rafmacalaba/datause-encoder-data huggingface dataset:
due to any reason. We describe this method and our implementation in more detail in Appendix 1, Part B.

# **4.2. Data Description and Construction of Variables**

We bring together various data sources for the empirical analysis. Our main data set is the Rural-Urban Migration in China (RUMiC) survey, which consists of three independent modules: a migrant household module, an urban household module, and a rural household module. It collects rich data on the socioeconomic characteristics of rural-urban migrants and their left-behind children, including information on co-residence status, schooling status, and household expenditures on various types of school fees for their children. We restrict our sample to households that have at least one school-age child (age 6-16) as we focus on the impacts of school fees on child migration. This leaves us with a working sample of 1,349 households. We provide a more detailed description of this survey and other data sources in Appendix 2.

While we analyze two rounds of the RUMiC data set, we focus in this paper on the 2008 (first) round for two main reasons. First, as discussed earlier, local governments typically relied on raising revenues through school fees before 2008 to compensate for the lack of funding transfer from the central government to pay for the education of migrant children. The abolition of school fees in 2008 resulted in local governments being no longer able to collect revenues this way, at least in theory. Thus there could be (almost) no correlation between schools fees and budget deficits, which violates the relevance condition of our IV for 2009. Second, the linkage between shocks to public education spending and school fees is also likely to be weakened during an economic crisis. This is because local governments would typically be constrained by competing spending priorities in such times, thus would unlikely have total discretion over their education budget. For example, they might not be able to spend the surplus from the education budget generated in the previous year on education in the following year, 20
**Figure 1: Coefficients of variation for Level 1 variables from the different observation instruments**

<!-- Start of picture text --> 0.00 0.25 0.50 0.75 1.00 1.25 1.50<br>SDI<br>Share of time teaching<br>Share of time teaching and learning<br>Good teacher demeanor<br>Good pedagogical practice<br>Classroom environment<br>Materials and infrastructure<br>Use of materials<br>Stallings<br>Share of time teaching<br>Share of time teaching and learning<br>Classroom environment<br>Materials and infrastructure<br>CLASS<br>Emotional support<br>Classroom Organisation<br>Instructional Support<br>Overall CLASS score<br>Student engagement<br>Teach<br>Share of time teaching<br>Share of time teaching and learning<br>Classroom culture<br>Instruction<br>Socioemotional skills<br>Overall Teach score<br><!-- End of picture text --> All Rural Urban and Semi-Urban 35
# **A Tale of Two Species:**

# **Revisiting the Effect of Registration Reform on Informal Business Owners in Mexico**

Miriam Bruhn<sup>1</sup> JEL codes: O12, O17, L26 Keywords: Informal sector, business registration reform, entrepreneurship Sector Board: Financial Sector (FSE)

> 1 Development Research Group, The World Bank. mbruhn@worldbank.org. I thank David McKenzie for valuable comments.
<!-- Start of picture text --> 800 800<br>600 600<br>fchld fadol fadlt<br>400 400 mchld madol madlt<br>head’s assets (in 000 taka), wife’s assets=0 head’s assets (in 000 taka), wife’s assets=0<br>200 200<br>0 0<br>60 60<br>fchld fadol fadlt<br>40 40<br>mchld madol madlt<br>20 20<br>wife’s assets (in 000 taka), head’s assets=0 wife’s assets (in 000 taka), head’s assets=0<br>0 0<br>1.4 1.2 1 .8 .6 1 .8 .6 .4 .2<br>calorie adequacy ratio calcium adequacy ratio<br>1.4 1.2 1 .8 .6 1 .8 .6 .4 .2<br>calorie adequacy ratio calcium adequacy ratio<br><!-- End of picture text --> Figure 3: Adequacy Ratio & Spouses’ Assets, Lowess Fit, Bandwidth=0.8 Note: mchld, male child, fchld, female child mdol, male adolescent, fadol, female adolescent, madlt, male adult, fadlt, female adult 35
actual RER on the ratio of net foreign assets to GDP, productivity differentials and terms of trade. The actual RER is proxied by the real effective exchange rate ( _REER_ ), as defined by the domestic price index of country _i_ vis-à-vis the price index of its main trading partners multiplied by the nominal exchange rate of country _i_ , where _eit_ is the nominal exchange rate of country _i_ ( _vis-à-vis_ the US dollar) in period _t_ , _Pit_ is the consumer price index of country _i_ in period _t_ , _dkt_ is the nominal exchange rate of the _k-_ th trading partner of country _k_ in period _t_ (in units of local currency _vis-àvis_ the US dollar), and _Pkt_ 0 is the wholesale price index of the _k-_ th trading partners in period _t_ . The _nominal exchange rate_ , _e_ , is proxied by the average price of the dollar in local currency (line _rf_ of the International Monetary Fund's International Financial Statistics (IFS)). Domestic and foreign prices, _P_ , are proxied by the consumer price index of the country (line _64_ of IFS). According to this definition, an increase in _q_ implies a real appreciation of the domestic currency.

