rafmacalaba/datause-encoder-data huggingface dataset:
- Doraszelski, U. & Jaumandreu, J. (2019), Using Cost Minimization to Estimate Markups, CEPR Discussion Papers 14114, C.E.P.R. Discussion Papers. **URL:** _https://ideas.repec.org/p/cpr/ceprdp/14114.html_

- Draca, M., Sadun, R. & Van Reenen, J. (2006), Productivity and ICT: A review of the evidence, CEP Discussion Papers dp0749, Centre for Economic Performance, LSE.

- Dutz, M., R.K., A. & Packard, T. (2018), The jobs of tomorrow” technology, productivity and prosperity in Latin America and the Caribbean, Staff report, World Bank.

- Edmond, C., Midrigan, V. & Xu, D. Y. (2015), ‘Competition, markups, and the gains from international trade’, _American Economic Review_ **105** (10), 3183–3221.

- Edmond, C., Midrigan, V. & Xu, D. Y. (2018), How costly are markups?, Technical report, National Bureau of Economic Research.

- Hempell, T. (2005), ‘What’s spurious, what’s real? measuring the productivity impacts of ICT at the firm level’, _Empirical Economics_ **30** , 427–464.

- Hilbert, M. R. & Katz, J. (2003), ‘Building an information society: A Latin American and Caribbean perspective’.

- Iacovone, L., Perreira-Lopez, M. & Schiffbauer, M. (2016), Competition makes IT better: Evidence on when firms use IT more effectively, Policy Research Working Paper 7638, World Bank.

- ITU (2017), ‘The challenge on managing digital content’. Paper for the ITU-TRAI regulatory roundtable.

- ITU (2020), ‘Regulatory tracker’. data retrieved from ITU, `https://www.itu.int/net4/ itu-d/irt/` .

- Katz, R. & Jung, J. (2021), ‘The impact of policies, regulation, and institutions on ICT sector performance’, _International Telecommunications Union_ .

- Khandelwal, A. K., Schott, P. K. & Wei, S.-J. (2013), ‘Trade liberalization and embedded institutional reform: Evidence from chinese exporters’, _American Economic Review_ **103** (6), 2169–95.

**URL:** _https://www.aeaweb.org/articles?id=10.1257/aer.103.6.2169_

- Lee, F. C. & Shy, O. (1992), ‘A welfare evaluation of technology transfer to joint ventures in the developing countries’, _The International Trade Journal_ **7** (2), 205–220.

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the quantity gap). Note that the sectors with the greatest potential contribution to growth are electric power and roads —even for the top performers in the region.

**Figure 10. Growth benefits of narrowing the gap vis-à-vis world’s top decile in infrastructure**

_(In percentage points per annum)_ <!-- Start of picture text --> 4.5%<br>4.0%<br>3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>Sub-Saharan SSA LICs SSA LMCs SSA UMCs SSA Bottom SSA Bottom SSA Top SSA Top<br>Africa 10% 25% 25% 10%<br>(SSA)<br>K-Tele K-Power K-Roads Q-Power Q-Roads<br><!-- End of picture text --> _Note: The coefficient estimates are taken from column [3] of Table 6 (Calderón and Servén 2010)._

# **D. Fostering investment and closing the financing gap**

Sub-Saharan Africa ranks at the bottom of all developing regions in virtually all dimensions of infrastructure performance: it trails the rest of the developing world not only in terms of the quantity of infrastructure but also in the quality of and access to these services. The evidence shows that there is a severe infrastructure bottleneck to be addressed and that the potential growth benefits of addressing these bottlenecks are large.

Narrowing the large infrastructure gap in Sub-Saharan Africa requires the mobilization of financial resources. The private sector has limited involvement in the different infrastructure sectors across the region —except for South Africa. If the additional spending needed to close the infrastructure gap falls on the public sector, it imposes a heavy burden. First, these countries have limited domestic resource mobilization. Total government revenues fall short of 20 percent of GDP in many SSA countries. Second, in the presence of scarce resources, governments need to assess the benefits of infrastructure investment vis-à-vis other pressing demands (say, education, health, among others).

