rafmacalaba/data-use-mentions-extended huggingface dataset:
Legal Origin and Latitude explain 2.16% of the total variation in firms’ responses that amounts to about 13% of the cross-country variation although Legal Origin has a more significant drop in explanatory power when compared to the full sample of 80 countries (-44%) than Latitude (-3.6%).

# **Insert Table 4**

Taken together, all the institutional factors explain approximately 52% of the cross-country variation in this reduced sub-sample, while they explained 50% of the cross-country variation in the full sample that included the former Socialist countries.

At the firm level (not shown in the table), legal organization of the firm explains 0.94%<sup>20</sup> which is more than ownership structure (0.44%). Size explains 0.28% and there is very little variation explained by the industry sector in which the firm operates (-0.03%). When we look at the marginal contribution of the firm effects in Table 4 (second row in each panel), the variables with the maximum explanatory power across models is legal organization. The strongest country-firm interactive effect is that of Latitude-Legal Organization (0.93%).<sup>21</sup>

# **_5.1.2. Using Settler Mortality_**

Given the strong performance of the latitude variable, we use another measure of geographical endowments, **Settler Mortality** , as stressed by Acemoglu, Johnson and Robinson (2001). Table 5 shows the results for a sub-set of countries for which data on settler mortality is available. The sample is now restricted to 40 countries with 3222 observations. This sample of countries is similar to the sample of non-socialist economies in Table 4.

# **Insert Table 5 here**

We construct the table using the five point categorization applied to settler mortality. The results of Table 5 once again dramatically change the order of explanatory

> 20 Legal organization is still the dominant
generated by the elicitation exercise.

Whether training takes place in a village is a random event, conditional on the willingnessto-pay of the decision maker _D_ for randomly selected potential trainee _z_ . The _price_ , _choice_ , and _decision maker_ lotteries are such that a decision maker cannot affect the price she pays for training, the identity of the recipient, and the likelihood that she herself is the decision maker. But her willingness-to-pay choice can affect the probability that a recipient _z_ , selected via _choice-lottery_ , receives training; the higher the willingness-to-pay of a decision maker for _z_ , the more likely it is that _z_ receives training. Whether training takes place in a village is therefore a random event, conditional on the willingness-to-pay of the decision maker _D_ for randomly selected potential trainee _z_ . This allows me to employ the following empirical specification to evaluate the impact of training one farmer on the adoption of other sampled farmers in the village<sup>15</sup> : The dependent variable is a dummy equal to 1 if farmer _i_ in village _v_ has adopted the following recommended practices to control the Fall Armyworm: spraying pesticides, pouring ash (or sand or soil) on infected plants, crushing eggs or caterpillars. _Trainingv_ is a dummy equal to 1 if training has taken place in village _v_ . _Trained Farmeri_ is equal to 1 if _i_ has received training. _Spillover Farmeri_ is equal to 1 if _Trained Farmeri_ is zero. Indicator 1 ( _wDz,v_ = _p_ ) is equal to 1 if the willingness-to-pay of decision maker _D_ for potential trainee _z_ is equal to _p_ , where _p ∈{_ 0 _,_ 2 000 _, . . . ,_ 40 000 _}_ .<sup>16</sup> Results from estimating 10 are presented in Table 1 and based on responses to a phone follow-up survey carried out in December 2018. In the survey we only ask adoptionrelated questions to farmers who say at least one of their plots had a Fall Armyworm infection during the current season; for this reason
0.41|19.8%|0.43|21.39%|
|Net Market Income|0.40|19.8%|0.42|21.44%|
|Disposable Income|0.40|19.7%|0.42|21.42%|
|Consumable Income|0.40|19.9%|0.41|23.70%|
|Final Income|0.38|---|0.40|---|

_Note: Poverty headcount calculations use the national poverty line._ The rest of the report is organized as follows. Section 2 provides an overview of the fiscal structure of Uganda, describing both the main taxes and transfers in the country, and the CEQ methodology and the allocation assumptions are presented in Section 3. The poverty and inequality results are presented in Section 4, and the progressivity and marginal contribution of each fiscal instrument is discussed in Section 5. Section 6 compares the 2012/13 and 2016/17 fiscal incidence analyses. Finally, Section 7 concludes and lists some policy considerations.

# 2. Fiscal Structure

This section presents a categorization of tax and other revenue collections and public expenditures in Uganda for the fiscal year 2016/17 –the UNHS survey enumeration year – and identifies the fiscal policy items included in the fiscal incidence analysis.

