rafmacalaba/datause-encoder-data huggingface dataset:
also matches the observed volatility of output over the medium term both in Mexico (0.038 vs. 0.037 in the data) and in the US (0.02 vs. 0.015 in the data). Note that there is nothing in our calibration strategy that forces the model to matching the variance of output at lower frequencies. Matching these moments implies that the model induces the right amount of propagation of high frequency shocks into the medium term.

The model does a good job in reproducing the volatility observed in the data in variables other than output. It does a remarkable job in matching the volatility of consumption in Mexico both at the high frequency (0.03 vs. 0.031 in the data) and over the medium term cycle (0.038 vs. 0.04 in the data). This is of special interest given the attention that the international macro literature has given to these moments.

It also generates series for investment, the relative price of capital, bilateral trade ‡ows, the extensive margin of trade and FDI ‡ows that on average have similar volatilities to those observed in the data. For those instances where there is some di¤erences, the empirical volatilities fall within the 95% con...dence interval for the standard deviation of the simulated series.<sup>32</sup>

# Co-movement

Table 2 reports the contemporaneous correlation between the HP-...ltered Mexican variables and HP-...ltered output in both Mexico and the US. Broadly speaking, the model does a very good job in capturing the contemporaneous co-movement patterns both within Mexico but also between Mexico and the US. First, the model generates the strong co-movement between US and Mexican GDPs observed in the data. Speci...cally, the average correlation between these variables in our simulations is 0.71 with a con...dence interval of (0.4 , 0.9) that contains the correlation observed in the data (i.e. 0.43).

Second, the model generates the observed correlation between consumption and output in Mexico (0.76 vs. 0.78 in the data). Note also that, in both model and data, Mexican consumption is insigni...cantly correlated with US GDP. This indicates that US shocks do not contribute to the high volatility of Mexican consumption. This instead is the result of the response of Mexican consumption to domestic shocks.

A key driver of the volatility of consumption are the dynamics of the price of capital induced by domestic shocks. It is reassuring that the model matches closely the negative co-movement between Mexican output and the price of new capital (-0.45 vs. -0.54 in the data). Note also

> 32Note that the model also underpredicts the volatility of the extensive margin of trade, though given the distance between the measure in the model and in the data that should not be a surprise. Note also that, as discussed in section 4.2, the model generates a process for US consumption that is too volatile.

26
<!-- Start of picture text --> Figure 6. Comparison of Alternative Systems*<br>0.6<br>0.5<br>0.4<br>0.3<br>Reformed PAYG with Coverage Expansion<br>ulti-Pillar--no b<br>0.2 _<br>Uniform Rate for Partially Funded DB<br>Uniform Rate for Multi-Pillar<br>0.1<br>-<br>0<br>1995 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>mBased on Tables 3, 4, 6 and 7<br><!-- End of picture text --> ESTELLE2.XLS 50
household consumption in 2001 ( _column 2_ ), past employment status ( _column 3_ ), and customer affiliation with financial institutions ( _columns 4-6_ ).

Results in the first two columns of Table 4 are consistent with the earlier differences in the means. The newly self-employed is more likely to be male, aged 43, residing in an urban area, married, with some formal education, in good health, and with past self-employment experience. The effects of the proxies for optimism and social capital persist, and are significant both when included together and separately. The inclusion of the household consumption variable in _Column 2_ indicates a significant positive effect of past wealth on current self-employment. This suggests that selfemployed rely on their own financial means in making their decision.

_Column 3_ examines the impact of past employment experience and indicates that ceteris paribus, informal sector employees are more likely to enter self-employment compared to employees in paid employment. On the contrary, individuals coming from unemployment-inactivity are the ones least likely to start their own business. The interpretation can either consider informal sector employees to be more entrepreneurial, or highlight the lighter opportunity costs that the transition to self-employment entails for formal sector employees, who are already receiving higher premia and other nonpecuniary provisions. In order to shed some more light on this issue, we also examine the interaction between labor market origins and access to finance, and examine the survival chances of the new start-ups, with prior status as an explanatory variable.

