rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3. Which group deserves most support from the government?**

|||families<br>with<br>||working||||
|---|---|---|---|---|---|---|---|
||disabled|children|elderly|poor|unemployed|warveterans|nobody|
|**EU**||||||||
|France|0.31|0.14|0.14|0.31|0.07|0.00|0.02|
|Sweden|0.30|0.14|0.23|0.10|0.19|0.00|0.01|
|Germany|0.20|0.52|0.07|0.12|0.06|0.01|0.02|
|Italy|0.18|0.18|0.24|0.13|0.25|0.00|0.01|
|Great Britain|0.16|0.15|0.30|0.19|0.05|0.13|0.01|
|**NEW EU**||||||||
|Poland|0.36|0.22|0.23|0.08|0.06|0.02|0.00|
|Czech Republic|0.31|0.36|0.24|0.04|0.02|0.01|0.02|
|<br>Bulgaria|0.26|0.29|0.25|0.05|0.14|0.00|0.00|
|Romania|0.26|0.22|0.34|0.10|0.06|0.03|0.00|
|Croatia|0.23|0.20|0.19|0.09|0.22|0.05|0.00|
|Lithuania|0.22|0.36|0.21|0.08|0.10|0.01|0.01|
|Latvia|0.21|0.41|0.21|0.03|0.11|0.02|0.00|
|Estonia|0.18|0.46|0.09|0.09|0.15|0.00|0.00|
|Slovakia|0.17|0.45|0.19|0.11|0.05|0.00|0.01|
|Slovenia|0.15|0.29|0.14|0.30|0.10|0.02|0.00|
|Hungary|0.14|0.45|0.21|0.08|0.08|0.01|0.02|
|**NON EU**||||||||
|Kyrgyzstan|0.50|0.14|0.16|0.03|0.09|0.09|0.00|
|Mongolia|0.45|0.11|0.19|0.14|0.09|0.02|0.01|
|Tajikistan|0.42|0.09|0.21|0.13|0.07|0.05|0.00|
|Montenegro|0.42|0.10|0.17|0.07|0.21|0.01|0.01|
|Kazakhstan|0.39|0.19|0.17|0.07|0.07|0.10|0.00|
|Georgia|0.35|0.13|0.28|0.01|0.19|0.04|0.00|
|Uzbekistan|0.35|0.23|0.11|0.09|0.15|0.05|0.00|
|Macedonia|0.35|0.09|0.07|0.08|0.37|0.03|0.01|
|Belarus|0.34|0.36|0.12|0.07|0.03|0.08|0.00|
|Ukraine|0.33|0.28|0.19|0.05|0.07|0.07|0.01|
|Kosovo|0.31|0.06|0.11|0.06|0.33|0.13|0.00|
|Azerbaijan|0.31|0.13|0.14|0.07|0.27|0.08|0.00|
|Moldova|0.30|0.14|0.40|0.05|0.06|0.04|0.00|
|Serbia|0.29|0.23|0.14|0.10|0.20|0.03|0.00|
|Albania|0.28|0.11|0.19|0.12|0.27|0.02|0.00|
|Russia|0.27|0.33|0.21|0.07|0.03|0.08|0.00|
|Armenia|0.25|0.20|0.28|0.04|0.13|0.07|0.00|
|Bosnia and||||||||
|Herzegovina|0.25|0.09|0.22|0.05|0.34|0.04|0.00|
|Turkey|0.23|0.09|0.15|0.19|0.20|0.13|0.01|
|Total|0.26|0.26|0.19|0.12|0.10|0.05|0.01|
|_Legend: Between 0_<br>_Source:_LITS II|_and 0.1_|_Betw_|_een 0.1 and 0_|_.2_<br>_Betw_|_een 0.2 and 0.4_|<br>_More_|_than 0.4_|

13
Policy Research Working Paper 9331

# **Abstract**

Malaysia has been grappling with understanding how many foreign workers reside in the country and thus faces challenges in formulating evidence-based foreign worker policies. This paper uses micro-level remittance transaction data collected from money transfer service providers to estimate the number of foreign workers. Most foreign workers remit a large portion of their earnings to support family members back home. They are low-income earners, sensitive to remittance costs, and opt for money transfer service providers to remit money rather than regular banks, where transfer services are more expensive. Therefore, the remittance data provide a useful source to conduct the investigation. Existing estimates range from two million to five million foreign workers; our results narrow that range to between 2.99 million and 3.16 million foreign workers in Malaysia as of 2017–18. The estimated state and nationality distributions of foreign workers are consistent with the Ministry of Home Affairs data, lending support to the validity of the estimates. Nevertheless, the Bank Negara Malaysia remittance data could potentially underestimate the number of workers in states with low access to money service providers and nationalities that have access to alternative money transfer mechanisms such as commercial banking and informal transfer channels.

