rafmacalaba/datause-encoder-data huggingface dataset:
# Annex 1. Fiscal accounts in Mexico

The National Institute of Statistics and Geography (INEGI) compiles all administrative records on the source and use of all financial resources in municipalities. This information is openly available through the State and Municipal System Databases (SIMBAD) on the INEGI website. Figure A1 presents the maximum degree of disaggregation of public accounts at the municipal level available in SIMBAD.

In terms of the resources available to the municipalities, the analysis aggregated accounts the following way:

- Own income: those that are generated from taxes, contributions to social security, products, and uses

- • Unconditional transfers: federal participation (considered in Branch 28): federal resources assigned to states and municipalities that are not conditioned on their use and destination

- Conditional transfers (mostly through _Ramo_ 33) including FAIS resources. Branch 33, federal contributions respond to demands in: education, health care, basic and educational infrastructure, public safety, and social assistance; the FAIS contribution can be disaggregated from this item

- Other resources, such as unexecuted accounts from other periods and other financial instruments

- In terms of expenditure, accounts were grouped in: current expenditure, transfers and subsidies, investments, debt, and other expenditures.

# **Figure A1.** Fiscal accounts, municipal level

<!-- Start of picture text --> Ramo 33<br>Contributions of federal<br>Municipal own revenues Others resources and states<br>Total revenues<br>Total expenditure<br>Recurrent expenditure Investment expenditure Others<br>)<br>FAIS FAFM<br>Taxes<br>Social security contributions Entitlements Products User fees and charges Aprovechamientos ( Other revenues Financing Initial availability Reallocation<br>Federal contributions<br>Debt<br>transfers<br>Personal services supplies other government Movable, immovable Public investment Financial investment and other provisions Other fiscal<br>Materials and Subsides, grants and and intangible Disponibilidad<br><!-- End of picture text --> _Source:_ Constructed using SIMBAD (State and Municipal System Databases), National Institute of Statistics and Geography (INEGI).

_Note:_ FAFM = Municipal Strengthening Contribution Fund.

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cases where a target population’s true poverty rate falls outside the 95% confidence interval around the estimated poverty rate. But with the parametric bootstrap approach underpinning estimates in Columns 2-4 the failure rate is higher (7 cases). For this pseudosurvey the parametric bootstrapping approach appears to produce standard errors that are too “optimistic” - suggesting greater precision of estimates than is warranted.

Given this evidence of a tendency for the parametric bootstrapping procedure to produce confidence intervals that are somewhat too narrow, we employ from now on, unless noted explicitly otherwise, the non-parametric approach outlined in Appendix 1. Additional comparisons, not reported here, confirm that conclusions derived with this simulation procedure hold also for estimates based on the considerably more computationally-intensive numerical gradient approach outlined in ELL(2002). The important point to take away here is that simulation methods do seem to matter (with respect to standard errors, if not point estimates). Further research is underway to understand better why the different simulation methods do not always agree.<sup>18</sup> Table 4 looks more closely at the confidence intervals estimated around welfare estimates produced with our non-parametric simulation scheme. If the confidence intervals accurately reflect the true uncertainty in the estimates, the fraction of cases of the “truth” falling within a confidence interval around an estimate should be approximately equal to the corresponding confidence level. Note however that twenty ‘target populations’ are drawn for each of the ten ‘surveys’ and so the experiments are not entirely independent.

For each welfare measure and each of the ten pseudosurveys the number of instances is counted when true welfare in each of the 20 target populations falls within two standard deviations around the target population’s estimated welfare level. For example, in the case of pseudo survey 1, the true welfare estimate (mean, headcount, squared poverty gap, and General Entropy Class inequality measure with parameter 0) always fall within the confidence interval around the estimated welfare measure. In Table 2 we saw that for pseudosurvey 2 this occurred 95% of the time (19 out of 20 cases) for the headcount, and Table 4 shows the same was observed for the mean, while

> 18 The most recent version of POVMAP2 now offers the user the choice of the  “classical” numerical gradient or  the parametric bootstrapping procedures outlined in Section 3.  .

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# **Figure 5 Share of Households in the Largest Lineage Group (1995) and Numbers of Civil Disputes and Criminal Cases in 2003**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of Households in the Largest Patrilineal Clan (1995)<br>Civil Disputes Crime Cases<br>12 1.5<br>10<br>1<br>8<br>.5<br>6<br>Number of Civil Disputes (2003) Number of Crime Cases (2003)<br>4<br>0<br><!-- End of picture text --> Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 42_
credit in relatively equal proportions (see the bottom of column (2) where the mean and median of the ratio are close to 1), but there is important degree of variation around the central tendency, with the 25th and 75th percentile values located at 0.6 and 1.6, respectively. As expected, the correlation between the two measures of trade credit use is negative but small (-0.32).

