rafmacalaba/datause-encoder-data huggingface dataset:
Using qualitative information, performance-based measures and employment growth, Diao et al. (2016) identified a group of firms in the Micro, Small and Middle-Sized Enterprises (MSME) sector in Tanzania– the so-called “in-between”. These firms operate mainly in the sectors of manufacturing and trade services, employ approximately the same number of employees as compared to those in the formal sector and have significant potential for future growth.

Few studies have specifically investigated inequality in the informal sector. Most that do have focused only on the causality between rising inequality and the size of the informal economy at the macroeconomic level (Chong and Gradstein 2004; Winkelried 2005).

Classifying firms in the informal sector and recognizing the sector’s role as a setting environment for profitable activity may contribute to economic growth (AfDB 2013). Accounting for more than half the labor force in Sub-Saharan Africa, the informal sector represents prospects for better living standards among many people. Nonetheless, there is evidence that registering with tax authorities (formalization) increases the profits of firms with two to five workers and the middle third of capital stock (the midsize group), while leading to losses among smaller and larger informal firms (McKenzie and Sakho 2010). Discovering how to reduce inequality and poverty between and within the three distinct categories of informal firms—top performers, constrained gazelles, and survivalists—is thus an important policy matter. It could enhance the productivity of firms and help governments coordinate strategies and policies to support the formalization of the sector.

# **3. Data and descriptive statistics**

## **3.1. 1-2-3 survey**

The empirical investigation reported in this paper relies on survey data from the 1-2-3 survey collected in the Democratic Republic of Congo in 2012. The 1-2-3 survey was conducted by the National Institute of Statistics, in partnership with other actors, including Afristat and the World Bank.<sup>2</sup> The 1-2-3 survey is a representative, multilayer survey that covers three nested surveys, three phases involving separate statistical populations: individuals, informal production units, and households.

Phase 1 provides detailed information on employment, unemployment, and household and individual sociodemographic characteristics. It includes a household module, an individual questionnaire for individuals ages 5 or above, and a community questionnaire. Phase 2 is a specific survey covering the heads of informal production units. This informal sector survey collects information on the characteristics of firm owners, production units, economic performance, business requirements (costs, investment), and employees. This phase is important as it allows measuring the Congolese informal economy. Phase 3 is a survey on household

> 2 See INS. http://www.ins-rdc.org/sites/default/files/Rapport%20enquete%20123.pdf.

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18 time to be a consistent predictor of pupil performance.<sup>93</sup> In the Philippines and Ethiopia more pupil-oriented teacher behavior led to improved use of time devoted to learning and had a significant impact on learning processes and resulted in higher achievement levels.<sup>94</sup> But studies have not captured all of the important dimensions of time loss and gain in lower-income countries, so the magnitude and significance of the relationship between instructional time and educational achievement may seem uncertain.<sup>95</sup> For example, in Pakistan it was found that teaching time by itself was a poor predictor of student achievement, and that the effective use of time was a more accurate predictor.<sup>96</sup> Reduced instructional time may be related to higher dropout rates. For example, in Egypt girls attending multiple-shift schools with fewer instructional hours were five to six times more likely to drop out before completing lower secondary education than girls attending a single-shift school.<sup>97</sup>

**Figure 2-5. Time lost in book distribution in Honduras**

_Source: author, rural school 2003_ _Tutoring._ To make up for instructional time lost to strikes, absenteeism, and lack of feedback parents have resorted to private tutoring. This is a major phenomenon worldwide that has grown dramatically in recent times and has affected the priorities of those who tutor. Families with the necessary resources are able to secure not only greater quantities but also better quality of private tutoring. Children receiving such tutoring are then able to perform better in school, and in the long run to improve their lifetime earnings. However, children of low-income families often do not receive such benefits and may drop out of school at an earlier age.<sup>98</sup> This is one more way that the better off get more instructional time than the poorer students.