Results in _Columns 4-6_ indicate that the decision to become an entrepreneur is unrelated to an existing relationship with financial institutions, confirming that these institutions rarely finance entry into self-employment.<sup>9</sup> However, remittances from abroad exhibit a significantly negative effect on the probability of an individual becoming self-employed, in accordance with the “disincentive effects” of remittances noted in the literature. This effect is economically significant – an individual who switches from not

> 9 Since receivership of several types of loans and remittance money are not mutually exclusive, we have also conducted the analysis incorporating one variable at a time. All results presented are robust to these separate specifications and are available upon request. The inclusion of interaction terms between wealth and the affiliation with financial institution renders insignificant estimates of these interaction terms. This is likely to reflect the general climate of distrust towards the financial institutions that exist in BiH. During the war all household savings vanished and after the war several informal pyramid schemes cost households money. Furthermore, the network of banks and microfinance institutions are still considered very expensive.

14
<!-- Start of picture text --> Step 1) Start with the daily time-series | Step 2) Retain the day of each year<br>of inferred cooling demand with the maximum demand (i.e. peak<br>2 15 2<br>@ 14<br>=€<br>3<br>io) ® Ke}3 12<br>5 ° 3 10<br>E €<br>< <<br>0 8<br>2010 2020 2030 2040 2010 2020 2030 2040<br>Step 3) Compute the system design capacity from the annual maximum demand<br>Record used<br>§ 14 | \<br>5 1<br>2 10 i 1 Using the data from years prior to the<br>Ke) ' design year, Compute the empirical 1-in-X<br>= 40 | year event (i.e. the 1 — 1/X percentile)<br>e<br><x ! 1<br>8<br>2010 2020 2030 2040<br>Step 4) Evaluate whether demand is greater than design capacity over interval<br>between system re-design<br>T<br>£<br>fe) i I<br>€ 5 | I<br>3 |<br>& 1<br>0 i<br>2020 2040 2060<br>Step 5) Repeat steps (3) and (4) for the next interval between system re-design<br><!-- End of picture text -->
Policy Research Working Paper 11022

# **Abstract**

This paper documents the life cycle of formal and informal plants using five waves of the Mexican establishment census. Formal plants begin operations with three times more workers than informal plants and exhibit faster growth rates. Throughout their life cycle, formal establishments more than double their size, while informal plants increase their size by only 77%. A general equilibrium model is developed to quantify the aggregate economic losses stemming from these growth rate disparities. In the model, plants grow through productivity investments, and informality emerges from incomplete enforcement. In equilibrium, informal plants exhibit flatter life cycle profiles to avoid detection and taxation. Model parameters are calibrated to match key properties of plant size distribution and the life cycle of plants in Mexico. Quantitative results indicate that a revenue-neutral full enforcement increases aggregate output and the overall growth rate by sixteen and twenty-five percent relative to the benchmark, respectively.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at fsarikay@asu.edu, mtamkoc@worldbank.org, and jtorrescoronado@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|**Education**|100%|94%|100%|100%|100%|87%|69%|93%|
|---|---|---|---|---|---|---|---|---|
|**Accounting**|100%|92%|100%|100%|92%|87%|69%|91%|
|**Tourism**|100%|100%|100%|100%|100%|96%|94%|99%|
|_Source:_ _FDI Regula_<br>_Note_: 100 = full for<br>*: number of econ|_tions_database 2<br>eign ownership<br>omies included i|012<br>allowed<br>n each region|||||||

# **ii.** _Restrictions per region_

**Figure 1. Average equity restrictions on FDI ownership by region**

<!-- Start of picture text --> 100 96 94 93 93<br>88<br>85<br>82<br>74<br>80<br>60<br>40<br>20<br>0<br>Eastern High-income Sub-Saharan Latin America Global South Asia East Asia and Middle East<br>Europe & OECD Africa & the average the Pacific & North<br>Central Asia Caribbean Africa<br><!-- End of picture text --> _Source_ : _FDI Regulations_ database 2012 _Note:_ 100 = full foreign ownership allowed Looking at the data from a global perspective, there are marked differences between the different regions. Eastern Europe and Central Asia (ECA), the highincome OECD economies (OECD), Sub-Saharan Africa (SSA) as well as Latin America and the Caribbean (LAC) emerge as the most open to foreign investment. On the other hand, South Asia (SAR), East Asia and the Pacific (EAP) as well as the Middle East and North Africa (MENA) tend to have more restrictions to foreign equity (Figure 1).

