rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3. Management practices by area (Manufacturing)**

<!-- Start of picture text --> Management score<br>1.50 2.00 2.50 3.00 3.50 4.00<br>2.02<br>1.54<br>Mozambique 2.00<br>2.01<br>2.20<br>2.22<br>2.24<br>Ethiopia 2.40<br>2.25<br>2.05<br>2.23<br>2.05<br>Ghana 2.24<br>2.11<br>2.37<br>2.54<br>2.45<br>Kenya 2.66<br>2.43<br>2.57<br>2.49<br>2.23<br>Nigeria 2.50<br>2.39<br>2.64<br>2.25<br>1.93<br>Tanzania 2.34<br>2.10<br>2.41<br>2.32<br>2.12<br>Zambia 2.39<br>2.21<br>2.41<br>3.28<br>3.25<br>United States 3.52<br>3.19<br>3.18<br>Management (overall) Operations Monitoring Target-setting People-management<br><!-- End of picture text --> Source: World Management Survey. Adapted from Lemos & Scur (2014).

The weaker management practices implemented by Mozambique’s firms include lack of lean manufacturing, limited development of a talent mindset, lack of planning and targeting, and the limited use of documentation to capture lessons. The variability in relation to the mean in operations and monitoring practices is very large, in particular, for lean manufacturing. Higher scores are found for talent recruitment, retaining talent, and developing talent (Figure 4).

One important gap in Mozambique is the limited supply of consulting services in the areas of relevance for management practices. When those consulting services are available, they are often expensive because of their fixed costs, which is associated with the limited market and small 9
because they are adversely affected by the depreciation of currencies that closely track the renminbi.

An interesting related finding is that we do find statistically significant and negative coefficients on the interaction terms for the other countries (not shown). Therefore, the spillover effect we estimate is not specific to China, and is more general. Unsurprisingly, the indices of competition are much lower for all the other countries. Therefore, the magnitude of the spillover effect which is given by the coefficient multiplied by the index is much smaller for the other countries than for China.<sup>17</sup> In Table 5, we test for robustness across exporters, defined in geographic terms. We split the sample into four regions and find that the results hold across all. The coefficients on the China spillover effect are greater for Asia than for Sub-Saharan Africa but it is difficult to say whether these differences are due to the fact of supply conditions in the exporting region or due to differences in the product composition of their exports and/or their geographic destination.<sup>18</sup> In Table 6, we check if the results are robust to the degree of product disaggregation. In the core specification, the data are at the HS 4-digit level. In Table 6, we use data at the HS 2-digit level. The indices of competition are measured by aggregating across 6-digit lines within the 2-digit category. The sample size shrinks from over 3.6 million to about 860,000 observations. But the interaction term remains negative and significant.

> 17 See Section V for detailed discussion on the magnitudes of the coefficients. 18 The differences are also statistically significant (based on a estimating a stacked specification with triple interaction terms with regional dummies).  We find some suggestive evidence that the differences between Asia and sub-Saharan Africa, for example, may be due to the fact that the latters’ exports tend to be less homogenous than those of Asia. We also tested for robustness across importers, defined in terms of advanced and other countries, and the results hold for each category of importers.

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Economic segregation can be thought to affect educational attainment through two distinct channels (Mayer 2002): through the distribution of resources between schools, and through the composition of students within schools. In this paper, I use a pupil level data-set for sixth graders in Argentina to explore the second mechanism. The effect of the composition of classmates will be identified using the variations across classes within schools, so that the first set of factors – related to the political economy of school financing, and quality of monitoring and institutions – will be controlled for rather than tested. The identification strategy follows closely that of Ammermueler and Pischke (2009). The aim is to understand the impact of increased socioeconomic segregation on both the achievement gap between poor and non-poor and the average academic achievement of children at school. To do this, I need to depart from the standard peer-group effects approach, allowing for heterogeneity in the effects both in terms of the socioeconomic background of the recipient student and in terms of the distribution of socioeconomic characteristics within the classroom.

