rafmacalaba/data-use-mentions-extended huggingface dataset:
|

# _Source:_ Authors’ calculations

# **6 Discussion and conclusions**

LMICs have struggled with the quality of education systems, with a learning crisis: learning basic skills is declining even as access has expanded (World Bank 2018a; Le Nestour, Moscoviz, and Sandefur 2022; UNICEF 2022). Regularly assessing learning and acting on this evidence are cornerstones of improving quality (World Bank 2018a). QAS are a critical mechanism to achieve these goals and ultimately to improve quality, particularly at the pre-primary level, when there are no national assessments to draw on (World Bank 2013; Raikes, Neuman, and Burton 2019). There has, however, been limited evidence on how QAS work in practice in LMICs. This paper investigated how consistently QAS measure quality, and lessons learned on how QAS should be used in LMICs, in light of their measurement challenges.

# **_6.1 Summary_**

Leveraging parallel data collection efforts in schools, we investigated the consistency of QAS tool data with more detailed diagnostic research tool data. Consistency varied substantially across items, with almost perfect consistency only for two out of 35 items and substantial consistency for only five items. Measures of foundational skills were some of the items with 21
# **C. Additional Worker Results**

Figure C1: Average GDP Growth in Firms’ Destinations <!-- Start of picture text --> 3<br>2<br>1<br>0<br>-1<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Year<br>Average Growth of GDP in firm's destinations(%)<br><!-- End of picture text --> _Note:_ The graph shows the average of the growth of GDP in firms’ destinations in each sample year.

Figure C2: Pre-GFC Trends in Firm Employment and Wages Depending on Firm GFC Foreign Shock <!-- Start of picture text --> 4.8<br>11.8<br>4.6<br>11.6<br>4.4 11.4<br>4.2 11.2<br>11<br>4<br>2004 2005 2006 2007 2008 2004 2005 2006 2007 2008<br>1st Shock Quartile 2nd Shock Quartile 1st Shock Quartile 2nd Shock Quartile<br>3rd Shock Quartile 4th Shock Quartile 3rd Shock Quartile 4th Shock Quartile<br>(a) Firm total employment (b) Firm total real wages<br>Firm total employment (log) Firm total real wages (log)<br><!-- End of picture text --> _Note:_ The graphs plot the evolution of firm total employment (log) (in panel a) and firm total real wages (log) (in panel b) before the GFC in 2004-2007 for four groups of firms defined by the quartiles of the distribution of firm GFC foreign shocks.

43
, 2019; Damania et al., 2017).

Access to finance is another channel by which precipitation shocks could affect the private sector. Using a global sample of firms, Kling et al. (2021) find that frequent climate shocks might impair the ability of banks to predict outcomes, and thus may increase the cost of capital due to additional risk. A study on Mexican firms found that weather shocks can also generate liquidity shortages, increasing the chances of loan default, and reducing access to future credit by lowering credit scores (Aguilar-Gomez et al., 2023).

3
thought to be a way to cope with the uncertainty involved in the baseline projections, their implementation is problematic for, at least, two reasons. First, the framework ignores the co-movements between the macroeconomic variables that determine the evolution of external debt. For example, a negative shock to GDP growth is assumed to have no impact on other macroeconomic variables, like the real exchange rate, the current account, or the interest rate. These variables are kept constant at their baseline projections. Moreover, shocks in the current framework do not have a meaningful economic interpretation. Where do shocks come from? What is their structural interpretation? Second, some argue that the stress tests are too stringent because they consider highly unlikely and pessimistic scenarios. For example, one of the stress tests in the current framework involves a simultaneous worsening in four determinants of debt for two years.<sup>3</sup> There is no rationale for the duration or the size of the proposed shock.

This paper proposes to alleviate these problems by pooling data from many countries and estimating the shocks and co-movements faced by a generic low income country. The methodology is an extension to that proposed by Garcia and Rigobon (2005). I rst derive a debt accumulation equation and identify key variables that determine the evolution of foreign debt.<sup>4</sup> Next, I estimate a panel vector autoregression with common slope coecients and covariance matrix to model the evolution of the determinants of debt. Finally, I perform simulations to generate a large number of future debt trajectories for a given country and compute several statistics on external debtmedian forecasts, condence regions, the probability of debt crossing some threshold, and so forth. These statistics can be used to assess whether external debt is, or is likely to be, too large relative to a predetermined criterion.