Taking a closer look at the respective regions, significant intra-regional differences begin to emerge. The strongest

**Figure 2.  Foreign equity ownership index in East Asia and the Pacific**

<!-- Start of picture text --> Solomon Islands 100<br>Papua New Guinea 100<br>Cambodia 100<br>Hong Kong SAR, China 98<br>Singapore 92<br>Regional average 82<br>Malaysia 79<br>Taiwan, China 78<br>Indonesia 73<br>Thailand 68<br>Vietnam 66<br>Philippines 50<br>Source: FDI Regulations database 2012<br>0 50 100<br><!-- End of picture text --> 8
<!-- Start of picture text --> November 2020<br>th<br>was 2,915<br>Second Wave - Survey Round<br> November 2020 – 20<br>th<br>13 The actual number of completed surveys in the second wave<br>At the end of October 2020<br>the COVID-19 vaccines in Colombia<br>Public announcement of successful trial of one of October 2020<br>th<br>First Wave - Survey Round<br> October 2020 – 20<br>th<br>The actual number of completed surveys was 3,413<br>Colombia 14<br> March 2020<br>th<br>6<br>First case of COVID-19 in<br><!-- End of picture text --> 21
1577 firms, and reducing it to 1 (mean = standard deviation) reduces it to 395 firms. Based on this and experiences in other countries, my recommendation is therefore to aim for an initial pool of firms whose standard deviation of sales is no greater than their mean. However, as we have seen, the number of firms in any particular industry in most African countries can be quite small, so we are unlikely to get treatment and control groups of 300 firms each using such an approach. This brings me to the second step needed:

# **4.2 Collect a Lot More Data on These Firms**

The typical SME project collects very little data on the firms in the project, and often none on comparable firms not participating in the project. Even in cases where experiments are being planned, the default option is often to conduct a baseline survey, a midline survey (at say 1 year) in some cases, and then an endline survey. While such an approach is often appropriate for educational and health interventions in which outcomes like anthropometrics and test scores are highly persistent, but it is less well-suited to learning about dynamic firms, whose sales and profits can differ dramatically from one month to another due to seasonality, idiosyncratic demand shocks, shocks to the supply of labor, and a host of other reasons.

In McKenzie (2011), I provide theory and evidence to show the increases in power possible from collecting more waves of data than the usual baseline and follow-up. There are two important uses for more frequently collected data. The first, and better known, is for understanding the trajectory of program impacts, and for helping to unpack causal chains. For example, if we want to know whether an effort to formalize firms increases firm growth by allowing firms better access to formal credit, having multiple data points allows measurement of whether changes in credit precede firm growth or instead follow it. The second, and less practiced, use of more frequent data is to collect multiple measurements on noisy and weakly autocorrelated outcomes. For example, in a microenterprise survey, measuring several months of profits allows noise in a given month to be averaged out, increasing the power to detect genuine effects of a program.

With larger firms it may be possible to collect even more frequent data on key performance indicators like daily production, electricity usage, quality defects, and sales. Such data can then be used for both purposes above – averaging out daily fluctuations, and also 15
# **Appendix A.**

**Comparing Trends in School Safety:** The items measuring school safety were not identical in the TIMSS 2007, 2011, and 2015 questionnaire, which may result in concerns about the reliability of the trends generated for school safety.<sup>11</sup> To check the robustness of the findings, factor decomposition was used to analyze two distinct indexes of school safety, one for 2007 and one for 2011 using all the variables available. After averaging these two new indexes by country, the trends in these indexes were assessed by country within and across the region. Results revealed that the school safety index in 2011 was lower than the school safety index of 2007 for MENA countries, while the opposite was true for countries outside the region. Computing a difference-in-difference indicates that school safety dramatically dropped in the MENA region before and after the start of the Arab Spring, regardless of the methodology chosen ( **_Table 5_** ). The average drop is about half a standard deviation.

