rafmacalaba/datause-encoder-data huggingface dataset:
In 2011, these divisions housed 53 percent of the entire population, including 48 percent of the rural population (BBS, 2015). These divisions constitute 38 percent of the lottery applicants and 50 percent of the lottery winners. These divisions are also the two most prosperous divisions of Bangladesh. To the extent that migration improves outcomes, the results can be interpreted as a lower-bound of the impact on the entire country.

# **Sampling strategy and field protocols**

The relative scarcity of T1 and the nature of the administrative data we received guided our sampling and field protocols. We received administrative data from BMET in two separate extracts. The first extract was for the lottery losers (control group) which included information on the applicants’ names, their parents’ names, phone numbers, and the name of their unions. The second extract was the published data on lottery winners (group T1 and T2) which contained the same information except for their phone numbers. We were able to get matched phone numbers for this group from BMT only for 76 percent of group T1 and 16 percent of group T2.

To deal with this, we opted for a combination of phone and field-based tracking of respondents. In each of the sampled unions, enumerators were instructed to find all the T1 individuals. Applicants in the T2 and control groups were randomly ordered, and enumerators were instructed to follow the order in finding respondents. Enumerators would keep going down the randomized order until the number of successful interviews in that group (T2 or control) matched the number of successful interviews in the T1 group. This way, the final survey would have similar sample sizes across treatment groups within each union.

To find the respondents in a sampled union, enumerators first tried calling the applicants for whom we had phone numbers. Each applicant would be called up to five times over the course of several days. If somebody picked up the phone, we also asked if they knew the phone numbers of additional people in the treated groups. Lottery applicants not found by phone were searched in the field. Enumerators would use the information available to locate the respondents. This would include making visits to and consulting with local union officials and asking local residents.

With this protocol, we were able to interview 3,512 lottery applicants, of which 1,127 were Phase 1 winners (group T1), 1,138 were Phase 2 and Phase 3 winners (group T2), and 1,247 were the lottery losers (Control group). We conducted interviews with the applicants themselves if they were present, or with knowledgeable family members if they were absent.

11
Here the (informal) employment status dummy equals 1 when worker j in region i (or sector i) and year t is employed (informally employed), and 0 otherwise. Weights capture the extent of representativeness of worker j in the whole sample. Both simple and weighted measures are constructed with the latter giving more weight to more representative observations in the sample. As shown above, while BPS provides three definitions of informal employment, the simplified definition is preferred for the analyses as it is less linked with Indonesia’s sectoral composition. These data series are then used to show the levels, trends, and features of informality in Indonesia.

**3. Levels and trends of informality: EMDEs versus Indonesia**

# **3.1 Informality in EMDEs**<sup>**5**</sup>

19. **Informal economic activity is more pervasive in EMDEs than in advanced economies (AEs).** In AEs, it accounted for less than one-fifth of official GDP and total employment in 2019, whereas about one-third of economic activities and employment in EMDEs happens in the informal sector (Elgin et al. 2021). Informality is common in all EMDE regions but takes different forms. On average, the informal economy’s share of output is highest in Sub-Saharan Africa (SSA), Europe and Central Asia (ECA), and Latin America and the Caribbean (LAC). The share of self-employment, however, is highest in SSA, South Asia (SAR), and East Asia and the Pacific (EAP).

20. **The share of informal output and employment has contracted in EMDEs since the 1990s.** The downward trend is observed both in AEs and EMDEs (Ohnsorge and Yu 2022). In the case of EMDEs, the downward trend is more prominent in output informality, whereas employment informality fell more noticeably during the recent two decades than during the 1990s.

**Figure 1. Informality in EMDEs**

<!-- Start of picture text --> A. Informality across EMDE regions B. Trend of informality in EMDEs<br>Percent of GDP / employment Percent of GDP/ employment<br>55<br>80 Output Employment Output Employment<br>50<br>60<br>45<br>40 40<br>35<br>20<br>30<br>0<br>EAP ECA LAC MNA SAR SSA<br>Sources: Elgin et al. (2021, updated), International Labor Organization (2023), World Bank.<br>1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019<br><!-- End of picture text -->

> 5 In this section, output informality for EMDEs is captured by MIMIC-based estimates. The DGE-based estimates are highly positively correlated with the MIMIC-based estimates and give similar results. The discussion here is restricted to the period before 2020 to avoid the impact of COVID-19.