The relative measures (i.e. the ratios with respect to the mean across industries) of _Payables Financing_ ( _Pi_ ) and _Short Term Debt to Payables_ ( _Si_ ) for the 28 ISIC industries in the U.S. are reported in Table 2. The median in column (1) is close to 1, which suggests that the distribution of _Payables Financing_ across industries is relatively symmetric because the mean is one by construction. In contrast, the median in column (2) is below one, indicating that the distribution is skewed to the left. As it was with the country level variables, the degree of variation across industries is reasonable but not large. The 25th and 75th percentiles of _Pi_ across industries correspond to 0.87 and 1.21, respectively. Again, this variation is slightly larger for _Si,_ with the corresponding figures at 0.71 and 1.15.

# 4.2 Sectoral correlations

The main measure of sectoral comovement is the correlation of the growth rates of real value added across 28 industries covering the complete manufacturing sector. The data used to build these correlations come from the United Nations Industrial Development Organization (2005), Industrial Statistics Database (henceforth UNIDO). This database contains information on nominal value added, employment, number of establishments, wages and salaries, gross output, index of industrial production, and gross capital formation for 179 countries and 28 three-digit ISIC manufacturing industries during 1963 and 2003 but the actual coverage of the data is smaller because of missing information. I use data for the period 1980-2003 so that the comovement is measured over approximately the same period where trade credit data is available, and for reasons explained below keep only those industries with at least 15 years of data during this period.

To compute the correlations, I first calculate the annual growth rates of real value added for each of the 28 industries in the different countries included in the database during 1980-2003. Following Rajan and Zingales (1998), I deflate the nominal value added of each industry in a country with the overall Producer Price Index for that country (from International Monetary Fund, 2005). Next, in each country I keep only those industries with at least 15 observations of this growth rate, and calculate their within-country, across-years correlation. To fix ideas, let _gict_ denote the growth rate of industry _i_ in country _c_ between years _t −_ 1 and _t._ The estimate of the correlation between industries _i_ and _j_ in country _c_ corresponds to where _g_ ̄ _ic_ is the time-average of _gict, Tij_ is the number of observations in which there are data for both sectors _i,_ and _j,_ and _Ti_ is the number of observations in sector _i._ The reason to keep 13
# **3.2 Data from the Private Employment Service Provider**

The private employment service provider collects two sets of data on program participants. The first set contains information regarding the participants’ work experience, skills, and potential barriers to employment. These data are gathered during the profiling interview whereby details on formal education, main and alternative occupations, and self-identified obstacles related to these occupations are recorded. Additionally, information on other skills, such as foreign language proficiency and possession of a driving license, is also collected. Based on the information gathered during the profiling interview, the private provider’s counselors categorize beneficiaries as "less employable," "employable," or "highly employable", respectively. This categorization is based on the counselor’s assessment of the relevance of jobseekers’ skills (technical and soft skills), the relevance of their education to their preferred occupation, and prevailing labor market conditions.

The second set of data includes complete administrative records of the services provided by the counselors to each participant. These activities include conducting a profiling interview, developing an individual action plan, counseling, monitoring, and (depending on the jobseeker) matching. Data on services provided is available for 1,496 jobseekers. However, data on employability assessments is available for 1,399 individuals (or 93.5 percent of the sample). There is no differential attrition between treatment and control groups.

# **3.3 Survey Data**

In addition to the two sources of administrative data, a baseline and end-line survey of program participants was conducted. These surveys collected detailed information on the jobseeker and their household and included modules on: (i) Household demographics and socioeconomic status; (ii) jobseeker’s employment history; (iii) experience with the PES and private employment service providers; and (iv) a battery of questions to characterize each of the jobseeker’s “Big 5” personality traits.<sup>5</sup> The baseline survey had a response rate of 59.3 percent (914 out of 1,541 beneficiaries participated in the survey). There was no difference in response rates across the two treatment groups, likely because the survey was administered before any services were provided by the private employment service provider. Table 2 below shows the timelines of the program and impact evaluation related activities.

> 5Given the relatively high rates of refusal to participate in the end-line survey, we have not used these data for analysis of the impact evaluation. As there were no systematic differences across treatment and control groups, analysis of the endline survey could be included as an additional robustness check.

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of the indices for comparison, ���, as each one of them is a perfect linear combination of the others. The base index constitutes the base for the comparisons with the others. Table 4 presents descriptive statistics of the variables of interest included in the econometric model for the group aged 15 to 18 years old.

