rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5. Quartiles of predicted probability of formality by model and data source, informal firms**

|**Predicted probability of formality:**|**<25%**|**25% to <50%**|**50% to <75%**|**75%+**|
|---|---|---|---|---|
||**Model**|**1**|||
|**ELMPS 2012 in est.**|5.8|26.2|62.4|5.5|
|**ELMPS 2018 in est.**|6.8|42.6|46.7|3.8|
|**Economic Census**|48.4|39.8|11.0|0.8|
|**Establishment Census**|30.9|51.7|16.1|1.4|
||**Model**|**2**|||
|**Economic Census**|50.2|39.0|9.9|1.0|
|**ELMPS 2012 in est.**|19.7|37.0|30.6|12.7|
|**ELMPS 2018 in est.**|17.3|44.1|30.1|8.5|
||**Model**|**3**|||
|**ELMPS 2012 in est.**|27.2|36.5|23.2|13.0|
|**ELMPS 2018 in est.**|30.1|37.8|23.3|8.9|

Source: predictions based on models in Table 2, Table 3, and Table 4

# **4.4. Profiles: Grouping and Clustering Analysis**

In what follows we attempt to characterize which informal firms operating inside establishments are least likely to be formalizable and which have a higher potential for formalization.<sup>22</sup> We do this first by grouping informal firms into different profiles (collections of characteristics that explain formality) based on Model 2 for the EcC data and Model 3 for the pooled ELMPS data. We also use cluster analysis to classify informal firms into 10 clusters based on the variables in EcC Model 2 and ELMPS Model 3. After examining how the mean characteristics of these clusters compare to those of all informal firms within establishments (shown in Table 13 and Table 14 in the appendix), we attempt to assign these clusters names that best describe the firms that fall within them. Finally, we identify, separately for each data set, which groups or clusters of informal firms are least likely and most likely to be formalizable based on the proportion that falls into each quartile of the probability of being formal.

As shown in Table 6, according to the ELMPS, the firms most represented in the bottom quartile of the probability of formality are one or two-person firms that are relatively young (<5 years of age) and that

> 22 The very high informality rates for firms operating outside fixed establishments precludes this kind of analysis for these firms since few of them are likely to be appropriate candidates for formalization.

38
# 1. Introduction

Past decades have seen Malaysia experience a long period of rapid economic growth. GDP per capita grew from about MYR 7,000 in 1970 to MYR 43,000 in 2018 in constant Malaysian ringgit (WDI 2020). This is a more than sixfold increase. For the period studied in this paper, 2004–2016, cumulative real GDP growth was about 77 percent, while real per capita incomes grew by around 46 percent (WDI 2020). Interestingly, and somewhat unusually compared to many other countries experiencing rapid economic growth, survey-based income inequality as measured by the Gini coefficient fell from about 0.46 in 2004 to 0.41 in 2016. These two forces have combined to all but eliminate extreme poverty at the international poverty line of USD 1.90 at 2011 PPPs. Malaysia introduced a new national poverty line in 2020, which is roughly USD 10 per person per day at 2011 PPPs. Based on this new official poverty line, the poverty headcount fell from 7.6 percent in 2016 to 5.6 percent in 2019 (DOSM, 2020).

While this reduction in the headline poverty rate is well-documented, not much is known about poverty dynamics in Malaysia – i.e., statistics that capture the movements in and out of poverty of individuals or households over time. Studies of this kind have been published for other countries in the region, such as Indonesia and Vietnam (Imai et al. (2011), Justino et al. (2008), Purwono et al. (2021), Suryahadi et al. (2003)). On Malaysia, there have been studies based on retrospective data that investigate socio-economic intergenerational mobility (Khalid 2018, Hamid et al. 2019). However, this study is the first to provide information on poverty dynamics in Malaysia based on official micro-level survey data.

