rafmacalaba/datause-encoder-data huggingface dataset:
nonfinancial private sector to total domestic credit (excluding credit to money banks); and _BANK,_ the ratio of deposit money bank domestic assets to deposit money bank domestic assets plus central bank domestic assets.<sup>7</sup> While all of these measures are positively associated with financial development, each one captures a slightly different aspect of it. _LLY_ is a measure of the overall size of the financial system, while _BANK_ measures the relative importance of banks within the financial system. _PRIVY_ and _PRIVATE_ measure the extent to which financial services are provided to the private sector, with _PRIVATE_ being a more direct measure of how credit is allocated to the private vs. the public sector. Thus, _LLY_ and _PRIVY_ are more general measures of overall development, while _PRIVATE_ and _BANK_ attempt to gauge the nature of the development.

We use annual data from 70 countries for the period 1956 to 1998. The majority of our data is extracted from the IMF's International Financial Statistics. The exceptions are the average level of real GDP, real consumption and real investment per capita _(MEAN),_ which are calculated from the Penn World Tables 5.6, and the political regime and stability variables _(POLITY),_ which come from the 1996 version of the POLITY III Dataset.<sup>8</sup> In order to calculate our volatility measure, the standard deviation of the growth rate of GDP and its components, we need to collapse several years of data into one time period. An ideal characterization of the amplitude of the business cycle in each country and each time period would require a large number of annual observations to capture both the upturns and downturns of the business cycle. However, we face a tradeoff: as we increase the number of years in each period, perhaps increasing the accuracy with which we characterize volatility, we reduce the number of periods we can use in our fixed effects estimation, reducing its efficiency. Nonetheless, we are able to create 4 time periods while still measuring volatility over a relatively long period of time7See King and Levine (1993a) for a detailed description of the calculation of these measures.

8The POLITY III dataset includes ten different variables which aim to measure the level of institutional democracy and autocracy across countries over time. The results presented here use the variable _XRCOMP,_ the extent to which executives are chosen through competitive elections. Our results are robust to using alternative measures from this dataset: The _POLIY_ variable was never statistically significant in any of the estimations possibly because, for many countries, these variables do not change much over time. Therefore, it is difficult to identify their impact in a fixed-effects estimation. The complete data can be accessed at http://garnetl.acns.fsu.edu/ phensel/intlpoli.html.

7
is no reason to expect that these variables that happen to be highly correlated with each other also are those that have the largest effects on the outcome of interest.<sup>5</sup> A different approach to solving the degrees‐of‐freedom problem would be to simply choose a subset of the many disaggregated indicators that seem most plausible _a priori_ , and try to estimate a regression of the outcome of interest on this smaller set of preselected variables. The advantage of this approach is that it tries to more precisely estimate the effect of a few of the disaggregated indicators on the outcome of interest by imposing the restriction that the remaining indicators have no effect. However, the problem of course is that this will give valid estimates of the partial effects of the included variables only if they are orthogonal to all of the other disaggregated indicators that matter for outcomes but are not included in the regression. This is the standard problem of omitted variable bias. Moreover, this approach of preselecting a subset of regressors throws open the door to specification searching or data mining for a subset of variables that happens to confirm the researcher’s or policymaker’s priors.

To avoid these problems of specification searching, we instead use Bayesian Model Averaging (BMA) to systematically document the partial effects of many disaggregated indicators on outcomes of interest. We do this using disaggregated indicators from Global Integrity and Doing Business, in three steps. First, we identify a set of potential outcome variables for each of these two sets of disaggregated indicators. These variables capture outcomes we think a policymaker might reasonably want to influence by reforming the areas captured by these indicators. For reasons we elaborate below, we choose seven closely‐related subjective measures of corruption as outcome variables linked to the Global Integrity Index, and seven closely‐related subjective measures of the quality of the regulatory environment as outcome variables for Doing Business. Second, for each combination of outcome variable and disaggregated indicator set, we use BMA to obtain estimates of the partial effects of each of the disaggregated indicators on the corresponding outcome of interest. Third, once we have identified the partial effects of the disaggregated variables on each outcome, we compare these results across different outcome variables and across different levels of disaggregation.