To perform the simulations, Garcia and Rigobon (2005) use a Monte Carlo method and assume that shocks are drawn from a normal distribution with mean
of crude or refined oil. However, as noted again later, most of these variations did not lead to any clearly distinguishable pattern in the results. In addition, each country has its own unique legacy of energy subsidies, history of reform efforts, and composition of subsidies across energy sources. For example, 10 of the 12 countries have subsidies for different combinations of electricity, fuel, and/or gas (IMF 2023; IEA 2023). Table 1 provides more details about the different types of energy subsidies that exist in each country. The negative impacts of these subsidies on efficiency, equity, and the environment are discussed throughout the rest of this subsection (see Appendix C for more details).

# **[Insert Table 1]**

Energy subsidies make up a sizable share of government spending in these 12 countries, and the basis for the efficiency argument for reducing energy subsidies is that they are often considered an inefficient use of limited public resources. Comparing government spending on health care to that on energy subsidies is instructive. In 10 of the 12 countries, government spending on energy subsidies is equal to or larger than what is spent on health care. In fact, in seven of the countries (Angola, Bangladesh, Egypt, Indonesia, Kazakhstan, Nigeria, and Pakistan), spending on energy subsidies is considerably higher than spending on health care (see Figure C1 in Appendix C). Even in the two countries where health care spending is larger than spending on energy subsidies (Argentina and Ecuador), energy subsidies still account for more than 2 percent of GDP, which is roughly 10 percent of all government spending.

Energy subsidies disproportionally benefit richer households in these 12 countries, and this is the basis for the equity argument for reducing energy subsidies. Ultimately, subsidies result in higher levels of inequality: in these 12 countries, the richer a household is, the greater the share of government spending on energy subsidies it receives (see Figure C2 in 14
**Figure 12. Gini indices (left) and international poverty rates (right) in Moldova and peers by income levels, percent**

<!-- Start of picture text --> 60 50<br>45<br>50 40<br>35<br>40<br>30<br>30 25<br>20<br>20 15<br>10<br>10<br>5<br>0 0<br>Belarus Moldova Romania Russia Ukraine Belarus Moldova Romania Russia Ukraine<br>2015 2017 2016 2014 2016 2015 2017 2016 2014 2016<br>market market + pensions disposable consumable final market market + pensions disposable consumable<br>Note: The international poverty line: Belarus – 10 USD/day in 2005 PPP, Moldova – 5.5 USD/day in 2011 PPP, Romania – 5.5 USD/day<br>in 2011 PPP, Russia – 4 USD/day in 2005 PPP, Ukraine – 5.5 USD/day in 2011 PPP.<br>Source: authors’ calculations using HBS-2017 data for Moldova and respective CEQ analysis for other countries: Belarus – Bornukova et<br>al., 2017; Romania - Inchauste et al., 2018; Russia – Popova et al., 2018, Ukraine - Bornukova et al., 2019.<br><!-- End of picture text --> <mark>As in other countries, among the various fiscal interventions, pensions in Moldova are the main contributor to inequality reduction (F</mark> igure 1 <mark>3). Moreover, this effect is the highest among the five countries. While progressivity of pensions is in line with other countries for which data are available, the highest contribution is explained by highest relative size of the pensions in Moldova comparing to other countries. Social contributions also have the highest equalizing effect in Moldova and direct transfers are the second highest after Ukraine (<
# A. Policy Experiment Sample