8
Duflo, E. 2012. Women Empowerment and Development. Journal of Economic Literature, 50(4): 1051–1079 Duranton, G. and M. A. Turner, 2012. Urban Growth and Transportation. Review of Economic Studies, 79 (4): 1407-1440 Duranton, G. E. Ghani, A. Goswami and W. Kerr. (2015). The Misallocation of Land and Other Factors of Production in India. World Bank Policy Research Working Paper # 7221.

Duranton, G. E. Ghani, A. Goswami and W. Kerr. (2016). A Detailed Anatomy of Factor Misallocation in India. World Bank Policy Research Working Paper # 7547.

Edmonds, E., N. Pavcnik and P. Topalova. 2010. Trade Adjustment and Human Capital Investments: Evidence from Indian Tariff Reform. American Economic Journal: Applied Economics, 2(4): 42-75.

Ederington, J., J. Minier and K. Troske. 2009. Where the Girls Are: Trade and Labor Market Segregation in Colombia. IZA Discussion Paper 4131, Institute for the Study of Labor.

Elson, D. 1999. Labour Markets as Gendered Institutions: Equality, Efficiency and Empowerment Issues. World Development, 27(3): 611-627.

Estrin, S. and T. Mickiewicz. 2011. Institutions and Female Entrepreneurship. Small Business Economics, 37(4): 397-415.

Field, E., S. Jayachandran, and R. Pande. 2010. Do Traditional Institutions Constrain Female Entrepreneurship? A Field Experiment on Business Training In India. American Economic Review Papers and Proceedings, 100(2): 125-129.

Fontana, M. 2009. The Gender Effects of Trade Liberalization in Developing Countries: A Review of the Literature. In M. Bussolo, and R. De Hoyos, Gender Aspects of the Trade and Poverty Nexus: A Micro-Macro Approach. Palgrave Macmillan.

Fernandes, A. 2007. Trade Policy, Trade Volumes and Plant-Level Productivity in Colombian Manufacturing Industries. Journal of International Economics 71(1): 52–71.

Gaddis, I. and J.Pieters. 2014. The Gendered Labor Market Impacts of Trade Liberalization: Evidence from Brazil. World Bank Policy Research Working Paper # 7095.

García-Santana, M. and Pijoan-Mas, J. 2014. The Reservation Laws in India and the Misallocation of Production Factors. Journal of Monetary Economics, 66(0): 193-209.

Goldberg, P., A. Khandelwal, N. Pavcnik and P. Topalova. 2010. Multiproduct Firms and Product Turnover in the Developing World: Evidence from India. The Review of Economics and Statistics, 92(4): 1042-1049.

37
Table 3: Is the surge driven by lower prices or higher quantities?

||(1)|(2)|(3)|(4)||(5)|
|---|---|---|---|---|---|---|
|VARIABLES|ln(_SMit_) =|ln(<sup>_SPit_</sup><br>_Pit_ <sup>)</sup>|+<br>ln(_Pit_)<br>+|ln(<sup>_SQit_</sup><br>_Qit_ <sup>)</sup>|+|ln(_Qit_)|
|1 Qtr before fle|0.11***|-0.03**|-0.03***|0.22***||-0.06**|
||(0.03)|(0.01)|(0.01)|(0.03)||(0.02)|
|2 Qtrs before fle|0.16***|-0.03**|-0.02*|0.22***||-0.01|
||(0.03)|(0.01)|(0.01)|(0.03)||(0.02)|
|3 Qtrs before fle|0.14***|-0.04***|-0.01|0.18***||0.01|
||(0.03)|(0.01)|(0.01)|(0.03)||(0.02)|
|4 Qtrs before fle|0.13***|-0.03**|-0.00|0.12***||0.05**|
||(0.03)|(0.02)|(0.01)|(0.03)||(0.02)|
|5 Qtrs before fle|0.11***|-0.01|-0.00|0.11***||0.01|
||(0.03)|(0.02)|(0.01)|(0.03)||(0.02)|
|6 Qtrs before fle|0.04|-0.01|-0.01|0.09***||-0.02|
||(0.03)|(0.02)|(0.01)|(0.03)||(0.02)|
|7 Qtrs before fle|-0.04|0.01|0.00|0.01||-0.06**|
||(0.03)|(0.02)|(0.01)|(0.03)||(0.02)|
|Observations|16596|16596|16596|16596||16596|
|_R_<sup>2</sup>|0.839|0.583|0.979|0.745||0.925|

Estimates include month, year, petition and HS-10 fixed effects Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **5.2 Decomposition of Imports: Prices and Quantities**