I find that, first, peer effects are indeed heterogenous; wealthier pupils are more sensitive to peer effects overall. Second, poor children do best with peers who are richer, but not too rich. This result is consistent with hypotheses of social distance and competition; people that are too far away in the social spectrum have lower (or even negative) effects on others (Akerlof 1997). The combined prediction of these two effects implies a strong equityefficiency tradeoff from school integration. Increasing socioeconomic segregation raises both the overall average test score and the gap between the rich and poor pupils, where the latter is appreciably more sensitive to the allocation of students than the former.

The paper proceeds as follows. The next section describes the data employed and presents basic stylized facts on the relation between test scores and segregation within provinces and localities. Section 3 presents the standard approach to estimating peer-group effects and the alternative model proposed. Section 4 describes the empirical specification and the estimation strategy chosen. Section 5 and presents the results from the estimations and the next section the simulations to show the effects of increased segregation on average test scores and the gap in test scores between the wealthiest and the poorest groups. Section 7 concludes.

3
likely that – as in the rest of both developed and developing worlds - new firms may have high failure rates in the first few years and find it difficult to grow. In terms of employment, while informal enterprises employ the bulk of the urban labor force, their growth has been slow if not stagnant in the last two years.

33. _(i) Growth in Number of Firms_ : Figure 12 shows that the vast majority of the formal firms started after 1999, with the largest burst in numbers occurring between 1999 and 2000, and after a slight decline in 2001, there was again an increase in 2002, followed by another decline in 2003 and the first three quarters of 2004.<sup>5</sup> Although the survey was in the field only until September 2004, the decline in numbers of enterprises between 2003 and 2004 seems to point to the effects of the withdrawal of UNMISET. Informal enterprises show a similar trajectory, though with a much less sharp decline after 2003 compared to formal units, probably because they cater to the local market, rather than the expatriate market. Overall, the numbers prior to 1999 should not be given great attention, as they most likely simply reflect dominant Indonesian ownership of businesses during the occupation period.

<!-- Start of picture text --> Figure 12: Growth of Enterprises 1981-2004<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>FormalY ear Informal<br>Source: UES, 2004.<br>1981 1984 1986 1991 1994 1998 2000 2002 2004<br>Number of Enterprises<br><!-- End of picture text --> 34. _(ii) Employment Dynamics_ : Over the last two years, the bulk of enterprises – both formal and informal - remained unchanged in their number of workers, though there appears to be a net increase in hiring in 10 percent of formal enterprises (see Figure 13). The informal sector in particular is virtually stagnant, with neither job creation nor destruction, reflecting the family nature of establishments. In the context of increasing inflow into the labor force, with growing cohorts of youth, the failure of firms to create net employment (or even to generate job turnover, which might be a source of productivity growth) is problematic for the economy.

> 5 Clearly this is in reference to non-Indonesian firms.

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# **Figure A5: Fiscal Position-Dependent Multipliers during Expansions**