One particular feature of Malaysia’s development model concerns the preferential treatment of the Bumiputera ethnic groups, the population segment that was economically disadvantaged both before and after independence. For example, average consumption was and remains lower for Bumiputeras than for Chinese or Indian Malaysians, the other main ethnic groups.<sup>1</sup> Malaysia’s New Economic Policy (NEP), introduced in 1971, had the twin objectives of reducing overall poverty and improving the economic position of the Bumiputeras. The NEP aimed to raise the participation of Bumiputeras in the formal education system, tertiary education and professional occupations, as well as expand their ownership of and control over capital (Lee, 2017). We will elaborate on this context in the next section.

> 1 See Anand (1983) or Ikemoto (1985) for a detailed historical treatment, or Jomo and Hui (2014) for a more recent overview. The demonym _Malaysian_ refers to all citizens of Malaysia, regardless of their ethnicity. The term _Malay_ refers to the Malay ethnic group, the largest in the country. _Bumiputera_ is an ethnic label that groups Malays with other indigenous groups (such as _Orang Asli_ in Peninsular Malaysia and _Orang Asal_ in the states Sabah and Sarawak). The Bumiputeras form the majority of Malaysia’s population.

1
56

## Keny

The World Bank previously assumed a small flow of net immigrants for each five-year period, while the United Nations assumed no net immigration in any period. Although the differences between the two sets of estimates are very small, it should be pointed out that the official population projections from the Central Bureau of Statistics assume that there is no net immigration between 1980 and 2000 (Central Bureau of Statistics, 1983).

# Zimbabwe

The World Bank previously estimated that net emigration would be 5,000 in 1980-1985 and again in 1985-1990, whereas the United Nations assumed a value of 25,000 in 1980-1985 and zero thereafter. Again, the differences are small. However, the official projections from the Central Statistical Office (1986) assume there is no net international migration for the period 1982-2032. The Central Statistical Office points out that according to official statistics there were 12,123 net emigrants in 1983, 11,412 net emigrants in 1984, and 1,454 net emigrants in the first six months of 1985. Although the publication suggests that unofficial movements may reduce or even negate net emigration, I would recommend a net emigration assumption of 25,000 for 1980-1985 and no net emigration for the rest of the century.

# Cote d'Ivoire

The World Bank previously estimated considerably larger net immigration to Cote d'Ivoire than the United Nations for 1980-1985, 1990-1995, and 1995-2000, but a similar level for 1985-1990. Net immigration may have decreased somewhat because of the economic crisis of the early 1980s (Hill, n.d.) and depressed prices for cocoa. However, I do not have sufficient information to be able to choose rationally between the two different estimates. It should be noted, though, that Russell and Jacobsen (1988) conclude that there is unlikely to be any significant reduction in immigration to Cote d'Ivoire (while at the same time noting the possible confounding effect of high unemployment rates among educated Ivoirians).

# Siouth Africa

The United Nations assumes no net international migration between 1980 and 2000, whereas the World Bank previously assumed net immigration to be 50,000 for 1980-1985 and 1985-1990, 40,000 for 1990-1995, and 26,000 for 1995-2000. According to the Bulletin of Statistics, immigration totalled 30,483 in 1983, 28,793 in 1984, and 17,284 in 1985. About two-thirds of the immigrants come from Zimbabwe and the United Kingdom. At the same time, there were 8,247 officials emigrants in 1983, 8,550 in 1984, and 11,401 in 1985. The emigration figures are probably underestimated, however, given the large number of legal immigrants from South Africa to the United Kingdom, Australia, Canada, the United States and other countries. Overall, I would recommend somewhat lower net immigration assumptions of 40,000 for 1980-1985, 20,000 for
**Table 1: Maquiladora Employment 1998**

