rafmacalaba/datause-encoder-data huggingface dataset:
is 25%. This is typical for this cluster of countries.<sup>21</sup> Sri Lanka’s labor force participation rate of 59% is lower than average and substantially lower than East Asian countries like Cambodia (85%) and the Lao People’s Democratic Republic (81%). On the Pillar 3 indicators of total unemployment, part-time unemployment and unemployment with advanced education, Sri Lanka is typical of this group of countries.

An examination of Pakistan’s comparative ranking in Pillar 1 (Tables 3A and 3B) indicates that its educational attainment and percent of population with bachelor’s degrees is lower than average as compared to its cluster. Most striking, however, is its LAYS score of 5.10 which is substantially lower than average and, in fact, almost a full point lower than the next lowest score in the sample (Bangladesh’s LAYS score of 6.0). Given that the mean and standard deviation of LAYS scores in this cluster is 7.18 and 1.17, Pakistan’s LAYS score places it in the lowest 4<sup>th</sup> percentile of this sample. Pakistan’s labor force participation and unemployment rates are also worse than the average placing it among the second lowest country in the overall Asian Skills Index.

India’s pupil-teacher ratio and LAYS score is about average for its cluster while the percentage of population with bachelor’s degrees, given India’s relative size, is quite impressive at 9.11%. These indicators rank India’s performance on Pillar 1 at about average. However, India’s relative standing on Pillars 2 and 3 is worse than average on every indicator. India’s labor force participation rate is barely over 50%, 30% of Indian youth are not in education, employment or training, unemployment with advanced education is 15% (more than twice the average in this

> 21 Myanmar’s high position in the ASI is due to the fact that its total unemployment, as well as its unemployment among those with advanced education, is extremely low at 1.79% and .80%. The total unemployment rate in 2019 was even lower at .50%. (see <u>https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS?locations=MM)</u>

25
that estimating economic mobility using LASSO-PMM may approximate actual welfare indicators in settings where cross-sections are routinely collected, but where panel data are unavailable.

Given these validation results, we apply the LASSO-PMM method to harmonized crosssection data from 43 countries across the world in circa 2010 and 2015. Compared to previous methods, the models we estimate have greater explanatory power, which increases our predictive capabilities. In general, aggregate poverty rates are accurately approximated when using crosssections across a heterogeneous group of countries. Several additional messages emerge from the dynamic analysis. First, there has been considerable mobility in recent years; approximately 22% of individuals changed their economic status in all countries under analysis. Second, even in a period of generalized poverty reduction, about 9% of the population fell into poverty. Third, a large share of the population is immobile; about 24% of all individuals remained in chronic poverty. Fourth, initial average income for the chronic poor was slightly lower but grew significantly less than for those who moved out of poverty. Lastly, those who were not poor but fell into poverty had an income substantially lower and decreased more than those who were not poor and remained out of poverty.

The remainder of this paper is organized as follows. Section 2 summarizes existing crosssection welfare dynamic predicting methods and presents the LASSO-PMM approach. Section 3 describes the harmonized data sources used to validate and apply the method. Section 4 presents the results of estimating the LASSO-PMM approach and performs a wide array of validation tests to determine its sensitivity and robustness with 36 panel data sets. Section 5 applies the LASSOPMM approach to cross-section data from 43 countries and discusses the findings. Section 6 concludes.

5
# **1 Introduction**

In textbook models of economic growth and development, a powerful underlying assumption is that market exchange takes place almost exclusively in non-violent settings where property rights are secure.<sup>1</sup> That premise is at odds with the actual experience of many countries and the households and firms within them. The 2011 World Development Report (WDR) counted a billion and a half people living in countries affected by fragility, conflict or violence, and it identified organized violence as an impediment to long-run economic growth (World Bank, 2011).