To this point the interpretation of demand and supply shocks as underlying the behavior of subject imports and total imports is relatively provisional since we have only considered information on expenditures. To scrutinize this interpretation we further decompose the behavior of imports into changes in price and quantity. These results appear in Table 3. Is the decline in total imports immediately before the filing likely to be due to a negative demand shock? It appears that this is the case with evidence from the first row of columns (3) and (5) of Table 3 confirming a decline in both average unit price and average quantity from all sources in the quarter immediately before filing (i.e. in equilibrium both price and quantity have declined).<sup>25</sup> The picture that emerges for imports from all sources is consistent with a negative demand shock

> 25Because this shock affects imports from all sources it is likely that its origins are domestic. Observed movements in price and quantity are consistent with a domestic demand shock. There is scope for other explanations on the supply side, however, such as the breakdown of collusive arrangements in the domestic market. We return to this argument later.

18
**Table 13 Effects of gold production at the district level on employment**

|Panel A: Using|production i<br>|n theprevious<br>|period<br>|||||
|---|---|---|---|---|---|---|---|
||(3)<br>not<br>working|(4)<br>agri-<br>culture|(5)<br>service or<br>sales|(6)<br>profess-<br>ional|(7)<br>manual<br>work|(1)<br>earns<br>cash|(2)<br>works all<br>year|
|gold period|0.003|-0.009**|0.003*|0.004***|-0.002|-0.001|0.008**|
|district|(0.004)|(0.004)|(0.002)|(0.002)|(0.004)|(0.002)|(0.003)|
|observations|19,175|19,175|19,175|19,175|19,175|19,270|15,991|
|R-squared|0.207|0.327|0.127|0.137|0.037|0.213|0.278|
|PANEL B. Usi|ng productio<br>|n in the same<br>|year<br>|||||
||(3)|(4)|(5)|(6)|(7)|(1)|(2)|
||not<br>working|agri-<br>culture|service<br>or sales|profess-<br>ional|manual<br>work|earns<br>cash|works<br>allyear|
|gold year|0.012|-0.033|0.020|0.019*|-0.018|-0.010|0.041***|
|district|(0.022)|(0.025)|(0.013)|(0.011)|(0.015)|(0.009)|(0.008)|
|observations|19,175|19,175|19,175|19,175|19,175|19,270|15,991|
|R-squared|0.207|0.327|0.128|0.137|0.037|0.213|0.278|

_Note:_ Robust standard errors clustered at the district level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. *** p<0.01, **p<0.05, *p<0.1.

# **7.3 Investigating spillovers**

The districts are small, and some mines are located in border regions. We thus expect there to be spillovers across district borders. For example, a mine can change demand for labor, agricultural produce, and services across the district border, and induce reallocation of work across districts. We explore neighbor spillovers by estimating the following equation: Y��� � β�gold_prod�� � β�neighbor_gold_prod�� � ∝�� g� �λX�� �ε��� �5� That is, we add gold production for the mining districts to their neighbors, and _β2_ measures the effects of gold production in these districts, as well. That is, if _<mark>β</mark> 2_ <mark>i</mark> s statistically and economically significant, it would imply that increased production in a neighboring district has spillover effects on the district in question.

In Annex table 4, we have added the gold production of the mining areas to their neighbors and we estimate the spillover effects of mining production in a district on employment in adjacent districts. As before, panel A shows effects of increasing production in the previous period, and 34
# Table 1: Contribution-Benefit Link

||Until De<br>Baseline|cember 2014<br>Tax evasion|From J<br>Baseline|anuary 2015<br>Tax evasion|
|---|---|---|---|---|
|**(a) Increase in monthly wage items**|||||
|Gross wage|1|1|1|1|
|Total cost to employer|1.27|0.62|1.27|0.62|
|Net wage|0.66|0|0.66|0|
|PIT and SSC on wage|0.62|0.62|0.62|0.62|
|**(b) Increase in monthly beneft items**|||||
|Gross beneft|0.26|0.26|0.70|0.70|
|Net beneft|0.20|0.20|0.54|0.54|
|PIT and SSC on beneft|0.06|0.06|0.16|0.16|
|**(c) Total increase of wage items (6 months)**|||||
|_Gross wage_|_6_|_6_|_6_|_6_|
|Total cost to employer|7.62|3.69|7.62|3.69|
|Net wage|3.93|0|3.93|0|
|PIT and SSC on wage|3.69|3.69|3.69|3.69|
|**(d) Total increase of beneft items (24 months)**|||||
|Gross beneft|6.30|6.30|16.80|16.80|
|Net beneft|4.82|4.82|12.85|12.85|
|PIT and SSC on beneft|1.48|1.48|3.95|3.95|
|**(e) Overall gains and losses**|||||
|_Employer_|_-7.62_|_-3.69_|_-7.62_|_-3.69_|
|_Employee (= Net wage + Net beneft)_|_8.75_|_4.82_|_16.78_|_12.85_|
|_Government (= PIT and SSC on wage - Net beneft)_|_-1.13_|_-1.13_|_-9.16_|_-9.16_|

