rafmacalaba/datause-encoder-data huggingface dataset:
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# **Figure 6 HEs are the majority of all NFEs owned by households**

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Burkina Cameroon Congo Ghana Mozambique Rwanda Uganda Weigthed<br>Faso average<br>HE owner only HE with family employees NFE larger than HE<br>Distribution of Enterprises<br><!-- End of picture text --> _Source: Authors‟ calculations_

# **Figure 7 HE sector of activity, urban and rural areas**

<!-- Start of picture text --> Urban Rural<br>Mining/Nat.Res./ Mining/Nat.Res./<br>Construction/Ene Construction/En<br>18 14<br>4 rgy 6 ergy<br>16<br>Manufacturing Manufacturing<br>27<br>Wholesale/retail Wholesale/retail<br>53<br>62 Other services Other services<br><!-- End of picture text --> _Source: Annex tables 16 and 17_ The majority of HEs are in the trading sector (Figure 7). Other common activities are manufacturing – primarily transformation of agricultural goods or natural resources such as making charcoal, bricks, or grinding grains, but also artisanal activities such as making custom furniture; construction; and services such as food service (making and selling snacks or lunches), tailoring, transport, and personal services (barbering and hairdressing). Partly due to opportunities to process agricultural products, manufacturing is a more common HE activity in rural areas. On average, across countries both genders are equally represented in each sector of industry, however deeper analysis in Ghana reveals that within each sector of industry there is clear gender segregation - for example, females are more likely to do tailoring, and men to work in construction (Fox et al, 2011). Though street vendors and markets are the most visible signs of HE activity, these activities are harder to observe as 36 to 47 percent of HEs are operated by owners out of their own homes. This number is even higher among women, and in rural areas 13
# 5.1. Raising physical capital

UNCTAD (2014) estimated that unfilled global investment needs amount to up to 3 percent of global GDP. In particular, all EMDE regions have sizable investment needs (Vashakmadze et al. 2017). Depending on the availability of financing, these could be filled through either public or private investment or a combination of both in publicprivate partnerships. Increasing public investment can be an effective policy tool to support short-term demand while also helping to raise future potential growth (World Bank 2017a; Calderón and Servén 2010a, 2010b, and 2014). Although the rapid increase in public debt over the past decade has constrained fiscal space in most countries, there remains scope to shift existing government expenditures toward public investment to make government operations more growth-friendly (World Bank 2017b; Kose et al. 2019). Moreover, in many countries, government revenue ratios remain low, indicating that in some cases tax revenues could be raised, including by expanding tax bases or improving the quality of tax administration (World Bank 2015).

In addition, policies can support productivity-enhancing private investment. Innovationrelated investment tends to be low in EMDE firms, partly because of limited availability of complementary inputs such as trained engineers or effective organization techniques (Cirera and Maloney 2017). Policy efforts to expand the supply of complementary inputs and capabilities and to raise the returns on investment through intellectual property right protection may foster private investment.

If, over the next decade, each country raised its investment growth as much as its largest increase over any historical ten-year interval, global investment-to-GDP ratios would rise by 2.3 percentage points of GDP. Investment-to-GDP ratios would rise somewhat more in EMDEs, by 2.9 percentage points of GDP. It is estimated that such an investment boost would raise global potential output by 2 percent by 2027, reversing the slowdown under the baseline scenario. EMDE potential output would rise even more (5 percent cumulatively by 2027; Figure 9).

Implicit in these scenarios is the premise that the additional investment will be used productively. In the context of EMDEs, there is some evidence that absorptive capacity can limit the success of large scaling-up of public investment, although this adverse effect is small in lower-income and capital-scarce countries (Presbitero 2016).

# 5.2. Raising human capital

Measures to raise human capital could lift both labor supply and TFP growth: A better educated workforce is more securely attached to the labor market and more productive. In particular, a better-educated workforce may be better able to adjust to technological disruptions that reduce employment and wages by replacing jobs (Acemoglu and Restrepo 2017a).<sup>17</sup> In the framework used here, human capital has two dimensions, educational

> 17 The impact of such technological disruptions on output may not be clear-cut. For example, in aging societies, technological change to replace jobs may relieve pressures resulting from a shrinking labor supply (Acemoglu and 13
15 between treated and untreated children. We find a monotonic effect of the treatment on completed years of schooling that is very similar to the OLS estimates (Columns (5) to (8)). Although the IV estimates tend to be rather imprecise, these exercises suggest that parental differential treatment of their offspring does not appear to be biasing our results.