The recent academic literature points out that insecurity can influence a nation’s development and growth through many different channels. These include a fall in output due to the loss of physical capital and drop in investment by firms; the misallocation of human capital from production to protection; the erosion of human capital due to the shift in public spending toward the military and away from social goods like education and health care; the disruption of labor markets and transportation networks; the drop in consumer demand; and the weakening or destruction of the political, social, and economic institutions that markets require, including property rights (Blattman and Miguel, 2010; Miguel and Roland, 2011).<sup>2</sup> A separate channel that has recently received attention in this literature is via the industrial organization of sectors and firms (Amodio and Di Maio, 2017; Besley and Mueller, 2017). How persistent are the effects of insecurity, and how does this contribute to firm growth and survival? Do firms make strategic decisions on where and when to invest based on patterns of violence? And does insecurity affect all firms equally, or are certain firms more

> 1Terms like “civil war” and “rebellion” generally do not appear in the leading textbooks on economic growth (see, for example, Jones (2002)). One of the best-selling texts on development economics devotes just one paragraph to political instability including inter-state and civil wars, coups d’etat, and rebellions (Perkins et al., 2013).

> 2The link between such institutions as property rights and long-run growth has been of particular concern to economists since at least the pioneering work of North (1981). Svensson (1998), for example, extended North’s research by modeling the effects of political instability on property rights and, in turn, on investment rates. Gradstein (2004) argues that the enforcement of property rights and economic growth are self-reinforcing. Building on this and related research, the WDR emphasizes the crucial role of improved governance in securing people and property if growth is to be achieved (World Bank, 2011).

2
and _αt_ are survey wave fixed effects. We impute zeros for outcome variables when the household does not cultivate the crop, with the exception of yields and fertilizer use; we therefore interpret estimated impacts on yields and fertilizer use as a combination of direct and selection effects.

**Balance and attrition** We establish that our village-level randomization achieved balance for key baseline farmer characteristics, both individually and jointly, across demonstration and control arms (Table 2). In Appendix Table A28, we show that inference in our tests of balance is not affected when we implement randomization inference, and we therefore instead present robust standard errors clustered at the village and associated p-values throughout our analysis.<sup>16</sup> In Appendix Table A7, we establish that attrition was low (12.5% to 18.9%) one and two years post-demonstration and not significantly affected by assignment to demonstration.

**Results** Pooling across the two years following the initial demonstration year, we find that assigning demonstration resources increases adoption of the new seed among farmer group members by 4.5 percentage points, or a 145% increase over the control mean (Column 1 of Table 3). This estimate conservatively suggests that adoption of the new seeds, conditional on cultivating the promoted crops, increased by 20 percentage points.<sup>17</sup>

Consistent with the objective of enabling substitution away from dry season paddy and towards pulses through the promotion of the new seeds, we find suggestive (but statistically insignificant) evidence of increased cultivation of promoted crops (Column 2 of Table 3) and decreased cultivation of paddy (Column 6 of Table 3). While neither of these coefficients are significant, we consider them further when we estimate heterogeneous effects with respect to experience with promoted crops.

We find no average impacts of being assigned to demonstration on yields or profit 16We present estimates of Equation 1 with randomization inference p-values included in Appendix Table A30. Inference is in general unaffected, although our estimates of the impacts of demonstration on fertilizer use fall marginally below conventional thresholds of statistical significance.

> 17We implement this calculation, and similar calculations throughout this Section 4, under the assumption that all demonstration-induced adoption of the new seeds is joint with demonstrationinduced cultivation. Our estimated effect of demonstration on cultivation in Column 2 of Table 3 does not allow us to rule out either this possibility, or the possibility that all demonstration-induced adoption of the new seeds is _conditional_ on cultivation. Under the latter possibility, our estimate would imply adoption of the new seeds, conditional on cultivating the promoted crops, increased by 35 percentage points.