The financing needs to close the infrastructure gap in the region are estimated to be approximately US$93 billion per year over the next decade —that is, about 15 percent of the region’s GDP (Foster and Briceño-Garmendia 2010). To date, less than half of that amount is being provided: actual investments in infrastructure amount to US$45 billion annually —with more than half being funded by the public sector. This leaves a financing gap of US$48 billion per year. Estimates show that about one-third of the infrastructure gap can be met through operational optimization —thus narrowing the gap to US$31 billion (5 percent of the _Infrastructure in Sub-Saharan Africa - 25_
# **1 Introduction**

Despite the government’s implementation of an ambitious agenda of economic and social reform, the performance of Mexico has been mediocre in economic growth and poverty reduction since the early 1990s. In 1992–2014, the country’s gross domestic product (GDP) expanded at an annual average rate of 2.5 percent. Per capita, this translates into an annual growth rate of only 0.9 percent, making Mexico the secondworst performer in continental Latin America during the period. This mediocre performance was mirrored in income poverty reduction. In 1992, the official poverty headcount ratios for extreme and total poverty were, respectively, 21.4 percent and 53.1 percent. More than 20 years later, in 2014, these ratios remained virtually unchanged, at 20.6 percent and 53.2 percent, respectively.<sup>1</sup> This poor performance in income growth and the long-run stagnation in poverty rates leave the impression that little has changed in the living standards of the population, especially among the poorest. This is particularly surprising, given that Mexico experienced many changes during this period, including multiple economic crises, the implementation of an ambitious set of structural reforms, and innovations in redistributive social policy. It may be, however, that these aggregate figures are masking trends at the subnational level. To understand clearly how income and poverty are changing, one needs to zoom in to a higher level of spatial disaggregation and unpack how these patterns vary within Mexico.

Specifically, this paper zooms in to explore whether some municipalities have been persistently lagging behind in pockets of poverty (income convergence) and whether poorer, converging municipalities have been able to translate their relative income gains into poverty reduction (poverty convergence). The analysis of convergence in the mean per capita incomes of municipalities follows the framework proposed by Barro and Sala-i Martin (1991). It aims at an understanding of whether poorer municipalities have been capturing the income gains resulting from modest growth and social spending and whether there has been a reduction in regional disparities. The analysis of convergence in poverty headcount ratios applies the poverty convergence decomposition by Ravallion (2012) to assess the effects of initial poverty on both the income growth process and the sensitivity of poverty reduction to income growth.

While several studies have looked at short-term (growth) convergence in income among states in Mexico, this paper contributes by leveraging a unique data set on

> 1All figures on GDP growth rates come from the World Bank’s World Development Indicators database. Official income-based poverty rates are from Mexico’s National Council for the Evaluation of Social Development Policy (CONEVAL).

2
utilization, including user fees and health outcomes. Section 4 presents empirical results on incidence of sickness and health outcomes before and after abolition of use fees, and Section 5 concludes.

# **2. Background, literature and data sources**

To set the scene, this section reviews the discussion in the literature on the pros and cons of user fees for health care as well as the evidence on the impact of this measure. We then describe the evolution of Uganda’s health system over time, in particular the imposition and subsequent elimination of user charges for health services, and use DHS data on health outcomes to place the quality of Uganda’s health services in a comparative international perspective.

# **2.1 User fees for health services: Conceptual foundations and empirical evidence**

While equity concerns have originally motivated many developing country governments to provide health services for free, such services were often not well targeted, implying that increasingly large amounts of resources had to be expended to provide them. With the imperative to balance budgets, such services often were no longer affordable. To prevent fiscal constraints from undermining the quality of the health system, policy makers have increasingly emphasized the need to focus public support on provision of a basic package of primary health care and to impose some cost recovery in the form of user fees wherever possible (Gwatkin, 2000). A justification for such policies was provided by studies that showed demand for health care to be relatively inelastic with respect to price (McPake, 1993; Akin et al. 1985; Heller, 1982; Birdsall and Orivel, 1983; Jimenez, 1985), implying that potential users would be willing to pay for services that were of a sufficiently high quality. This general policy direction has been expressed in the Bamako Initiative, which was supported by a large number of governments (Gilson _et al_ ., 2000; McPake _et al._ , 1993).

