rafmacalaba/data-use-mentions-extended huggingface dataset:
agreements) that can result in subtle differences in both the frequency of their use and of their trade-reducing potential.

Section 2 begins by analyzing the focal policy of the trading system - the import tariff. This section examines different dimensions of tariff data for our sample of 31 economies to provide an initial response to each of our chapter’s first four questions. We introduce the most-favored-nation (MFN) ad valorem import tariff - i.e., the ‘headline’ border policy instrument that countries impose under the WTO. While applied tariff levels are relatively low in historical terms, significant crosscountry heterogeneity remains; furthermore, the negative correlation between applied tariffs and income per capita is even more pronounced once we consider the countries’ ‘legal’ commitments that are the upper limits for their tariffs. Next, we characterize cross-sectional variation in applied tariffs across sectors and inter-temporal variation across countries over 1993-2013. We then introduce which countries and sectors apply _specific_ - or per unit - duties, and we describe what makes them distinct from ad valorem tariffs. Finally, we characterize the lower-than-MFN tariffs that countries apply under a variety of preferential trade arrangements. Because these allow countries to take on additional liberalization, we illustrate the extent to which countries apply tariffs so as to discriminate among trading partners.

In Section 3 we introduce the other major (non-tariff) border barriers, and we use them to further inform our chapter’s main themes.<sup>5</sup> Contemporary use of temporary trade barrier policies

> 5One set of border policies that we do not directly address are export policies, and in particular export taxes or export quotas. Some discussion of export subsidies will arise in Section 5 by implication of our discussion of domestic subsidies, especially where they impact two exporting countries competing in a third market. On subsidies more

4
125|209|240<br>138|121|
|Labor productivity|Connections per<br>employee|176|45.1|91.4<br>148.9|203|
|Hidden costs|% of total revenues|163|176|149<br>43|140|
|||**Uganda**||Scarce water<br>resources|Other developing<br>regions|
|U.S. cents per m<sup>3</sup>|**2001 **||**2006**|||
|Residential tariff|47||66|60|3–60|
|Nonresidential tariff<br>_Source:_Demographic and Health|80<br>Survey and AICD water and|sanitation utilities|104<br>database(w|121<br>ww.infrastructureafrica.org/ai|<br>cd/tools/data<br>).|

_Source:_ Demographic and Health Survey and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). Access figures from Demographic and Health Surveys (1995, 2001, and 2006).

28
which the marginal product is very high at low levels but declines very rapidly. In this case one would expect to see a very high marginal product for that input, but also observe that the input was rarely in range of low use, and hence the empirical finding is of limited usefulness. For instance, Glewwe (1996) conducts a study of the determinants of school quality in Ghana using a large scale data set and the _only_ statistically significant school level effects on math and reading test scores are from the fraction of classrooms that have blackboards, and the fraction of classrooms that cannot be used when it rains. However, even in Ghana, 90 percent of schools have blackboards and only 15 percent of middle schools have leaking classrooms.

# **_)_** Direct evidence

The 10 to 100 fold divergences in marginal product per dollar across inputs, and that these are in a consistent pattern across inputs, rule out both the simple and uncertainty optimizing models. However, one could still return to the multiple output model and conjecture that the observed pattern of marginal products in producing scores corresponds to the inverse pattern in the marginal products in producing some other output. In this section we first say why we think this objection is unfounded, and then show additional evidence that bears directly on the difference between the multiple schooling output and our model, and which leans in favor of a positive model with teacher influence.

results suggest that for score on reading test the degree of significance of the effect of an input is significantly negatively related to the mean value of the input. For the score on the math test, the estimated magnitude of the effect is significantly positively related to its standard deviation.

27
households that stayed behind in the same region of origin. To account for selection bias, we exploit the well-documented evidence that migrants tend to settle where other migrants from the same village (or town) of origin reside. We make use of historical migration networks at destination as an exclusion restriction: we use district-level information from the 1984 Census to construct our measure of network, captured by the historical internal immigration rate at destination up to 16 years before the migration episode occurred.

Our estimates indicate that migration has a positive impact on consumption levels. On average, migrant households have a consumption level 77 percent higher than non-migrant households, and the effect is driven by households originally from Inland Ghana. In other words, there is a big gain in moving out of the Northern regions of the country. The impact is larger among male-headed households and households headed by higher educated individuals. We also find that migration substantially reduces distance from the poverty line.