Of course, the instrumental variable strategy is of no help in disentangling the effects of preprimary education on the outcomes of interest from the effect of other interventions which may be correlated with average locality-cohort variability in preschool attendance. As a further robustness, we restricted the sample to siblings with at most 4 years of difference in age. The idea is that the closer the age difference between siblings, the most likely it is that they have been exposed to similar experiences in primary and secondary school. The results of analogous models to those estimated in Table 5 for this sub-sample are reassuringly similar to the results for the whole sample. (Results are available upon request from the authors).

# **5. Discussion and conclusions**

This paper uses micro data from the Uruguayan _Encuesta Continua de Hogares_ (ECH) to study the short and medium term effects of preschool attendance on school progression among children aged 7-15. We use a rather unique feature of the data that collects retrospective information on the number of years of preschool attended to estimate the impact of this variable on school stay-on rates and the number of school years completed. A major government intervention aimed at universalizing pre-primary education warrants sufficient variation in the data to identify precisely the effects of interest.

A major challenge in identifying the causal effect of preschool exposure on subsequent school progression stems from the difficulty of distinguishing between unobserved heterogeneity - whereby better-off or more able children are both more likely to attend preschool and to perform better in school - and state dependence, that is the effect of interest. In order to control for such 23
# **1. Introduction**

A large literature has actively discussed the role of trade in fostering economic development. The seminal article of Frankel and Romer (1999) showed using data for 1985 that trade, instrumented with countries’ geographic characteristics, has a quantitatively large and robust positive effect on income. Irwin and Tervio (2002) also find similar results for a sample covering the period between 1913 and 1990. However, many have questioned these findings. Rodríguez and Rodrik (2001) argue that the geography-based instrument used in these studies is likely to be correlated with other geographic variables that affect income through non-trade channels. They provide evidence that, when any of three summary indicators of geography is introduced as a control, the result that trade has a positive effect on income disappears. Acemoglu et al. (2001) contend that these geographic instruments are closely correlated with countries’ experiences during colonial times, which in turn help explain the international differences in governance and institutions. Moreover, Rodrik et al. (2004) show empirically that when different institutional arrangements across countries are taken into account, there is a lack of statistical significance on the coefficient of trade on income. More recently however, Noguer and Siscart (2005) argue that geographical controls must enter the trade-income equation directly in order to avoid estimation biases. They find that countries that trade more reach higher levels of income and the results are robust to a wide array of geographical and institutional controls. While the debate has not yet settled, most recent empirical papers tend to find a causal effect from trade to income levels and growth.<sup>1</sup> Trade theory tends to support this the positive trade-income nexus, though specific theoretical studies differ in their predictions about the underlying channels through which trade affects income. For instance, larger economies can export greater quantities of each good (the intensive margin), a wider set of goods (the extensive margin), or higher-quality goods.<sup>2</sup> It is possible that larger economies simply export more of each good, at lower prices on average on world markets. Hence, efficiency gains associated with specialization or economies of scale for instance may be attained as countries increase their degree of trade openness. It is also possible that larger economies export more varieties to more countries or that they export higher-quality goods at higher prices. Hummels and Klenow

> 1 For papers on trade and income, see for example Alcalá and Ciccione (2004), Felbermayr (2005), and Dufrénotet al. (2010). For papers on trade and growth, see for example Dollar (1992), Edward (1992), Wacziarg (2001), Easterly and Levine (2001), Dollar and Kraay (2003), Lee et al. (2004). Singh (2010) provides a recent review of this literature. 2 For example, Armington’s (1969) models emphasize the intensive margin, monopolistic competition models (e.g. Krugman, 1981) focus on the extensive margin, and vertical differentiation models (e.g. Grossman and Helpman, 1991a) explore the quality margin.

