rafmacalaba/datause-encoder-data huggingface dataset:
protection program in place prior to the pandemic in order to protect the food security of poor households.

The paper is organized as follows. Section 2 describes how we implemented these surveys. Section 3 provides contextual information on the pandemic in Ethiopia, how it affected the households in our sample and how we define PSNP participation. Section 4 describes our estimation strategy; section 5 presents our results and we end with concluding remarks in section 6.

# **2. Survey method and implementation**

The PSNP is Ethiopia’s flagship rural food security program, operational since 2005. Most beneficiaries are employed in labor-intensive public works for six months per year with a small fraction of beneficiaries (approximately 15 percent) receiving unconditional payments (Berhane et al., 2014; Berhane et al., 2020). Payments are largely made in cash, but in-kind (food) payments are also used in localities that have poor access to markets. It is targeted both geographically – operating in _woredas_ (districts) which are considered chronically food insecure – and individually, with households selected for inclusion based on a series of criteria of which household food insecurity is particularly important. Households targeted for PSNP participation are historically food insecure, have low household asset holdings (e.g., land, oxen) and limited income from alternative sources of employment (Berhane et al., 2014; Berhane et al., 2020). Beginning in late 2018, in the Highland regions of Ethiopia (Amhara, Oromia, Southern Nations, Nationalities and Peoples (SNNP), and Tigray), additional activities were introduced into the PSNP that included the provision of information on improved maternal and child nutrition practices with the aim of making the program more nutrition sensitive.

In March and August 2019, we conducted face-to-face surveys in 88 woredas where the nutrition sensitive PSNP was supposed to operate. Three rural _kebeles_ (sub-districts) were randomly selected from each woreda and, within these, one enumeration area (EA) was randomly chosen.<sup>2</sup> For each EA, a list of households was constructed. Inclusion in this list was based on the following criteria that were implemented sequentially. First, a household was eligible if it had a child less than 24 months of age. Conditional on having a child in this age range, households were

> 2 _Kebele_ is the smallest administrative unit in Ethiopia.

4
||(5.61)***|(5.59)***|(3.74)***|(5.48)***|(5.74)***|
|---|---|---|---|---|---|
|Mother’s height|0.039|0.038|0.034|0.038|0.039|
||(4.23)***|(4.03)***|(3.44)***|(3.79)***|(6.22)***|
|Father’s height missing|0.110|0.059|0.239|0.057|-0.029|
||(1.31)|(0.71)|(1.74)*|(0.68)|(0.36)|
|Mother’s height missing|-0.034|0.039|-0.095|0.039|0.118|
||(0.25)|(0.30)|(0.71)|(0.29)|(1.10)|
|Mother’s years of education|0.011|0.000|-0.014|0.002|0.000|
||(0.78)|(0.01)|(0.71)|(0.14)|(0.00)|
|Father’s years of education|0.032|0.029|0.032|0.032|0.036|
||(2.04)**|(1.85)*|(2.05)**|(2.03)**|(2.74)***|
|Constant|-16.292|-16.122|-14.757|-16.441|-14.533|
||(9.43)***|(8.28)***|(7.04)***|(9.19)***|(9.60)***|
|Observations|2984|2871|1357|2871|2871|
|R-squared|0.21|0.22|0.12|0.22||

**Note:** Absolute value of t statistics in parentheses. * indicates significance at 10% level; ** at 5% and *** significant at 1% level of confidence. Columns 4 and 5 use predicted values of log of per capita consumption and feeding posts to deal with the endogeneity and measurement error problems associated to their variables. The Partage / feeding post variable is a community level variable. Consequently the set of instruments are community variables. They include: mean of the (log) household expenditures, drought and crop disaster in the past ten years, presence of a daily market on community, AIDS being a main cause for adult mortality and average female and male height for those aged 25-32. The Partage / feeding post variable was then regressed on the instruments listed here and other community variables in the model. Log per capita consumption is instrumented with log income and the presence of a good (wood, iron sheets, concrete, asbestos) roof in the house. Predicted per capita consumption was then obtained after regressing log per capita expenditure on its instruments and the various community and household regressors. Standard errors corresponding to T-statistics in columns 4 and 5 are obtained by using the bootstrapping method, required because of the complication of instrumenting community variables. The total number of different individuals observed between 1 and 4 times is 1206.