23
<!-- Start of picture text --> Plot of PPP (in logs) against Plot of PPP (in logs) against<br>real GDP (in logs) real GDP (in logs)<br>1993 1996<br>oa<br>++.ae*<br>*<br>oa t ° * +<br>LW * * ** * * *<br>' oe<br>a +<br>Plot of PPP (in logs) against Plot of PPP (in logs) against<br>real GDP (in logs) real GDP (in logs)<br>2005 2011<br>a<br>w te Pha oe * oe, 2*%2 oe<br>a) * * py *, #,<br>ae<br>a<br>' SS<br>5 10 15 20 5 10 16 20<br>In_realGDPPC<br>—— yhat *® In_PPP<br>Graphs by year<br><!-- End of picture text --> <!-- Start of picture text --> Plot of PPP (in logs) against Plot of PPP (in logs) against<br>exchange rate (in logs) exchange rate (in logs)<br>1993 1996<br>ao<br>Fel- ih wt oe? > oe *<br>*<br>of,Fe) ek ee re<br>'+<br>o<br>Plot of PPP (in logs) against Plot of PPP (in logs) against<br>exchange rate (in logs) exchange rate (in logs)<br>2005 2011<br>o<br>~<br>*<br>Fe) +*<br>o ot gt + + +<br>ry<br>oOo<br>' aas 1<br>-5 0 5 10-5 0 5 10<br>In_exrate<br>—— yhat ® In_PPP<br>Graphs by year<br><!-- End of picture text -->
Table 4 Investment and **savings** in Sub-Saharan Africa, 196648 (average annual percentage of GNP)

||1966-73|1974-80|1981-84|1985-87|1988|
|---|---|---|---|---|---|
|Sub-Saharan Africa||||||
|Gross domestic investment|16.7|22.3|16.7|_15.0_|16.6|
|Gross national savings|13.0|17.2|9.0|8.8|8.0|
|Gap a/|3.7|_5.1_|7.7|6.2|8.6|
|Nigeria||||||
|Gross domestic investment|14.8|22.8|13.8|10.3|13.6|
|Gross national savings|10.3|23.7|8.8|8.4|8.7|
|Gap a/|4.5|-0.9|_5.0_|1.9|4.9|
|Other IBRD countries||||||
|Gross domestic investment|22.2|29.5|26.4|21.8|19.6|
|Gross national savings|14.2|12.2|13.7|14.7|11.7|
|Gap a/|8.0|17.3|12.7|7.1|7.9|
|IDA-only countries||||||
|Gross domestic investment|16.1|18.8|16.8|16.1|16.3|
|Gross national savings|13.8|10.6|7.3|6.9|5.7|
|Gap a/<br>- - - - - - - - - - - - - - -|2.3<br> - - . . . . .|8.2<br>...--<br> - - . . . .|9.5<br> <br> . . - - .|9.2<br>- - - - - -|10.6<br> - - - - -...|

Note: a/ Defined as investment minus savings

# Source: World Bank data files

The decline in savings in Sub-Saharan Africa can be explained by three factors: a) declining income, which leaves fewer resources for public and private consumption and savings; b) the govemment's negative savings that resulted from growing recurrent budget deficits; and c) some misclassification of foreign inflows that may make the level of domestic savings look lower than it actually is.

National accounting identities define all net foreign capital as investment (by definition, net capital inflows finance the gap between domestic investment and national savings). From the perspective of changes in the stock of national savings, this definition may be correct -- the increase 14
2 or the countries' present and past environmental policies can be deemed deficient. Country comparisons are also made difficult by the lack of comparable data for some indicators.

# 2. World trade in environmentally sensitive goods

Competition for world markets has been keen over the past twenty years. While industrial countries seem to have more or less maintained their share in total exports, a major change has happened in manufactures. Developing countries'<sup>2</sup> share in world exports of manufactures rose from 8 to 18% between 1970 a.nd 1990 (Annex table 2). The role of lower environmental standards and expenditures in developing countries than in industrial countries in this is likely to be small. The average share of environmental expenditure3 in industrial countries has been a modest share of GDP, around 1-1.5%. Differences in labor, capital and other resource endowments are more likely to have contributed to the international division of labor between industrial and developing countries. Policy reforms in developing countries have- also increased productivity and efficiency. Total trade shares and those of the sensitive categories during the past twenty years were also influenced by changes in commodity and especially in oil prices.<sup>4</sup> Only about half of the environmentally sensitive goods belong to the manufactures category as traditionally defined in international trade statistics.

