rafmacalaba/datause-encoder-data huggingface dataset:
||(0.526)|(0.515)|(0.518)|(0.524)|
|---|---|---|---|---|
|**Sector**(Accommodation and food service<br>activities, Construction):|||||
|Communication and other services|-2.333**|-2.111**|-2.305**|-2.129**|
||(1.026)|(0.975)|(0.996)|(0.960)|
|Manufacturing, Mining, Water, Energy,|||||
|Agriculture|0.502|0.630|0.587|0.435|
||(1.368)|(1.424)|(1.438)|(1.358)|
|Transportation and storage|-5.801***|-5.643***|-5.726***|-5.640***|
||(1.438)|(1.522)|(1.413)|(1.550)|
|Wholesale and retail trade, Repair of motor|||||
|vehicles and motorcycles|1.222|1.130|1.201|1.001|
||(1.248)|(1.248)|(1.288)|(1.145)|
|**Region**(Dar es Salaam)|||||
|Dodoma|0.945|0.0536|0.127|-0.221|
||(1.796)|(1.374)|(1.778)|(1.528)|
|Tanga|-4.005***|-4.431***|-4.390***|-4.543***|
||(0.660)|(0.733)|(0.755)|(0.813)|
|Controls|YES|YES|YES|YES|
|Observations|660|660|658|658|
|R-squared|0.105|0.114|0.107|0.122|

_Note:_ Controls include age of the firm and number of employees Standard errors (in parentheses) are clustered at the ward level:<sup>*</sup> p<0.1<sup>**</sup> p<0.05<sup>***</sup> p<0.01

## 5.2.3. The geographical extent of a supply network can determine risk

Firms can also reduce the risk of local shocks by varying the spatial extent of their supply network. However, for the surveyed firms, local suppliers play a dominant role. About 50 percent of the firms in Dodoma, Tanga and Dar es Salaam are less than 10 kilometers away from their average supplier, making them vulnerable to local disruptions. For 80 percent of firms, the average supplier is less than 50 kilometers away. Only a few firms maintain a dispersed supply network—only for about 10 percent of firms, the average supplier is over 200 kilometers away. For the case of Tanzanian firms covered by this survey there is no detectable significant relationship between disaster risk perception or actual flood experience and distance to the average supplier, when employing linear regressions (table D.3, appendix D). Instead, differences in supply network extents across firms can mainly be explained through region and sector fixed effects, with firms in Dodoma and Tanga maintaining on average more spread out supply networks than those in Dar es Salaam.

# 5.3. Investing in backup capacity can ensure business continuity but also results in risks

By investing in water tanks and electricity generators, firms can ensure business continuity even when infrastructure systems are disrupted by disasters (Hallegatte et al. 2019). The survey data show that about 64 percent of the firms that depend on water for their production own a water tank, and 30 percent of firms own power generators. The evidence presented in this section shows that water tank and generator ownership are strongly driven by economic factors, such as the local price of water and electricity or firms’ usage levels. There is some evidence that experiencing a flood on business premises increases the probability of water tank ownership, but decreases the probability of owning a power generator.

16
**Table A7 continued**

|**Question**|**Description**|**Score**|**Rationale**|
|---|---|---|---|
|Is the data in<br>open and<br>machine-<br>readable file<br>formats?|We automatically compare them against a list<br>of file formats that are considered machine-<br>readable and open. A file format is called<br>machine-readable if your computer can<br>process, access, and modify single elements in<br>a data file.The Index considers formats to be<br>“open” if they can be fully processed with at<br>least one free and open-source software tool.<br>Potentially these formats allow more people<br>to use the data because people do not need<br>to buy specific software to open it. The source<br>code of these format does not have to be<br>open.|20<br>points|Both features (machine-readable and open format) are<br>key aspects of the open definition. Machine-readability is<br>a major enhancement of technical usability. However, if a<br>file is only usable with proprietary software (such as<br>ArcGIS) ‘normal’ users are exempt from using them. Open<br>formats put no copyright, monetary restrictions or other<br>restrictions on their use (important for people who<br>cannot / do not want to afford proprietary software).|
|How much<br>human effort is<br>required to use<br>the data. (1 =<br>little to no effort<br>is required, 3 =<br>extensive effort is<br>required)|The submitters tell us their use case and the<br>steps they took to make the data usable<br>(example: “I have to reformat the data”).|Not<br>scored|The question is a subjective assessment. Furthermore,<br>usability depends on context and the purposes for which<br>a person wants to use the data.|