2
TABLE 4. GMM Estimates with Instrumented Diaspora

||(1)<br>|(2)<br>|(3)<br>|
|---|---|---|---|
||1<sup>st</sup>stage results|GMM|GMM|
|||Co-inventorship|R&D offshoring|
|ln(Diaspora)||0.223***<br>(0.0774)|0.120<br>(0.170)|
|ln(Distance)|-0.268***|-0.247***|-0.132|
||(0.0315)|(0.0649)|(0.0985)|
|ln(diaspora 1960s)|0.0128<br>(0.0102)|||
|ln(diaspora 1960s)^2|0.00534***<br>(0.00163)|||
|ln(low-skilled diaspora)|0.0549***<br>(0.00971)|||
|ln(low-skilled diaspora)^2|0.0137***<br>(0.00195)|||
|Controls|Yes|Yes|Yes|
|Constant|14.86***|-6.866|-0.0369|
||(1.967)|(5.998)|(8.833)|
|Observations|26,160|26,160|26,160|
|Controls|Yes|Yes|Yes|
|Sending FE|Yes|Yes|Yes|
|Receiving FE|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|
|F-test|344.58|||
|p-value|0.0000|||
|Hansen's J chi2||3.00468|6.13152|
|p-value||0.3909|0.1054|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia, and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. Instruments are centered around their mean, and they are as follows: (i) the size of the bilateral diaspora between countries i and j in the 1960s—and its square, and (ii) the size of the unskilled diaspora original from country i residing in country j in 1990 (migrants with only primary or lower levels of education)—and its square.

One way to see whether my interpretation of the different effects of inventor diaspora is plausible is to include tertiary educated stocks of migrants as extracted from census data. The data come from Docquier et al. (2009) and were originally built from two census rounds, 1990 and 2000, in which the regressions were used, respectively, for the years 1991–2000 and the years 2001–2010 (recall that censuses record data only every 10 years). As can be seen in table 5, column (2), the coefficient for the inventor 30
is a more cost-effective way than a diary to collect FAFH information. However, the specificities of the best option should still be based on the context. On the one hand, moving beyond one line comes at a cost: the least costly alternative – individual recall – is around 7% more expensive, while the best performing arm is around 33% more costly. On the other, each of the two improved treatment arms tested in this experiment is associated with different field and implementation logistics that can be more or less appropriate depending on the setting (i.e. geographic distributions of teams, transportation costs, time spent in each location, number of visits usually made to the household, prevalence of cell-phones, etc.). Finally, there is still much to be learned. For example, it remains an open question whether some tweaks to the individual recall arm can improve its accuracy, or whether eliminating or changing some of the components of the salience and bounding arm can lower its costs without sacrificing accuracy. Additionally, the external validity of the results is unknown, and the scope of the research question was limited for better capturing the _value_ of FAFH. Much less is known still about how to measure _quantities_ consumed, and its implications for the measurement of the caloric and nutritional value of food consumption.

In sum, this work highlights the inaccuracy associated with collecting FAFH consumption from a single question in a survey, and the high potential of using lessons from survey methodology and behavioral sciences to identify cost-effective alternatives that can better inform policy making over time. When considering approaches to measuring food away from home, policy makers should consider their own context, costs, and needs to decide on how to improve methods of data collection. This study offers a few lessons and provides useful insights into ways to go about it.

# **8. Bibliography**

<mark>Backiny-Yetna, P., Steele, D., and Djima, I. (2014)</mark> _<mark>. “</mark>_ <mark>The impact of household food consumption data collection methods on poverty and inequality measures in Niger”. World Bank Group Policy Research Working Paper 7090.</mark> <mark>Bee, Adam, Bruce D. Meyer, and James X. Sullivan (2012). “The Validity of Consumption Data: Are the Consumer Expenditure Interview and Diary Surveys Informative?” No. w18308. National Bureau of Economic Research.</mark> <mark>Beegle, Kathleen, et al. (2012). "Methods of household consumption measurement through surveys: Experimental results from Tanzania."</mark> _<mark>Journal of Development Economics</mark>_ <mark>98.1: 3-18.</mark>

- <mark>Borlizzi, Andrea, Del Grossi, Mauro Eduardo, Carlo, Cafiero (2017). “National food security assessment through the analysis of food consumption data from household budget and expenditure surveys: the case of Brazil’s Pesquisa de Orçamento Familiares 2008/09”.</mark> _<mark>Food Policy.</mark>_

34
different source).1<sup>4</sup> The estimates refer to the period 1990-99. Data come from the ITU/BDT database and from infoDev. All money variables (GDP and costs) are expressed in US$.