# B. Mechanism Experiment Sample

<!-- Start of picture text --> Agadez<br>Tahoua<br>Tillabéri % (od<br>Niatee opyy SeMaradiS<br><!-- End of picture text --> <!-- Start of picture text --> Agadez<br>Tahoua<br>e<br>Tillabéri<br>gae ng5 , MaradiOo<br><!-- End of picture text --> <!-- Start of picture text --> Notes: Authors’ creation; boundaries from OCHA Common Operational Data<br><!-- End of picture text --> <!-- Start of picture text --> Notes: Authors’ creation; boundaries from OCHA Common Operational Data<br><!-- End of picture text -->
# 2.3 Agglomeration in African economies

Ghana, along with the rest of the African continent, has urbanized very rapidly. 9% of the Ghanaian population lived in urban areas in 1931 (Songsore, n.d.) and this increased to 51% by the time of the 2010 population census. The World Bank estimates that the urban population of Ghana increased by 3.5 times between 1984 and 2014 (World Bank, 2015). It is thus expected that the benefits of agglomeration would be evident in Ghana and other African economies that are rapidly urbanizing, as they have been evident in the recent experiences of many Asian economies (eg Au and Henderson (2006) on Chinese cities). East Asian economies have shifted from a primarily agricultural base to a manufacturing‐based economy and then to a services‐based economy, ie they have undergone structural transformation. However, a number of studies have demonstrated that there has been no structural transformation in African economies (McMillan and Rodrik, 2011), despite levels of urbanization similar to those in Asia. This has been argued to be because of low levels of agricultural and manufacturing productivity (Jedwab, 2013). Increasing urbanization without industrialization suggests that urbanization has not been accompanied by a growth in productivity generated by agglomeration across the continent.

Jedwab (2013) argues that urbanization in Ghana and Ivory Coast has been driven by the formation of consumption cities, cities that have evolved due to the production of resource export commodities (cocoa in the two countries he studied). In Jedwab’s stylized model resource exports generate a surplus which is spent on urban goods and services, drawing people into cities and creating urbanization without either an increase in agricultural yields from a green revolution or increased productivity in manufacturing. This is thus a model in which urbanization does not lead to agglomeration economies, although the absence of agglomeration economies is not tested directly.

Collier et al (2018) test for agglomeration economies in cities using a firm‐level dataset constructed from the World Bank
Our gravity regressions also include trade policy measures that enter as control variables: a) the U.S. statutory most favored nation (MFN) tariff rate taken from USITC (2018), and b) dummy variables indicating countries that are members of a preferential trade agreement (PTA) with the U.S. Finally, we include in the structural regressions product-level elasticities of substitution estimated by Fontagné et al. (2022).

## **3.3 Input-output relationships in PR**

Calculation of the welfare costs of the JA requires information that is more comprehensive than what is available in the trade flow data. A critical element for such calculations is data on PR’s purchases of its own output, activity that is not included in the trade data. We employ a Puerto Rican input-output (IO) table that separates expenditures on local output from expenditures on imports (including imports from U.S. sources). The IO table was produced by the Junta de Planificación de Puerto Rico for the years 2006-2007.<sup>17</sup> The table reports final demand expenditures (consumption, investment, etc.) for every 4-digit NAICS sector in the PR economy. The table also reports - for each purchasing NAICS code and final demand category - expenditures on local production and on imports, respectively. We match the trade data to the NAICS codes, and calculate U.S. and rest of world (ROW) shares of imports for each cell of the table, including purchases by final demand.

# **4 Fleet composition in the Caribbean**

The LLI data on vessel characteristics contains information on vessels’ place-ofbuild, flag-of-registry and ownership. We combine the vessel characteristics and the 17The Puerto Rican government has been under severe financial stress in recent decades, limiting its ability to produce economic statistics in a timely and credible manner. The 2006-2007 table was the most recent table available when we did the calculations. A 2011-2012 table has finally been produced, but it is imputed and
Learning about their absolute values, however, is inhibited by the existence of a single equilibrium condition in the model to identify the two unknowns. Since Hsieh and Klenow [2009]’s approach to measuring idiosyncratic distortions does not require knowing the equilibrium prices and aggregates,<sup>21</sup> one can get an estimate of the distortion slope _γ_ directly from the data and use the model’s equilibrium condition to back out the entry barrier that matches the equilibrium’s average firm size with the average firm size in the data. We implement this strategy later in the paper.