The association between instructional time and achievement lead some educators to argue in favor additional hours and a longer school year. In higher-income countries, the length of the year may not greatly affect performance (for example, Japan has 210 school days and Taiwan only 180). But poor students with language delays may benefit from longer instructional days when used well. Some countries (notably Uruguay) have “full-time” schools that keep students for about eight hours and offer many enrichment activities. Low-income students in Uruguayan full-time schools were 10% more likely to get 93. Armitage et al. 1986, in Fuller 1987 94. Tan, Lane and Coustère 1997; Verwimp 1999 95. Anderson 1976; Karweit 1976, 1983 96. Reimers 1993 97. Lloyd et al. 2003 98. Bray 2006
**<u>Table 1. Study characteristics</u>**

|||A<br>|ll<br>|Excl.<br>|CCDRs<br>|
|---|---|---|---|---|---|
|||Share|SD|Share|SD|
|Country or region|Sub-Saharan Africa|0.243|0.432|0.121|0.331|
||Global|0.171|0.380|0.364|0.489|
||East Asia and Pacific|0.157|0.367|0.182|0.392|
||South Asia|0.129|0.337|0.182|0.392|
||East Asia and Pacific|0.100|0.302|0.030|0.174|
||Europe and Central Asia|0.071|0.259|0|0|
||Middle East and North Africa|0.071|0.259|0|0|
||UnitedStates|0.057|0.234|0.121|0.331|
|Type of shock|Climate change|0.571|0.498|0.212|0.415|
||Natural disaster|0.214|0.413|0.242|0.435|
||Temperature|0.329|0.473|0.303|0.467|
||Heat|0.200|0.403|0.061|0.242|
||Typhoon/cyclone/hurricane|0.086|0.282|0.121|0.331|
||Rainfall|0.086|0.282|0.061|0.242|
||Flood|0.229|0.423|0.182|0.392|
||Drought|0.200|0.403|0.152|0.364|
||Earthquake|0.057|0.234|0.061|0.242|
||Landslide|0.057|0.234|0.030|0.174|
|Level of analysis|Country|0.377|0.488|0.313|0.471|
||Subnational|0.269|0.448|0.226|0.425|
||Household|0.346|0.480|0.355|0.486|
||Individual|0.212|0.412|0.129|0.341|

_Note_ : CCDR = _Country Climate and Development Report_ . Sample has 70 studies. When the CCDRs are excluded, the sample has 33 studies. Each row represents the share of studies in our sample with the specific characteristic. Country or region indicators, type of shock indicators, and level of analysis indicators are not mutually exclusive.

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Table 4 reports analogous estimates, but allowing for a 1-year lag in the transmission of green technologies (as measured by mentions in the corresponding ECTs). The evidence shows a broadly similar pattern with positive and significant coefficients for the variables of interest. The sole exception for the coefficient on the interaction term in column (8), which suggests that when firms are simultaneously buyers and suppliers of a green firm, they are significantly likely to become green irrespective of the level of emissions of the green firm to which they were linked.

Since firms may have various types of linkages at the same time, it may be difficult to isolate the role of each type of linkage when considering the full sample. For robustness, in Table 5 we examine if the baseline results are sensitive to the exclusion of firms with multiple linkages. Once again, we observe that the results remain qualitatively very similar.

The majority of firms in our sample have affiliates in other countries and hence are multinational enterprises. In Table 6 we restrict the sample to include only linkages with MNEs. Once again we observe qualitatively similar results. In Table 7, we consider only linkages with non-MNEs. In this case, we are left with a relatively small number of observations, but we still observe positive and significant coefficients for most linkages considered.

In Table 8 we consider instead a sub-sample in which the firm that is linked to the green firm is a MNE, while the green firm could be or not an MNE. Once again, the baseline results remain valid. Finally, in Table 9 we consider a sub-sample in which the linked firm is not an MNE, while the green firm may or not be an MNE. In this case, we are again left with a relatively small number of observations, but we observe once again a positive and significant coefficient for most linkages considered.

Taken together, these estimates suggest that firm-to-firm linkages play an important role in the transmission of green technologies across multinational enterprises, especially when the green firm had high emissions to begin with. We also observe some evidence that these linkages matter when one of the firms involved is not an MNE, but in this case the relationships of interest are estimated from a small sample and are less precise.