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The first implementation of the World Bank poverty mapping software was actually done in SAS (Demombynes, 2002). Poverty indicators and their corresponding standard errors were obtained by a bootstrap procedure relying on simulation. This simulation approach is very reminiscent of multiple imputation methods, where every single relevant parameter necessary for simulating vectors of welfare is drawn from its corresponding estimated asymptotic distribution (or an approximation to it). This approach is the one currently used in the World Bank’s tools for implementing the ELL (2002) approach: Stata’s sae package and PovMap. Specifically, the steps of the implemented bootstrap procedure designed to obtain the traditional ELL point estimates and their estimated noise are:

1. Fit the nested error model (1) to the survey data. This yields the vector of initial parameter estimates

- _θ_ ˆ0 = (ˆ _β_ 0 _,_ ˆ _ση_<sup>2</sup> 0<sup>_,_ˆ</sup><sup>_σ_</sup> _e_<sup>2</sup> 0<sup>)</sup><sup>_,_</sup>

where the 0 subscript is used hereafter to indicate that the estimates come from the original household survey. In the implemented version, the model is fit via FGLS as specified in Nguyen et al. (2018).

2. Draw new model parameters as follows. First, regression coefficients are drawn from The variance of the household-level errors is drawn, according to Gelman et al. (2004, pp. 364-365), from where _χ_<sup>2</sup> _n_<sup>_∗_</sup> _−K_<sup>denotesarandomnumberfromachi-squareddistributionwith</sup><sup>_n−K_degreesof</sup> freedom. Here, _n_ is the number of observations in the survey data used to fit the model and _K_ is the number of correlates used in the model. The variance of the cluster effects _ση_<sup
the low-cost values and the regulatory monitor (Table 2 Panel A), the figure shows that there remains a large underestimation when using the low-cost monitor data adjusted using the Jaffe calibration, which is confirmed by a larger RMSE when comparing the regulatory and Jaffe-adjusted data (Table 2 Panel B).

We find that aggregating the data temporally improves alignment, as it did with the satellite and regulatory data comparison. This can be seen both visually in Figure 6 Panel D, where the data is aggregated weekly, and also in the higher correlation of 0.89 at the weekly level (Table 2 Panel A). Comparing at a higher temporal frequency at the hourly level leads to lower alignment (Figure 5). Nevertheless, the locally calibrated data performs much better at the hourly level (correlation of 0.74) as compared to the satellite data (correlation of 0.54) (Table 2 Panel A).

Third, we compare the PM2.5 values from the low-cost monitors to the PM2.5 estimates produced using the satellite MERRA-2 data. In a similar result to the comparison between the regulatory-grade and low-cost monitors, we find that the low-cost monitors record much lower levels of pollution compared to the satellite data (Figure 7). This underestimation was particularly severe in early 2020 and early 2021, when dust storms were frequent, where the unadjusted Purple Air PM2.5 values do not increase substantially despite peaks in PM2.5 in the satellite data (Figure 7A).

To confirm that the substantial divergence between the PM2.5 measurements from the two data sources is due to an underestimation of dust by the Purple Air monitors in our context, we generate a modified PM2.5 estimate using all of the MERRA-2 components except dust.<sup>20</sup> Comparing the MERRA2 estimates of PM2.5 with and without dust suggests that much of the PM2.5 pollution in Dakar is due to dust (Figure A2). The PM2.5 estimate excluding dust from MERRA-2 and
the worked for a and under 20 were sample wage, just percent self-employed. Applicants were working about nine hours a week and earning 15 GHS a month from all sources. Garment making and cosmetology were the two most popular trades, which is unsurprising given the gender composition of our sample. Of the applicants, 44 percent expressed interest in an apprenticeship in garment making, 35 percent were interested in cosmetology, and the rest were interested in construction trades (welding, masonry, and carpentry).

In order to provide evidence for the internal validity of our randomization, we test whether our treatment and control groups are similar on observable characteristics measured at baseline. For a given characteristic we run an ordinary least squares (OLS)

> 10Additional details of the World Bank STEP survey can be found at `http://microdata.worldbank .org/index.php/catalog/step/about` .

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Figure 1.7a: Natural Gas Non-residential Tariffs, US$/TCM <!-- Start of picture text --> 500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2005 2006 2007 2008 2009 2010<br>Ukraine Poland Russia Lithuania Belarus<br><!-- End of picture text --> Source: Energy Regulators Regional Association (ERRA), Ministry of Energy of the Republic of Belarus.

Figure 1.7b: ULC dynamics: Russian manufacturing and Belarusian industry (2005=1) <!-- Start of picture text -->
1.3<br>1.2<br>1.1<br>1.0<br>Belarus<br>0.9 Russia<br>0.8<br>0.7<br>0.6<br>2005 2006 2007 2008 2009 2010<br>Source: World Bank staff calculations on Belstat and Rosstat data.<br>Notes:  Based on the producer price index as wage deflator.<br><!-- End of picture text -->

**On the demand side, there has been an increase in demand for more sophisticated products in the Russian market** . The evolution of the Russian market suggests that there is a high income elasticity of demand for quality transport equipment.  To the extent that Belarus does not compete in this market its industry is bound to lose market share.