**Table 5. Comparing Trends in School Safety Between 2007 and 2011 in MENA and Elsewhere**

||Safety<br>index of<br>2007|Safety<br>index of<br>2011|Difference|
|---|---|---|---|
|Countries outside MENA participating in 2007 and||||
|2011|0.08|0.23|0.14|
|MENA countries participating in 2007 and 2011<br>(treatment group)|0.03|-0.34|-0.37|
|**Difference-in-difference**|||**-0.51**|

Source: Analysis using TIMSS 8<sup>th</sup> grade data, 2007 and 2011.

**Table 6: Pairwise correlations of the PISA disciplinary climate by country and by wave**

|2000|2003|2009|2012|2015|
|---|---|---|---|---|
|2003|0.7884|1|||
|2009|0.7767|0.813|1||
|2012|0.7772|0.7916|0.7874|1|
|2015|0.5533|0.6377|0.7689|0.781|

Source: Analysis from PISA data 2000-2015

> 11 Eighth grade data were used consistently for the 2011 and 2015 analysis.

14
satisfy the exclusion restriction.

Consistent with the prediction of a simple theoretical model, our results reveal a larger negative impact of migration on local labor outcomes than the one documented for developed countries: we find that a 10 percentage points increase in the net in-migration rate decreases the employment probability of non-migrants in the destination region by 7.8 percentage points. Our results further show that this negative impact is significantly stronger in Ugandan regions less conducive to capital mobility (i.e., showing below-median road density): a 10 percentage points increase in the net in-migration rate in these areas decreases the probability of being employed of non-migrants by more than 10 percentage points.

The paper proceeds as follows. In Section 2, we develop a simple theoretical model that shows that the impact of an influx of migrants on the employment probability of the nonmigrants is more negative in economies less conducive to capital mobility. In Section 3, we present our data. Section 4 describes our empirical strategy. Section 5 presents our results. Section 6 provides robustness checks. In Section 7, we summarize our conclusions and their policy implications.

# **2 Theoretical model**

The purpose of this simple theoretical model is to show that the impact of an influx of migrants on the employment probability of the non-migrants is more negative in economies with lower road density.<sup>6</sup>

> 6The predictions of our model remain unchanged if, instead of focusing on road density, we focus on the density of transportation infrastructure generally speaking. We choose to focus on road density because roads play the dominant role worldwide as a percentage of freight. For instance, roads carry over 60% of freight in the US (US Bureau of Transportation Statistics (2006)) and 75% of freight in Africa (World Bank (2011)).

5
|**Appendix table 1: Descriptive stati**|**stics for matched a**|**nd unmatche**|**d baseline**<br>Unmatch|**household**<br>ed|**surve**|**y sample (ker**|**nel matchi**<br>Matche|**ng)**<br>d||
|---|---|---|---|---|---|---|---|---|---|
||Label|Treatment|Control|p-value|Sig.|Treatment|Control|p-value|Sig.|
|Female head|dfemhead|0.449|0.372|0.008|***|0.439|0.421|0.486||
|Household size|hhsize|4.192|4.300|0.365||4.252|4.123|0.212||
|Share of boys (age <= 14)|share_boy|0.095|0.120|0.004|***|0.099|0.108|0.283||
|Share of girls (age <= 14)|share_girl|0.094|0.115|0.013|**|0.097|0.094|0.661||
|Share of male adults (age 15-65)|share_madu|0.332|0.323|0.564||0.330|0.335|0.688||
|Share female adults (age 15-65|share_fadu|0.393|0.347|0.001|***|0.396|0.390|0.660||
|Share of male olds (age > 65)|share_mold|0.034|0.033|0.911||0.031|0.034|0.628||
|Head’s age|hage|52.406|49.679|0.001|***|52.227|50.988|0.086|*|
|Any plot-level investment|d_invest|0.343|0.373|0.260||0.345|0.324|0.395||
|Female managed plots|owner_ft|0.416|0.351|0.024|**|0.409|0.411|0.946||
|Main plot w. paved road access|dpvdroad|0.106|0.009|0.000|***|0.035|0.026|0.325||
|Main plot with tap water access|dprvtap|0.829|0.538|0.000|***|0.814|0.817|0.911||
|Main plot has electricity|dnoelect|0.197|0.239|0.057|*|0.199|0.206|0.737||
|Main plot has flush toilet|dflusht|0.136|0.059|0.000|***|0.130|0.108|0.190||
|Main plot has pit toilet|dpit_vip|0.792|0.838|0.035|**|0.796|0.829|0.088|*|
|Main plot w. indoor bath/shower|dindorbath|0.860|0.891|0.088|*|0.864|0.838|0.144||
|Knows about land leases|dknowllse|0.557|0.453|0.000|***|0.550|0.590|0.118||
|Lease and more valued than w/o|dpaymorelse|0.762|0.831|0.004|***|0.756|0.756|0.993||
|More willing to sell land w. lease|dslemorelse|0.709|0.731|0.411||0.710|0.730|0.390||
|Heard about 2010 Land Act|dknowlact|0.179|0.105|0.000|***|0.179|0.162|0.405||
|Likelier to improve land w. lease|dlikeinvlsenew1|0.721|0.830|0.000|***|0.741|0.724|0.490||
|Annual expenditure (USD)|exp|4,834|3,557|0.001|***|4,512|4,350|0.547||
|Would use mortgage to improve|dlctln3|0.284|0.218|0.011|**|0.282|0.284|0.925||
|Would use mortgage to buy land|dlctln2|0.107|0.088|0.276||0.100|0.104|0.819||
|Land area in square meter|gpsarea|1,297|1,566|0.010|***|1,306|1,278|0.690||