We show robust evidence that such impact is attributable to a physical mobility effect that goes beyond the effect associated with moving out of agriculture or moving out of a rural area. Most of the migrants, in fact, do not move to an urban area from a rural location, and the large majority does not change sector of employment at destination. Internal migration in Ghana is beneficial to household welfare, remarkably when migration occurs from Inland Ghana to other regions. Policies aimed at limiting internal migratory flows are not recommended, as migration can bring positive effects and the policies would represent a limitation to individuals’ freedom.

At the same time, however, internal migration comes with important social costs that need to be factored in. First, as described in previous sections, those migrating are generally younger, more educated and more entrepreneurial than those deciding to stay. It follows that areas that are already poor see an outflow of important resources that could have contributed to the local development (Awumbila, 2007). True is that via remittances poor areas might eventually get some compensation for this brain
surveys conducted in fiscal years 1994, 2000, and 2005. The services sector has only more recently been surveyed in fiscal years 2001 and 2006. In all cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. This section describes some key features of these data for our study, and we refer to readers to Ghani et al. (2011c) and its data appendix for greater details.For additional detail on the manufacturing survey data, we refer the reader to Nataraj (2009), Kathuria et al. (2010), and Hasan and Jandoc (2010). Dehejia and Panagariya (2010) provide a detailed overview of the services data and its important characteristics.

Our work considers portions of the Indian economy surveyed by the National Sample Survey Organisation (NSSO). This survey includes the essential questions on gender of the establishment owner that we describe shortly. Due to the nature of India’s data collection, the NSSO collects information on a representative sample of all services establishments and on a representative sample of the unorganized sector of manufacturing. Amanufacturing business is considered part of the unorganized sector if it has fewer than ten employees and uses electricity. If the establishment does not use electricity, the threshold is 20 workers. The unorganized sector accounts for over 99% of Indian manufacturing establishments.<sup>2</sup> For most of our estimations for the services sector, we mimic the unorganized nature of our manufacturing sample for comparability. We do this by classifying services establishments with fewer than five workers and/or those listed as an “own-account enterprise” (OAE) as the unorganized sector. OAE enterprises are firms that do not employ any hired worker on a regular basis. The choice of five employees as the size cutoff recognizes that average establishment size in services is significantly smaller than in manufacturing. Using this demarcation, the unorganized sector makes up approximately 75
The detailed estimates for the probits are in the Appendix; results are given there with and without the net income variable (which enters as a quadratic in log income), given the concerns about its endogeneity. (We return to this issue.) Controlling for household income per capita, we find that DB participation is more likely for larger households, living in smaller dwellings, who do not own their dwelling, have an “old style” toilet, are still using coal for cooking, have no heating, no computer, have a female head of household, have a disabled or sick head of household, or a head with little schooling or who works in services or social security/welfare, or a head who is retired, works at home, has been laid off or is unemployed. DB households have lower financial wealth, are more likely to feel that their income is “less than they need to make ends meet,” are more likely to think that their income has improved, have a lower share of wages in income, have more unemployed or students in the household but fewer retired people. Most cities have significantly lower participation rates than Beijing, controlling for household characteristics. (Later we investigate the differences across cities.) It appears that the program is putting heavier weight on certain characteristics, such as poor dwelling attributes and lack of financial wealth, than is implicit in household income per person from the UHSS. To the extent that these effects reflect measurement errors in incomes or a broader concept of “income” that is motivating the program’s targeting at local level, it can be argued that the program is doing a better job of reaching the poor than our calculations based solely on the survey-based incomes would suggest. We return to this point in section 5.

There are also indications that the program is doing better at reaching the chronically poor than those who may be vulnerable to poverty in the future. This is suggested by the fact that people who feel that they are on a
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br>25|
|**Emporchi**|6,375,130|94|6,804,283|69|4,481,230|**18**|987,764|<br>11|**!Syntax**<br>**Error,)**|<br>37|
|**Total**|**!Syntax**<br>**Error,)**|**100**|**!Syntax**<br>**Error,)**|**100**|**!Syntax**<br>**Error,)**|**!Syn**<br>**tax**<br>**Err**<br>**or,)**|**!Syntax**<br>**Error,)**|**!Syn**<br>**tax**<br>**Erro**<br>**r,)**|**!Syntax**<br>**Error,)**|**!Syn**<br>**tax**<br>**Erro**<br>**r,)**|

Private ports for exclusive use by owners2 Private ports for public use _Source:_ Own calculations using data from Tortello (1998).