_NFA_ data is drawn from Lane and Milesi-Ferretti (2001, 2007). This database comprises a set of foreign asset and liability stocks for a large group of industrial and developing countries spanning over the 1970-2005 period. The construction of the data is thoroughly documented in Lane and Milesi-Ferretti (2001, 2007), and the NFA position of country _i_ in year _t_ is defined as: where the letters _A_ and _L_ denote assets and liabilities, respectively. Thus, the net foreign asset position is the sum of net holdings of direct foreign investment, _FDIA_ - _FDIL_ , plus net holdings of portfolio equity assets, _EQYA_ - _EQYL_ , and the net position in non-equity related assets ( _i.e._ ''loan assets''). In turn, the net position in non-equity related assets consists of international reserves, _RA_ , and the net loan position, _LA_ - _LL_ .

For _productivity differentials_ we use labor productivity differentials weighted by trade patterns. Then, we develop the data on labor productivity of traded and non8
value added – and statistically significant. In other words, once the initial productivity level differences caused by self-selection of weaker firms into CIMO were controlled for, participation in the program was associated with a positive impact on performance.

Tables 5 and 6 report the corresponding production functions results using the alternative measures of output, but estimated for the 1993-95 period (first panel), and for the full five-year 1991-95 period when a sub-sample of treatment and control group firms from the first study were added (second panel). Like the previous results, CIMO had a negative impact on the productivity of participants when the production function was estimated **in levels** . The estimated θ parameter was not only negative and large (-53 to -60 percent), but statistically very significant as well. **In first-differences** , however, estimates of the θ parameter became much smaller (-5 percent) or not statistically different from zero, depending upon the value added measure used. When the data were augmented to include some firms from the first study (the 1991-95 panel), the estimated θ parameters of CIMO were not statistically different from zero. It is unclear if this absence of an impact is a reflection on the program’s lack of effectiveness, or the selection of an inappropriate control group for the CIMO firms.

**Table 5. Production Function Estimates for 1993-95 and 1991-95 Dependent variable: Log(Value Added)**

|**Variable**|_L_<br>Coeffici|_evels_<br>ent|**1993-19**<br> <br>z-statistic|**95**<br>_First d_<br> Coeffici|_ifferences_<br>ent<br>z-<br>statistic|_L_<br> Coefficie|**1991-**<br>_evels_<br>nt z-statistic|**1995**<br>_First_<br>Coeffici|_differences_<br>entz-statistic|
|---|---|---|---|---|---|---|---|---|---|
|Log(Capital assets)|0.184|*|13.8|0.069|*<br>3.5|0.187|*<br>16.7|0.071|*<br>4.5|
|Log (Employment)|0.875|*|36.2|0.504|*<br>9.3|0.854|*<br>40.5|0.466|*<br>10.9|
|CIMO Variable|-0.527|*|-9.8|-0.051|<br>-1.9|-0.485|*<br>-9.8|-0.006|<br>-0.3|
|Constant|9.368|*|49.6|0.298|*<br>4.9|8.918|*<br>53.0|0.311|*<br>6.5|
|1995 dummy|-0.151|*|-8.3|-0.379|*<br>-13.7|-0.065|*<br>-3.5|-0.386|*<br>-16.3|
|No. of observations|362|1||2394||4805||3424||

_Note:_ Industry dummy variables included but not reported.