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# Introduction

Larger firms pay higher wages. Moore (1911) first observed this regularity among Italian textile mills, and a subsequent literature has established the large‐firm wage premium (LFWP)‐‐‐or the positive difference between wages paid by large firms and those paid by small ones‐‐‐as a stylized fact of the labor market. Here, we offer the first systematic comparison of the LFWP across developing economies, using a common regression specification and harmonized controls from 69 surveys of individual income across 25 developing economies compiled by the World Bank.

A worker’s wage summarizes their productivity as well as any rents associated with employment in a particular firm. Prior LFWP literature (e.g., Brown and Medoff, 1989; Abowd, et. al., 1999, <mark>Troske, 1999</mark> ) has been primarily concerned with identifying whether the observed LFWP is due to the sorting of higher ability workers into larger firms (worker effects), or due to large firms paying higher wages to otherwise similar workers (firm effects). Overall, sorting is understood to explain at least half of the effect, though its contribution may vary depending on national context, as we confirm here.<sup>2</sup> Identifying this sorting effect requires a panel of workers with firm identifiers, which is not commonly available, especially in smaller low‐income economies or in a format that is comparable across many countries at once. We address this issue in the cross‐section by employing a heuristic benchmarking technique that reports the magnitude of potential sorting on unobserved worker quality.

The analysis reveals five stylized facts about the LFWP in developing economies. First, conditional on occupation, education and experience, the LFWP is comparable to the average gap between male and female wages, or two‐thirds of the gap between urban and rural wages. Second, the LFWP has been declining over time, consistent with what is observed in advanced economies (Bloom, et. al., 2018). Third, the LFWP is also declining in national income.<sup>3</sup> <mark>Fourth, there is large variation across countries in the share of the premium explained by sorting on human capital (i.e., education and experience), conditional on occupation. Fifth, the LFWP in developing economies is low in manufacturing relative to commerce, a pattern that is reversed in advanced economies (B</mark> erlingieri, et. al., 2018) <mark>. In concluding</mark>

> 2 For instance, <mark>in Brazil, Menezes‐Filho, Muendler, and Ramey (2008) find the premium to be entirely explained by selection, whereas in Ghana and Kenya, Söderbom, Teal, and Wambugu (2005) find that a substantial portion remains unexplained.</mark> 3 The LFWP has been estimated in several developing economies, but its variation with income has not been tested systematically. See, for instance, <mark>Dobbelaere (2004) on Bulgaria; Funkhouser (1998) on Guatemala; Söderbom, Teal, and Wambugu (2005) on Kenya and Ghana; Schaffner (1998) on Peru; and Velenchik (1997) on Zimbabwe.</mark>

2
∆�� can be either positive or negative, and it is informative to separate positive-direction job creation rates from negative-direction job destruction (or contraction) rates. This distinction has been widely used and can be calculated separately for cases where ∆�� is positive and negative (using the absolute difference).

We distinguish _dir_ to indicate the direction of ∆��, and so includes (i) establishments with positive employment growth, or (ii) establishments with negative employment growth. These measures are referred to here as the _gross job creation rate_ and _job contraction rate_ , respectively.

# **3. Share of net job growth, share of gross job creation, and share of job contraction**

A statement frequently heard among policy makers is that firms of a certain type contribute X% to job creation (or contraction). This metric simply represents the relative contribution to each rate by each sub-cut s. Using the formulae for the rates for net job growth, this rate is expressed as: Most often, the cut is considered at the economy-level and provides a measure for the contribution of various sub-cuts – small and medium enterprises (SMEs) versus large, young versus old, services versus manufacturing, etc.

