of the SITC classi...cation (see Table A.3). The results in columns (1) and (3) suggest that a rainfall shortage leads to tari¤ reductions in most of the products considered. When considering both simple and weighted import tari¤s, we ...nd positive and signi...cant e¤ects for all product categories,

> 18 We de...ne this variable based on the full country-level data on the log of rainfall for this period. We have considered alternative thresholds to de...ne the dummy variable and have obtained qualitatively similar results.

> 19 Extremely high realizations of rainfall in a short period of time can cause ‡oods. Floods may (or may not) reduce agricultural output in a given year. While a ‡ood typically reduces agricultural output in the months it occurs, it also tends to increase agricultural productivity in the following growing season, what has the opposite e¤ect (Banerjee, 2007). Based on annual data on country-level rainfall, it is however di¢ cult to separate ‡oods from consistently higher volumes of rainfall spread over the year.

19
The deficit of a working age adult can explain the lower agricultural income in female-headed households in our sample. Surprisingly, however, female-headed households report higher incomes from wage work. It could be that female-headed households where men are missing may also be more likely to suffer from a lack of access to land, making wage work or self-employment more important as means of income. It is interesting, however, that while we see limited signs of differences in wealth, we do not see differences in terms of assets or land ownership or value.

# _Pooled estimates_

As we discuss, our choice of outcomes is based on the overlap between variables where the literature suggests possible effects and the data available in VARHS. Thus, we analyze RTP3 impacts on agricultural production patterns, employment outcomes, and household income.<sup>16</sup> In discussing impacts on agriculture, we estimate impacts on crops and livestock in both production and trade. Following <mark>Shamdasani (2016)</mark> , we also distinguish between export and non-export crops. Unfortunately, our data do not allow for construction of reliable indicators of educational or health outcomes. To better understand mechanisms behind the observed results, especially when they vary by gender, we drew on the insights from qualitative work.

# _Agricultural production and trade_

Our analysis suggests that the RTP3 project had statistically significant impacts on both crop production and the amount of crop production traded (Table A.6). Besides being statistically significant, the impact is large in magnitude. The size of the treatment effect on crop production is about 15 percent of the baseline mean for male-headed households, or the equivalent of about US$100 annually. However, for femaleheaded households, the impact is much smaller: less than 0.5 percent of the baseline mean.

A few factors could explain this difference. Since there are fewer working-age adults in female-headed households (Table A.4a), these households have less of one of the critical inputs into agricultural production: household labor.
error stemming from aggregation is not easily predictable, as it is linked to cognitive processes that respondents employ in aggregating such information. Since the present study aims at measuring the 1.4.2 and 5.a.1 indicators, disentangling the behavioral mechanism falls beyond our scope. These mechanisms, however, are at the core of a second study based on a robust empirical analysis targeting several types of heterogeneities in misreporting (Gourlay et al., 2021).

The analysis proceeds by comparing the effect of collecting data at the aggregate level (Arm 2), through proxy respondents (Arm 3), and with both proxy respondents and aggregate-level collection (Arm 4), relative to a selfreported parcel-level approach (Arm 1), which represents the gold standard for these types of surveys. First, we investigate how the different components of land tenure security vary across treatment arms, indicating the effect of the related survey designs. Second, we present the resulting SDG indicators, both for the overall population and by gender. Finally, we examine the gender gap in the land tenure components and SDG indicators, exploring how these gaps vary with different respondent approaches and levels of land aggregation.

As the results in Table 3 suggest, the point estimates of the land tenure components, namely legal documentation, right to sell, right to bequeath, and perception of security, vary across the four treatment arms, but these differences are generally not statistically different from the means of Arm 1, with the exception of Arm 3.<sup>11</sup> Data collected

> 10 The tenure typology does not vary substantially in our data, with more than 95% of parcels reported as private tenure. Tenure type is not available for the modules where land data is collected at the aggregate level. For this reason, we have excluded disaggregation by tenure type from the analysis.

> 11 Due to an incorrect enabling condition in the CAPI application, the question asking whether a specific person’s name was included on a