The shift toward greater cost recovery prompted a rapid growth in empirical studies aiming to determine whether cost recovery promotes or prevents access to health care by the poor (see Shaw and Ainsworth, 1995; Reddy and Vandermoortele, 1993; Gilson _et al_ ., 2000; Akin _et al_ ., 1989 for summaries). Proponents of user fees argued that at least part of the benefits from better health are private goods and that even charges that fall far short of full cost recovery can prevent those seeking health care from overusing the system while, especially if appropriately channeled, providing incentives for better performance of medical staff (Audibert and Mathonnat, 2000; Killingsworth _et al._ , 1999; de Ferranti, 1985). The general consensus, as supported by recent empirical evidence, seems to be that in a number of cases the poor are willing to pay for high quality of health services, such that user fees may not hamper their access to health care. For example, evidence from Bangladesh points to instances where the poor 3
|Started or increased online<br>business activity|-0.604**|-0.634**|-0.599**|-0.648**|0.529|0.408|0.441|0.441|-0.459|-0.524|-0.552|-0.433|-0.802|-0.777|-0.814|-0.889*|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(0.276)|(0.272)|(0.276)|(0.269)|(0.482)|(0.493)|(0.485)|(0.480)|(0.405)|(0.404)|(0.394)|(0.409)|(0.546)|(0.508)|(0.521)|(0.533)|
|Started or increased|||||||||||||||||
|delivery of goods, services<br>|0.435|0.371|0.454|0.523*|-0.466|-0.387|-0.402|-0.419|0.092|0.115|0.242|0.005|1.368**|1.339**|1.442**|1.449**|
|or carryout|||||||||||||||||
||(0.285)|(0.249)|(0.280)|(0.271)|(0.431)|(0.413)|(0.426)|(0.407)|(0.494)|(0.505)|(0.492)|(0.498)|(0.629)|(0.624)|(0.686)|(0.584)|
|Started or increased remote<br>work|0.084|0.125|0.090|0.108|0.294|0.303|0.326|0.313|-1.034***|-1.037***|-1.009***|-0.984***|-0.229|-0.134|-0.176|-0.275|
||(0.161)|(0.171)|(0.163)|(0.160)|(0.320)|(0.322)|(0.332)|(0.352)|(0.355)|(0.370)|(0.372)|(0.343)|(0.345)|(0.340)|(0.359)|(0.358)|
|Exporting directly (at least<br>10% of sales)|0.259|0.289*|0.247|0.238|0.469|0.508|0.509|0.499|0.110|0.174|0.164|0.220|1.073|0.777|1.080|1.007|
||(0.160)|(0.162)|(0.162)|(0.160)|(0.525)|(0.531)|(0.522)|(0.503)|(0.659)|(0.695)|(0.670)|(0.696)|(0.804)|(0.588)|(0.743)|(0.765)|
|Constant|0.278|0.251|0.271|0.293|-2.598***|-2.600***|-2.596***|-2.562***|-1.444***|-1.456***|-1.641***|-1.439***|-3.285***|-3.176***|-3.436***|-3.283***|
||(0.239)|(0.234)|(0.215)|(0.218)|(0.464)|(0.476)|(0.491)|(0.459)|(0.353)|(0.367)|(0.384)|(0.362)|(0.498)|(0.463)|(0.465)|(0.508)|
|Number of observations|469|469|469|469|473|473|473|473|522|522|522|522|427|427|427|427|
|r2_w|0.340|0.358|0.335|0.355|||||||||||||
|r2_b|0.066|0.035|0.077|0.074|||||||||||||
|r2_o|0.066|0.038|0.074|0.073|||||||||||||
|rho|0.669|0.697|0.674|0.701|||||||||||||

Notes: This table reports panel regressions with individual firm fixed effects for the Georgia COV-ES data. Sector information used as the interaction terms is taken from the baseline ES. Columns 3 through 8 report coefficients from logistic regressions. Sampling weights from the ES are used. Standard errors are shown in parenthesis. All standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 6 for more details.