<!-- Start of picture text --> A. On Impact B. 1 Year<br>0.3 0.6<br>0.2 0.4<br>0.1 0.2<br>0.0 0.0<br>-0.1 -0.2<br>-0.2 -0.4<br>-0.3 -0.6<br>12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4 12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4<br>C. 2 Years D. Long Run<br>1.2 3.0<br>0.8 2.0<br>1.0<br>0.4<br>0.0<br>0.0<br>-1.0<br>-0.4<br>-2.0<br>-0.8 -3.0<br>-1.2 -4.0<br>12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4 12.4 24.8 32.2 38.5 42.9 47.9 56.1 65.5 80.5 107.4<br><!-- End of picture text --> _Note:_ The graphs show the conditional fiscal multipliers during expansions for different levels of fiscal position at select horizons. These are based on estimates from the IPVAR model, where model coefficients are jointly conditioned on fiscal position and the phase of the business cycle. Government debt as a percentage of GDP is the measure of fiscal position and the values shown on the x-axis correspond to the 5<sup>th</sup> to 95<sup>th</sup> percentiles from the sample. Expansions are determined by the Harding-Pagan (2002) business cycle dating algorithm. Fiscal position is strong (weak) when government debt is low (high). Solid lines represent the median, and dotted bands are the 16-84 percent confidence bands 33
<!-- Start of picture text --> FIGURE V-3 : PROGRESSIVITY OF TAXES AND TRANSFERS<br>0.70 0.050<br>0.60<br>0.040<br>0.50<br>0.40 0.030<br>Kakwani<br>0.30<br>0.20 0.020 coefficient<br>0.10 0.010<br>0.00<br>0.000<br>-0.10<br>-0.20 -0.010 Marginal<br>contribution<br>Source: World Bank Group and staff estimates based on ENPH 2017UP STAFF ESTIMATES BASED ON ENPH 2017<br>Kakwani Coefficient Subsidies Education Health Redistributive effect<br>Regressive <---------> Progressive Direct taxes and C.S.S. Direct transfers Indirect taxes Unequalizing <---------> Equalizing<br><!-- End of picture text -->

# **_a) Direct taxes and indirect taxes_**

The sum of all direct taxes and contributions to the social security system is progressive since the Kakwani coefficient associated with that value is positive (see Table 1 of Annex A). Furthermore, when each of the direct taxes and social security contributions is examined, it is observed that all direct taxes, as well as contributions, are progressive, as seen in Figure V-4, all the associated Kakwani coefficients are positive. Additionally, given that the marginal contribution coefficients are positive, it is possible to deduce that all direct taxes and all social security contributions help to reduce inequality. The income tax is more progressive and redistributive than the other direct taxes. As far as social security contributions are concerned, the most progressive contribution is made by employers to health care, and the most redistributive is the contribution to health care paid by employees.

<!-- Start of picture text --> FIGURE V-4: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF INDIRECT TAXES<br>(FROM MARKET INCOME + PENSIONS TO DISPOSABLE INCOME)<br>0.6000 0.0060<br>0.5000 0.0050<br>0.4000 0.0040<br>0.3000 0.0030<br>0.2000 0.0020<br>0.1000 0.0010<br>0.0000 0.0000<br>Kakwani Coefficient<br>Marginal Contribution<br>Source: World Bank Group and staff estimates based on ENPH 2017<br>Coeficiente de Kakwani Redistributive effect<br>Regresivo <---------> Progresivo Income tax Property tax Vehicle tax<br>employer employee<br>Unequalizing <---------> Equalizing<br>employer employee<br>Contribution to healthcare - Contribution to healthcare - Contribution to ARL - Contribution to ARL -<br><!-- End of picture text --> However, direct taxes and contributions in Colombia are less progressive and redistributive than in some other countries in the region such as Chile, Ecuador, and Brazil (Figure V-5-Panel A). Additionally, direct taxes and social security contributions contribute to the increase in poverty. As shown in Table 1, Annex 35
# **Table 2 Decomposition of change in manufactures GVC income due to change in production structure and final demand**

||Change in re|al GVC income bet<br>and 2008<br>keein|ween 1995<br>keein|
|---|---|---|---|
||Total<br>change<br>(mil US$)|pg<br>production<br>structures<br>constant|pg<br>final<br>demand<br>constant|
|Germany|47,328|111,625|-64,297|
|France|37,886|79,199|-41,313|
|United Kingdom|6,895|8,637|-1,742|
|Italy|65,104|83,767|-18,664|
|Spain|44,140|49,120|-4,980|
|Netherlands|24,840|26,925|-2,086|
|Belgium|3,426|12,606|-9,180|
|Sweden|15,012|18,434|-3,422|
|Austria|12,593|15,799|-3,206|
|Greece|10,095|10,977|-882|
|Denmark|5,217|6,910|-1,694|
|Finland|8,085|9,549|-1,465|
|Ireland|18,897|16,383|2,514|
|Portugal|3,498|3,620|-121|
|Luxembourg|2,959|1,600|1,359|
|All EU15|305,974|455,153|-149,180|
|Poland|52,261|47,986|4,275|
|Czech Republic|26,973|23,229|3,743|
|Romania|20,689|18,584|2,105|
|Hungary|16,020|14,371|1,649|
|Slovak Republic|12,564|11,338|1,226|
|Other EU12|16,325|12,621|3,704|
|All EU12|144,832|128,130|16,702|