# **III. Literature Review of the Evidence on Employment Stock and Growth**

# **1. Where are people employed? Share of Employment Stock**<sup>**8**</sup>

Among the issues that have garnered the most attention in the literature is the role of firm size and whether large firms or SMEs are the largest employers in the private sector. Various theories predict that the answer to this question depends to a large extent on the economy’s stage of economic development. Biggs and Oppenheim (1986) for instance argue that the size composition of firms evolves with the process of structural transformation, noting that – generally – the size distribution of manufacturing firms increases with the level of economic development. Several historical studies of the evolution of specific industries in the US and UK corroborate these findings (Sokoloff 1984; Berg 1992; Lloyd-Jones and Le Roux 1980). However, evidence

> 8 Annex I includes a comprehensive table of this literature, findings, coverage, and methods.

6
Empirical work has concentrated to date on two themes:

- (i) The impact of ICB transparency on financial market responses eg the degree to which interest changes have become more predictable;  and

- (ii) The impact of greater transparency on macro-economic outcomes.

For much of this work, the measures of transparency used have been taken from Fry et al (2000) or subsequent developments. The types of variable included in the transparency index are whether or not the CB provides

- **_Explanations of policy decisions_** – how closely they are announced relative to the decision being made, the publication of minutes and voting patterns, etc

- **_Published forward looking analyses_** – the role of forward-looking analysis of economic developments, the clarity of inflation forecasts, the publication of inflation forecasts, published risk assessments and discussion of past forecasting errors, etc.

- **_The frequency of published assessments of economic developments_** – whether they are published monthly, quarterly or less frequently.

# **6.3.1 The Impact of ICB Transparency on Financial Market Responses**

The main findings to date are as follows:

- (i) _Geraats (2002) reports that several studies have shown that greater information disclosure by ICBs is associated with a decline in the response of market interest rates to changes in the official interest rate._ This has been found for Canada, the UK and the US<sup>62</sup> .

- In the UK-US study cited, the main decrease is at the short end of the yield curve on interest rates, indicating that greater transparency on information has reduced information asymmetries about the economy.

- (ii) _For 18 inflation targeting central banks, Fracasso, Genberg and Wyplosz (2003) estimate that higher quality inflation reports (quality as measured by graduate student appraisals) are associated with smaller interest rate surprises_ .  This is cet par, after including control variables on the country levels of inflation and interest rates as well as market and inflation volatility.

- Quality is judged on 5 indicators, each based on a 1-10 scale:

- a) How convincing the inflation report was;

- b) How well the expertise of the bank came through;

- c) How complete the report was;

> 62 See Geraats op cit, p. F558-9 and the references cited by her – specifically the studies (a) by Muller and Zelmer and (b) by Haldane and Read .

49
Among the 204 firms suffering tsunami damage, 168 (82 percent) repaired or replaced some damaged enterprise assets within three months of the tsunami. On average, owners suffering damage reported spending US$111 on enterprise recovery in the three months following the tsunami. Excluding those spending nothing in this period, the average expenditure was $135. While a substantial sum relative to pre-tsunami income levels, the amount represents less than 15 percent of the assets lost or damaged by the tsunami. The largest initial effort went to equipment and working capital: an average of US$39 was spent on equipment, representing 25 percent of the losses in this category. Relative to the losses suffered, the smallest investment was in land and buildings, where owners reported spending US$29 compared with losses of US$362.<sup>9</sup> What were the sources of funds which enterprises used to replace lost or damaged equipment? On average, just over half of the funds spent in the first three months came from own savings (51 percent). An additional 15 percent were obtained through loans from family members (9 percent) or friends (6 percent). One-fifth of the resources (20 percent) came from grants or loans from tsunami relief agencies. The remaining 14 percent was spread among credit from suppliers (6 percent), loans from microfinance organizations (2 percent), moneylenders (2 percent), banks (less than 1 percent), remittances from relatives abroad (1 percent) and other sources. The largest spenders relied more heavily on loans from family members and credit from suppliers, while those spending the least relied more on own savings. As a percentage of the total amount spent by all 176 entrepreneurs with complete data, own savings represents only 36 percent of expenditures. Loans from family members (13 percent) and friends (20 percent) are together almost as important, and credit from suppliers, at 11 percent of the total funds spent, is also more important than the unweighted averages suggest.

