rafmacalaba/datause-encoder-data huggingface dataset:
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- <mark>Davis, S. J., and Haltiwanger, J. (1999). On the driving force between cyclical movements in employment and job reallocation.</mark> _<mark>American Economic Review, 89</mark>_ <mark>, 1234-1258.</mark>

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30
22% in Kunming to 70% in Shenzhen except for firms in Wenzhou where the average Other financing is only 3.38%.<sup>18</sup> It is interesting that Shenzhen records the highest amount of Other financing since this city is located in the Guangdong province which has the highest number of private enterprises including underground lending organizations as suggested by the McKinsey study.

Looking at aggregate financing patterns in panel B, the use of Internal funds ranges from 46% in Benxi to 81% in Zhengzhou. When we use the Allen et al. (2005) categorization, we find that several cities have in excess of 90% of their financing coming from sources other than banks. Figure 2 reveals the financing patterns across the different cities in increasing order of their use of bank financing. Figure 2 shows that after Internal funds, firms use Equity or Bank finance. Use of informal sources, investment funds and operations finance is very low. We control for the variation in financing patterns across different cities in our regressions using city dummies.

Table 2 also shows that the very large firms use more of Bank financing (30%) than the micro and small firms (14-15%). Publicly listed companies finance a little over 33% of their new investments through Bank Financing and 67% from Self Fund Raising sources. By contrast, cooperatives fund only 18% of their new investments from banks and 82% from sources other than banks. When we look at ownership, domestic private firms use 20% Bank financing and 80% Self Fund Raising compared to state firms that use 26% Bank financing and 74% Self Fund Raising. In both cases, Other makes up a bulk of the Self Fund Raising. Older firms (firms > 20 years) use more of Bank financing

> 18 Four of the 18 cities were designated as export processing zones- Shenzhen, Dalian, Hangzhou, and Wuhan. Their use of Other Financing ranges from 24% in Hangzhou to over 70% in Shenzhen suggesting that Other might also be funds from overseas investments (Hong Kong, Taiwan (China), Macao).

17
the market for export goods to avoid collapse and even reemerge as recovery begins. In fact, falling wages may have aided in the recovery of the sector.

# **References**

Cai, F., Wang, D., & Zhang, H. (2009). _Employment Effectiveness of China’ Economic_ _Stimulus Package._ Working Paper _._ Institute of Population and Labor Economics, Chinese Academy of Social Sciences.

Chen, X. (2009). Financial crisis caused by 20 million migrant workers unemployed. _WTOGuide_ , (3),18.

deBrauw, A., Li, Q., Liu, C., Zhang, L., & Rozelle, S. (2008). Is feminization of agriculture occurring in China? Debunking the myth and measuring the consequence of women’s participation in agriculture. _China Quarterly_ , _194,_ 327-348.

deBrauw, A. and J. Giles. 2008a. Migrant opportunity and the educational attainment of youth in rural China. The World Bank. Policy Research Working Paper 4526.

deBrauw, A. and J. Giles. 2008b. Migrant labor markets and the welfare of rural households in the developing world: Evidence from China. The World Bank. Policy Research Working Paper 4585.

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Frankenberg, Elizabeth, James P. Smith, and Duncan Thomas. (2003). "Economic Shocks, Wealth, and Welfare," _Journal of Human Resources_ , University of Wisconsin Press, vol. 38(2).

25
food and non-alcoholic beverages, while utilities expenditure is deflated using the consumer price inflation for energy (capturing housing, water, electricity, gas and other fuels). Remaining nonfood consumption expenditure is deflated using the headline average consumer price inflation. The year 2022 is used as the base year.

Hence, the consumption expenditures as measured in our experiment in 2022 are taken in their nominal values, while the expenditures in previous rounds are deflated. The expenditure values elicited during the TZNPS 2020/21, conducted between December 2020 and January 2022, are deflated in accordance with the 2021-2022 inflation. Similarly, the expenditure values elicited during the TZNPS 2019/20, conducted between January 2019 and January 2020, are deflated in accordance with the 2020-2022 inflation. In what follows, all expenditures are reported in year2022 Tanzanian shillings (TSH), and total annual consumption per adult equivalent is compared to the TZNPS 2020/21 poverty line deflated to prices in 2022.

Table 1 provides descriptive statistics for TZNPS 2020/21, 2019/20 rounds and for each of the survey treatment arms, coupled with the results from the tests of mean differences among the TAs. The “good” news is that across treatments, comparisons of the prospective poverty predictors that are required for Models 1, 2, 8 and 9 largely do not reveal statistically significant differences. The only exceptions are participation in wage work, and bicycle ownership, between TA 1 and TA 2; radio ownership, urban–rural residence and utility expenditures (though with marginal differences) between TA 2 and TA 3; and access to piped water, between TA 1 and TA 3. These findings are in stark contrast with those of Kilic and Sohnesen (2019)<sup>8</sup> and do not raise flags regarding the