The good news is that we find BMA to be an effective tool for identifying a relatively small number of disaggregated indicators that display strong partial correlations with a given outcome of

> 5 Lubotsky and Wittenberg (2006) elaborate on this point, showing that the use of multiple proxies as explanatory variables in a linear regression generally dominates the use of a single summary of those proxies.

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non-agriculture. Thus, although these figures are suggestive about the pathways which may be the most important, they are not definitive--all that is shown is the net position rather than the actual flows themselves. It is impossible to observe directly the number of workers shifting sector with existing data from SUSENAS (although panel data will be available starting from 2004.

Furthermore, the lack of panel data does not allow us to identify the key characteristics that allowed individuals to increase their welfare by shifting sectors. In other words, the aggregate evidence tells us little about the micro-economic determinants, most notably education, location, etc., of sectoral employment shifts and implied welfare gains. For more definitive results on these topics a panel of data is needed, which is explored below.

# DATA

To explore the microeconomic determinants of exiting poverty requires a panel data set over a reasonable length of time. Fortunately, the Indonesia Family Life Survey (IFLS) was first conducted in 1993 (IFLS1) and then again in 1997 (IFLS2), 1998 (IFLS2+) and finally in 2000 (IFLS3). For IFLS1, 7,224 households were interviewed with data collected on 22,000 individuals in those households. This sample is representative of about 83% of Indonesian population in 13 provinces. In 1993, IFLS did a face-to-face interview with the household head, the spouse, a sample of their children and a sample of other household members. In 1997, 94% of IFLS1 households were reinterviewed consisting of all 1993 “main” respondents and all 1993 household members born before 1967. In 2000, IFLS managed to re-contact 95.3% of IFLS1 households interviewed: all 1993 “main” respondents, all 1993 household members born before 1967 and a sample of other 1993 household members. The sample size in 2000 was 10,400 households and 39,000 individuals.

The IFLS collects data at two levels. First, information is collected at the individual and household levels. This information consists of data on: consumption, income and assets; education, migration, and labor market outcomes; marriage, fertility and contraceptive use; health status, use of health care and health insurance; relationships among co resident and non co resident family members; processes underlying household 14
Table 2: Earnings

||(1)<br>Earnings|(2)<br>Earnings|(3)<br>Earnings|(4)<br>Earnings|
|---|---|---|---|---|
|Treated|17521.1<sup>_∗∗_</sup>|18739.2<sup>_∗∗_</sup>|19431.8<sup>_∗∗_</sup>|11699.1<sup>_∗_</sup>|
||(8017)|(8157)|(7669)|(6495)|
|Constant|77228.4<sup>_∗∗∗_</sup>|44791.5<sup>_∗∗_</sup>|22968.0|12632.6|
||(5760)|(22241)|(21895)|(16819)|
|Standardized coeff.|0.227|0.243|0.251|0.180|
|Mean outcome control|77228.4|77228.4|77228.4|113351.4|
|Baseline controls||✓|✓|✓|
|Baseline outcome|||✓|✓|
|N|368|368|368|263|
|R-sq|0.013|0.045|0.150|0.454|

_Notes_ : Standard errors in parentheses, *, **, *** denote significance at 10%, 5% and 1% levels. _Baseline controls_ : sex, age, completed middle school, number of beds in household, number of children in household, number of adults in household, total (formal and informal) household debt, total household savings, household income. Dependent variable in all specifications is earnings reported by workers at end-line. Unemployed individuals and individuals with zero earnings are excluded from the sample in column (4).

longer-term impacts on the labor market trajectory of workers.

# **3.2. Mechanisms**

We now turn to potential mechanisms that could explain the improvements in the labor market outcomes that we observe. We consider three possible mechanisms. First, the training could have directly affected conscientiousness-related skills and this in turn affected labor market outcomes by making workers capable of being more responsible and hard-working and thereby more productive. One part of the initial training session was dedicated to increasing motivation to change conscientiousness and discussing possible rewards such as getting a raise or helping the company complete the work better or faster. Second, the training could have affected other qualities, such as agreeableness or openness, and these led to participants keeping their jobs. In turn, longer tenure allowed workers to acquire conscientiousness skills and these impacted their earnings. Third, it is possible that the firm kept track of who was trained and on the basis of this, retained workers and paid them higher wages. We discuss each of these potential channels in turn.

