rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1:** Balance between treatment and control groups

|**Outcome**|**Obs**|**Control Group**<br>**Mean**|**Treatment**<br>**Difference**|
|---|---|---|---|
|Household head: age|2104|42.094|-0.249|
||||(0.484)|
|Household head: no education|2113|0.811|-0.012<br>|
||||(0.017)|
|Household head: some primary education|2113|0.102|-0.013<br>(0.013)|
|Household head: completed at least primary school|2113|0.087|0.025<sup>_∗_</sup><br>(0.013)|
|Household head: female|2113|0.007|0.000<br>(0.004)|
|Household head: producer|2113|0.936|0.003|
||||(0.010)|
|Polygamous household|2113|0.417|-0.006<br>|
||||(0.021)|
|Number of spouses|2113|1.464|-0.034<br>(0.032)|
|Age of frst spouse|1790|35.169|-0.644<br>(0.492)|
|First wife: no education|1983|0.944|-0.018|
||||(0.011)|
|Men in household|2113|1.763|-0.053<br>(0.047)|
|Women in household|2113|1.721|-0.011<br>(0.053)|
|Boys aged 6-16|2113|1.553|-0.171<sup>_∗∗∗_</sup><br>(0.063)|
|Girls aged 6-16|2113|1.209|-0.020<br>(0.058)|
|Number of elderly individuals over age 65|2113|0.095|0.000<br>(0.014)|
|Children under 5 years old|2113|1.829|-0.050<br>(0.066)|
|Number of shocks in last year|2113|1.856|-0.115<sup>_∗_</sup><br>(0.061)|
|Household assets (PCA)|2113|8.415|0.095<br>|
||||(0.132)|
|Pre-program cotton area (ha)|1804|3.648|0.027<br>(0.092)|
|Pre-program cotton production (kg)|1804|3769.341|10.267<br>(133.957)|

Note: Robust standard errors reported in parenthesis. *, **, and *** indicate significance at the 90, 95, and 99% confidence intervals. Pre-program cotton area and production come from cotton producer database.