Note: Change in real GVC income from Table 1. The change is decomposed by keeping the production structures constant while final demand changes, and by keeping the final demand constant, while production structures change. This additive decomposition can be done keeping 1995 or 2008 levels constant, and average weights are used.

_Source:_ Author’s calculations based on World Input-Output Database, April 2012.

# **4.3 Revealed comparative advantage in GVCs**

An interesting issue is to what extent Europe is specializing in particular activities within specific product GVCs. The standard tool to analyze this is revealed comparative advantage (RCA) analysis. Traditionally, this is based on comparing a country’s share in world exports of a particular product group or industry to its share in overall exports. It is often used for informing industrial and trade policies by predicting which domestic sectors would benefit from further global market opening, and which would 24
for market capitalization are negative in the high-income sample. At the same time, we recognize that this does not necessarily rule out a causal link. Our process for indentifying potential instruments simply failed to come up with candidates that worked well for stock market capitalization. In short, the positive coefficient for that variable in the OLS regressions might or might not reflect a causal link with firms‘ labor growth but our analysis did not uncover an instrument that could reveal the ̳true‘ relationship.

We also present LIML results for private credit/GDP in Appendix C. Although the statistical tests from the GMM models supported the relevance and validity of our instruments (implying that recourse to LIML is not necessary), the LIML results for low-income countries are similar to the GMM results, though significance levels are a bit lower. Still, the one result that is consistent across all estimation techniques in this section is the positive, significant relationship between private credit and firm labor growth in low-income countries.

# **C. Rajan-Zingales Methodology**

Another widely used method for identifying a causal link between financial sector development and growth comes from Rajan and Zingales (1998), focusing on how industry-level growth rates vary with both dependence on external sources of finance and the level of financial sector development in a country.<sup>14</sup> The key identifying assumption is that capital markets in the United States are relatively frictionless, and thus an industry‘s reliance on external finance in that country is a reliable indication of its true technological demand for external financing. Industrylevel dependence on external finance computed from U.S. data is therefore the key exogenous source of variation in their analysis.

The equation that we estimate is: where is the percentage change in the number of workers employed by firm _i_ in industry _j_ in country _k_ over the two years prior to the enterprise survey, as described above. represents

> 14 See, for example, Fisman and Love (2007) and Kroszner, Laeven, and Klingebiel (2007) for applications of this method, and Xu (2011) for summary of the use of this method in identifying the effects of business environment in general.

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confirmation, self-esteem or self-serving bias study prediction tasks concerning choice _outcomes_ – e.g., the returns to stock choices, entrepreneurial efforts, technology adoption, etc. or the welfare gains from consumption choices among goods or services (Foster and Rosenzweig, 1995; Handel, 2013; Hanna et al., 2014; Handel and Kolstad, 2015; Bhargava et al., 2017; Hastings et al., 2017; Kohlhas and Walther 2021). However, estimation of respondent learning about outcomes requires accurately specifying the outcome data generating process, typically represented as a production, cost, or profit function. The possibility always exists that a respondent's beliefs accurately reflect unobservable farmer attributes that cause their outcome distribution to deviate from the analyst's estimates. Our experimental design, by contrast, rules out the possibility of unobserved heterogeneity by studying directly measurable, observable, and valuable agricultural inputs. No unobservables should materially influence answers to questions such as: what is the size of this plot? Nor should it influence responses to questions about how much fertilizer or labor the farmer applied to the plot before or during planting. Errors in reporting observable production inputs almost surely reflect either misreporting or systematic behavioral errors that lead to mistaken beliefs. NCME in directly measurable inputs are less likely confounded by other, unobserved arguments to the respondent's mental model of the relevant data generating process.