The share of environmentally sensitive goods in total exports varies greatly among countries (Table 1). High shares of sensitive goods in exports can make a country's overall export performance more sensitive to changes in environmental standards at home and abroad. In the Nordic countries environmentally sensitive goods are a very high share of total exports -

> Developing countries in the paper refer to all countries not included in the industrial country category.

> Environmental expenditure indicates all public and private expenditure for pollution abatement and control as reported to the OECD Secretariat by its member countries. It is defined **as** the first-order, out-ofpocket expenditure of those economic entities that inplernat control measures and undertake compliance activities. The reporting is done according to guidelines provided by the OECD to help comparability. Categories included are water, waste, **air,** noise, other pollution. The data excludes expenditure on nature protection. For more detail on definitions see OECD(1990, 1993). Despite the efforts at OECD for comparability of data crosa-country definitions and reporting vary **a** great deal and therefore cross-country comparisons of data should be done with much caution.

- 4 Data for 1980 is distorted by the oil price increases in the 70's and is therefore left out. The share of petroleum productu in the sensitive category was 11% in 1970, 24 % in 1980 and 16 % in 1990 based on data on world imports of the goods.
The remainder of the paper is organized as follows. In Section 2 we discuss why and how firm survey data are useful in the context of improving our understanding of Africa’s manufacturing sector. In this section we also describe briefly the main data collection projects that have been fielded in Africa since the early 1990s. In Section 3 we review the research results on manufacturing firms in Africa. We begin by looking at the business environment, focusing on market size, risk, access to credit, labor, and infrastructure. We then turn to issues to do with how firms that operate in such an investment climate choose to organize themselves and how firms do business. We end the section by looking in some detail at what we have learned about three key aspects of firm performance, namely firm growth, investment and technology acquisition, and exports. We conclude in Section 4 with an extended discussion of the policy lessons.

# **2. Why Firm Surveys?**

The main channels through which Africa’s private sector can contribute to poverty reduction are by generating wage growth and more jobs. Everything else equal, increasing wages and employment result in higher total labor costs, which tend to erode firms’ profits unless accompanied by better performance. This is why improved economic performance is vital in this context and a major reason why this has come to be a central concern to policy-makers and development analysts alike over the last decade. The survey data on African firms have been extensively used for research under this theme. Over the last ten to 15 years three major research programs can be identified - all connected to the World Bank one way or the other - which have involved the collection of survey data on African firms on a large scale. The first was the Regional Program on Enterprise Development (RPED) surveys, carried out in Burundi, Cameroon, Côte d'Ivoire, Ghana, Kenya, Tanzania, Zambia, and Zimbabwe between 1992 and 1995. Each survey typically covered about 200 firms, and firms were interviewed three years in a row in most countries (thus yielding panel data). Four industrial sub-sectors were covered – food, wood, textiles and metal – and large as well as small firms, including informal ones, were included. Data were collected on a wide range of variables, including sales and output, capital stock, entrepreneur characteristics, employment by occupational category, labor turnover, wages, and conflicts with workers. Data collection as part of the RPED slowed down after 1995, 4
- 64 -