Table 4. Descriptive Statistics of History of Past Decision Indices, _15 to 18 y.o_

|||_Males_|||_Females_||
|---|---|---|---|---|---|---|
|_Variables_|_Nini_<br>_(n)_|_Study Only_<br>_(s)_|_Work Only_<br>_(h)_|_Nini_<br>_(n)_|_Study Only_<br>_(s)_|_Work only_<br>_(h)_|
|�<sup>�</sup>:Index Nini|0.419|0.016|0.155|0.683|0.020|0.289|
|�<sup>�</sup>:Index Study Only|0.196|0.857|0.081|0.156|0.913|0.113|
|�<sup>�</sup>:Index Work Only|0.348|0.011|0.700|0.144|0.004|0.541|
|�<sup>�</sup>:Index Work & Study|0.038|0.117|0.063|0.017|0.063|0.058|

_Notes_ : complete information on the variables of interest for the first and fifth interviews of the rotating panel (see text for description of data). Weights are used in the estimation. A person is considered a _Nini_ when s/he is not working nor studying. Indices take values from 0 to 1. _Source_ : Authors’ calculations based on the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

In each case, the higher the value of the index, the more exposure the individual has had to that state. The value of each index is higher, relative to the other indices, when looking at the corresponding column that refers to that status (e.g., when looking at the Study Only index in the Study Only column). Except for the decision to work, women exhibit higher indices than those for men, which means that, on average, women have more experience than men of being Ninis and of studying during the previous year than do men, while men have more experience of working.

# **4. Results**

# **4.1 The Decisions of Youth and Their Associated Factors in Mexico**

Appendix Table A1 presents reduced form estimations of the linearized decision function ��,��∙� in equation 1 for youth aged 15 to 18, by gender. Results for 19 to 24 year olds are presented in Appendix Table A2 for comparison. The tables show marginal effects, or marginal changes in the case of dummy variables (evaluated at sample means of the covariates), of the underlying multinomial logit regressions. We have slightly adjusted the time use states discussed in the theoretical model in Section 2, to accommodate the fact that not many 15 to 18 year olds work and study at the same time. Hence, the dependent variable in the econometric models considers only three alternatives of time use - _Nini_ , _study only_ , and _work only_ - and exclude information for those who work and study _._ Standard errors are robust to 20
consistent with the TFP growth for Bangladesh (2.41%) from USDA’s agricultural productivity database over the same period (USDA, 2017).

_Table 5: Estimated TFP growth rates and its components: 2000-2008, in percent, using the extended model_

|**Index**|**LandQrtl. 1**|**LandQrtl. 2**|**LandQrtl. 3**|**LandQrtl. 4**|**Full sample**|
|---|---|---|---|---|---|
|**TFPG**|1.14|2.18|3.34|4.08|2.86|
|**TC**|2.08|3.00|3.74|4.58|3.20|
|**TEC**|0.12|‐0.21|‐0.09|‐0.75|‐0.16|
|**SEC**|‐1.05|‐0.58|‐0.29|0.27|‐0.17|

_Source_ : Authors’ estimates using the 62-village panel survey. _Note_ : TFPG is total factor productivity growth, TC is technical change, TEC is change in technical efficiency and SEC is change in scale efficiency.

The second point is that, as noted earlier, the production frontier has moved relatively faster for the larger farmers. Starting from a lower base, technical change has thus helped the larger farmers catch up with the smaller farmers in terms of average productivity. Further, despite the frontier moving faster, it also appears the bulk of the larger farmers are yet to benefit from these technical advances and have fallen further behind in terms of technical efficiency. Finally, it is important to note that with the larger farmers catching up, while the negative trend in TFP remains – that is the inverse farm-size and productivity relationship still holds – it has diminished over time.

The final set of results of interest are the policy variables in the inefficiency function. None of the demographic variables attain statistical significance at any reasonable level, suggesting that the fixed effects model adequately controls for inter-personal variation across farm households. Importantly, the non-farm participation coefficient is not statistically significant, indicating that households that participate in the off-farm work achieved neither higher nor lower levels of inefficiency.

Among other policy variables, households engaged in sharecropping are less inefficient (i.e, more efficient) – a consistent result in all models. Rented land does not show the same result, indicating these households are no better or worse off in terms of technical efficiency than the base category, which is pure owners. The fragmentation index has a highly significant negative coefficient, consistently across farms, indicating it is associated with _less_ inefficiency. This result is counterintuitive from a transaction cost point of view, but makes sense if the households are leasing in lands of better quality than what they own, or of different quality for different (diversification) 23
in any WMA, but it is not possible to identify from which WMA a specific consumer good originates. However, this assumption is reasonable given SA’s relatively small and well-connected economy.