Table A8: Weights of Features of the MENA Micro Access Indicator (MENA MAI)

|**Element**|**Feature**|**Weight in overall score**|
|---|---|---|
|User experience|English translation|1/6<br>1/2|
||Micro data tab/dashboard/library|1/6|
||Survey calendar|1/6|
|Data openness|Firm data|1/14<br>1/2|
||Price data|1/14|
||Consumption data|1/14|
||Labor Force data|1/14|
||Health data|1/14|
||Population Census|1/14|
||Economic Census|1/14|

52
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# **3.1. Who are the vulnerable to poverty in Mexico?**

In part because of modest economic growth and the expansion of more progressive social spending, Mexico faced a reconfiguration of its social groups (mainly) over the last decade. As shown in Figure 2, the reduction of poverty meant that between 2000 and 2012 the Mexican middle class rose by almost 12 percentage points (from 26 to 38 percent). At the same time though, there has been an expansion of the population in vulnerability in 5.4 percentage points. In fact, in 2012 roughly twothirds of the population in Mexico remained in a situation of economic insecurity: 22.2 percent in poverty, and 43 percent in vulnerability—this is the situation of nearly half of the Mexican states where the sum of poverty and vulnerability ranges from 64.5 percent in the State of Mexico to 84.2 percent in Chiapas (Figure 3).

**Figure 3: Distribution of socioeconomic groups by states** _Percentage of population; Mexico, 2012_

<!-- Start of picture text --> Nuevo Leon 8.3 31.7 54.6<br>Distrito Federal 5.8 39.2 50.3<br>Sonora 10.5 35.2 50.2<br>Baja California 10.8 38.5 47.1<br>Baja California Sur 14.5 34.0 46.9<br>Colima 11.6 39.1 46.7<br>Coahuila 12.6 40.4 44.6<br>Quintana Roo 17.4 37.6 42.4<br>Chihuahua 17.0 40.2 39.9<br>Tamaulipas 16.2 41.2 39.6<br>Sinaloa 16.7 41.4 39.2<br>Jalisco 16.5 42.0 38.8<br>Queretaro 17.3 41.2 37.6<br>Aguascalientes 17.6 43.3 36.1<br>Campeche 23.0 39.7 34.9<br>Morelos 16.0 48.0 34.4<br>State of Mexico 17.4 47.1 33.7<br>Guanajuato 20.7 45.0 32.7<br>Nayarit 25.6 40.2 32.3<br>Tabasco 31.1 35.7 31.3<br>Yucatan 18.2 49.4 30.0<br>San Luis Potosi 29.1 39.6 30.0<br>Veracruz 29.9 40.8 27.7<br>Zacatecas 35.0 36.6 26.6<br>Michoacan 27.4 44.8 26.4<br>Hidalgo 29.6 43.4 26.1<br>Durango 28.6 45.5 24.3<br>Tlaxcala 26.9 49.9 22.7<br>Oaxaca 40.3 36.9 21.6<br>Puebla 35.9 42.7 20.4<br>Guerrero 49.4 32.6 17.4<br>Chiapas 52.9 31.3 15.2<br>0.0 20.0 40.0 60.0 80.0 100.0<br>< $4.0 $4 - 10 $10 - 50 > $50.0<br><!-- End of picture text --> _Source_ : Author’s estimations based on MPS-ENIGH 2012 10
When all of these funds consolidated, Fondo PYME kept most of their support mechanisms, within a single, less complicated operational scheme. Between 2001 and 2006, the total resources allocated to these funds rose from US$41 million to US$205 million, and the number of supported SMEs went from 62,000 to 166,000.