> 8 Kilic and Sohnesen (2019) report on a randomized survey experiment that was conducted in Malawi in 2016 and that shows that <mark>observationally equivalent, as well as identical, households in fact answer the same questions differently depending on whether they are interviewed with a short questionnaire or its longer counterpart. The authors find large and statistically significant differences in reporting across a range of topics and question types, which can lead to a difference of 3 to 7 percentage points in predicted poverty estimates, depending on the imputation model. The authors, however, demonstrate that the imputation model using only the predictors that are elicited prior to the</mark>

11
Table A4: Peer Effect and Intergenerational Educational Mobility (Robustness): Alternative Definition of Peer Group: _±_ 5 Years Age Band

||**Rural(R)**|**Urban(U)**|**R–U**|**Rural(R)**|**Urban(U)**|**R–U**|
|---|---|---|---|---|---|---|
|Father Edu|0.309***|0.546***|-0.236**|0.295***|0.488***|-0.194*|
||(0.075)|(0.098)|(0.111)|(0.073)|(0.096)|(0.111)|
|Father Edu Sq|0.013***|0.012***|0.001|0.013***|0.012***|0.001|
||(0.002)|(0.002)|(0.003)|(0.002)|(0.002)|(0.003)|
|Father Edu_×_Peer|-0.018**|-0.039***|0.021*|-0.020**|-0.035***|0.015|
||(0.009)|(0.011)|(0.012)|(0.009)|(0.010)|(0.012)|
|Peer Edu|1.003***|0.621**|0.382|0.723***|0.507*|0.216|
||(0.206)|(0.298)|(0.340)|(0.187)|(0.278)|(0.315)|
|Peer Edu Sq|-0.006|0.02|-0.026|0.008|0.022|-0.013|
||(0.013)|(0.016)|(0.019)|(0.012)|(0.015)|(0.018)|
|Constant|-0.958|-0.039|-0.919|-0.918|-0.102|-0.816|
||(0.864)|(1.424)|(1.604)|(0.741)|(1.340)|(1.462)|
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|9907|5087|14994|9907|5087|14994|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). A peer is someone who was born in the respondents district within _±_ 5 years of respondents birth. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

4
negative impact on the labor market as it still employs a third of the domestic labor force, and provides at least partial subsistence to about half the population (Prohnitchi and Oprunenco, 2010).

Drawing on the MAMS database, the economic structure of the Moldovan economy in 2008 is summarized in Table 1. Primary exports represent only a moderate fraction of the total primary production while a quarter of the domestic demand for primary products is satisfied by imports. Therefore, the bulk of agricultural products are produced for the home market. This has important implications for the impact of changes in the agro-industrial sphere on the broader economy.

Moldova lacks own energy sources and imports all of its supplies of petroleum, coal, and natural gas, largely from Russia. It has been estimated that, by 2020, Moldova will only be able to meet around 10-12 percent of its energy needs from own (renewable) energy sources (Prohnitchi and Oprunenco, 2010). The availability and price of energy is thus a major factor causing uncertainty about the future economic development. In Table 1, fuel is part of an aggregate that also includes chemicals and fueland chemical-based products); for this broader sector, 80 percent of domestic demand is satisfied by imports.

The value added and employment shares indicate that private services have the highest level of value added per employee, while the opposite is true for agriculture. This relatively low level of labor productivity of is in part due to the fact that Moldova, despite its fertile soil and favorable climate, has not been able to reach yields that are on par with levels reached in EU or Central and Eastern European (CEE) countries (Prohnitchi and Oprunenco, 2010). Given this, there should be considerable scope for raising agricultural productivity.

**Table 1. Structure of production in 2008**

||Share of<br>production|Share of<br>value added|Employment<br>share|Share of<br>exports|Exports of<br>output|Share of<br>imports|Imports of<br>demand|
|---|---|---|---|---|---|---|---|
|**Primary**|12.2|11.4|31.4|10.7|14.9|8.5|25.1|
|**Industry and manufacturing**|36.8|21.4|19.3|61.6|27.4|80.8|51.7|
|Mining products|||||13.7||82.8|
|Food stuffs|||||21.4||25.5|
|Beverage and tobacco|||||55.0||36.8|
|Textile, clothing, leather|||||93.6||91.6|
|Wood and paper, publishing|||||21.1||52.8|
|Fuels, chemicals|||||77.6||80.2|
|Metal, rubber, plastic prod.|||||37.0||65.1|
|Manufacture of machines and equip.|||||58.6||81.4|
|Electricity, gas and water supply|||||||26.6|
|**Services**|51.0|67.2|49.3|27.8|9.4|10.6|8.5|
|**Government services**|7.4|12.5|16.1|||||
|**Private services**|43.6|54.7|33.2|27.8|11.0|10.6|10.0|
|Construction services|||||1.1||1.4|
|Hotels and restaurants|||||32.1||38.2|
|Transport|||||33.2||26.3|
|Post and telecommunications|||||19.7||14.5|
|Financial intermediation|||||1.7||4.6|
|Real estate services|||||||1.4|
|Renting, IT-services, res. and dev.|||||10.7||21.1|
|Other business services|||||2.1||8.7|
|Recreational,cultural and sportingact.|||||||2.1|
||100.0|100.0|100.0|100.0|16.7|100.0|33.0|

Source: Own calculations based on Moldova MAMS SAM 2008.