* Denotes significance at 5 % level.

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# **Table A8. Impact of living in the GMA and, once in the Lower Zambezi Park System -** **<u>GMA, of participating in the VAG/CRB on the non poor households</u>**

||Lower Zambezi|Treatment Regres<br>capita co|sion mod<br>nsumptio|els for the impac<br>nexpenditure of<br>|t on per<br>|
|---|---|---|---|---|---|
|||||Participating|in|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|<br>VAG/CRB|<br><sup>_b_</sup>|
|||(1)||(2)||
|Hage|Age of the household head|-0.004<br>||-0.002<br>||
|||(0.004)||(0.008)||
|fhhh|Female-headed dummy|-0.065||0.103||
|||(0.097)||(0.145)||
|maxedu|Education of the most educated member|0.021|*|0.000||
||(years)|(0.012)||(0.010)||
|c0to14|Number of children less than 15 years old|-0.128<br>(0.026)|***|-0.149<br>(0.036)|***|
|f15to60|Number of female members 15-60 years|-0.105<br>(0.033)|***|-0.056<br>(0.055)||
|m15to60|Number of male members 15-60 years|-0.083<br>(0.033)|**|-0.102<br>(0.036)|***|
|m61plus|Number of members older than 60 years|-0.171<br>(0.060)|***|-0.138<br>(0.163)||
|kroad|Distance to the nearest main road in km|-0.009<br>(0.004)|**|0.000<br>(0.010)||
|kbsch|Distance to the nearest basic school in km|0.003<br>(0.004)||0.007<br>(0.003)|***|
|kheal|Distance to the nearest health centre in km|-0.003<br>(0.002)||-0.004<br>(0.002)|*|
|vacast|Value of consumer durable assets in ZMK|0.031<br>(0.026)||0.211<br>(0.039)|***|
|dcoop|Participation in cooperatives dummy<br>|0.327<br>(0.066)|***|0.239<br>(0.101)|**|
|gma or|Participation dummy variable<sup>_a,b_</sup>|-0.362||0.494||
|dvcrb||(0.239)||(0.744)||
|cons|Intercept|14.238|***|13.897|***|
|||(0.223)||(0.239)||
|Number of|observations|540||230||
|Log-likelih|ood value|-765||-229||
|Goodness|of fit Chi-Square|.||.||
|Rho||0.26||-0.37||

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

23
In Lima, retail trade, personal services, and manufacturing account for more thati 80 percent of the businesses and almost 85 percenc of the family workers (Table 1). The single most important activity is retail trade. The pattern is the same in the rest of Peru. In other urban areas the proportions are 86 percent (retail trade, 52 percent) and 90 percent. In rural areas 80 percent of entrepreneurial families and workers are in retail trade (43 percent) or manufacturing, particularly textiles. These are typical informal sector endeavors in most developing countries. What distinguishes Peru from other countries is the unusually large proportion of women and households in these activities.

# 2.2 The Role of Women

The Peru Living Standards Survey shows that women dominate the informal economy. Schafgans (1989) notes t'at women make up about 45 percent of the labor force. The vast majority work in family-owned firms and farms. Table 2 shows that 82 percent of the 5,952 employed women worked in family-owned nonfarm businesses (28 percent) or on family farms (54 percent). The remaining 18 percent were wage earners, mostly in urban areas. In Lima 45 percent of employed women were wage earners in contrast to 19 percent in other urban areas. In rural areas only 5 percent of employed women worked as salaried employees, usually as schoolteachers and clerks in the public sector. There is practically no labor market for women in rural areas. In Lima, 38 percent of the women were employed in nonfarm businesses and 17 percent in agricultural activities; in other urban areas the corresponding proportions were 44 and 37 percent, and in rural areas 11 and 84 percent.