First, to measure the direct effect of the intervention on conscientiousness, we measured conscientiousness-related skills before the training at baseline and after the training at mid-line using a 32-item questionnaire measuring skill-based versions of four facets of conscientiousness: responsibility, industriousness, organization and punctuality (Soto et al., 11
being self-evident, the use of data on time allocation either to model economic behavior or to understand the dynamics of economic change over time has only recently begun to attract the interest of economists”.

# **4.3 Heterogeneity**

The most significant feature of the preceding model is that the decisions of family members depend on household specific domestic prices, as well as on market wage rates and prices. Thus, variation in domestic prices can offer an explanation for heterogeneity in female labor supply and domestic work observed across households with the same wage rates, non-labor incomes and demographic characteristics. It can also provide an explanation for heterogeneity in consumption and saving decisions observed to be associated with female labor supply. The standard household model does not provide an adequate framework for analyzing the policy implications of heterogeneity in these endogenous variables. By default, the model attributes such heterogeneity to differences in preferences (thereby ruling out welfare comparisons) or, quite implausibly, to measurement and optimization errors in empirical work.

By specifying household decisions as functions of domestic prices, the approach also highlights the importance of recognizing that domestic inputs and outputs, in common with those of market production, are inherently observable. Both domestic and market activities can involve varying degrees of on-the-job consumption but this does not provide an argument for abandoning the collection of data on either. Thus, in the absence of convincing evidence to the contrary, there is no apriori reason for believing that domestic production parameters are inherently more difficult to identify than those of market production and that no attempt should be made to identify them separately from preference parameters.

Since information on domestic output is typically missing in time use datasets, a household production model can be estimated only on the basis of some essentially arbitrary assumption about productivity and therefore about the price of domestic output.

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The change in the industrial employment share remains a statistically significant determinant of aggregate employment growth controlling for all of these additional factors (Table 4).

**Table 4:** Covariates of the change in employment and labor force to population ratio, 1990-99 to 2010-18

||_Dependent _|_variable:_|
|---|---|---|
||Employment|Labor force|
||(1)|(2)|
|Change in industry employment share|_−_0.148<sup>_∗_</sup>|_−_0.227<sup>_∗∗∗_</sup>|
|Change in share over 50 in working-age population|-0.03|-0.159|
|Change in female employment share|0.458<sup>_∗∗∗_</sup>|0.517<sup>_∗∗∗_</sup>|
|Change in log GDP per capita|0.835|0.311|
|Log GDP per capita|0.271|0.944<sup>_∗∗∗_</sup>|
|Constant|-3.70<sup>_∗∗∗_</sup>|-8.489<sup>_∗∗_</sup>|
|Observations|100|100|
|Adjusted R<sup>2</sup>|0.30|0.51|
|_Note:_|_∗_p_<_0.1; _∗∗_p_<_0|.05; _∗∗∗_p_<_0.01|

Note: Changes are calculated as the difference between the period averages for each economy during 1990-99 and 2010-18