||**Employment Share**|**N/P Emplo**|**yment Ratio**|
|---|---|---|---|
|**State**|**Maquila/Census**|**Census**|**Maquila**|
|Baja California Norte|0.868|0.153|0.078|
|Tamaulipas|0.769|0.239|0.086|
|Chihuahua|0.742|0.152|0.084|
|Sonora|0.644|0.212|0.065|
|Queretaro|0.552|0.422|0.083|
|Coahuila|0.485|0.217|0.056|
|Durango|0.340|0.17|0.052|
|Aguascalientes|0.286|0.261|0.041|
|Yucatan|0.227|0.266|0.055|
|Baja California Sur|0.226|0.319|0.031|
|Zacatecas|0.154|0.326|0.070|
|Nuevo Leon|0.142|0.285|0.090|
|Tlaxcala|0.103|0.243|0.068|
|Puebla|0.101|0.198|0.047|
|Jalisco|0.087|0.323|0.126|
|San Luis Potosi|0.073|0.308|0.027|
|Guerrero|0.060|0.282|0.022|
|Guanajuato|0.048|0.192|0.051|
|Morelos|0.023|0.348|0.092|
|Sinaloa|0.022|0.401|0.148|
|Mexico State|0.020|0.352|0.121|
|Hidalgo|0.008|0.186|0.069|
|Distrito Federal|0.004|0.506|0.108|
|National Average|0.242|0.293|0.073|

**Notes:** Maquilas include services as well as manufacturing.  In 1998, and over the 1990-2003 period, services average 4% of total maquila employment. The employment ratio is the nonproduction/production worker ratio.   INEGI does not report data for all states, and we presume this reflects an insignificant number of maquiladoras and therefore enter "0" for these states.  The Mexican states of Campeche, Colima, Michoacan, Nayarit, Oaxaca, Quintana Roo, Tabasco, and Veracruz have maquila/census employment shares of 0.000 and are not included in the table.  The “Average” includes all 31 states and the Federal District.

47
reasoning skills, based on a test using the Raven’s progressive matrices. This is not surprising, since these skills are likely not generated by exposure to more investment in educational activities.

Applying the Strengths and Difficulties Questionnaire (SDQ) to this sample of children reveals that there is no impact - either practical or statistical - on children’s internalizing behavior or externalizing behavior (Table 4).<sup>16</sup> However, the program reduces children’s pro-social behavior problems by -0.18 s.d. For context, the gap between high and low INPRES districts is 0.32 s.d. Thus, the estimate is practically very large, with over 50% improvement in pro-social behavior, and statistically significant.

The Early Development Instrument (Table 5) corroborates the findings from the SDQ. The coefficients on four out of the five EDI domains are practically small and statistically insignificant. However, there is a substantial positive effect on children’s social competence. Children of mothers who were exposed to high doses of the program have 0.2 s.d. higher social competence than children of mothers who were exposed to low doses of the program. The children are 8 p.p. less likely to be in the bottom quartile of the EDI domain distribution for their age. This is equivalent to 72% of the gap between high and low INPRES districts.

# **4.3 Mediators**

Next, we examine several channels of transmission between maternal education and child development, by drawing on insights from economics, psychology, and sociology. We begin by examining characteristics of the children’s father. The two columns in Table 6 differ only in that column 2 adds an interaction between the treated cohort and the number of school-aged children in 1973. The program did not affect the presence of fathers in the household and age of fathers. However, there is evidence that mothers who were exposed to higher doses of the program have children with more educated men. The program increased educational attainment of fathers by three-quarters of a year and primary school completion rate of fathers by 8 p.p. This lends some evidence to assortative mating as one channel of transmission for the benefits of exposure to INPRES. Yet, we cannot

> 16Recall that the SDQ is scored such that lower scores indicate better child development outcomes.

22
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" urban areas now have electricity service. The relationship between income and coverage is remarkably similar for electricity, in-house water connections, and sewer. As monthly household incomes increase from US$100 to US$250, coverage of all these infrastructure services rises rapidly. As expected, coverage is much higher in urban than in rural areas for electricity, water, sewer, and telephone service.