Eq. (1) is fairly standard and only the last term (dependent lagged variable) deserves some further comment. Such term comes from a Gompertz model of technology diffusion, quite common in the estimation of the sales of relatively new products. It derives after positing that the number of new adopters of a certain good or service in a given period depends both on the maximum number of potential users (usually a fraction of the total population) and on the number of existing users that may "spread" further adoption. These models typically produce life-cycle diffusion curves over time, with an introductory phase characterized by slow growth, a growth phase reflecting the highest rate of consumer acceptance, and a maturity phase where the product reaches its point of saturation. The Gompertz and the Logistic curves are probably the most popular in a wider class of curves.'<sup>5</sup> Epidemic models can be criticized on several grounds, mainly because they posit an exogenous process that does not model explicitly demand and supply factors, relying on primitive treatments of information acquisition. We simply intend to check if the lagged dependent variable resulting from the stock adjustment process reflects a diffusion process for the internet . If such process is found in the data, one could be tempted to argue that Latin America is currently not doing particularly well simply because it started the adoption process relatively late in time. If there are some forces that drive an exogenous process of diffusion (perhaps not well specified, e.g. imitation), internet adoption should then converge towards an equilibrium level, raising the further question on the rate of convergence. Our exercise is also motivated by recent work of Kiiski and Pohjola (2001) who used a Gompertz model to test Internet diffusion in OECD countries and in the whole world. The basic difference between our approach and Kiiski and Pohjola's is that by using the panel structure of the data, we can control for unobserved country specific characteristics that are potentially correlated with the included regressors, and in particular with the lagged dependent variable.

In eq. (1) the coefficient a5 of the lagged dependent variable should give information on the diffusion process. If there is diffusion, it is expected to be positive but smaller than 1. In the absence of diffusion, the coefficient should be 0.

The models are estimated via GLS with weights given by country population. Standard errors are robust to arbitrary heteroskedasticity. Because of the bias of the within group

> **14** A host is a domain name that has an IP address record associated with it. This would be any computer system connected to the Internet (via full or part-time, direct or dialup connections). 5 See Stoneman (1995) for a comprehensive survey.

1 1
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 1

# **Motivation and overview**

How many rural people live in Latin American and the Caribbean (LAC)? The question is simple to pose but not well-defined. “Rurality” has many aspects and connotations. For policy purposes we need to specify precisely which aspects of rurality interest us, and define criteria for identifying these aspects. Moreover, we should not expect that any of this criteria exhibit marked break points between rural and urban areas or people. However defined, rurality is a gradient, not a discrete condition.

Here we apply two criteria for defining the rurality: population density, and remoteness from large cities. We argue that these criteria constitute important gradients along which economic behavior and appropriate development interventions might vary substantially. Where population densities are low, markets of all kinds are thin, and unit costs of delivering most social services and many types of infrastructure are high. Where large urban areas are distant, farmgate (or factory-gate) prices of outputs will be low and prices of inputs will be high, and it will be difficult to recruit skilled personnel to public service or private enterprises. Thus remoteness and low population density together define a set of rural areas that face special challenges in development.

Remoteness and population density alone, however, cannot begin to capture the heterogeneity that exists within rural areas. Many aspects of physical geography shape opportunities and constraints for rural development, and environment-development linkages. Here we focus on two summary measures of physical geography: agroclimatic suitability for cropping, and forest cover.

With rurality criteria and descriptors defined, it is possible to tabulate rural populations using one of the existing global gridded population coverages. The two main candidate datasets – Landscan, and the GPW – each have advantages and disadvantages. We chose to use the GPW 3, in part out of anticipation of a forthcoming GPW refinement which should give an improved measure of population distributions.

Using GPW 3, we tabulate the cumulative distribution of population and land area by population density. We perform these tabulations for LAC as a whole and for the larger individual countries. We partition the population distributions by remoteness, cropping suitability, forest cover, and biome. We do not nominate a threshold population density cutoff for determining rurality, leaving that to the user of this information.

# **Criteria for rurality**

‘Rurality’ and ‘urbanity’ are comfortable but imprecise terms. On the surface, they seem to express a clear and meaningful dichotomy. We anchor the concepts with contrasting images of an isolated, bucolic farm on the one hand, a conglomeration of skyscrapers and slums on the other. The opportunities, constraints, and living conditions in these two locales are