**The combined effect of supply and demand shifts has been a sharp decline in the market share of Belarusian machinery and transport imports in total imports of Russia during the last years** ( **see** Figure 1.8a, 1.8b and 1.8c).

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4. Guidelines for good practices in the design of laws and implementation methods should be devised, and should include assessments of the specific procedures that are applied in different countries and mappings from existing guidelines to those recommended by major international organizations, such as the World Bank and UNICITRAL. This type of analysis should identify the important and desirable features of laws and procurement processes—including, for example, the reliance on standards and norms that apply to procedures along the lines that were developed for construction procurement in South Africa (see Watermeyer, 2004a, b). Related to this, a critical assessment of the costs and benefits of existing guidelines that are applied by institutions, such as the major development banks, would be very useful. What do these guidelines actually achieve? Are they outdated given the rise of the Internet and the potential for government to business portals to introduce market forces into the procurement process through real-time reverse auctions and the like?

5. The factual record on outcomes from procurement regimes in developing countries (including the number of contracts put out to tender, indicators of the use of selective or limited tendering, the use of preference margins in contracts, the number of firms submitting bids for a contract, the propensity to award a contract to the lowest bidder, and the propensity to source from abroad or from domestically favored groups) should be assembled. Of course, countries differ markedly in the amount of data that they collect and make available publicly, but with the growing use of computers in procurement systems, more developing countries are assembling such data. Subject to any concerns about confidentiality and the like, bringing these data together would facilitate policy analysis. Data on the observed outcomes from state procurement could be complemented by information obtained from surveys of business people and others’ perceptions of the “fairness” or otherwise of national procurement systems.

6. Given the role that discriminating between suppliers for state contracts plays in many nations’ procurement regimes, more research is needed that identifies the costs and benefits of such discrimination
**The Mechanics of Progress in Education:**

# **Evidence from Cross-Country Data**

# **1. Introduction**

In all countries education plays a key role in social and economic life, so it is not surprising that most government **s** eek, at least in rhetoric, to build education systems that offer the best possible services to as wide a segment of the population as possible. Despite the common goal, however, countries achieve very different results. On almost any measure of schooling outcome-whether related to coverage or student learning-low-income countries typically lag behind high-income countries. What are the sources of rich countries' advantage in education? What is the role of such factors as demographi **c** onditions, government fiscal capacity, prices, and policy choice? Does the impact of these factors change as countries grow rich, and if so, how? What do the patterns reveal about the nature of policy choice in education?

Answers to these questions are explored in this paper. In the process we hope to discover patterns in the relation between country wealth and various aspects of educational development, and to clarify the mechanics of progress in education as a country grows rich.' At the same time, we also expect to improve our understanding of policy options to improve education. Given the breadth of these issues, our study represents a partial treatment, both because it focuses on system-wide features rather than on pedagogical processes at the classroom level, and because it relies solely on quantitative data.<sup>2</sup> These limitations notwithstanding the findings reveal several key insights. The first is that systematic links exist between country wealth and most indicators of educational

> 'For a discussion of similar questions from the perspective of a production-demand framework based on data from 1960 to 1980 see Schultz, T.P. 1988 "Expansion of public school expenditures and enrollments: inter-country evidence on the effects of income, prices, and population growth," Economics of Education Review 7
. Consequently, only programs that meet employer needs will be able to identify WBL placements for their students.

There has been increasing focus on the trade-off between immediate employment gains of vocational-oriented education and general skills required for lifelong learning. Hanushek et al. (2017) argue that skills generated by technical-oriented training may facilitate the transition into the labor market but may become obsolete at a faster rate and that initial labor market advantage of vocational relative to general education decreases with age. The study pooled individuals from the 11 countries with sizeable vocational education systems and found that individuals with general education initially face worse employment outcomes but experience improved employment probability as they become older relative to individuals with vocational education. The pattern is most pronounced in the apprenticeship countries of Denmark, Germany, and Switzerland. In these countries, the easier entry into the labor market is balanced by noticeably greater withdrawal at older ages. Using Austrian social security data, the study showed that after a plant closure, the relative employment rates of displaced blue-collar workers (with more vocational training) are above those of white-collar workers at younger ages but below them at ages above 50.

Such a trade-off is likely to become more pronounced in light of technical advancement and automation. Kattan, Macdonald, and Patrinos (2018) show that while automation puts jobs at risk in general, upper-middle-income countries are at the highest risk. This risk can be mitigated through a boost in cognitive and socioemotional skills such as openness to experience. It might be difficult to acquire these skills only through technical and job-specific training, but high-quality education that focuses on general skills can help acquire the skills.