possibilities for functional form by using both quasi quadratic Cobb Douglas and Fourier specifications. In addition they introduce an element of dynamic adjustment by fitting first order autoregressive error structures. The basic model however is again a two stage cost function which gives fuel shares in total energy and then relates total energy used to other factors of production. The data cover the period 1962 to 1979. The partial cross price elasticities are calculated for 1974 fael shares and are shown in table 4.10. Coal was included with oil because of its minor importance. The model also allows for Hicksian technical progress. The results show that a single functional form cannot always be made to fit adequately (indicated by blanks in the table). Mountain and Hsaio compared their results with previous work on Canada and were able to conclude that in general their elasticities of substitution were larger. It La certainly noticeable that in comparison with the aggregate industry estimates of Pindyck and Considine, the use of disaggregated data plus an allowance for technical progress and for autocorrelation together produces very much higher values of the cross price elasticities. One important finding is that where quite different flexible forms are used the elasticities evaluated at the same fuel shares are close to each other. Also the results for the different industries are surprisingly close bearing in mind that no restriction has bee;i placed to limit the results from the various industries. Mountain, Stipdonk and Warren [19891 using a similar methodology with data spanning 1962 to 1984 for Canada, reach similar conclusions.
treat some interests and topics as valid and to devalue and delegitimize others. Mansbridge (1998) also raises this concern when she argues that assuming that deliberation must be deliberation about the common good narrowly restricts deliberation to talk framed from the standpoint of a single, all-encompassing “we” that might, after all, not be inclusive of the interests of marginalized groups and individuals. The inadmissibility of ‘private’ interests prevents them from being expressed, understood and debated over, which goes against the central premise of deliberative democracy that all interests and concerns should be publicly expressed and debated to yield a consensus regarding what is common good.

With these critiques in mind, and building on but departing from the genre of validating and revisionist extension accounts emerging from valuable contributions by sociologists like Baiocchi, Heller and Polletta, in this paper, based on empirical evidence drawn from the _gram sabha_ , we introduce the concept of _oral democracy._ This concept is proposed not as a utopian ideal, but as a representation of some prominent models of “actually existing democracy” (Fraser 1990). It is also proposed as an analytical frame to guide our investigations into empirical instances of talk-centered democracy without the encumbrances of the theory of deliberative democracy. Oral democracies, as they exist, are geared toward giving all citizens the equal right of talking to the state and the equal right to speak to each other as members of a polity. The goal is also to engage in an interactive and participatory process of decision-making and scrutiny on matters of community welfare and development. While deliberative democracy theory focuses on mutual conversation among citizen-peers outside the purview of the state, thereby, neglecting to focus on citizens’ interactions with the state, political systems based on oral democracy recognize that talking to the state is an important opportunity that has been inaugurated by this talk-centered model of democracy. Indeed this study of Indian village assemblies shows that 11
observations in 2010) measures the growth of issuers relative to non-issuers; therefore, it captures whether the distribution of issuers shifts more to the right than that of non-issuers between 2003 and 2010. Thus, the coefficient on this interaction term is of special interest.

Consistent with the kernel density estimates, the results of the quantile regressions show that issuing firms are ex ante larger than non-issuing firms. Importantly, not only are the issuers larger than non-issuers in the top of the firm size distribution, but these size differences also exist at every decile, including the bottom ones. The results are statistically significant for all estimates, except for the bottom deciles of the distribution of the number of employees. These differences are also economically significant. For example, issuing firms at the 1<sup>st</sup> decile are larger than non-issuing firms at the same decile of the FSD by 63% in assets, 24% in sales, and 7% in the number of employees.

Issuing firms not only start larger than non-issuing firms, but they also grow much faster than non-issuing firms at all deciles of the distribution. In other words, there is actually ex post divergence in firm size between issuers and non-issuers. The coefficients on the interacted term are larger than one and statistically significant for every estimated decile, with the exception of the top decile of the FSD in all three specifications (assets, sales, and the number of employees), thus providing evidence that issuing firms grew faster than non-issuing firms over the same period. There is in fact a sizeable additional growth for issuing vis-à-vis non-issuing firms between 2003 and 2010 even after taking into account the initial differences in size between issuers and non-issuers. For example, as shown by the estimated interacted term, the ratio of firm size between issuers and non-issuers at the bottom 1<sup>st</sup> decile of the distribution of assets increased 115% over this period; issuing firms were 63% larger than non-issuing firms in 2003 and became 250% larger by 2010. The implied differential in annualized growth rates is substantial. Non-issuing firms at the 1<sup>st</sup> decile of the distribution of assets grew 1.6% per year between 2003 and 2010, whereas issuing firms at the same decile of the distribution grew 13.3% per year over the same period. Qualitatively similar differences are estimated for sales and the 17