# **6 Concluding remarks**

While there is a growing recognition that green technologies are key for reducing the carbon footprint of economic activity at the global level, there is still little systematic evidence on their emergence and diffusion across firms, sectors, and countries, and on the economic mechanisms underlying this process. In this paper, we have used textual analysis of patents and earnings conference calls matched with multi-country, firm-level panel data for 2012-2021 to provide 12
# **2. Context and Details of the Program**

The Republic of Yemen is the poorest country in the Arab world, with a per-capita GDP of US$1,473 (2013). As in most developing countries, micro and small enterprises constitute the majority of private firms, with 88 percent of firms estimated to have less than five workers, 4.7 percent between 5 and 9, and only 3.5 percent having more than 10 workers. Smaller firms tend to be less productive, and may face more constraints to taking up technology and innovation.

In the two years preceding the launch of the program studied here, the Republic of Yemen had undergone a revolution as part of the Arab Spring, marked by protests, armed clashes, and a contraction of the economy. Real GDP shrank 12.7 percent in 2011, and only recovered 2.4 percent in 2012. In a survey of 200 firms in June, 2012, one year before the matching grant program was launched, 72% of firms surveyed reported declining sales, and there was a 12% average decline in employment across firms surveyed. Most firms (about 4 out of 5), reported availability of electricity, macroeconomic instability, and political instability as major or severe constraints to business (Stone et al, 2012). Coupled with an already existing difficult business environment, and productivity levels which are among the lowest in the region,<sup>3</sup> these economic conditions made it particularly difficult for businesses to develop and grow and motivated the project here.

# **2.1 The Matching Grant Program**

The Enterprise Revitalization and Employment Pilot (EREP) was designed as a two year pilot project aimed at improving firm capabilities and the employability of recent graduates. The matching grant component of this program aimed to provide grants for business development services (BDS) to 400 enterprises, 200 in each of two years.<sup>4</sup> Firms could apply for a grant of up to $10,000, as a 50 percent subsidy towards the cost of procurement of business development services, training, and goods (as a minority component) to improve business practices, technology, or products, or to reach new markets, as defined in a Business Development Plan submitted by the firm.

> 3 Compared with Lebanon, Egypt, Saudi Arabia and Syria – see Yemen Investment Climate Assessment Update, World Bank, 2011.

> 4 Firms could also apply for subsidies to hire interns, with some firms applying only for these internship subsidies, some applying for the BDS grants, and some applying for both. 20% of the firms in our treatment group also applied for interns. We discuss the impact of the internship program on youth employment in Assaf et al. (2015).

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12 with almost no change for AAA firms. This supports the inference that the big increase in working capital loans to A firms did not support a higher level of activity, but was a result of rolling over interest payments as they fell due, and financing growing stocks.

<!-- Start of picture text --> Figure 6 shows the time path<br>of investment loans from banks. AA<br>firms exhibit a jump in borrowing for Nominal Investnent Loans<br>investments at the start of the ETP (Dec. 1989 = 100)<br>and then stagnation thereafter. 1200<br>Between December 1989 and mid-<br>1991, investment loans grew fastest 1000<br>for A firms. It is only after mid- 800<br>1991 that investment loans to AAA<br>firms began growing faster than those 600 -<br>for A firms. These results show that 400<br>"good" firms are investing, but that<br>A firms have been receiving loans as 200 -<br>well, despite rapidly dropping oc- A<br>capacity utilization and shrinking Dec-89 Apr-90 Aug Dec90 Ap,41 Aug-91 Dec-91 Apr-2<br>profitability. AAA + A AA |<br><!-- End of picture text -->

The dynamics of net lending (inter firm credit measured as receivables minus payables - Table 8) Figure 6 confirms the view that such credit served as a substitute for bank loans. This shows up especially dramatically in 1992, when the curtailment of bank loans led to a sharp increase in inter firm borrowing by A firms. Lending by the better off AAA firms stabilizes in 1992, suggesting that greater caution is being exercised by them.