Furthermore, failure to update completely in response to demonstrably accurate, corrective information about an objectively verifiable value, such as the size of a plot, signals asymmetric or incomplete learning – or even complete learning failures, i.e., no updating at all – carries important implications for information-based interventions, such as extension messaging, market information services, cadastral surveys, public health, and nutrition education. The literature on attention and learning typically focuses on learning about outcomes, technologies, or some other phenomena that are not directly observable. By studying response to corrective information about a directly observable variable, we offer a more direct test of incomplete learning of various sorts.

Consistent with many prior studies (e.g., Carletto et al., 2013; 2015; 2017; Kilic et al. 2017; Abay et al. 2019; Abay et al., 2021; Dillon et al., 2019), we find pervasive, mean-reverting NCME in farmers' self-reported plot areas and considerable asymmetric rounding to easy-to-remember half-acre increments. In view of the importance of land for social status and income generation in these agrarian communities, these findings suggest behavioral anomalies that have been documented in other contexts, in particular, inattention to important details and self-esteem bias.

4
# **Abstract**

This paper aims at analyzing decentralization reforms in the education sector in Latin America (their status, impact and on-going challenges) by making use of the accountability framework developed by the 2004 World Development Report. It starts by identifying three main groups of models according to the sub-national actors involved, the pattern adopted in the distribution of functions across sub-national actors and the accountability system central to the model. It then reviews the impact of these models, according to the available empirical evidence, and explores determinants of this impact, extracting lessons useful to the design of future reforms. It concludes that the single most important factor in ensuring the success or failure of a reform is the way the accountability relationships are set to work within each of the models and provides some lessons on how to get these relationships to work effectively. It also provides three main general lessons for the selection of “successful” models: (a) avoiding complicated models; (b) increasing school autonomy and the scope for “client power”, maintaining a clear role for the other accountability relationships; and (c) putting more emphasis on the “management” accountability relationship and the sustainability of the models.

II
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Benchmarking ICT indicators**

||Unit|Low-income countries|**DRC**|Fragile states|
|---|---|---|---|---|
|GSM coverage|% population|42.42|**65.00**|62.55|
|International bandwidth|Mbps/capita|3.01|**0.19**|0.88|
|Internet|subscribers/100 people|0.13|**0.03**|0.07|
|Landline|subscribers/100 people|7.47|**3.90**|8.99|
|Mobile phone|subscribers/100 people|6.44|**3.88**|8.01|
|US$||**DRC**|Without submarine cable|Other developing regions|
|Price of monthly mobile ba|sket|**11.0**|11.12|9.9|
|Price of monthly fixed-line|basket|**28.17**|13.58|—|
|Price of monthly 20-hour In|ternet package|**74.00**|67.95|11.0|
|Price of international call to|U.S., per minute|**0.33**|0.86|0.67|
|Price of inter-Africa calls p|er minute, mean|**0.52**|0.72|—|

_Source:_ Minges and others 2009, derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ Mbps = megabits per second; — = data not available.

# **Challenges**

Completing the expansion of GSM network coverage is particularly challenging because of the spatial characteristics of the country. Analysis done around 2006 suggested that up to 80 percent of the population could be reached on a commercially viable basis, but the remaining 20 percent are dispersed across remote areas that cannot be covered without some degree of public subsidy. This “coverage gap” is among the largest found for any country in Africa (figure 5). As of 2010, a number of further developments are jeopardizing the further expansion of mobile coverage. A vigorous price war between mobile operators had pushed prices below economic levels to under US$0.04 per minute. In order to stem losses, operators have stopped coverage rollout and indeed reduced are coverage in some cases.

17