18
|pala<br>(9)<br>0.0365<br>(0.0677)<br>0.757<br>0.0260<br>(0.0691)<br>0.0258<br>(0.0701)<br>0.742<br>853<br>HH<br>ampala.<br>ozhukov<br>ds living<br>2, 5, 8),<br>d errors<br> 0_._1, **|
|---|
|ple = Kam<br>(8)<br>0.0706_∗_<br>(0.0389)<br>0.213_∗∗∗_<br>(0.0570)<br>0.421<br>0.0811_∗∗_<br>(0.0411)<br>0.0772_∗_<br>(0.0405)<br>0.227_∗∗∗_<br>(0.0542)<br>0.390<br>853<br>EA<br>stricted to K<br>hod of Chern<br>ost househol<br>ts (Columns <br>tion. Standar<br>ted by * _p <_|
|_refugees_<br>Sam<br>(7)<br>0.0792_∗∗_<br>(0.0292)<br>0.254_∗∗∗_<br>(0.0557)<br>0.342_∗∗∗_<br>(0.0381)<br>0.390<br>0.0779_∗∗_<br>(0.0330)<br>0.0695_∗∗_<br>(0.0294)<br>0.271_∗∗∗_<br>(0.0523)<br>0.346_∗∗∗_<br>(0.0355)<br>0.359<br>853<br>Context<br>mns (7-9) are re<br>e LASSO met<br>members and h<br>EA fxed efec<br>ate interpreta<br>vels are indica|
|_ceptions of _<br>ivale<br>(6)<br>0.191_∗∗_<br>(0.0872)<br>0.764<br>0.165_∗∗_<br>(0.0799)<br>0.183_∗_<br>(0.0893)<br>0.758<br>647<br>HH<br>ent and Colu<br>sing the doubl<br>er household <br>mns 1, 4, 7), <br>dized to facilit<br> Signifcance le|
|_hosts’ per_<br>ple = Nak<br>(5)<br>0.128_∗∗_<br>(0.0539)<br>0.295_∗∗∗_<br>(0.0591)<br>0.352<br>0.131_∗∗∗_<br>(0.0472)<br>0.132_∗∗_<br>(0.0487)<br>0.312_∗∗∗_<br>(0.0653)<br>0.335<br>647<br>EA<br>kivale settlem<br>ist selected u<br>ex among oth<br> efects (Colu<br>s are standar<br>er sampling.|
|_ent variable: _<br>Sam<br>(4)<br>0.0875_∗∗_<br>(0.0341)<br>0.316_∗∗∗_<br>(0.0587)<br>0.317_∗∗∗_<br>(0.0497)<br>0.329<br>0.0810_∗∗_<br>(0.0322)<br>0.0906_∗∗_<br>(0.0323)<br>0.334_∗∗∗_<br>(0.0640)<br>0.337_∗∗∗_<br>(0.0417)<br>0.310<br>647<br>Context<br>cted to the Na<br> (Panel A), a l<br>perception ind<br> context fxed <br>eraction indice<br>wo-stage clust|
|_Depend_<br>nda<br>(3)<br>0.100_∗_<br>(0.0589)<br>0.754<br>0.0868<br>(0.0590)<br>0.0868<br>(0.0597)<br>0.747<br>1500<br>HH<br>4-6 are restri<br> in Table A.3 <br>rages of the <br> fxed efects: <br>ption and int<br>account for t|
|mple = Uga<br>(2)<br>0.0919_∗∗∗_<br>(0.0317)<br>0.258_∗∗∗_<br>(0.0432)<br>0.380<br>**Lasso**<br>0.0963_∗∗∗_<br>(0.0309)<br>0.100_∗∗∗_<br>(0.0314)<br>0.268_∗∗∗_<br>(0.0431)<br>0.365<br>1500<br>EA<br>mple, Columns<br>ng list defned <br> except the ave<br> three sets of <br> 9). The perce<br> area level to|
|Sa<br>(1)<br>**Panel A - Controls listed in table A.2**<br>Interaction index<br>0.0762_∗∗∗_<br>(0.0222)<br>Perceptions within HH<br>0.291_∗∗∗_<br>(0.0423)<br>Perceptions among neighbours<br>0.332_∗∗∗_<br>(0.0282)<br>R-squared<br>0.353<br>**Panel B - Controls selected with double **<br>Interaction index<br>0.0800_∗∗∗_<br>(0.0210)<br>**Panel C - No controls**<br>Interaction index<br>0.0769_∗∗∗_<br>(0.0219)<br>Perceptions within HH<br>0.302_∗∗∗_<br>(0.0414)<br>Perceptions among neighbours<br>0.336_∗∗∗_<br>(0.0268)<br>R-squared<br>0.337<br>Observations<br>1500<br>Fixed efects<br>Context<br>Notes: Columns 1-3 consider the full Uganda sa<br>We consider three sets of control variables: a lo<br>et al. (2017) (Panel B), and no control variables <br>in the same community (Panel C). We consider<br>and households fxed efects (Columns 3, 6, and <br>in parentheses are clustered at the enumeration<br>_p <_0_._05, *** _p <_0_._01.|

38
new and complex features as they were extended to a broader population of households, many with more limited resources.

Data from Loanperformance.com shows that there has been a sharp increase in late payments among recently originated (2006) subprime loans, but these increases have been well before rate changes would take place Demyanyk and Van Hemert (2007). So at least some of blame is due to other characteristics of the loans and/or borrowers. It will take months or years before it is known which characteristics have been the most important.

# **Not Fully Recognizing the Adverse Selection Problem**

**There is evidence that a number of borrowers gave misleading information about their income and operated under the assumption that they could refinance their way out of problems.** When housing prices started declining, they discovered that this was more difficult than expected. That so many loans have been delinquent almost immediately after origination suggests that not all borrowers have been victims. In some cases, the borrowers went along with deceptive schemes to fool the underwriter (and loan purchaser).

**There is evidence that a number of borrowers took out loans knowing in advance that they could not afford the mortgage payments when an ARM reset took place.** These borrowers were effectively speculating on housing prices, hoping for an increase in housing values so that they could sell their properties for a gain before the reset occurred.