> Table 8: Net Interfirm Lending To Sales _(%,_ selected months)

|i_______|_<br>Dec-89|Jun-90|Dec-90|Jun-91|Dec-91|Jun-92|
|---|---|---|---|---|---|---|
|AAA|68.5|100.3|54.6|81.2|54.1|54.1|
|AA|59.4|104.6|43.7|53.6|-97.4|-6.1|
|A|64.7|71.4|49.9|37.7|-32.8|-33.2|

Note: Manufacturing firms are net lenders to distributors and to other manufacturers.

The last leakage at the micro level is tax arrears. Table 9, which gives arrears as a percentage of taxes due, shows that this has been substantial for both AA and A firms. Further, since A firms by definition do not owe income tax in 1992, it can be concluded that the arrears are in respect of dividend and PPWW payments. In contrast, AAA firms are virtually current on tax payments.
# **1. Introduction**

An educated workforce is necessary for a high national standard of living. Increasing the human capital of the poor is likely one of the most effective ways to reduce poverty and increase upward economic mobility.

Over the last 25 years, school enrollment, at all levels, has increased universally, and most children in low- and middle-income countries now complete primary school. Enrolling in school, however, does not guarantee that children acquire the competencies set out in the official curriculum. In fact, a large share of children in low-income countries complete their primary education lacking even basic reading, writing, and arithmetic skills. For example, when Grade 6 students in 15 Southern and Eastern African countries were tested in 2007 as part of the Southern and Eastern Africa Consortium for Monitoring Education Quality (SACMEQ) regional assessment exercise, less than 50 percent scored beyond the level of “reading for meaning,” and less than 40 percent scored beyond “basic numeracy” (Hungi et al. 2010). Among sixth-grade students who were tested as a part of the CONFEMEN Programme for the Analysis of Education Systems (PASEC) regional assessment in 10 Francophone countries in Western and Central Africa in 2015, less than 45 percent surpassed the competency level in reading or mathematics deemed “sufficient” to successfully continue their schooling (Malpel et al 2016). UNESCO has dubbed this failure the “global learning crisis” (UNESCO 2013).

A growing body of evidence suggests that teacher quality, broadly defined, is a key determinant of student learning.<sup>1</sup> Little is known, however, about what specific dimensions of teacher quality matter and even less about how teachers perform along these dimensions – facts we argue are crucial in order to guide both research and policy design.

This paper reports on an ongoing research program intended to help fill this void. Using data derived from direct observations, unannounced visits, and tests, from primary schools in seven Sub-Saharan African countries—Kenya, Nigeria, Mozambique, Senegal, Tanzania, Togo, and Uganda—which together represent close to 40 percent of the region’s total population, we answer four questions: (1) How much time do teachers actually spend teaching? (2) Do teachers have the relevant subject content knowledge to teach basic and higher-order language and mathematics skills? (3) Do teachers have the pedagogical knowledge and skills

> 1 See for example Rockoff (2004); Rivkin et al. (2005); Aaronson et al.  (2007); Metzler and Woessmann (2012); Chetty et al. (2014); and Das and Bau (2016) for evidence based on quasi-experimental data. These findings are supported by a growing experimental literature reviewed in, for example, Kremer, Brannen, and Glennerster (2013); Glewwe and Muralidharan (2015); Bruns, Filmer and Patrinos (2014) and Evans and Popova (2016), showing that traditional educational inputs have little impact on test scores but incentivizing teacher effort and supporting specific aspects of pedagogy do.

2
The paper is organized as follows. Section 2 describes the scope and methodology underlying the research agenda on deep trade agreements. Section 3 highlights a novel set of stylized facts that can be inferred from a first look at the new data, while Section 4 offers some insights into future applications and areas for analysis. Concluding remarks follow.

# 2. Scope and methodology

The number of policy areas covered by PTAs has increased in the last two decades. Until the late 1990s, when the number of PTAs started increasing, the majority of new agreements covered fewer than 10 policy areas. Since the 2000s, most new PTAs have covered between 10 and 20 policy areas, with some having even more than 20 (Figure O1). In a study of 28 trade agreements signed by the US and the EU, Horn et al. (2010) identify up to 52 policy areas that have been covered by at least one of the agreements. The inclusion of new policy areas in PTAs is not random. As shown in Mattoo et al. (2017), trade agreements covering few policy areas generally focus on traditional trade policy, such as tariff liberalization or customs (Table O1). Agreements with broader coverage (between 10 and 20 policy areas) tend to include trade-related regulatory issues, such as subsidies or technical barriers to trade. Finally, agreements with more than 20 provisions often include policy areas that are not directly related to trade, such as labor, environment and movement of people.