_Source:_ Own computation from MSU 2013 baseline survey. Note: The standardized mean and median covariate bias before matching were 17.6 and 15.5 percent, respectively. The kernel matching technique reduces the mean and median percentage bias to 3.8 and 3.2, respectively. One-to-one and 2-nearest neighbor matching strategies reduce the mean and median percentage bias (4.9 and 4.1; and 5.6 and 4.3, respectively), suggesting that the kernel-based matching performs slightly better than the latter two approaches.

38
The rest of this paper proceeds as follows. The next section describes the data and the methodology. The third section presents basic results on extreme poverty rates by age, and how these are divided by region and country. The following section considers how the estimated poverty rates for adults and children change when using different household equivalence scales, while the fifth and final section concludes.

# **2. Data and methodology**

The analysis on child poverty presented below is derived from a combined sample of 104 surveys containing records on 7.7 million individuals from 89 developing countries, taken from the September 2016 vintage of the Global Micro Database (GMD). The GMD is a collection of globally harmonized household survey data recently developed by the Data for Goals group of the World Bank’s Poverty and Equity Global Practice. Full details on the background of the GMD are given in Castañeda et al (2016). A crucial feature of the GMD is that the welfare aggregates are the same as those used to compute the poverty estimates published by PovcalNet and the World Development Indicators.<sup>7</sup> These aggregates are based on household per capita income or consumption, depending on which concept is used to measure national poverty in a particular country. The sample of GMD used in this analysis consists of surveys collected in 2009 or later. The year 2009 was selected to balance the competing goals of increasing coverage while minimizing error due to extrapolating poverty figures forward when lining up to 2013.

Table 1 shows the sample’s coverage in number of countries, of population and of child population, based on figures from the UNDESA Population Division. The bottom panel indicates that countries in the sample have a total population of 5.25 billion persons and 1.69 billion children. This represents around 82 percent of the developing world’s child population in 2013, which was 2.05 billion. 85.4 percent of the 767 million extreme poor reported in PovcalNet are contained in the GMD sample.

**Table 1: Coverage of PovcalNet and GMD. Population and Child Population in 2013**

||World|Developing countries|GMD sample|
|---|---|---|---|
|Number of countries|214|190|89|
|Population (millions)|7157.3|6233.7|5249.1|
|Children (millions)|2239.6|2052.5|1686.1|
|Extreme poor (millions)|766.7|766.7|655.0|
|Percent of children that are poor|||19.5%|
|Percent of extreme poor that are children|||50.2%|
|Extremely poor children (millions)|384.9|384.9|328.7|

Notes: See Appendix 1. The estimated number of extremely poor children is obtained by multiplying the total number of extreme poor by the percent of extreme poor in the sample that are children. As in PovcalNet, developed countries are assumed to contain no extremely poor persons.

> 7 The term welfare refers to either income or consumption per capita, depending on which measure is used by the country for their national estimates.  For more information on how these welfare aggregates are constructed, see Ferreira et al (2015)

4