33
Results framework rating (1||||||-0.226*||
|high – 4 low)||||||(0.128)||
|Extent of changes to project|||||||-0.290**|
|components|||||||(0.133)|
|Constant|2.985***|2.565***|2.640***|2.768***|2.335**|3.159***|<br>3.189***|
||(0.810)|(0.786)|(0.793)|(0.772)|(0.912)|(0.886)|(0.850)|
|Observations|120|120|120|120|120|120|120|
|Adjusted R-squared|0.09|0.067|0.067|0.110|0.046|0.073|0.086|
|||**Depend**<br>|**ent variable: I**<br>|**EG 1-6 outcom**<br>|**e rating**<br>|||
|||**(8)**|**(9)**|**(10)**|**(11)**|||
|**Design index**||**0.223*****<br>**(0.071)**||||||
|Project Characteristics Index<br>|||0.121**|||||
|for comparison<sup>1</sup>|||(0.057)|||||
## **Box 1: The Intuition behind Synthetic Panels**

Analysis of economic mobility over time requires panel data. However, such data are not typically available in many settings. In Nepal, even though the NLSS surveys contained panel components, they suffer from heavy attrition and the resulting population is not representative for the population in any of the rounds. The synthetic panel methodology was introduced to address this key data limitation. Several efforts to validate the results of this methodology against actual panel data have been conducted and the general consensus that has emerged is that synthetic panels provide a promising way forward.

We describe the key elements of how the method works using the example of two LSMS rounds in Nepal, let’s say the 2003/04 and 2010/11 rounds. The essential idea is to use an imputation methodology to predict the consumption of households interviewed in 2003/04 in 2010/11. This prediction is done by first estimating a consumption model for 2010/11. This entails a regression of household per capita consumption on a set of observable household characteristics such as household size, demographic composition, education or asset holdings, etc. The predicted 2010/11 consumption (of households interviewed in 2003/04) is based on two components. The first component is obtained by applying the estimated coefficients on observable characteristics of households interviewed in 2003/04. Implicitly, the question that is being asked is, what would the consumption of the 2003/04 households be if the “returns to their characteristics” were as observed in 2010/11.

But of course, in addition to the deterministic component, there is also an idiosyncratic element to household consumption, e.g., shocks experienced by the households or other unobservable characteristics such as entrepreneurship, industry etc. In order to account for that, a second component has to be included in the predicted consumption. This component is estimated based on the correlation coefficient of the estimated error term obtained from the consumption model for the two years.
CP = PF 1 * PS 1-l GO = RT71* C Y2 *JE > 1 Y2 SD = HD5*GI's EM = OO 1<sup>* CB E2 *DB'-e _</sup> 2 where a, 3, y, 6 and £ are weights indicating relative importance of components to overall governance assessment.

The citizens participation index is composed of two indices -- one that assesses the degree of political freedom within a country and one that assesses the level of political stability of a country. Political freedom assesses the ability of citizens to influence the quality of governance they receive. The political stability index was composed by a commercial group with the perspective of an investor in mind. This perspective may understate the ability of citizens to participate in governance decisions in some countries but it is a reasonable indicator of continuity of citizen participation.

The orientation of governments towards the provision of public goods and services is assessed through three indices; judicial efficiency, bureaucratic efficiency, and lack of corruption. Improving judicial efficiency has been recognized as a pre-requisite for a country's development at least since the 1960s (Blair and Hansen, 1994) and the costs of bureaucratic inefficiency and corruption have been well-documented (de Soto, 1989). All three of these indices are based on surveys which attempt to gauge the degree to which public sector employees are focused on serving the populace rather than enriching themselves or their political parties.

Social development within a country is assessed through two widely known components, the United Nations' human development index and gini coefficients (which quantify the degree of income inequality). The human development index combines estimates of life 4
Figure 1: Employment Size, Store Types & Formality

- (a) # Employees on Formality in Retail Censuses

<!-- Start of picture text --> Rwanda<br>Cameroon<br>Peru<br>Mexico<br>0 1 2 3 4<br>Log Employment<br>1<br>.8<br>.6<br>Share Formal .4<br>.2<br>0<br><!-- End of picture text -->

- (b) # Employees by Store in Mexico

- (c) % Paying VAT by Store in Mexico

<!-- Start of picture text --> (1) Non-Market N.A. (1) Non-Market N.A.<br>(2) Non-Brick & Mortar (2) Non-Brick & Mortar<br>(3) Convenience Stores (3) Convenience Stores<br>(4) Specialized Stores (4) Specialized Stores<br>(5) Large Stores (5) Large Stores<br>0 1 2 3 4 5 0 .2 .4 .6 .8 1<br>Log of Median Number of Employees Share of Firms Reporting VAT Payments<br><!-- End of picture text --> These panels shows the association between formality status, employment and firm type. Panel A shows the share of formal firms as a function of log employment, using retail censuses of four core sample countries (Cameroon, Mexico, Peru and Rwanda). Formality is defined as ’being registered with the tax authority’ in Cameroon and Rwanda, and ’paying Value-Added-Taxes on sales’ in Mexico and Peru. Panels B and C use the 2013 Mexican retail census, which classifies retailers in similar categories as our broad place of purchase taxonomy. The figures show the log median number of employees by place of purchase (Panel C) and the share of firms paying Value-Added-Taxes on sales (Panel C).
whether data of this nature are likely to be helpful when a government crafts its response to the cold winds of global recession.