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# 2 Stylized facts

The term diaspora (in ancient Greek, "a scattering or sowing of seeds") refers to dispersion of any people or ethnic population, voluntarily or by force, from their traditional homelands and the ensuing developments in their culture in the destination, mostly as a minority. In the economic sense, the diaspora refers to migrants who gather in relatively signi...cant numbers in a particular destination country or region. Some examples are the Turkish Gastarbeiter in Germany, South Asian workers in the Persian Gulf and Cuban migrants in the US.

Following this de...nition, we consider the size of a diaspora as the population (aged 25+) born in country i and living in country j. We use the Docquier, Lowell and Marfouk (2007, referred to as DLM from now on) database which extends and updates Docquier and Marfouk (2006). Based on census and register information on the structure of migrant communities in all OECD countries in 1990 and 2000, DLM database provides the stock of immigrants from any given country in each of the OECD countries by education level. The dataset covers only the adult population aged 25 and over, thus excludes children and students who emigrate temporarily to complete their education. In addition, migration is de...ned on the basis of the country of birth rather than citizenship<sup>4</sup> .

The main strength of the DLM database is that it distinguishes between three levels of education for migrants. High-skilled migrants are those with post-secondary education. Medium-skilled migrants are those with upper-secondary education completed. Low-skilled migrants are those with less than upper-secondary education, including those with lower-secondary and primary education or those who did not go to school. The main characteristics of the diaspora that we consider in this paper are the following:

- The size of the diaspora, measured as the population aged 25+ born in country i and living in the OECD country j (= i).

- The education level of the diaspora, proxied by the log-ratio of the proportions of high-skill to low-skill migrants.

- The concentration of the diaspora, measured as the Her...ndhal index applied to the distribution of the diaspora across di¤erent destinations.

Table 1 shows the 20 largest bilateral migrant communities residing in the OECD countries, both by overall size and by di¤erent education levels. The distinction

> 4Even though this is the standard de...nition of a migrant, especially in the economics literature, the dataset does not include second generation children who are born in the destination country even though they might constitute an important part of a diaspora in the sociological sense. This is simply due to absence of comprehensive administrative data in tracking of the migrants’children. However, we expect diaspora sizes inclusive and exclusive of second generation to be highly correlated.

5
longer-term. From a policy perspective, understanding whether and how autonomy encourages the use of agricultural best practices is important as policy makers re-design agricultural input subsidy and extension programs, which are ubiquitous in developing countries. From an academic perspective, we contribute to the debate by documenting the importance of autonomy for technology adoption and providing some evidence on the possible mechanisms at play, although more research is needed.<sup>15</sup> The rest of the paper is organized as follows. Section 2 describes the context and data used while section 3 provides the details of the design and rationale for the various experimental arms. Section 4 describes the empirical strategy and sections 5 and 6 present the short and long term results, respectively. Section 7 concludes.

# **2 Context**

This project was a collaborative effort between three organizations: our partner NGO “Qué Funciona para el Desarrollo” or QFD; Ipampa S.C., a long-standing local private extension service company, and Agropecuaria Amozoc, a commercial fertilizer dealer. The project was implemented in 13 municipalities of the Mexican state of Tlaxcala, chosen for having substantial rainfed smallholder farmer populations (see Figure 1) with relatively low maize yields (2.7 tons per hectare on average).

In January 2015, QFD advertised the program widely by displaying posters prominently in public locations and handing out informational leaflets. QFD also organized a total of 34 promotional meetings in the principal towns in each municipality. The promotional meetings lasted between 60–90 minutes and typically took place in a large public space (e.g. a municipal auditorium). During the meetings, the research team introduced and explained the intervention, described the eligibility requirements, and the lottery design. A total of 1,299 farmers that attended the promotional meetings and potentially interested farmers were asked to complete a short form.