**Figure O1: Number of policy areas covered in PTAs, 1970-2017**

<!-- Start of picture text --> 25 300<br>250<br>20<br>200<br>15<br>150<br>10<br>100<br>5<br>50<br>0 0<br>More than 20 Before 10 and 20 Less than 10 Cumulative<br>Number of Agreements<br>Cumulative Number of Agreements<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ calculations based on Hofmann et al. 2019.

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5 If the mean welfare ratio is equal to one, it indicates that on average households have consumption at the level of the poverty line. Growth is then measured by changes in the welfare ratios over time. Finally, Gini indices are also computed using normalized consumption levels, such that: G = 2 cov (Ci /Z, Fi )/W (3) where Fi is the normalized rank (taking a value between zero and one) of household i in the distribution of consumption, and the covariance is computed using the household weights. Note that at the area level, the Gini index computed with the lower poverty line (GL) is equal to the Gini with the upper poverty line (GU) since using one or the other regional poverty line just scales up all consumption measures without affecting inequality. However, at the national, urban, and rural levels, the Gini indices do depend on the poverty lines used since the poverty lines and the consumption distributions are not equal between areas.

# _1I.3 Results_

Table I gives poverty measures at the national, urban, and rural levels (measures for the fourteen geographical areas are given in appendix). We find decreasing poverty in the early 1980's, increasing poverty in the late 1980's and early 1990's, and again decreasing poverty thereafter (the results are similar with both sets of poverty lines). Broadly speaking, these results are consistent with previous research<sup>5</sup> .

Table 2 provides welfare ratios and Gini indices of inequality. The trends for welfare ratios are similar to those observed for poverty, which is not surprising since welfare ratios represent mean levels of consumption. Nationally, the welfare ratios increased from 1983-84 to 1985-96, then decreased from According to Rahman and Haque (1988), poverty decreased from the mid 1970's to the mid 1980's. For the later period, estimates based on group data for the Household Expenditure Surveys published by the Bangladesh Bureau of Statistics indicate that in both rural and urban areas, poverty increased in the late 1980's and early 1990's (Khundker, Mahmud, Sen, and Ahmed, 1994; Hossain and Sen, 1992). This was confirmed using the unit level data of the surveys up to 1991-92 (Wodon, 1995, 1997). Additional work based on a smaller survey conducted by the Bangladesh Institute of Development Studies within rural areas also show an increase in poverty in the late 1 980s, followed by a decrease between 1990 and 1994 (Rahman and Hossain, 1995; Rahman, Hossein, and Sen, 1996).
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 21. Senegal’s existing infrastructure spending is particularly high**

As percentage of GDP <!-- Start of picture text --> SSA<br>LIC-Fragile<br>LIC-Nonfragile<br>ECOWAS<br>Resource-Rich<br>MIC<br>Senegal<br>Niger<br>Mali<br>Ghana<br>Liberia<br>Benin<br>Nigeria<br>Burkina Faso<br>Cote d'Ivoire<br>Cape Verde<br>0 2 4 6 8 10 12 14<br>% of GDP<br>Capex O&M<br><!-- End of picture text --> _Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ LIC = low-income countries; MIC = middle-income countries; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M= operations and maintenance; CAPEX = capital expenditure.

Senegal’s existing spending amounts to 11 percent of its GDP (figure 21). This is close to the average for low-income, nonfragile states, even if it is substantially higher than some of its West African neighbors such as Côte d’Ivoire and Nigeria. Relative to its peer group, Senegal is relatively more reliant on the public budget for power and water investments, and relatively less reliant on ODA for transport, power, and water needs (figure 21). In the case of ICT, Senegal predominantly relies on the private sector for financing. The largest share of infrastructure spending goes to power (39 percent) followed by ICT (21 percent) and power (23 percent). The transport and water sectors receive 11 to 13 percent of total funding each (figure 22).

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