8
r h r h 1 2 r r (1) rr rrh YY = XX ββ + DD ββ + νν + εε Where: r h represents the outcome of interest - log per capita consumption of household h or height for age Z score, within district d and region r. This value of consumption has been spatially deflated using YY estimated local prices. r h represents the vector of household-level geospatial variables selected for the model. XX represents a set of regional dummy variables. DDrrr is a conditional random effect, conditioned on the sample data. It is specified at the target area level a, and assumed to be normally distributed. νν r is a classical household-specific error term, assumed to be normally distributed. rrh εε The povmap package in R is used to perform the estimation (Edochie et al, 2023). Povmap is an extension of the EMDI package (Kreutzmann et al., 2019) that is publicly available on the Comprehensive R Archive Network (CRAN). To calculate point estimates, the package first estimates a weighted linear mixed model to calculate estimates of the coefficient vectors 1and 2 and the variance of and r . Survey weights are incorporated into model estimation following ββ ββ Guadarrama et al (2018), as described in Skarke and Kreutzmann (2021). Next, survey weights are rr rrh νν εε normalized such that the sum in each district is equal to the unweighted number of observations, following “method 2” in Pfefferman et al (1998). The estimated model parameters include the conditional distribution of the area effect , conditional on sample per capita consumption r h. The procedure takes 100 draws of and ννrrr h and adds them to r 1 2 to generate simulated YY values of the outcome for the full set of households. For each of the 100 simulations, the implied rr rr νν εε XX ββ̂ + XX ββ̂ poverty rate is calculated across all households contained within each district, using the household size variable in the census as aggregation weights when averaging across households. The poverty line used is 1,570,739, which translates to about 96 cents per day at current market exchange rates. This was selected such that the overall poverty rate in the survey data is consistent with the reported 75.2% national rate. For stunting, the relevant threshold is height-for-age Z score of -2. The implied poverty or stunting rates for each district are then averaged across the 100 simulations. Because the random area effect is conditioned on the sample data, the estimates place a larger weight on the model predictions from the model when they are precise relative to the direct survey estimates, and conversely a larger weight on the survey estimates when they are more precise relative to the model predictions.

The estimates obtained from the simulation were then benchmarked to match the survey estimates at the regional level published in World Bank (2023), to arrive at poverty estimates for each district. The benchmarking procedure used the “benchmark complement” option in the povmap package. This 8 | P a g e
# **Table 1:  Summary Statistics**

||||**Std.**|||
|---|---|---|---|---|---|
|**Variable**|**Obs**|**Mean **|**dev.**|**Min**|**Max **|
|Overall Management Score|9,015|0.515|0.208|0|1|
|Share of family management (0 to 1)|9,015|0.407|0.450|0|1|
|All management is family Y/N|9,015|0.319|0.466|0|1|
|Share of same family ownership (0 to 1)|9,015|0.527|0.464|0|1|
|Owner, Manager or Board Political Connection Y/N|9,015|0.076|0.264|0|1|
|Female top manager Y/N|9,015|0.148|0.355|0|1|
|Firm Formally Registered when Started Operations|9,015|0.959|0.199|0|1|
|Top manager experience in sector (years)|9,015|21.296|11.364|1|60|
|Log of age of firm|9,015|2.851|0.686|0|5.088|
|Log of size|9,015|3.890|0.917|0|11.067|
|Direct exports 10% or more of sales Y/N|9,015|0.264|0.441|0|1|
|Foreign ownership Y/N|9,015|0.112|0.316|0|1|
|Establishment has checking or savings account Y/N|9,015|0.974|0.160|0|1|
|Establishment has a line of credit or loan Y/N|9,015|0.476|0.499|0|1|
|ISO Certification Ownership Y/N|9,015|0.327|0.469|0|1|
|Firm identifying labor regulations as a major or severe constraint Y/N|9,015|0.094|0.292|0|1|
|Manufacturing Sector Y/N|9,015|0.393|0.488|0|1|
|Firm expected to make payment to get things done Y/N|8,493|0.111|0.314|0|1|
|No or One competitor (Y/N) (domestic)|6,856|0.062|0.241|0|1|
|Introduced a process innovation Y/N|8,954|0.230|0.421|0|1|
|MG1 Problem resolution|9,015|0.707|0.278|0|1|
|MG2 Number of performance indicators monitored|9,015|0.428|0.342|0|1|
|MG3 Level of ease or difficulty to achieve production or service provision<br>targets|9,015|0.535|0.377|0|1|
|MG4 Knowledge of production or service provision targets|9,015|0.353|0.385|0|1|
|MG5 Basis of bonuses|9,015|0.380|0.398|0|1|
|MG6 Length of focus of production targets|9,015|0.573|0.404|0|1|
|MG7 Promotion of non-mangers|9,015|0.729|0.399|0|1|
|MG8Dismissal|9,015|0.412|0.459|0|1|