_Note:_ Table shows the costs and benefits associated with a 1 dollar increase of an employee’s monthly gross wage during the first six months of pregnancy. The first and second columns show costs and benefits under the system used until December 2014. Under this system, the benefit calculation window was the calendar year preceding the receipt of the benefit. For example, for a woman who gave birth on October 1, 2014, the reference period was January to December 2013. The effect of an increase in the gross wage on benefits depended on the month of birth, and here we show the average effect. The third and fourth columns show costs and benefits under the system used from January 2015. Under this system, the benefit calculation window is the six-month period between nine and four months before birth. For example, for a woman who gave birth on October 1, 2016, the reference period was January to June 2016. The first and third columns show a baseline scenario with no income tax evasion. The second and fourth columns show a scenario with income tax evasion. In this case, reported wages are increased, resulting in higher income tax and social security contribution payments, without a change in net wages. This would arise if the same net wage was previously paid as an envelope wage. Panel (a) considers the increase in monthly wage items associated with a 1 dollar increase in the gross monthly wage. For wages, a 16% personal income tax (PIT) rate, a 18.5% employee social security contribution (SSC) rate, and a 27% employer SSC rate apply. Panel (b) considers the increase in monthly benefit items. For parental leave benefits (PLB), only the 16% PIT rate applies. For child care benefits (CCB), the 16% PIT rate and the 10% employee SSC rate apply. Panel (c) considers the increase in wage items, aggregating over 6 months (for comparability across the 2012-2014 and 2015-2017 periods). Panel (d) considers the increase in benefit items, aggregating over 24 months. Panel (e) considers the overall impact on employers, employees, and the government.

19
corporate clients. Another indicator of the lack of market saturation is the fact that most banks have not yet developed niche strategies to target SMEs (Figure 4.2).

Figure 4.2: Sector-Specific and Geographic Focus in Dealing with SMEs <!-- Start of picture text --> Does the bank have a sector or geographic specific focus in dealing with<br>SMEs?<br>100%<br>88%<br>80% 75%<br>60%<br>40%<br>25%<br>20%<br>13%<br>0%<br>Yes No<br>Sector Focus Geographic Focus<br>Source: Authors’ analysis of bank questionnaires (simple averages across all banks).<br><!-- End of picture text -->

# **4.3 Government Programs**

Although banks stressed the overall positive role of government programs, particularly FNG and Bancóldex, a rigorous assessment of their impact on the SME market has not yet been made. Virtually all interviewed banks agreed on the positive role played by FNG and Bancóldex in helping them mobilize financial resources to SMEs. In particular, a number of policy actions in recent years<sup>28</sup> have enabled FNG to significantly expand its operations, while banks have expanded their use of Bancóldex discounting facilities in order to increase the maturity of SME financing. In addition, special Bancóldex credit lines – such as the “ _aProgressar_ ” program – and regional alliances/partnerships provide assistance and long-term funding for entrepreneurial training, technological innovation, and research and development related to SMEs. However, in spite these positive developments, banks consider that more government assistance is required in areas such as capacity-building (e.g. financial literacy to improve the overall quality of financial statements/business plan proposals, business skill training for SME owners etc.) and providing incentives for SMEs to formalize (see below). In addition, it is important to note that the effective contribution of government-sponsored SME financing programs in terms of enhancing access and other key objectives compared to a suitable baseline has not yet been assessed. This is an important consideration, especially in light of the scaling up in the operations of Bancóldex and FNG activities going forward, as affirmed by their respective business plans and as mentioned in the CONPES document 3484 (August 2007).

> 28 These include the admissibility of its guarantees for loan loss provision purposes (1999), an increase in its capitalization (2000), its inclusion as a financial institution subject to SF oversight (2004), as well as the development of new products and operational improvements that automated the granting of guarantees. See Llisteri et al. (2006) for a description.