This paper is a product of the Poverty and Equity Global Practice and 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/prwp. The authors may be contacted at syi@worldbank.org or ksimler@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
Here, _X_ is the covariate of interest and _N_ refers to the number of observations. As a rule of thumb, a normalized mean difference of 0.25 or less indicates good balance, whereas a normalized mean difference exceeding unity implies a severe imbalance that cannot be handled using standard regression techniques (Rubin 2001; Imbens and Wooldridge 2009).

The baseline socioeconomic and demographic profiles of households, along with their normalized mean differences across various treatment arms and the control arm, are reported in table 1. We have reported the weighted means with weights based on ownership of toilets to derive population estimates. A more detailed comparison of the baseline characteristics of households as well as their GP-level characteristics for both weighted and unweighted data is presented in tables A.1 and A.2 in the appendix. Our results indicate that our sample is well balanced. The absolute value of the maximum normalized mean differences of household baseline characteristics is 0.13 in the unweighted data and 0.15 in the weighted data and that of village-level characteristics is 0.2, all indicating a good baseline balance in the sample.

We also looked into the impact of attrition on baseline balance. Attrition in our data comes from two sources—attrition of GPs and attrition of households. The attrition of GPs occurred either due to the census reclassification of GPs into towns (meaning they were no longer eligible for inclusion in the project) or due to the implementation of the project in control GPs. Attrition of households was due to migration, a split in a household, or a lack of availability at the time of the interview. Table A.3 reports attrition for the three treatment arms and control arm. The overall attrition rate for our sample was 13.1 percent. It varied between 10.9 percent for treatment arm 3 and 14.3 percent for the control arm. A 10 percent attrition rate was incorporated during the power calculations at the design stage. Moreover, during sample selection we included 50 additional GPs (10 each in the three treatment arms and 20 in the control arm) to account for possible attrition during project implementation, so the attrition in our sample is unlikely to affect the power of our estimates. A comparison of baseline characteristics for households in various treatment and control arms, post attrition, shows a balance similar to the sample without attrition (table A.4). So, our sample remained well balanced even after attrition. We also compared the characteristics of the households who were present at midline with those who were lost due to attrition (table A.5). We did not find any significant differences between these two groups across a large majority of characteristics. But there were significant differences in characteristics such as asset ownership, occupation of the household head, and toilet access. Households with no land, mobile phone, or access to toilets, and for which the household head’s primary occupation was listed as either cultivation or salaried work, were more likely to be absent at midline. To somewhat account for these 17
||**Table 8 Broad**<br>|**band use intensit**<br>|**y and selected outco**<br>|**mes**<br>||
|---|---|---|---|---|---|
|VARIABLES|(1)<br>Laborers|(2)<br>Professionals|(3)<br>Value of production|(4)<br>Output per worker|(5)<br>TFP|
|Intensive & Broadband (1=Yes)|‐0.013|0.017|0.003|0.005|0.008|
||[0.016]|[0.019]|[0.013]|[0.010]|[0.006]|
|Broadband (1=Yes)|0.017***|0.007|0.019**|0.003|0.034***|
||[0.006]|[0.011]|[0.008]|[0.008]|[0.004]|
|Observations|50,466|28,967|52,661|52,616|50,753|
|R‐squared|0.007|0.003|0.009|0.002|0.005|
|Number of plants|9,037|5,915|9,378|9,369|9,139|
|Plant FE|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|
|Sector FE|YES|YES|YES|YES|YES|
|City FE|YES|YES|YES|YES|YES|
|Std. Errors|||Cluster Plant/State|||

Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1 I now turn to the main results. Table 9 shows that firms that report using broadband internet appear to have a higher labor demand than those with slower internet access; about 2.2 log points. However, there does not appear to be any relationship between broadband use and outsourced, management and permanent labor demand, which suggests internet use is particularly related to production workers’ labor demand. Temporary labor displays the highest correlation with broadband use. Since TFP has been estimated from a structural model, under the set of assumptions explained before, the results can be interpreted as a causal effect on labor demand.