15
state, sector (census versus sample) and industry, a joint return can be furnished. In the population of firms, fewer than 2% of the observations report more than one factory, and we will use the term The key variables we construct from the ASI data are output, material input, labor, and capital.<sup>4</sup> We drop closed firms from the dataset, and we include only firms with positive values of the key variables. To address a few extreme outliers, we also trim the top 0.5% of output and material input values.

We deflate output using industry-specific wholesale price indices (WPI) from the Government of India’s Handbook of Industrial Statistics. Similarly, our material input measures are deflated by constructing deflators using the WPI along with India’s 1993-94 Input-Output Transactions Table. Labor is measured as the total number of people employed by the firm, and capital is measured by deflating the book value of capital by the WPI for machinery.<sup>5</sup> Summary statistics for the population are presented in Table 3. Only open firms that have positive values of our key variables are included. Sampling weights are applied to the summary statistics in the first column, so the results are representative of the overall organized sector. The second column shows results for the firms that were sampled, without applying sampling weights. Since larger firms are surveyed more often than smaller firms, the mean and median values of output, capital, material inputs, and labor are much larger in the sampled population rather than the estimated population.

# **3.2 Creating a Panel**

The ASI data provide unique firm identifiers beginning in 1998. However, it has not previously been possible to track firms prior to 1998, and thus to follow them during the most significant period of reforms. We overcome this challenge by matching individual firms from one year of

> 4Output includes the ex-factory value of products, the increase in the stock of semi-finished goods, and the value of own construction; material input includes material and fuel.

> 5In Appendix A, we show that our results are robust to measuring capital using the perpetual inventory method described in Harrison (1994), as modified by Sivadasan (2009).

10
24 months, down from the 36 months allowed until 2008 (Table 2). Although Sweden has no statutory minimum wage (which is reflected by recording a zero for this aspect in the calculation of the index), minimum wages are negotiated as part of the collective agreements at the sectoral level and thus differ across sectors.<sup>7</sup> Figure 5: Sweden’s hiring regulations are less flexible than the OECD average but more flexible than the EU15 and Nordic averages <!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>2008 2011<br>Hiring regulations and minimum wage index Austria Denmark United States Belgium Canada Ireland Japan United Kingdom G7 average Italy OECD average Germany Netherlands Sweden EU15 average Nordic average Greece Iceland Spain Finland Norway France Luxembourg Portugal<br><!-- End of picture text --> Source: Gwartney et al. (2013).

Note: The Economic Freedom of the World hiring regulations and minimum wage index is calculated as the average of _Doing Business_ data on the three indicators shown in table 2. The index ranges from 1 to 10, higher values indicate more flexible regulations. Countries and country groups are in descending order by the index value in 2011. The Nordic average excludes Sweden.

Among the 20 countries in the EU15, G7 and Nordic region, nine have no limit on the maximum duration of fixed-term contracts, including Denmark, Japan, the United Kingdom and the United States—and therefore have among the most flexible hiring regulations. Among the other economies the maximum length of fixed-term contracts ranges from 12 months in Spain to 60 months in Finland. Like Sweden, three other economies—Germany, Iceland and Luxembourg—set a maximum of 24 months. These figures have remained the same for the majority of the economies since 2008. The exceptions are a few countries that undertook reforms to tighten regulations on

> 7 Some examples of sector-level minimum wages are SKr 15,387 for the steel and metal sector, SKr 16,630 for the retail sector and SKr 17,481 for the hotel and restaurant sector (Kullander, 2012). Since the minimum wages set by collective agreements in Sweden do not automatically extend to other sectors, Doing Business takes the labor law as the basis for determining the minimum wage.

12