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focus on nutrition for mothers in utero and best practices during pregancy, as well as practices with infants regarding breastfeeding, care when sick, and supplemental feeding when older. In the meeting, the health worker used cards portraying the issue at hand to lead a group wide discussion. For example, one card was a drawing of a woman breastfeeding, where the discussion might then revolve around issues related to frequency depending on age of the child, proper attachment, and ways to try to solve issues such as not creating enough milk. The overall curriculum was developed by Helen Keller International, based on World Health Organization standards.

The cash transfer was set at NPR 700 per month (approximately $7 USD), which represents about 8-20% of median monthly income in our four districts. This figure is approximately in line with standard CCT’s (Fiszbein, Schady, and Ferreira, 2009). The transfer was distributed at the regular village meeting, meaning that there was a conditionality on the cash transfer that can additionally be viewed as an incentive to take up the information treatment. The transfer was “labeled” as for use on the child. Given that the conditionality is on simply going to collect the money, not on any type of behavior, as is standard in conditional cash transfer programs, and that the transfer was only distributed for five months, this should not be viewed as a standard CCT program. Rather, the cash provided a short term safety net, spurring the critical question of whether a shorter, cost effective intervention can have similar improvements in child outcomes as would a more elaborate, prolonged CCT program.

# 3 Data

To estimate the impacts of the intervention, we gathered detailed data on eligible households in each experimental village. A household was deemed eligible if there was a woman present who was either pregnant or had a child younger than two years old. Baseline data collection occurred in August-October 2013, prior to the start of the intervention. In order to better measure effects of how the cash
and that, even among those who pass, we observe a strong ACASI effect on innocuous placebo questions. This lack of understanding will tend to _increase_ IPV reporting, since the rate of IPV is much less than 50%. And indeed, we do find a striking increase in reported IPV in one country (Malawi). However, this result is likely entirely spurious. This is deeply concerning because measurement error goes in the same direction as destigmatization, and so what looks like a decrease in stigma could be purely fictional. SI could therefore give very misleading results.

Our results, combined with our read of the literature, suggest that there may be greater benefit from having well-trained, empathetic enumerators than from SI in the context of measuring IPV. For example, in a natural experiment in Serbia, respondents of a WHO-run IPV survey ended up getting randomly assigned to either a previously inexperienced but well-trained enumerator 15
larger scale passed, they could provide significant economic benefits.

The remainder of the paper is organized as follows. Section two puts Malawi’s land tenure system in context by describing its evolution, highlighting questions to be analyzed, and drawing out their relevance for recent debates on land legislation. Section three presents the sample and descriptive evidence on smallholders’ and estates’ intensity of land use and productivity and the size and potential lease fees from estates. Section four discusses econometric results on determinants and productivity impacts of perceived tenure insecurity by male and female users. Section five concludes by drawing out implications for policy and future research.

# **2. Context, issues, and link to the literature**

While dating back to colonial times, Malawi’s dualistic agrarian structure was reinforced by the creation of estates on more than 20% of the country’s area in the late 1980s and limitations on smallholders’ market participation. While the latter were eased in the 1990s, debates on land tenure remained inconclusive and opinions on next steps remained divided. Quantitative evidence can help put issues in perspective and point

> 1 Data are from the 2010/11 Third Integrated Household Survey (IHS3). We chose willingness to pay because it is slightly lower than the median rent paid (US $55/ha and year) and because it is based on responses from close to 90% of the sample rather than only those participating in markets. 3
UR) reforms. First, UR (as with past GATT and future WTO) commitments relate to reductions in bound tariff rates, not applied rates, and bound rates can be higher (in agriculture’s case often several times higher) than applied rates. Yet many modellers used applied rates in calibrating their models and then reduced them by the extent of the promised bound tariff cuts, thereby overstating the magnitude of reform – in some cases by a huge margin, given the dirty agricultural tariffication that occurred. Recent concerted efforts by GTAP consortium member institutions and others have ensured both sets of tariffs are available from late 2004. Care is also needed in specifying reform options when non-linear tariff and subsidy cuts are to be implemented (Francois and Martin 2003, Jean, Laborde and Martin 2005) Second, a wide array of tariff preferences operate for various groups of developing country exporters and among members of preferential-trade areas. In the past these have been ignored by most modellers, because such data have not been available in a form that they could readily use. That too has been rectified with the Version 6 GTAP data base. The expansion in the number of LDCs separately represented in the GTAP data base also aids quantitative analysis of this sensitive issue.

Third, it is in agriculture where most of the remaining gains from goods trade liberalization are to be found.<sup>26</sup> Reforming that sector should have been straightforward

> 25 Criticism has come even from within the international economics profession. See, for example, Panagariya (1999).

> 26 According to GTAP modelling results reported in Anderson et al. (2001) using Version 5 of the GTAP database, fully two-thirds of the gains from eliminating all merchandise import barriers globally in 2005, after

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