**More broadly, there were plenty of incentives for borrowers, lenders, and investors to have protected themselves.** While it may be the case that investors in the AAA tranches of subprime structures had little incentive to look closely at risks, the investors in the subordinated tranches certainly had such an incentive. It looks like they will bear the brunt of losses, and it is unclear so far why they, who were supposed to be the specialists in risk management, were so wrong.

# **Not Fully Understanding the Shortcomings of Credit Rating Agencies**

**Credit Rating Agency models were misapplied.** The credit rating agencies (CRAs) (Moody’s, S&P, Fitch) provide ratings for credit-sensitive products such as subprime Asset-backed Securities (ABS). Investors around the world rely on the ratings agencies’ assessment of risk for the underlying collateral and the structure of the ABS tranches. Unfortunately, the ratings agencies were delayed in downgrading the ratings on ABS tranches, waiting until after the problems had already begun.<sup>28</sup> A partial explanation for the ratings agencies being slow to react to the deteriorating credit conditions in the subprime market is that their risk models are historically based. Subprime defaults during 2004-2006 were low and house prices were increasing (given little incentive for subprime borrowers to default). However, the slowdown and decline of house prices led to a

> 28 “Stopping the Subprime Crisis,” New York Times, July 25, 2007.

20
# **Table 8 CGAP indicators for financial inclusion – regulation, disputes, and financial inclusion reforms**

||**Unde**<br>**agency**<br>**(**|**r regula**<br>**'s superv**<br>**Yes/No)**|**tion**<br>**ision?**<br>|**Disputes**<br>**and reg**|**resolution**<br>**ulations to**<br>|**: provision**<br>**restrict u**<br>**(Yes/No)**|**s in existi**<br>**nfair treat**|**ng laws**<br>**ment?**|**Fi**|**nancial in**|**clusion refor**<br>**(Yes/No)**|**m underg**|**oing**|**Fina**<br>**inclu**<br>**Cons**<br>**prote**<br>**(Yes**|**ncial**<br>**sion:**<br>**umer**<br>**ction**<br>**/No)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Cooper-<br>Atives|MFIs|SSFIs<sup>39</sup>|Deceptive<br>advertising|Unfair or<br>high-<br>pressure<br>selling<br>practices|Abusive<br>collection<br>practices|Breach<br>of client<br>confiden<br>tiality|No fair<br>treat-<br>ment<br>provi-<br>sions|Consum<br>er<br>protecti<br>on|Financial<br>capability|Facilitating<br>SME<br>access to<br>finance|Enabling<br>micro<br>finance|Facilitatin<br>g rural<br>financial<br>access|Agency is<br>response-<br>ble|Dedicate<br>d team<br>or unit|
|**Malaysia**||**√**||**√**|**√**|**√**|**√**||**√**|**√**|**√**|**√**|**√**|**√**|**√**|
|**OIC**<br>**countries**|35%|59%|65%|51%|35%|27%|68%|24%|51%|38%|49%|57%|41%|76%|65%|
|**Developing**<br>**countries**|44%|59%|57%|70%|38%|42%|78%|16%|42%|37%|43%|49%|42%|64%|51%|
|**OIC-GCC**|35%|59%|71%|50%|32%|26%|71%|24%|50%|38%|50%|62%|41%|79%|71%|
|**low-**<br>**income**<br>**countries**|41%|72%|86%|52%|21%|31%|76%|21%|34%|28%|45%|72%|52%|79%|55%|

Source: CGAP Financial Access Report Data 2010 Note: The CGAP Financial Access Database covers questionnaires sent to 151 economies: 13 in East Asia and the Pacific, 27 in Europe and Central Asia, 20 in Latin America and the Caribbean, 14 in the Middle East and North Africa, 6 in South Asia, 40 in Sub- Saharan Africa, and 23 in the high-income OECD countries. Among them, 37 OIC countries are covered. For practical purposes, most small islands and economies at war were not included. The sample covers more than 94 percent of the world’s population and nearly 98 percent of global GDP.