The findings confirm that the very poor rarely have these infrastructure services. There are, however, exceptions. The very poor often do have electricity if they live in urban areas. The very poor in Eastern Europe and Central Asia have much higher levels of coverage than elsewhere in the world; they often have electricity, water, sewer, and telephone services. The results also suggest that if the poor have access to services in their communities, many will in fact decide to connect.<sup>7</sup> Where the very poor do not have formal infrastructure services, informal, private, and community infrastructure solutions fill the gap for many households. Few households in any of the fifteen countries in our sample report using unimproved water sources or candles for lighting. However, many households at all income levels and in both rural and urban areas used wood, thatch, or dung for cooking fuel. Few poor households without private telephones have public telephones in their communities, and the vast majority of the poorest rural households have no toilet, sewer, or septic facilities in their homes.

# 2. **The data: Livings Standard Measurement Study Surveys in fifteen countries**

The World Bank initiated the LSMS program in the 1980s to improve the quality of survey data available for policy research and analysis in developing countries. Since then more than twenty countries have administered nationally-representative household surveys based on the LSMS model of questionnaire design and quality control. The multi-country data set used in this analysis is cornposed 7In this paper, we reserve the term "access" to refer to a household's ability to obtain an infrastructure connection, should the household decide to do so. For example, a household has access to sewer service if there is a sewer network in the household's neighborhood.

3
Regarding EPA’s institutional arrangements, objectives and activities associated with their effects on exports, our results suggest the following. EPAs should have a large share of the executive board in the hands of the private sector, but a large share of their budget should be publicly funded. The proliferation of small agencies within a country leads to an overall less effective program. However, we found no statistically significant evidence regarding the allocation of EPAs’ expenditures across different activities or types of firms targeted by EPAs (small versus large or established exporters versus non-exporters).

The rest of this article is organized as follows. Section 2 describes our global survey of EPAs and provides some descriptive statistics to help understand the objectives, activities, and institutional structures that exist in EPAs around the world. Section 3 describes the econometric strategy. Section 4 discusses the empirical results, and section 5 concludes.

# **2 Survey of EPAs: Summary Statistics**

During the summer and fall of 2005 we conducted an eighteen-question survey of EPAs around the world.<sup>6</sup> Through the ITC website (www.intracen.org/tpo) we obtained a database with contact information. We complemented this list with the help of World Bank country economists who provided contact information for national EPAs. We contacted agencies or Ministries in 116 countries, and 92 answered our request (of which 4 responded that they could not respond). Each of the 88 surveys that we received was followed up with phone conversations to confirm and clarify some of the answers. The list of 88 agencies appears in the Appendix Table.

The survey contains five parts: i) institutional structure, ii) responsibilities of the agency, iii) the strategies followed, iv) resources and expenditures, and v) activities and functions. Below we provide summary statistics by region.

> 6The survey is available from the authors upon request.

4
# 5.3.3 Magnitude and intensity of food transfers in intervention districts

Effects of school feeding may depend on the magnitude or intensity of the food transfer provided. The World Food Programme reports that the OSF ration is intended to provide 100 grams of corn-soya blend and 12.5 grams of sugar each school day, with a target of 83 feedings per term. In the follow-up survey, villages reported the frequency of meals provided in the current term and the number of days that meals had been provided in previous terms (Table 9). Intensity of OSF varied in Phongsaly vs. Khua. While 34/35 (97%) of take-up schools in Phongsaly reported providing OSF meals every day in the current term, only 27/47 (57%) take up schools in Khua reported providing a meal every day. Indeed, 9 participating schools report never providing a meal this term. A second measure of OSF intensity, calculated as the number of days since Fall 2006 that the school reports providing a meal, is also higher in Phongsaly (median = 280) compared to Khua (median = 235), even though schools in Khua enrolled in the school feeding program earlier than Phongsaly schools. Overall, Phongsaly schools that participated in school feeding reported providing meals on 58% of all possible schools days since Fall 2006, compared to 49% of Khua schools. Together, these results suggest that program implementation difficulties in Khua may have compromised the effectiveness of the program.