These results show a negative relationship of productivity gains on labor demand for all types of workers, holding everything else constant. Columns 2 to 9 compare pairs of possible labor substitutes in the production process. Taken at face value, increases in productivity from past adoption of broadband internet would reduce to a higher degree outsourced labor than direct labor, low skilled than high skilled labor demand, production workers relative to managers, and temporary workers relative to permanent ones. These results suggest that labor types which may be easier to adjust, such as outsourced, unskilled and temporary would be the most likely to be substituted by broadband adoption. Interestingly, the size of the effect is similar for men and women, suggesting an absence of gender bias in the substitution between labor and productivity growth.

14
# **2. Context and Data**

# 2.1 MSME Finance in Indonesia

The financial sector in Indonesia is shallower than in other low- and middle-income countries or other countries in the region (Figure 1). Domestic credit to the private sector has averaged 37% of GDP in Indonesia over the past decade, compared to an average of 99% in low- and middle-income countries and 144% in developing East Asia and the Pacific. While domestic credit availability has been steadily increasing elsewhere in the region, in Indonesia the availability of domestic credit has remained stagnant.

Figure 1: Indonesia’s financial sector is shallow compared to peers **Domestic credit to private sector as a share of GDP** <!-- Start of picture text --> 200<br>175<br>150<br>125<br>100<br>75<br>50<br>25<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br>East Asia & Pacific (excluding high income) Low & middle income Indonesia<br>Domestic credit to private sector (% of GDP)<br><!-- End of picture text --> Source: World Development Indicators Over the past decade, MSME clients have comprised an increasing share of banks’ portfolios in terms of number of lending accounts but not in terms of the share of total lending. Lending accounts to MSMEs have increased from 7 million in January 2012 to over 40 million by the end of 2022 (Figure 2). Aligned with an increase in lending accounts, the total volume of credit to MSMEs has also tripled over this time period from approximately US$30 billion in 2012 to US$90 billion at the end of 2022 (Figure 3). However, although the share of lending accounts to MSMEs has increased from approximately one-fifth to one-third of banks’ total lending accounts, the overall share of lending volumes to MSMEs has remained relatively stagnant at about 19% of banks’ total lending volumes.

**7**
## Quintile 3: Comparison of Tax Regimes to the Baseline

|Quintile 3|2013|2014|2015|2016|2017|2018|2019|2020|
|---|---|---|---|---|---|---|---|---|
|Baseline|0|0|0|0|0|0|0|0|
|Sales Tax Increase|-0.18|-0.27|-0.13|0.06|0.00|-0.25|0.10|0.61|
|Corporate Tax Increase|-1.40|-1.18|-1.28|-0.72|-0.69|-0.27|-0.17|0.94|
|Tariff Increase|0.04|-0.01|-0.04|0.14|-0.09|-0.07|-0.04|0.53|

# Quintile 3: Comparison of tax regimes to the baseline

<!-- Start of picture text --> 1<br>0.5<br>0<br>2013 2014 2015 2016 2017 2018 2019 2020<br>-0.5<br>-1<br>-1.5<br>-2<br>Baseline Sales Tax Increase<br>Corporate Tax Increase Tariff Increase<br><!-- End of picture text --> Quintile 4: Comparison of Tax Regimes to the Baseline

|Quintile 4|2013|2014|2015|2016|2017|2018|2019|2020|
|---|---|---|---|---|---|---|---|---|
|Baseline|0|0|0|0|0|0|0|0|
|Sales Tax Increase|-0.15|-0.21|-0.11|0.02|-0.03|-0.22|0.05|0.46|
|Corporate Tax Increase|-1.26|-0.94|-0.95|-0.59|-0.59|-0.30|-0.26|0.59|
|Tariff Increase|0.04|0.00|-0.03|0.09|-0.07|-0.06|-0.04|0.42|

# Quintile 4: Comparison of tax regimes to the baseline

<!-- Start of picture text --> 1<br>0.5<br>0<br>2013 2014 2015 2016 2017 2018 2019 2020<br>-0.5<br>-1<br>-1.5<br>Baseline Sales Tax Increase<br>Corporate Tax Increase Tariff Increase<br><!-- End of picture text --> 36
Policy Research Working Paper 9003

# **Abstract**

This paper examines the impact of the recent recession in Belarus on poverty and broader measures of household welfare and compares the recent recession episode to previous economic crises in Belarus. The paper constructs a measure of vulnerability to poverty, based on an estimated probability of falling below the national poverty threshold not exceeding 10 percent, which is estimated for each year of the household survey data between 2014 and 2017. The analysis finds that the recession of 2014–16 was qualitatively different from earlier recent crisis episodes (2008–09 and 2010–11) in that it affected low-income households to a much greater extent, and the negative welfare effects lingered. The paper also documents that although the recession did not result in a substantial increase in absolute poverty by the official definition, it led to a considerable increase in the share of households that are vulnerable to poverty. The greater degree of vulnerability is also evident from the growing share of the population that faced a risk of poverty within a year (going in and out of poverty). Household types for which the impact of the recession was most pronounced are households with multiple children, single-parent households, residents of rural areas, as well as those who were not employed, partly employed, or low-skilled employees. Coping strategies that were employed by the population were largely related to reducing expenditures and, among vulnerable households, food expenditures in particular, as well as drawing down on their savings.