# **4.3 Theoretical framework: industrial employment share and employment growth**

This section outlines a static theoretical model that explains the empirical findings concerning technology shocks, employment growth, and the industrial employment share. It uses a simplified version of the framework of Acemoglu and Restrepo (2018, 2020), and closely follows the notation of Acemoglu and Restrepo (2017), which assesses the overall impact of automation on aggregate job creation. In their framework, the job-displacing effect from automation can in part be offset by two factors: higher relative demand for the products in the affected industry as goods are produced 24
Figure 4.4: TANZANIA, comparison of the ICA method and other imputation mechanisms for PF variables <!-- Start of picture text --> I. Kernel 1 estimates of output and input densities<br><!-- End of picture text --> <!-- Start of picture text --> A. Sales (log) B. Materials (log)<br>5 10 15 20 5 10 15 20<br>log-Sales log-Materials<br>Complete case ICA method Complete case ICA method<br>Random ICA EM alg. [1] Random ICA EM alg. [1]<br>EM alg. [2] EM alg [3] EM alg. [2] EM alg [3]<br>C. Capital stock (log) D. Employment (log)<br>5 10 15 20 8 10 12 14 16<br>log-Capital log-Employment<br>Complete case ICA method Complete case ICA method<br>Random ICA EM alg. [1] Random ICA EM alg. [1]<br>EM alg. [2] EM alg [3] EM alg. [2] EM alg [3]<br>.2<br>.2<br>.1 5 .1 5<br>.1 .1<br>D e n sity o f S ale s<br>D en sity o f M a te ria ls<br>.05 .0 5<br>0 0<br>.2 .4<br>.15 .3<br>.1 .2<br>Density of Capital<br>.05 D ensity of E m ploym e nt.1<br>0 0<br><!-- End of picture text --> Notes: 1 Epanechnikov kernel. Each point estimated within a range of 300 values. Source: Authors’ estimations with ICSs data.

58
outcome data for all lags and leads for all municipalities. For instance, the data set includes information on Quarter+5 only for the eight municipalities that implemented the reform in 2003 or earlier. For Quarter+6 the data cover only six municipalities, and for Quarter+7 and higher only two municipalities. Similarly, for the lags, the data cover all municipalities only for Quarter�8 and higher. For this reason, I limit the regression above to observations that fall between Quarter�8 and Quarter+6. Quarter�8 is also the omitted quarter in the regression, to which all other quarters are being compared.

Figure 7 shows the coe¢ cients on the lag and lead dummies for the regression with the lowrisk registered business owner dummy as the outcome variable. The two lighter colored lines are the 95 percent con...dence bands of the estimates. Between Quarter�8 and Quarter�1, the estimated di¤erences tend to be negative or close to zero. After implementation of the reform, the di¤erences become positive, re‡ecting the increase in registration. The only exception to the positive di¤erences after implementation is Quarter+5. As mentioned above, the data set covers fewer municipalities for this lead quarter than for previous lead quarters. The decrease in registration may thus be driven by missing data rather than by a actually reversal of the e¤ect of the reform. Note that the coe¢ cient is much greater for Quarter+4 than for other post-reform quarters. To make sure that the result in the regression in Column 4 of Table 5 is not only driven by Quarter+4, I rerun the regression without Quarter+4. The coe¢ cient drops slightly from 0.0037 to 0.0034, and it is signi...cant at the 5 percent level.

To check whether the measured increase of 0.37 percentage points in low-risk registered businesses from Table 5 seems plausible, I use the 2004 administrative data from the business registration center in Guadalajara. First, I calculate how many new businesses the increase of 0.37 percentage points implies for Guadalajara. Given that Guadalajara had a population between the ages of 20 and 65 of 879,019 in 2000, and assuming that the number of additional businesses which came into being due to the reform was equal to the average 0.37 percentage points, this implies that 3,252 new businesses in Guadalajara were created due to the reform. Then, I compare 3,252 to the total number of licenses issued by the registration center in Guadalajara. The center issued 16,613 new licenses in 2004. Given that the reform was implemented in Guadalajara in May 2003, 16,613 is a lower-bound for the total number of businesses created since the reform was implemented. The regression results thus suggest that most of the new licenses issued since the reform in Guadalajara (13361 and up) would have been issued in the absence of the reform as part of the normal turnover in businesses, and that 3,252 of them were issued as a result of the reform.

As mentioned in Section 2, for some municipalities, the COFEMER reports statistics on prereform and post-reform registration procedures. These data are available for 27 municipalities 19
the rural areas and other towns, there were large numbers at the bottom of the distribution who fell behind.

# **6.4 Factors Affecting Rural-Urban Differences in Trends in Income and Poverty**

What can we say about these trends in income growth and poverty reduction in terms of the changes in the relative incomes of different socio-economic groups. The following tables indicate the distribution of poverty by urban and rural areas and the various sources of income by poverty groups. It is clear from Table 6.6 that rural poverty has declined between 1987-92 although it is still high, while the contribution of urban poverty to national poverty is on the rise. All of the urban increase has come from the dramatic increase in poverty in Accra. The depth of this poverty in Accra has also dramatically increased by more than three fold. Table 6.7 clearly shows that the poor obtain an increasingly large share of their income from non-farm self employment, a sector which saw substantial growth, resulting in substantial declines in rural poverty.