**Table 4 Funds of the Ministry of Economy: Main Results 2001-2006**

|**Fund**|**Firms**<br>**200**|**Million**<br>**USD**<br>**1**|**Firms**<br>**200**|**Million**<br>**USD**<br>**2**|**Firms**<br>**200**|**Million**<br>**USD**<br>**3**|**Firms**<br>**Tot**|**Million**<br>**USD**<br>**al**|
|---|---|---|---|---|---|---|---|---|
|**FAMPYME**|25,692|25|21,141|23|12,745|15|59,578|63|
|**FIDECAP**|36,342|16|23,107|60|24,594|125|84,043|201|
|**FOAFI**|*|*|23,362|15|48,917|18|72,279|33|
|**FACOE**|*|*|*|*|6,606|7|6,606|7|
|**Total**|62,034|41|67,610|98|92,862|165|222,506|304|
||**200**|**4**|**200**|**5**|**200**|**6**|**Tot**|**al**|
|**Fondo PYME**|177,393|105|130,281|168|165,937|205|473,611|478|

* The fund did not exist in the specified year. **Source:** Secretaría de Economía (2006a).

ME manages other programs related to SME support, such as the National System of Business Incubators, which had a total of 308 institutions supporting firms, and the Program — of Training and Modernization ( _Programa de Capacitación y Apoyo Empresarial_ PROMODE) to increase the competitiveness of small retailers.

# **Table 5 PROMODE: Main Results 2001-2006**

||**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**Total**|
|---|---|---|---|---|---|---|---|
|**Workers trained**|91,240|176,506|146,142|156,014|192,875|94,431|857,208|
|**Entrepreneurs trained**|9,012|16,907|12,562|6,170|12,581|9,644|66,876|
|**Instructors trained**|2,593|1,512|1,492|1,246|1,433|404|8,680|
|**Total individuals trained**|102,845|194,925|160,196|163,430|206,889|104,479|932,764|
|**Total SME benefited**|N/A|N/A|16,528|18,889|17,691|19,113|72,221|

* Data from 2001 to June 2006. **Source:** Secretaría de Economía (2007c)

Another program run by ME is the Network of Centers for the Development of Business Competitiveness and Regional Centers for Business Competitiveness (CETRO-CRECE). This was created in 1996 to support Mexican SMEs development by identifying their needs and offering them the appropriate tools to become competitive. During its first years CETRO-CRECE was entirely financed with federal funds. However, by 2003, its financial structure had changed to 59 percent federal subsidies, 30 percent from clients, 8 percent from other private entities and 3 percent from state and municipal governments. It is the largest institution in Mexico offering consultancy services and training. Other types of support mechanisms the network provides include elaboration of feasibility studies, marketing strategies, sector and financial analyses, human resource studies, analysis and oversight of the productive process, management analysis and oversight, and business training. The CETRO-CRECE network supported 29,466 firms between 2001 and 2003 and the budget for the network during that period was 625.9 million pesos.

6
# Figure 5: Ricardian and Interest Rate Channels: Impulses Responses

# A. Sub-sample: Post-Crisis (2009-14)

<!-- Start of picture text --> Private Consumption CDS<br>0.5 150<br>0.4<br>100<br>0.3<br>50<br>0.2<br>0.1 0<br>0.0<br>-50<br>-0.1<br>-100<br>-0.2<br>-0.3 Weak fiscal position Strong fiscal position -150 Weak fiscal position Strong fiscal position<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text -->

# B. Lag Structure: 3 Quarters

## Private Consumption

<!-- Start of picture text --> 0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>Weak fiscal position Strong fiscal position<br>-0.5<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters<br><!-- End of picture text -->