Notice from the logic above that the identified entry tax rate is sensitive to three potential sources of bias: 1) measurement error in average size, 2) measurement error in the elasticity of TFPR _−_ TFPQ, and 3) model misspecification. The next section presents a series of adjustments to our firm-level databases that attenuate biases stemming from the first two sources. Furthermore, to minimize model misspecification, we conduct the quantitative analysis by appealing to a richer version of the model just presented that incorporates two key determinants of the size distribution: endogenous exit and endogenous firm dynamics.

Last, the theoretical results concerning the interaction between distortions will prove helpful when interpreting the magnitude of the TFP effects associated with altering the mix of distortions.

# **4 Data**

The identification of entry barriers hinges on two empirical inputs: the average firm size and the elasticity of idiosyncratic distortions with respect to firms’ physical productivity (i.e., the TFPR and TFPQ elasticity). For the purpose of extracting these inputs, we gathered a collection of firm-level databases from 19 countries, to which we have made adjustments so as to maximize the comprehensiveness and the comparability of the sample.

> 21Recall from Hsieh and Klenow [2009] that wedges are defined as deviations of marginal revenue products relative to industry averages. Thus, industry-wide variables related to the equilibrium, such as aggregate demand and wages, are subsumed in this average
# **Introduction**

Savings is alternatively defined as income minus consumption, the change in wealth, or the supply of capital. Given the comprehensive and consistent definitions of each of these terms, each definition of savings would represent the same concept and give rise to similar empirical measures (Gale and Sabelhaus 1999).

Understanding savings behavior is critical when designing economic policies that promote investment and growth. Most of the empirical literature that analyzes cross-country savings behavior concentrates on aggregate savings for a multitude of reasons. The first reason is due in part to the lack of consistent information on household behavior, as well as possible differences in the household savings in developing versus industrial countries (Loayza et. al. 2000).

The decline in savings will reflect on the state of economic growth and development. One interpretation asserts that the low savings rate will induce low savings behavior and will accordingly lead to low capital accumulation. On the macro level, it illustrates vulnerability of an economy’s dependence on foreign capital. At the micro level, it raises households’ fears to secure future consumption through pension systems for retirement or other purposes.

Another interpretation is that the sustainment of short-term aggregate demand is indicative of a low savings rate; however, worries that the savings rate will rebound tend to arise. The decline in personal savings, and the accompanying rise in consumption has helped fuel the economic expansion in the United States as well as propping up the global economy (Gale and Sabelhaus 1999).

Nordhaus (1996) proposed a third view asserting that current tools for measuring savings and investment are stone-age definitions in the information era. His view emphasized that measures of aggregate savings weakly correspond to the classical concept of savings. Accordingly, empirical analysis of different economic issues may require examining different measures of savings. In spite of that, his view supports the conceptual theory that low savings or higher consumption accelerates economic growth; however, both the minimal and poor quality of savings data makes it quite difficult to reach such conclusion when based solely on
<br>Spacing<br>Program|**Yes,**and company<br>will<br>obtain<br>sme<br>contraceptive.**at**<br>wholesale<br>prices|Yes|Yes|Not yet<br>determtned|Project<br>cost - $114,070,<br>ith<br>435,000<br>from Enter-<br>prite<br>Program.<br>The**costs**<br>of contraceptives<br>arenot<br>included.|
|Thailand:<br>Factory-based<br>Family Planning<br>Services<br>Project|Yes|No|Yes|Yes|Project<br>coat<br>- $260,255<br>0|
|OWTlCNG MIAuT IUMT|U<br>IN FAMILY_FNAIN_|||||
|Niseria:<br>Gulf Oil|No|Yes|No|No|Noinforuatton<br>predomi-<br>nately<br>technical<br>**assistance**|
|Zimbabwe:<br>Comercial .nd<br>Industrial<br>Medical Aid<br>Society|No|Yes|No|Yes|<br>$31,911 for employce survey|
|Indonesia<br>Feasibility of<br>Comercial<br>Clinics<br>Providing<br>Sterili-<br>zation<br>inJakarta|N/A|N/A|N/A|N/A|Project coat- $24,000|
|TheYKBEntrepre-<br>'neurial<br>Develop-<br>mentProject|Yes, byBKKBN|N/A|Yes, from PVO|Yes|Projectcost<br>-<br>$10,552|
|urkey's<br>ts<br>|
|---|---|---|---|---|---|---|---|---|---|
|Factor Intensity|1990|1996|2004|1990-95|1996-00|2000-04|1990|1996|2004|
|Natural Resource|49.1|35.1|13.4|6.9|-0.4|17.6|6.3|9.5|13.4|
|Unskilled Labor|41.1|31.0|35.8|11.0|15.5|30.8|3.1|4.2|11.1|
|Capital|4.3|16.2|14.0|43.9|2.4|37.0|3.0|13.2|19.0|
|SkilledLabor|5.4|17.7|36.8|36.3|21.3|41.5|2.3|9.4|14.0|
|All abovegoods|100|100|100|15.0|10.1|32.7|4.0|7.0|13.2|