This paper is a product of the Poverty and Equity 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/prwp. The authors may be contacted at bornukova@beroc.by, acojocaru@worldbank.org, mmatytsin@worldbank.org, and shymanovich@research.by.

_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
Mammen, Kristin, and Christina Paxson, “Women's Work and Economic Development”, Journal of Economic Perspectives14:4(2000), 141-164.

Marshall, Alfred, Principles of Economics (London, UK: MacMillan and Co., 1920).

- Michelacci, Claudio, and Olmo Silva, “Why So Many Local Entrepreneurs?”, Review of Economics and Statistics 89:4 (2007), 615-633.

- Minniti, Maria, “Female Entrepreneurship and Economic Activity,” European Journal of Development Research 22 (2010), 294–312.

- Minniti, Maria, “Gender Issues in Entrepreneurship”, Foundations and Trends in Entrepreneurship 5:7-8 (2009), 497-621.

- Minniti, Maria, and Wim Naudé, “What Do We Know about the Patterns and Determinants of Female Entrepreneurship across Countries?,” European Journal of Development Research  22 (2010), 277-293.

- Mitra, Reshmi, “The Growth Pattern of Women-Run Enterprises: An Empirical Study in India,” Journal of Developmental Entrepreneurship (2002).

- Mukim, Megha, “Industry and the Urge to Cluster: AStudy of the Informal Sector in India”, Working Paper (2011).

- Mukim, Megha, and Peter Nunnenkamp, “The Location Choices of Foreign Investors: A District-Level Analysis in India”, World Economy forthcoming (2011).

- Munshi, Kaivan, “Strength in Numbers: Networks as a Solution to Occupational Traps”, Review of Economic Studies78:3 (2011), 1069-1101.

- Munshi, Kaivan, and Mark Rosenzweig, “Economic Development and the Decline of Rural and Urban Community-based Networks”,The Economics of Transition13:3 (2005), 427-443.

- Munshi, Kaivan, and Mark Rosenzweig, “Traditional Institutions Meet the Modern World: Caste, Gender, and Schooling Choice in a Globalizing Economy”, American Economic Review 96:4 (2006), 1225-1252.

- Nataraj, Shanthi, “The Impact of Trade Liberalization on Productivity and Firm Size: Evidence from India’s Formal and Informal Manufacturing Sectors”, Working Paper (2009).

- Overman, Henry, and Diego Puga, “Labor Pooling as a Source of Agglomeration: An Empirical Investigation”, in Glaeser, Edward (ed.) Agglomeration Economics (Chicago, IL: University of Chicago Press, 2010).

- Parker, Simon, The Economics of Entrepreneurship (Cambridge, UK: Cambridge University Press, 2009).

- Pillania, Rajesh, Madhurima Lall, and Shikha Saha, “Motives for Starting Business: Indian Women Entrepreneurs Perspectives,” International Journal of Indian Culture and Business Management 3:1 (2010), 48-67.

Porter, Michael, The Competitive Advantage of Nations (New York, NY: The Free Press, 1990).

26
**Table 4:**

# **DTERMINANTS** OF **INSTITUTIONAL CREDIT ADVANCE TO RURAL SECTOR**

|Explanatory Variable|Institut<br>Rand|ional Credit<br>om effect|
|---|---|---|
|Aggregate real crop (real) price (lagged)/|-0.038|(-0.383)|
|Real price of fertilizer /|-0.207|(-0.833)|
|Real urban wage a/|.0.168|(-1.159)|
|Rainfall x 103|_-0.055_|<br>(0.637)|
|Roads a/|2.166|(5.181)*|
|Regulated markets_4I_|0.536|(3.598)*|
|Primary schools /|0.986|(1.418)*|
|Rural electrification _/|-0.357|(-1.979)*|
|Canal irrigation/|-0.189|(.0.987)|
|Commercial banks a/|0.801|(8.565)*|
|Cooperative banks A/|0.239|(1.904)*|
|Primary cooperative societies a/|.0.801|(-4.081)*|
|Land development banksg|-0.287|(-1.853)*|
|Year|-64.962|(-4.276)*|
|Year x irrigation potential|0.471|(3.847)*|
|Year x excess rain months|-2.399|(-0.723)|
|Year x length of rainy season|5.760|(1.688)*|
|Year x soil moisture capacity|14.308|(4.339)*|
|Year x flood potential|-1.957|(-2.006)*|
|Year x number of cold months|1.434|(0.486)|
|F-statistic|32.63||
|Hausman-Wu (Chi-square, 20 df.<br>Number of observations|25.05<br>**765**||