**Table 6.6.: Distribution of Poor by Urban and Rural Areas**

|Sector|Popula|tion<br>share|Inciden|ce of Poverty|Depth of|Poverty|Contribu<br>National|tion<br>to<br> Poverty|
|---|---|---|---|---|---|---|---|---|
||1987-88.|1991-92|1987-88|1991-92|1987-88|1991-92|1987-88|1991-92|
|Urban|34.2|33.2|27.4|26.5|8.1|6.8|25.3|27.9|
|Accra|8.3|8.2|_8.5_|23.0|1.7|_5.6_|1.9|6.0|
|Other urban|25.9|25.0|33.4|27.7|10.1|7.1|23.4|22.0|
|Rural|65.8|66.8|41.9|33.9|13.8|8.7|74.7|71.9|
|All|100.0|100.0|36.9|31.5|11.9|8.1|100.0|100.0|

_ue: WorldBank 1995a_

**Table 6.7.: Source of Household Income by Poor and Non-poor**

|**Income From**|**Po**|**or**|**Non-**|**Poor**|
|---|---|---|---|---|
||**1987-88**|**1991-92**|**1987-88**|**1991-91**|
|Wage employment|10.0|10.9|19.8|16.8|
|Farm Self-employment|**60.4**|52.9|47.3|42.0|
|Non-farm Self emp|19.5|25.7|21.3|30.2|
|Actual and Imputed rent|2.6|3.2|2.0|2.0|
|Remittances|6.4|6.5|6.7|7.3|
|Other|1.1|0.7|2.8|1.8|

_ource: WorldBank, 1995a_ 44
unclear what exactly one is measuring with the proxy. To achieve a more objective categorization, this paper uses a clustering exercise to find which variables differentiate landowners the most with the aim of finding an aggregate categorization of ownership.

# **3. Methodology**

In this section, we briefly discuss the two main methodologies used in this paper: Multiple Correspondence Analysis (MCA) and Hierarchical Clustering with Principal Components (HCPC).<sup>17</sup>

# **3.1 Multiple Correspondence Analysis Using Land Rights Data**

Factor Analysis methods, such as Principal Component Analysis (PCA) and MCA are dimensionality reduction techniques. These methods can be used in exploratory data analysis to identify important patterns in the data, particularly when there are a large number of correlated variables. MCA is an extension of PCA that is used when all variables are categorical with multiple levels without necessarily an order. The discussion below will focus solely on MCA given the type of data that we use.

Factor analysis methods have a long history of being used in other disciplines (Abdi and Williams, 2010) and in a number of economic studies to reduce the dimensionality of data. In labor economics, a single measure of skill can be derived using PCA when multiple skill variables are available (see Autor et al., 2003; Antonovics and Golan, 2012). In the macro literature, factor analysis is useful since multiple economic measures are highly correlated. Gregory and Head (1999) used dynamic factor analysis to find a single measure of common economic activity among G7 countries. Factor analysis can also be used in modeling asset returns and economic forecasting (for example Ludvigson and Ng, 2009). In the development context, PCA has been used to create a wealth index. The Demographic and Health Survey (DHS) uses PCA on household durables data to create a single wealth index measure separately for urban and rural households. Most studies that use PCA typically assign the first principal component as the index measure that summarizes the data.

For our purposes, MCA helps in better understanding land ownership in two ways. First, we are able to analyze which variables (based on their variance) distinguish different types of right bundles/profiles. Second, since MCA provides a transformation of the data from categorical to continuous variables, we can use the continuous variables for the hierarchical clustering method that will categorize individuals into groups with a similar rights profile. Common clustering methods, such as hierarchical clustering and k-means clustering, rely on continuous variables instead of categorical.

> 17 For a more theoretical discussion on these methodologies, please refer to Abdi and Valentin (2007), Husson et al. (2017) and Pagès (2014).

13