> 9 The data on assets lost in this paragraph differ slightly from those reported for microenterprises in Table 1, because the data here come from the baseline survey while those in Table one come from the October 2007 retrospective survey. The two means are actually quite close, however, with owners reporting business losses of $814 in the April 2005 survey and $897 in the October 2007 survey.

11
example, to certify that the effective level of benefits is equivalently generous to meet the SG obligations). As a result, many employers revisited their ongoing interest and cost effectiveness in maintaining their single employer DB schemes and many have since been closed to new members, and in a large number of cases, wound up transferring members to DC schemes All classes of funds (other than ‘small funds’) have been consolidating for some time. Corporate funds have declined particularly rapidly in both number and share of industry assets as costs of administration have increased and the introduction of near-universal employer superannuation has eroded any competitive benefits from offering in-house superannuation. Public sector funds have also experienced large declines. In contrast, industry funds and especially retail and small funds have registered large increases in market shares.<sup>32</sup> The consolidation trend was given substantial new impetus by the recent introduction of more stringent prudential regulations, including for fund governance and risk management, and the requirement that all funds be licensed by the middle of 2006. As a result, the number of funds, excluding ‘small funds’ and pooled trusts was 575 at endJune 2007, almost one-tenth of their number 12 years earlier (Table 11).

**Table 11** : **Number of Superannuation Funds by Type**

||1995|2000|2007|
|---|---|---|---|
|Corporate|4,211|3,389|289|
|Industry|152|155|74|
|Public Sector|97|81|40|
|Retail|541|293|172|
|Subtotal|5,001|3,918|575|
|Small Funds|100,447|212,538|365,992|
|Pooled Superannuation Trusts|||101|
|Total|105,448|216,456|366,668|

**Source: APRA**

The number of accounts continued to expand even after the attainment of near universal coverage in 1995 (Table 12). This signifies a trend to multiple account holding and may also imply a large number of inactive accounts. Retail funds have more than half of all outstanding accounts, followed by industry funds. The large presence of retail funds raises concerns about the operating efficiency of the second pillar because retail funds are notoriously more expensive than other types of funds and also suffer from a much wider dispersion of operating costs and fees.

> 32 The balance of statutory funds of life insurance companies is the difference between superannuation assets reported by life insurers and the assets that superannuation funds report as held with insurance companies. This mainly covers assets backing various types of annuities and capital funds.

38
is completely missing in the existing literature. It is the relative productivities of different sectors that ultimately have a crucial role to play in the determination of comparative advantage.

In Aghion et al (2005b), using the same industry-level, state-level data from India they show that delicensing led to an increase in interstate inequality in industrial output during the same period. This follows from Aghion et al (2005a) where they showed that output and employment in each industry increase relatively more in the pro-employer states than in other states. Their focus here is only on delicensing.

# **3 Indian Policy Framework**

# **3.1 The Trade Reforms in India**

In the 1980s, India experienced moderate economic growth, but accompanied by large macroeconomic imbalances reflected in the rapid rise in the fiscal deficit to GDP ratio, in foreign commercial debt and in the debt service ratio. These problems were further accentuated by a drastic rise in the price of oil as a consequence of the Gulf War. At this time, the general elections of 1991 brought to power a new government that inherited probably the world’s most complex and restrictive trade regime. By the time the new government assumed power, India’s external payments problem had assumed crisis-like proportions. The government requested the IMF for loans, which were granted but came attached with the strong conditionality of major and deep economic reforms. The reforms were initiated almost immediately. There were many members in the new cabinet who had been cabinet members in past governments that had tried to avoid IMF loans precisely because of these conditionalities. These governments were also strong believers in inward-looking trade policies and the use of tariffs as a primary source of revenues. Thus, the reforms came as a surprise.