Source: Computations based on Pan-Euro and EU import data from UN COMTRADE Statistics.

In contrast to export performance in 1990-95, skilled labor intensive products have subsequently become the major levers of export expansion to non-EU-15 Pan-European markets. While capital intensive products followed by skilled labor intensive exports drove the growth in 1990-96, the latter drove the expansion in 1996-2003. The share of EU-15 in Turkish PanEuropean exports of skilled labor products fell from 98 percent in 1990 to 916 percent in 1996 and 86 percent in 2004.

Simultaneously, the factor content gap between EU-15-destined exports and those to other pan-European markets has been falling. While through the
set of fiscal stimulus multipliers included 0.3 on<br>revenue, 0.5 on capital spending and 0.3 on other spending|
|World|<br>Agnello_et al._(2017)|Panel of 157 countries<br>(Gini, real GDP growth<br>rate, fiscal stimulus<br>packages)|1960-2010||An increase in inequality increases the probability of<br>government crises. However, this impact is decreased when<br>expansionary fiscal stimulus episodes are in place.|
|World|Aizenman & Jinjarak<br>(2011)|<br>Size of fiscal stimulus,<br>exchange rate<br>depreciation, trade<br>openness,fiscalcapacity|2007-2009<br>(main focus)||A lower debt/average tax base is associated with higher fiscal<br>stimulus and lower trade openness is associated with a higher<br>fiscal stimulus and lower depreciation rate during the crisis|

39
rate, and the importance of the plant in the local labour market). Regulators thus appear to show a reluctance to enforce standards in a way that may have an impact on employment.' .

Empirical studies on monitoring and enforcement issues have thus been of only recent interest and remain limited in numbers.<sup>7</sup> Moreover, they have been performed only in the context of developed countries: We are not aware of any empirical study on the impact and determinants of monitoring and enforcement activities of an environmental agency of a developing country at the plant level.<sup>8</sup> The current paper is a first step toward filling this important gap. Building upon MV (1990) and LR (1996), in this paper we analyse the role of inspections, pollution charges, and citizens' complaints as determinants of the environmental performance of industrial polluters in Zhenjiang, China.<sup>9</sup> Our analysis of plant-level data shows that inspections do have a statistically significant impact on the firms' environmental performance. In the current paper, our results suggest that pollution charges do not have a statistically significant impact on firms' performance. However, this result may simply be explained by the lack of variation in pollution charges within the city of Zhenjiang which therefore prevents us from effectively capturing the impact of pollution charges. Our results also demonstrate that complaints do impact inspections significantly. Given the impact of inspections on pollution, citizens' complaints therefore do have a significant impact on pollution control. These results may provide a key to China's future quality of environment. These results may provide a key to China's future quality of environment.

- 6 Dion et al. (1998) thus argues that both the public interest theory of regulation and the economic theory of regulation contribute to explaining the decision of regulators to monitor the environmental performance of regulated plants.

- 7 In their paper, Deily and Gray (1991) writes that this is _"thefirst_ empirical