Note: T-statistics are in parenthesis. Asterisk refers to a significant level of 10 percent or better. **A/** Coefficients **are** in elasticity **_form._** 22
- 7 -

**Figure 1**

KOREA: FINANCIkL DEEPENING

**RATIO Mdl/GNP. 1981-a6**

<!-- Start of picture text --> 42X- - -<br>402 -<br>363 - ____<br>383 --<br>34X<br>32X<br>222'<br>243<br>202<br>123'<br>8% - . - -.. . . .<br>1961 1965 1969 1973 1977 198i 1985<br>a U2/GNP<br><!-- End of picture text --> Figure 2 <!-- Start of picture text --> LEVELS OF FINANCIAL DEVELOPMENT<br>KOREA. JAPAN. THAILANO 1960-1986<br>0.9<br>0.8<br>0.7<br>0.4<br>0.3 -<br>0. 2 -<br>0 -<br>1960 1964 1968 1972 1976 1980 1984<br>KOREA M2/GDP J JAPAN M2/GOP ° THAILAND M2/GOP<br><!-- End of picture text -->
12

**Table 3: The Algerian Unemployment Insurance System**

|**Zilgibility0**<br>**i~ter**<br>**fort waster**|**la**<br>**Eligibit**<br>**requirementifor employer**|
|---|---|
|Laid off for economic reasons (i.e. involuntarily);|Firm must be current in payment of SS<br>contributions and Ul contributions;|
|Receives no other revenue, except for unpaid||
|leave;|Firms with > 9 employees must present<br>employment reduction or restructuring plan_(volet_|
|Ineligible for early or regular retirement;<br>Affiliated with Social Security system for at least<br>3 years;|_social)_to workers' participation committees and to<br>labor unions for agreement, for negotiation and<br>mediation, with recourse to arbitrage;|
|Paid UI contributions for at least 6 months prior to<br>lay-off;|Lay-off decision for each employee includes<br>severance package, notification of Ul fund, and<br>payment of entry fee;|
|Does not refuse job offer or reconversion;|Entry fee is equal to 80% of the gross monthly<br>salary for each year of work experience over three<br>years, up to a maximum of 12 months salary (i.e.<br>18 years of tenure).|
|Employer paid the entry fee to the UT fund;|Initial payment equivalent to 2 months salary must<br>be paid up front, with the balance to be paid over<br>the following year;|
|Registered "job seeker" with employment office<br>for at least 3 months.|Failure to comply results in penalties ranging from<br>5,000-10,000 DA per worker.|

- 0 Severance pay equal to 3 months salary, paid in lump sum by employer directly to the worker; 0 Benefits are subject to employee portion of SS contributions;

- 0 Continued eligibility for health insurance, maternity benefits, family benefit;

- 0 Duration period equal to 2 months per year of UI contributions paid to the last employer: 12 month minimum, 36 month maximum;

- 0 Monthly reference salary equal to<sup>_1_</sup> _2_ * (gross monthly salary+ minimum wage);

- 0 Duration period is divided into 4 equal quarters;

- 0 Graduated replacement rate of the monthly reference salary:

- 100% during the 1st quarter of duration period;

- 80% during 2nd quarter;

- 60% during 3rd quarter;

50% during 4th quarter;

- 0 Benefit level minimum = 75 % of the minimum wage, maximum = 3 times the minimum wage;

- 0 After exhaustion of benefits, continued eligibility for health insurance and family benefits for one year.
# **Table A2: Change in household size 1983-2011**

||1983|1993|2004|2007|2009|2011|
|---|---|---|---|---|---|---|
|All India|5.1|4.8|4.7|4.5|4.4|4.3|
|Urban|4.8|4.4|4.3|4.2|4.1|4.0|
|Analytic sample – wives aged 25-<br>55 ofurban householdheadhead|5.8|5.2|4.9|4.7|4.6|4.6|

21