The CGE model contains a measure of the exchange rate, which adjusts to ensure that SA’s current account balance remains fixed in foreign currency. The model’s exchange rate is an index capturing the relative price of tradables to non-tradables (i.e., the real exchange rate). Thus, for example, if total import demand rises in response to shifting consumer demand, this would, all else being equal, increase the country’s current account deficit. However, in the CGE model, the real exchange rate depreciates in order to raise the export prices received by domestic producers, while also raising import prices for domestic consumers. This stimulates an increase in exports needed to pay for additional imports, thereby maintaining the current account balance at its original level.

# _3.2.3 Household incomes and demographic structure_

The model distinguishes between various institutions, mainly government and a number of representative household groups. Households in each WMA are disaggregated across rural/urban areas and national expenditure quintiles<sup>14</sup> . Each representative household is an aggregation of the individual households captured in the 2001 Population Census and the 2000 Income and Expenditure Survey (reconciled with inflation and national accounts) (StatsSA, 2002 and 2001)<sup>15</sup> . Households receive income in payment for producers’ use of their factors of production<sup>16</sup> . Households pay direct taxes to government (based on fixed tax rates)<sup>17</sup> , save (based on marginal propensities to save), and make transfers to the rest of the world. Households use their income to consume commodities under a linear expenditure system (LES) of demand.

> 14 There are 190 representative households (five expenditure rural and urban quintiles in each WMA)

> 15 Since the household survey is not representative at the WMA level, per capita income/expenditure patterns were identified at the provincial level for rural and urban areas, and then multiplied by the number of rural and urban inhabitants reported by the population census. Household incomes from various income sources were manually adjusted proportionately to match the expenditure levels reported in the survey. 16 Note that the SAWSAM does not have an “enterprise” account and hence capital payments are paid directly to households. Land and irrigation water rents are similarly distributed across households.

> 17 Since the SAWSAM does not have a separate enterprise account, corporate taxes are taken directly from capital to the government direct tax account. Similarly, it was assumed that all industrial and domestic water value-added is paid to the government at a 100% tax rate.

16
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**Table 9**

**Percentage Change** in **Utility Level from Benchmark under a Universal Targeting Scheme**

||All|MEXP|MAGS|MVIVA|MADP|MFOR|MIND|MINAC|
|---|---|---|---|---|---|---|---|---|
|Export Croppers|33.24|78.51|-12.40|-16.89|7.82|40.30|-9.39|1.12l|
|Food Croppers in the Savannah|114.86|2.95|233.03|10.18|-6.44|-0.07|-38.61|-24.18|
|Other Food Croppers|100.27|-11.74|32.87|37.83|-13.30|-1.74|23.35|13.18|
|Government Employees|.70.82|.24.56|.18.36|-16.01|47.19|-1.92|-33.12|-11.64|
|Formal Sector|-79.35|-24.37|-13.38|-17.20|-13.79|3.39|-25.13|-6.53|
|Small Businesses|19.51|-15.93|-18.05|-6.85|-11.85|.<br>-1.03|158.91|-17.93|
|Inactive|58.18|22.42|.46.22|44.09|3.60|1.86|-88.16|59.73|

**Table 10**

**Poverty Index (P.) under a Universalistic Transfer Scheme**

||Base<br>Case|ALL|MEXP|N.!AV|MVIVA|MADP|MFOR|MIND|MINAC|
|---|---|---|---|---|---|---|---|---|---|
|Export Croppers|0.44|0.227|0.104|0.536|0.579|0.5|0.443|0.512|0.433|
|Food Croppers in the Savannah|0.637|0.425|0.614|0.019|0.56|0.f84|0.637|0.908|0.749|
|Other Food Croppers|0.345|0.046|0.433|0.176|0.159|0.444|0.356|0.215|0.264|
|Government Employees|0.087|0.699|0.176|0.147|0.138|0.026|0.092|0.23|0.121|
|Formal Sector|0.018|0.67|0.046|0.031|0.036|0.031|0.015|0.051|0.023|
|SmaUl Businesses|0.468|0.367|0.564|0.579|0.504|0.536|0.476|0.071|0.579|
|Inactive|0.371|0.159|0.264|0.719|0.195|0.352|0.363|0.997|0.154|
|TOTAL|0.37|0.301|0.323|0.386|0.3S6|0.407|0.374|0.428|0.368|

Table 9 shows that in the "ALL" scenario when transfers are distributed to all households simultaneously, both government and the formal private sector householtds lose
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