7
- Lee, Minsoo, Xuehui Han, Pilipinas F. Quising, and Mai Lin Villaruel. 2020. “Hazard analysis on Public-Private Partnership Projects in Developing Asia.” _Journal of Infrastructure, Policy and Development_ 4 (1): 50–72.

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33
# **2.2 Focusing on DS Registration**

Figure 1 shows that registration at all levels increases markedly with the number of employees. While most firms with 1 or 2 workers are entirely unregistered, a majority of firms with 10 or more workers are registered with each of the relevant agencies. Our goal is to understand the demand for formality among firms in this size range. We focus on one particular dimension of formalizing, registering with the DS. This DS registration most closely corresponds to the concept of formalizing underlying much of the discussion of formalization in the literature, because it establishes a legal and tax presence, and is the prerequisite for selling to the government and other firms which require registration, as well as for applying for most bank loans.

# **3. The Experiment**

# **3.1 Selection of the Sample**

We chose to carry out our study in the two largest cities in Sri Lanka—Colombo and Kandy. Since the goal of our intervention is to gauge the demand for formality among informal firms, we needed to construct a sample of firms not registered with the DS at the time of the baseline survey. There was no existing sample frame which could be used for this, so we carried out our own screening exercise.<sup>5</sup> We randomly selected 5 Divisional Secretariat Divisions in each city. In December 2008, we had research assistants go door-to-door in these areas to screen firms, with the goal of surveying approximately 50 unregistered firms in each selected DS. Firms were selected for the baseline survey if they were not registered with the DS, were not in seasonal agriculture or fisheries, had 1 to 14 paid employees, and had an owner aged 20 to 55 who worked at least 20 hours in the enterprise each week. Few firms of this size are owned by women, and so we chose to restrict the sample to male owners and to businesses jointly owned by a husband and wife. A sixth DS Division was added in Kandy due to difficulty finding enough informal firms which satisfied these criteria – there are lots of informal firms without paid workers, but fewer with paid workers.

An obvious concern in attempting to construct a sample of the unregistered is whether firms will reveal that they are not registered (and hence that they are breaking the law). We used university students to carry out the surveying, and they presented firm owners with a letter on University of Peradeniya letterhead emphasizing that this was a research study. In an environment where informality is commonplace among smaller firms we believe that most unregistered firms did tell the truth. Indeed, as we will discuss in more detail, our sample contains a non-trivial fraction of firms which were ―quasi-

> 5 We decided against using the sample from our previous survey, the SLLSE, since logistically it was infeasible for us to implement the experiment island-wide, and there were too few firms in the SLLSE that were in Kandy and Colombo and which were not part of a separate experiment.

7
**Figure 1: Average Loan Size (inflation adjusted) from different sources**

<!-- Start of picture text --> Figure 1: Average Loan Size (taka)<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>MFI Informal Bank<br>2000 2007<br><!-- End of picture text -->

**Figure 2: Sources of Rural Credit**

<!-- Start of picture text --> 2000<br>MFI Informal bank<br>27%<br>47%<br>26%<br><!-- End of picture text --> <!-- Start of picture text --> 2007<br>MFI Informal bank<br>15%<br>13%<br>72%<br><!-- End of picture text --> 36
The theoretical model also predicts that the quality of cane purchased by a private factory that uses ex post permits is higher than that of a factory that uses ex ante contracts. To test this hypothesis, the third set of empirics estimates cane quality including a private factory ownership dummy for the crop years 1992–3 through 2006–7. The base factory type is cooperative. The theoretical model predicts that the coefficient on private factory ownership is positive. Regional and year fixed effects control for variations across geographic areas and crop years.

# _Estimation Results_

Based on the results of Breusch-Pagan tests, homoskedasticity of the error terms could not be rejected for any of the empirical model specifications, so all models were estimated using ordinary least squares. In the first two sets of empirical models (tables 4 and 5), which used survey data, the standard errors are cluster-corrected at the village level to account for correlation among respondents from a single village. All of the coefficients have the anticipated signs with one exception, and most of them are significant at conventional levels.

The estimation results for the first set of regressions that include factory area dummies are reported in table 4. Models 1 and 2 use a single dummy variable for a farmer being in a private factory area, and models 3 and 4 use dummies for private factory reserve and non-reserve areas. Models 1 and 3 incorporate regional fixed effects, and models 2 and 4 use district fixed effects (coefficients for regional and district fixed effects are omitted for brevity).

Overall, the results support the predictions of the theoretical model. Including district fixed effects rather than regional fixed effects tends to reduce the magnitude and significance level of the cost increase in private areas. Models 1 and 2 suggest that the unit cost of cane cultivation is significantly higher in private factory areas: 36.75 Rs/metric ton or approximately 20