Take-home rations for boys and girls in this intervention included 15 kilograms of rice upon enrollment in September, 30 kilograms of rice at the end of the school year in May if attendance was 80%, and one can of fish each month if attendance was 80% for the month. Informal boarder rations provided an additional four kilograms of rice, two cans of fish and one bag of salt each month if attendance was 80% for the month. Participating villages reported generally similar ration amounts, although the timing of distribution differed from the WFP schedule in many cases.

23
# **1. Introduction**

With the development spotlight increasingly focused on Sub-Saharan Africa (SSA), there is renewed interest in understanding the constraints to higher growth in the region<sup>1</sup> . Recent findings have emphasized the role of the broader investment climate in shaping the opportunities and incentives for enterprises to invest, create jobs and expand (World Bank WDR 2004). This paper uses detailed enterprise-level data to examine how different dimensions of the investment climate –availability of infrastructure, access to finance, the regulatory environment and corruption -- affect patterns of employment growth. In addition to testing which dimensions are most associated with higher growth, there remains a question whether the priorities for reform are common across countries. Thus, the aim is to compare the impact of the different dimensions of the investment climate in SSA with those in other regions, particularly to test whether the top constraints faced by entrepreneurs in SSA have a common impact in low-income countries in other regions.

To examine the impact of the investment climate on employment growth, the paper uses data from over 17,000 enterprises in 31 SSA countries included in the World Bank’s Enterprise Surveys (ES). To put the experience of SSA into a larger global context, results are compared with those of 48 other low or lower-middle income countries (LOW) and 26 upper-middle or high income countries (HIGH) in other regions.2 These datasets are collected with a comparable instrument and sampling strategy, providing a unique source of disaggregated information on many dimensions of interest of the investment climate.

Covering the full range of enterprise sizes, they also allow for an analysis of how conditions vary across enterprise types that proves to be important in highlighting priorities for reform.

> 1 See the Africa Action Plan, commitments of the G8 to focus on SSA development, the Stern Commission for Africa, the World Bank’s Six Strategic Pillars, among others.

> 2 Three of the SSA countries in our sample are upper-middle income countries (Botswana, Mauritius and South Africa).  For the results presented here they are classified as SSA.  Results are robust to their exclusion or their inclusion with HIGH countries.  The one exception noted below is the comparison of productivity across regions, where South Africa’s higher productivity can affect averages.

2
# **1. Introduction**

Malnutrition is a persistent problem in developing countries. WHO estimates the fraction of malnourished children in developing countries at 33% as measured by the percentage of children stunted, i.e. children that fall below -2 standard deviations of the United States National Center for Health Statistics / WHO international reference median value (de Onis, Frongillo, and Blössner 2000). This has far-reaching consequences: malnutrition _in utero_ or in infancy can have a long-lasting negative impact on cognitive development and on their subsequent capacity to achieve sufficient income to provide for their own children when adults (Alderman, Hoddinott, and Kinsey 2006; Victora et al., 2008).

There is substantial agreement on the efficacy of a number of nutrition interventions (for a review see Allen and Gillespie 2001). There is little doubt, for example, that breastfeeding promotion saves infant lives, or that vitamin A prophylaxis reduces child mortality. However, there is little consensus that community based programs can reduce stunting or the risk of being underweight (Bhutta et al., 2008, Alderman, 2007).

This study assesses the impact of a package of health inputs on the anthropometric status of children in three regions in rural Senegal as evidenced by their weight-for-age. The relatively short time between the baseline and follow-up survey motivates the focus on two further measures of program success: health inputs and nutritional knowledge of the mothers. The program’s ability to influence these intermediary inputs not only helps explain the final outcomes but also indicates whether other health measures can be improved in the course of a program with a primary objective framed in terms of nutritional status. Furthermore, we stratify the sample by child age, i.e. whether the child 1