The major trade reform objectives announced by the Indian government in July, 1991 included the removal of most licensing and other non-tariff barriers on all imports of intermediate and capital goods, the broadening and simplification of export incentives, the removal of export restrictions, the elimination of the trade monopolies of the state trading agencies, the simplification of the trade regime, the reduction of tariff levels and their dispersion and the full 9
<u>Table 7: System Estimation Using 3SLS</u>

|<br>Variable|<br>(1)<br>License|<br>(2)<br>Payroll|
|---|---|---|
|Payroll registry|-0.050<br>(0.207)||
|License||0.644**<br>(0.313)|
|Log distance to SUNAT|-0.080***<br>(0.020)||
|Firms in E-Payroll / firms in SUNAT||0.021***<br>(0.005)|
|Log product per worker|0.031*|0.034|
||(0.018)|(0.021)|
|Log number of workers|0.073***|0.061**|
||(0.026)|(0.026)|
|Incorporated business|0.189**|0.221***|
||(0.076)|(0.066)|
|Age 5-8|0.033|-0.029|
||(0.037)|(0.047)|
|Age 9-12|0.113***|-0.020|
||(0.040)|(0.057)|
|Age 13-16|0.140***|-0.151**|
||(0.045)|(0.070)|
|Age 17-+|0.196***|-0.103|
||(0.039)|(0.078)|
|Constant|0.455*|-1.116***|
||(0.245)|(0.365)|
|Observations|888|888|
|R-squared|0.169|0.094|

_Note:_ Standard errors are in parentheses. Significance: *** p<0.01, ** p<0.05, * p<0.1.

# **6. Conclusions**

There are many pathways to formality for small entrepreneurs, and most of them are able or prefer to formalize gradually. When they can, they weigh at each step the relative costs and benefits of formalization, choosing the most cost-effective path to business (often) or labor (less often) formalization—but rarely both at the same time. Small entrepreneurs are ready to revert to informality if, after making the transition to formality, they realize that the costs outweigh the benefits.

Given the propensity of small entrepreneurs to formalize gradually taking a diversity of pathways, approaches tackling only one side of formalization are prone to failure. But successful policies not only need to be broad in scope and tackle the many obstacles affecting 25
Our main data source is the World Bank’s Women, Business and Law (World Bank, 2011) and a stratified random sample of firms in 58 developing countries (Enterprise Surveys, World Bank). Enterprise Surveys were conducted in various countries between 2006 and 2010 by the World Bank’s Enterprise Surveys using a common questionnaire and sampling methodology. Given the costly nature of these surveys, they are not conducted annually and each firm has only one observation in the sample. The surveys are designed to be representative of the non-agricultural private sector of the economies. Our sample is restricted to manufacturing firms alone (14,609 firms) due to data availability on women’s employment. We caution that a separate analysis is required to see if our results extend to other sectors such as agriculture. Other data sources used are discussed below.

The results that follow should be treated with due caution as they are based on the Ordinary Least Squares (OLS) estimation method and cross-section data, and hence may suffer from endogeneity problems.<sup>3</sup> While we control for a large number of variables and also provide a falsification test, we treat the results as robust associations and suggestive of a possible causal relationship.

All our regression results use Huber-White robust standard errors clustered on the country. A formal definition of all the variables used in our main regressions is provided in Table 1 and the summary statistics are provided in Table 2. A list of countries in our sample along with the sample size by country is provided in Table 3.

# _Dependent variable_

Our main dependent variable is the percentage of all permanent full-time workers at the firm that are women ( _Women workers_ ). The mean value of the variable is 32.8 and the standard deviation

> 3 As a robustness check, we do re-estimate the results in Table 5 using Tobit regressions. By and large the results do not change.

7