Selected questions are presented in the first and second rows of the table. The row for OIC countries means the percentage of OIC countries that replied yes to the questions. The same calculation applied to the groups of developing countries, OIC-GCC, and low incoming countries. OIC-GCC means OIC countries excluding the GCC countries covered by the CGAP database.

> 39 Specialized state owned financial institutions

54
consumption or expenditures on non-food items such as health, education and clothing, or to reduce purchases of agricultural or livestock inputs than actions that reduced asset holdings or increased indebtedness. Third, borrowing money to buy food was used by more than half of households reporting that income was much less than usual. Fourth, few households reported selling consumer durables, possibly because these are relatively illiquid. A larger proportion (21 percent) reported selling productive assets but note that this category includes livestock which also serves as a store of value.

# **_3.3. PSNP participation_**

We define PSNP participation in three different ways. First, we define access to PSNP based on self-reported receipt of any payments in the August 2019 survey. PSNP participation could be endogenous to the pandemic if, for example, the government expanded the program in response to the spread of the coronavirus; using pre-COVID-19 access to PSNP transfers to define participation in the program addresses this concern. This approach assumes that program participation remains stable during our study period. We compared the share of households receiving PSNP transfers in June 2020 and we find that 89 percent were also PSNP beneficiaries in August 2019.

Second, we use information on actual PSNP transfers made in the six months prior to the August 2019 survey and generate an indicator variable assuming a value of 1 for households who received transfers exceeding 100 birr (about 3.5 USD) and 0 for households receiving less than 100 birr or not at all. This definition reduces potential measurement errors due to misreporting of program participation. Third, we generate aggregate district level access to PSNP transfers. Among the 88 woredas (districts) which were supposed to be included in the PSNP, there were six woredas that did not make any payments because of delays in implementation and related logistical problems. This provides a relatively more exogenous variation in households’ access to PSNP transfers.

# **4. Estimation strategy**

To assess the impact of PSNP participation on household food security as well as maternal and child diets under COVID-19, we compare the temporal evolutions of food security and nutrition 11
job status changes with education level. The y-axis denotes the share of people with a particular job status. The education level on the x- axis is measured by average years of schooling. The share of agricultural, unpaid and self-employed workers falls as the level of education increase

# 2.1.1 Youth Employment Outcomes

We first present descriptive statistics of youth employment outcomes for countries in different income groups, using the standard World Bank classification, which divides countries into four categories: Lower income, middle income, upper middle income and high income.<sup>12</sup> High income countries are excluded from the analysis. As shown in Figures 2 and 3, there is substantial variation in youth labor market outcomes across these three income groups. In low income countries, more youth between 15-24 work and fewer are full time students than in middle income countries (Figure 2). In addition, most youth in low-income countries work as unpaid or self-employed workers, and more than 60% of youth work in the agricultural sector (Figure 3).<sup>13</sup> These results are consistent with other studies on youth labor market outcomes, which have shown that a large share of youth work in the informal sector or as unpaid workers.<sup>14</sup>

> 12 The World Bank uses Gross National Income per capita and divides countries based on the following definition: Low income: $1,025 or less; lower middle income: $1,026 -$4,035; upper middle income: $4,036 - $12,475; high income: $12,476 or more.

> 13 Labor market outcomes by region are shown in Figures 10 and 11 in the Appendix.

> 14 See for example O’Higgins (2001) or World Bank (2009).

7
# **Figure 10. Long-term outlook**

**A. Oil price forecasts, over time**

## **B. Potential growth response to contraction events in oil-exporting EMDEs**

<!-- Start of picture text --> US$/bbl, nominal Percentage points<br>120 0.0<br>Confidence interval<br>100<br>-0.5 Estimated impact<br>80<br>-1.0<br>60<br>Oil price<br>40 Jul-14 -1.5<br>Jul-15<br>20<br>Apr-18 -2.0<br>0<br>-2.5<br>2000 2005 2010 2015 2020 2025 2030 Year 1 Year 3 Year 5<br><!-- End of picture text --> Source: World Bank. A. Forecasts from various editions of World Bank’s “Commodity Markets Outlook” report. B. Contractions are defined as the years of negative output growth from the year after the output peak to output trough. Sample includes 9 oil-exporting EMDEs. Dependent variable defined as cumulative slowdown in potential growth after a contraction event. Bars show coefficient estimates, while vertical lines show shock +/- 1.64 standard deviations (10 percent confidence bands). The methodology is described in Chapter 3 of World Bank (2018a).