We begin by examining in more detail the timing of the 2008-2009 recession as it affected Thailand, and documenting the effects on output and unemployment. After developing a serviceable measure of welfare, we then use data from the Thailand Socio-Economic Surveys to construct a month-by-month time series for consumption, which we deflate to obtain real per capita consumption levels. This allows us to trace the effects of the recession on different sociodemographic groups, both on average, and controlling for a wide variety of household-level variables.

Our main, and somewhat surprising, conclusion, for which we argue more carefully below, is that the only major socio-demographic group hurt by the 2009 recession was younger wageearners in the greater Bangkok region, who were hit by the sharp, if relatively short, drop in export-oriented manufacturing. On average, the poor in Thailand actually benefitted from the recession, because the effects of lower prices of food and energy outweighed the other influences on their economic wellbeing. We offer some further thoughts, including on the role of government in the crisis, and the importance of rooting policy in actual data, in the concluding section.

# **Timing the Recession**

The annual data on GDP growth rates shown in Figure 1 indicate that economic growth in Thailand, and worldwide, slowed in 2008, and GDP fell in 2009. From the standard macroeconomic identity, we have: It is useful to decompose the growth of GDP into the contributions of each of these components, and this is shown in the bars in Figure 1. Most of the drop in Thai GDP in 2009 was due to a sharp fall in investment spending, which then rebounded in the following year. Real consumer spending rose in every year except 2009, when it dipped slightly (by 1.1%). The rise in net exports was the major contributor to GDP growth in 2006 and 2007; in 2009, Losers and Winners in
**Figure 6: Cluster buffer, sub-basins, and deforestation since 2000**

As precipitation and temperature have been known to be significant predictors of DD (Halpenny et al., 2012; Escamilla et al., 2011), we also construct climate controls for each cluster within its buffer area. In particular, we construct average annual temperature in Celsius and total precipitation in meters for each cluster, using the ERA5-Land product (Muñoz-Sabater et al. 2021). This data is provided as monthly aggregates at 11,132m resolution. To match the temporal resolution of the deforestation data, we further aggregate this data to create yearly means for temperature and total monthly precipitation.

# **2.4. Data for Peru Case Study**

Peru was chosen as a case study for our analysis, given that it is one of the top ten forest-rich countries in the world, which has experienced both rapid changes in land use and health outcomes over the last two decades. Compared to other countries in Latin America, only Brazil has larger forest cover, with Peruvian forests representing 54 percent of the country’s territory (FAO and SERFOR, 2017). However, the country has experienced rapid tree cover loss of about 3.15 million hectares between 2004 and 2020, equivalent to a 4 percent decrease in tree cover since 2000, resulting in 2.03 Gt of uncompensated CO2e emissions.<sup>8</sup> Five Amazon regions - Loreto, Ucayali, San Martin, Madre de Dios, and Amazonas - were responsible for 78 percent of all tree cover loss,<sup>9</sup> which was driven by the growing demand for fuel wood, exports of hardwoods, and expansion into the Peruvian Amazon for cultivation, livestock, and the extraction of minerals (Swenson et al., 2011; Piu and Menton, 2014).

Despite tree cover loss, child health indicators have improved in the last two decades. Particularly, the incidence of diarrhea and stunted growth decreased significantly
387|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 13: Effects on child development

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Gross motor score|Fine motor score|Language score|Total child development score|
|**Full sample**|||||
|ITT estimate|1.545***|2.748***|0.160|4.391***|
||(0.498)|(0.640)|(0.528)|(1.343)|
|Observations|1618|1618|1618|1618|
|_R_<sup>2</sup>|0.481|0.325|0.561|0.636|
|Control mean|43.524|20.689|34.816|99.029|
|Max score|52|54|46|152|
|**Have child aged 0-2**|||||
|ITT estimate|1.939***|1.318**|0.832|3.895***|
||(0.496)|(0.499)|(0.714)|(1.312)|
|Observations|717|717|717|717|
|_R_<sup>2</sup>|0.534|0.257|0.538|0.636|
|Control mean|39.139|16.959|26.584|82.682|
|Max score|52|41|46|139|

_Notes:_ Robust standard