20
the IV regressions yield Kleibergen Paap F-statistics that are well in excess of 10; Staiger and Stock (1997) proposed a first-stage F-statistic of 10 as a rule-of-thumb below which instruments are declared weak. A number of recent papers (Castelló‐Climent (2010); Halter et al. (2014); Ostry et al. (2014); and Dabla‐Norris et al. (2015)) have estimated effects of inequality on GDP per capita using lags as instruments. Kraay (2016) examines instrument strength and finds that the IV estimates reported in those papers suffer from weak instrument bias; i.e. the first-stage F-statistics are substantially below 10.

IV estimates based on weak instruments are biased towards least squares estimates (Bound et al., 1995). We show that the least squares estimate of the relationship between transitional GDP per capita growth and inequality yields a negative coefficient on inequality. Thus, least squares estimation suggests that the effect of income inequality on transitional GDP per capita is negative. This is the same result as obtained by recent papers that use lags as instruments. On the other hand, our identification approach that uses the residual variation in inequality not affected by GDP per capita as an instrument for inequality yields a positive coefficient on inequality.

# **3. Data**

**Income Inequality.** Our main indicator of income inequality is the Gini. This variable is based on the area between the Lorenz curve and a hypothetical line of absolute equality. In the empirical analysis we use two different Ginis from the Standardized World Income Inequality Database (Solt, 2015): (i) the market Gini that measures inequality in pre-tax, pre-transfer income; and (ii) the net Gini that measures inequality in post-tax, post-transfer income. These data are available from 1960 onward. As a robustness check, we will present estimates that are based on Gini data from the World Development Indicators (2017), available from 1980 onwards, and Gini data of Brueckner et al. (2015), available from 1960 onwards.<sup>4</sup>

> 4 Brueckner et al.'s (2015) primary data source is the UN-WIDER World Income Inequality Database. The authors

7
of GDP. Despite the lack of accuracy of these statistics, they hint to the size transfers had reached in the small economy of Mount Lebanon. Such a level of transfers had deep structural and permanent impact on the economy.

# **_3. The Impact of Transfers on the Economy of Mount Lebanon_**

Scholars from the beginning of the 20th century acknowledged the positive impact of transfers on household income and welfare. But they complained about the misuse of this money, pointing to an uncontrolled real estate development with an “excessive inflation of the prices of land.” This statement is simply indicating the occurrence of a price increase in the non-tradable real-estate sector due to a strong spending effect caused by large transfers into the domestic economy. With land being an input to the productive activity, the large increase in its prices had a harmful impact on agriculture and on many industrial activities. Also, the booming real-estate sector has initiated further adjustments with resources moving into construction activities. Scholars talk about some technology transfers to agriculture and industry by returning migrants, but they present their effect as being marginal and limited. In sum, transfers have reinforced the deindustrialization already experienced by Mount Lebanon due to the mono-industry of raw silk.

Table 3. Imports, exports, and transfers of Mount Lebanon

|Item – in Ottoman Pound|Imports & outflows|Exports & inflows|Balance|
|---|---|---|---|
|Food and livestock|760,000|78,000|-682,000|
|Tobacco,beverage & olive oil|80,000|198,000|118,000|
|Textile and silk|63,000|687,000|624,000|
|Manufacturedgoods|650,000|85,000|-565,000|
|Total|1,553,000|1,048,000|-505,000|
|Tourism Income/Expenditures|0|15,000|15,000|
|Transfers from/to America|300,000|1,200,000|900,000|
|Current Account Balance|||410,000|

_Sources: Lubnan, mabaheth ilmiah wa ijtimaya - Lebanon, scientific and social studies - 1918._ The eviction of tradable sectors became quasi total by 1910-1914 and while silk reached 61 percent of total exports, Mount Lebanon became a net importer of tradable goods financed by Net Transfers from the Americas. Table 3 summarizes the imports and exports of Mount Lebanon for an unspecified year between 1910 and 1914, most likely 1912. It shows a trade deficit equivalent to 30 percent of Mount Lebanon’s GDP and a ratio of Current Account surplus to GDP of 24.5 percent. Transfers and silk exports are clearly the main sources of revenues fueling the growing demand in Mount Lebanon that is met through increase in the imports of goods. For instance, statistics show that imports of food and beverage were three times larger than the exports of these same items. Also, imports of manufactured goods from Europe are almost eight times higher than exports of manufactured items other than textile and silk. This dependency on mono-exports and on transfers to finance imports of food and manufactured goods would have disastrous consequences for Mount Lebanon in times of economic or political crisis.

5