Between February and March 2015, interested farmers were visited by the research team. During the visit, the team collected a detailed baseline survey on a range of farmer characteristics and agricultural practices during the previous growing season (2014). After the survey, farmers were asked to register a subplot of one hectare for the program where they planned to grow maize. QFD cordoned off this subplot, GPS coordinates for the subplot were recorded,

> 15The literature on the impacts of cash transfer programs is also related (see Benhassine et al., 2015; Baird et al., 2019, and see Molina-Millán et al., 2019 for a review). This literature has examined the difference between unconditional and conditional transfers both in the short- and longer-terms focusing particularly on human capital, consumption, and well-being. In a conditional cash transfer program beneficiaries can _use_ the transfer amount as they please, but the cash is _received_ only if the beneficiary complies with the conditions (Fiszbein and Schady, 2009). In contrast, we provide in-kind grants that directly subsidize (or not) the desired activity.

6
_Difference in returns to workers’ characteristics_ A second approach, which is by far the most common in the literature, consists of allowing the returns to the workers' observable characteristics Xit to differ between the two sectors. Let β1 represent the returns in the public sector and β0 represent the returns in the private sector. Using the same notations as above, the model is as follows: yit = τt + ηt ∗Pit + β0 ∗Xit + β1 ∗Pit ∗Xit + εit.

We include the workers’ observable characteristics that have the most significant effect on earnings.<sup>7</sup> The OLS estimation results are shown in table 4. In 1993, the public sector has higher returns to education and lower returns to experience than the private sector. Between 1993 and 2006, the returns to education increase, and the returns to experience decline in both sectors, which is a common finding in other transition economies. At the end of the period, the returns to education are even higher in the public sector whereas the returns to experience are comparable in both sectors. This shift suggests an explanation for why the public-private wage gap increases. Public workers, who are better educated and more experienced than their private counterparts, benefit from a rise in the returns to skills. Note that once the differences in the returns to the workers' characteristics are taken into account, the public sector dummy becomes insignificant; if anything, it decreases over time. We will use these results to perform the Oaxaca-Blinder decomposition. However, an important limitation of this approach is that any unobserved characteristics that are correlated with Pit and Xit (e.g., cognitive abilities or political connections) bias the estimate of β1 −β0.

19
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that excludes the own-unit _l_ from calculation of the aggregate flow _mj,t_ , and this could provide more credible identification. Second, if the number of units _l_ over which each _mj,t_ is calculated is large, the “mechanical” correlation between the instrument and the outcome may be ignorable.

Finally, researchers also sometimes use more complicated versions of shift-share instruments, that are non-linear. Examples include Basso and Peri (2015) who use a nonlinear shift-share instrument for immigration flows, and Allen et al. (2018) who use a nonlinear instrument to predict border walls. It can be harder to be clear where identification is coming from in these non-linear cases, and their use requires extra care. Appendix D.4. of Borusyak and Hull (2021) discusses these cases, and note that a type of “re-centering" is usually needed to make these instruments valid.

# **4.4.2 Calculating correct standard errors**

It has also been recently shown that conventional standard errors in shift-share designs may be invalid, due to the fact that units _l_ that have similar exposure shares have similar residuals. For example, under the Borusyak et al. (2022) framework that relies on shifts being exogenous, the effective sample size depends on the number of shifters (number of independent exogenous shocks). Borusyak et al. (2022) and Adao et al. (2019) provide methods for constructing valid standard errors that account for such shared shock exposure across units, both of which are available as Stata packages. A key point here is that it is often the case that the effective sample size providing variation may be much lower than researchers have traditionally thought. For example, consider attempting to use a shiftshare instrument to examine impacts of migration on different Mexican communities, where the shares are the share of migrants that historically when to different U.S. cities, and the shocks are immigration enforcement actions in different U.S. cities. Then there may only be 10 or 15 U.S. cities that most migrants are choosing between, and some of these cities may coordinate their policy changes, so that effectively we can think of policies being randomly varied over only 4 or 5 clusters of cities. Correctly accounting for this will typically result in much larger standard errors.

Researchers have thought less about the validity of standard errors under the GoldsmithPinkham et al. (2020) framework of identification based on exogenous shares. However, often it will not seem plausible to think that the shares are independently distributed across towns or regions, since there is often some spatial correlation. We currently recommend that researchers discuss explicitly what they believe is the cause of shares varying across units, and the use this to consider clustering standard errors at higher levels of aggregation (such as state), or otherwise explicitly modeling the assignment process that gave rise to shares differing.

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