24
and every Indonesian village, quelling any latent unrest. As Malley (2001) notes, the New Order used “coercive bureaucratic and military measures to prevent social tensions from developing into open, violent conflicts and to subdue them when they did.” High-profile conflict in East Timor, Central and West Kalimantan, Central Sulawesi, Papua, the Malukus, and Aceh have attracted international attention (Wilson 2001). However, there has been an increasing awareness that many regions of Indonesia are prone to destructive conflict (Barron and Madden 2003; Smith 2003; Welsh 2003). A recent government census of more than 69,000 villages and neighborhoods provides a unique opportunity to get a better understanding of the distribution of (violent) conflict across Indonesia, to investigate the empirical considerations related to tracking local conflict, and to test hypotheses relating to factors that may potentially be associated with local conflict. More than 7 percent of villages and neighborhoods in the census reported local conflict during 2002.

A growing international literature has sought to investigate conditions that affect the probability of countries experiencing large-scale violence and, in particular, the likelihood of civil war (cf Collier 2002; Humphreys 2003; World Bank 2003a).<sup>1</sup> A range of arguments have been put forward as to the potential factors associated with such conflict. Economic arguments have focused on poverty, inequality, property rights, and the process of economic development and structure change. Social arguments have focused on the role of ethnicity and religion.

> 1 The recent international literature has investigated those characteristics that make countries more prone to violence.  The Correlates of War study at the University of Michigan and Pennsylvania State University (Singer 1990), and the Conflict Data Project at the University of Uppsala (Wallensteen and Sollenburgh 1998) have both compiled large cross-national datasets on wars and armed conflicts.  Ted Gurr and Montey Marshall’s work seeks to look at which state forms make civil war more or less likely (Gurr _, et al._ 2001).

3
# **4. Evaluating the performance of the sample**

In this section, we evaluate the performance of the HIES 2016/17 sample in terms of accuracy. More specifically, we evaluate the sample’s ability to estimate accuracy statistics for the targeted variables at the level of precision we selected. As mentioned previously, the key parameters used for the estimation of the sample size and the allocation of PSUs across strata are all based on the HIES 2010 data. Table 11 summarizes district level RSEs of mean nominal per capita and total household expenditure. The table also reports SEs of mean poverty headcount ratios (i.e. the three accuracy statistics selected for the HIES 2016/17) estimated using Equation (1) but using ���<sup>�</sup> � as the left-hand side variable and _n_ as the final sample allocation.

All district level nominal per capita consumption expenditure and total household expenditure show RSEs of less than 9 percent, thus below the targeted threshold of 10 percent. SEs of estimated district level poverty rates are also all lower than the acceptable threshold of 5 percent.

**Table 11 Annual district level summary accuracy statistics**

|**Accuracy statistic**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|RSE of mean nominal per capita<br>expenditure< 10 percent, district level|64|3.34%|1.28%|1.70%|7.89%|
|RSE of mean nominal total household<br>expenditure< 10 percent, districtlevel|64|3.54%|1.44%|1.85%|8.58%|
|SE of mean poverty headcount ratios<br><5 percent, district level|64|2.67%|0.89%|0.72%|4.78%|

Figure 1 shows the spatial distribution of district level RSE estimates of mean nominal per capita and total household expenditure, and the SE estimates of the poverty headcount ratios.<sup>22</sup> These figures suggest that the HIES 2016/17 sample generates accurate estimates of poverty.

> 22 The magnitude of all district-level statistics shown in Figure 1 is reported in Annex 3.

27
# TABLE 2

# COUNTRY PERFORMANCE

||Ret.<br>1970-80|of Growth<br>1981-84|<br>1985-88|Ra<br>DItow,.s<br><sup>1970-80 1</sup>|tio of<br>tic<br>Ss ing<br><sup>981-84</sup>|to GDP<br><sup>1985-88</sup>|<br>Inve<br>1970-80|Ratio of<br>stment to<br>1981-84|CDP<br>1985-88|Ratio of<br>1970-80 1|Exports<br>961-84|toCDP<br>1985-88|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|F<br>.<br>.....|A<br>Z'|II 1()|4 2(I)|16 7(I)|(4 6t (I)|I? 2(1)|24<br>1(3)<br>|19 9 (I)<br>|10 6 (I)<br>|24<br>7 (2)<br>|25I<br>_1f 2)_<br>_'_|20 I (1)<br>|
|||'1**6**|3 7|1127|147y|IS|2(a|19 0|I6|3|_2,)1_|34 6|
||.~~ o..~~%.'*<br>_2_**S**|I|2I|0<br>14'|09|I|96<br>|I',<br>|Lb<br>|'5<br>|12<br>||
|14o.,.,6,I.|<br>_6_|2 7|6 0|L2<br>1|10|19 I|62|2|21)|29**4**|30 I|3|
||I<br>11)|II2)<br>|I0(2)|(3 9 (3)|(2) 2|13 3 (3)|23 6(2)<br>|<br>72 0(2)<br>|2'lI (1)<br>|24 7(1)<br>|24 4 (3)<br>|<br>23 6**(1)**<br>|
|*<br>do...<br>.|**4 9**|~~~3<br>**9**<br>|3 9<br>|24 0|211|16 6|30a|2b 11|21_2_|34 4|25, 3|2|
|**44**|**S**())<br>|1~~~~<br>(I<br>|~I(<br>2 7 13)|1 46<br>(2)<br>|44<br>0(3)<br>|144(2<br>|2 6 (1)<br>|24**I,**(3)<br>|20 0 (2)<br>|29 9**(I)**<br>29 1|716**1)**(1<br>253|?-4<br>(2)<br>7/0|
||**46**|<br>**/6 **|<br>22|**1**<br>**4**|**44**<br>|**(1**<br>|**2?6**|_77?_|**_21_**<br>a<br>|<br>|<br>||
||**.1**<br>*.*.~~~~<br>**6**|.<br>.<br>**I**<br>I_2_|0 7|100|79~~~~~|~~<br>**96**<br>|a<br>|**I?**I<br>|ii 0<br>|1 6 3<br>|**II9**<br>**32? I**|**1**<br>**4**|
|**1**<br>**..**<br>.|||_3_9|**I**96|20 0|19_2_|_25_a|_2?3 _|23 2|36 6|||

Sou,ce: World Ban), data.

h. at,, **_are_** _calculated_ with date in constant<sup>local currency.</sup> Note: Th. f,qu,oa in parentheses<sup>indicate th. ranking. of th. country groups for</sup><sup>**that**</sup> indicator **('best a** on.).

(T22P.WP)FQ (PATOX90.WKI() X i-i-g0
Policy Research Working Paper 8551

# **Abstract**

Too often, academics and policy makers interpret formality as a binary choice and formalization as an irreversible process. Yet, formalization has many facets and shades on the business and labor fronts, and firms may not be able or willing to formalize all at once. This paper explores the joint process of business and labor formalization, using a unique panel data set of Peruvian micro enterprises. The paper finds that business formality does not imply labor formality, and vice versa. Further, there is significant churning in and out of different dimensions of formality within a relatively short period. Using an instrumental variable approach, the paper infers that business formalization affects labor formalization but not the other way around, and that enforcement is a key driver of formalization. Overall, the analysis shows that formalization is a gradual and reversible process, with small entrepreneurs weighing their possibilities in each pathway to business (often) or labor (less often) formalization, but rarely both at the same time.

This paper is a product of the Social Protection and Jobs Global Practice. 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/research. The authors may be contacted at jrigolini@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