## CDS

<!-- Start of picture text --> 100<br>Weak fiscal position Strong fiscal position<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters<br><!-- End of picture text -->

# C. Government Debt Gap

## Private Consumption

## CDS

<!-- Start of picture text --> 0.3 60<br>0.2<br>40<br>0.1<br>0.0 20<br>-0.1<br>0<br>-0.2<br>-20<br>-0.3<br>Weak fiscal position Strong fiscal position<br>Weak fiscal position Strong fiscal position<br>-0.4 -40<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text --> Note: The panel C is based on government debt gap (deviation from group mean) as conditioning variable. Strong fiscal positions are defined as government debt in the 10th percentile and weak fiscal positions are defined as government debt in the 90th percentile in the sample. These are based on estimates from the IPVAR model, where model coefficients are conditioned on fiscal position, with government debt as a percentage of GDP is the measure of fiscal position.

22
- 10 -

In order to assess the practical importance of the choice of base year, we proceeded to carry out 12 partial rebasings, recalculating CDP series at constant prices on the basis of value added series at constant prices, broken to n into 12 sectors, derived from the World Tables 1984. For tha* rpose we established a deflator for each sector, the prices of a~, year serving in turn for calculation of a global product series whose base was the correspondi-g year. 2/ In this way we were able to calculate for the period 1970-81 and for 73 countries, by regression, average annual growth rates on the basis of each of the years (i.e. 12 average growth rates per country). In more than half the cases (39 countries) tne gap between the maximum and minimum rates exceeds 0.5%. For 22 countries it exceeds 1%. In some cases, most of them oil-exporting countries, it is substantial (Nigeria 7.2%, Iran 4.2%, Congo 2.9%, etc.).

The illustrative model was estimated using the growth rates calculated for various base years for a sample of 40 countries. It was found first of all that the coefficient of determination varies between 0.54 and 0.62, the best estimates according to this criterion being obtained, curiously, by using as bases the years following the two oil shocks (1975, 1976, 1980, 1981). We next note that the value of the coefficients estimated hardly changes with the base year (with the exception of the constant) but that the "Student's" value for t changes appreciably. For example, the share of public expenditure in GDP, which is practically never significant, becomes so (at the 10% threshold) in base 1980. Thus, if the choice of the base year does not seem to entail any bias in estimating the coefficients, it can skew the selection of independent variables.

In brief, it is found that growth measurements differ substantially, depending on the base year adopted and the source used and, to a lesser degree, on the nature of the aggregate adopted. But, in conformity with the econometric theory of measurement errors (for example, Stewart and Wallis, 1981), the differences in the measurement of the dependent variable have little effect on the results of the econometric estimates of the factors affecting growth.

# 1.2 Choice of countries and periods

The cross-sectional analyses of economic growth are based on a wide variety of country sets and a wide variety of periods (cf. table 1, columns 2 and 3). The choices made result logically frm the nature of the question posed; however, they are very often constrained by availability of statistics.
<!-- Start of picture text --> 120<br>100<br>80<br>60 oe<br>-<br>--7”<br>40 =<br>20<br>°<br>09s 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050<br>—Malaysia<br><!-- End of picture text --> <!-- Start of picture text -->
1.0%<br>---- ~~<br>~ 090%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0.0%<br>2019 2024 2029 2034 2039 2044 2049<br>— -Median over 1985-2014 —=—=Business-as-usual baseline<br><!-- End of picture text -->
distributional consequences of the increase (the impact on income inequality and the identification of the winners and losers). We aim to identify effects on the wage distribution if only the minimum wage policy changes, keeping everything else constant; therefore, this exercise aims to identify the potential impacts of the policy, and it is different from a forecasting exercise where the objective is to replicate the actual distributions, given many changes in the economic environment. This part of our research is based on the administrative tax data and the method previously used by Robayo-Abril et al. (2020) to provide an ex-ante evaluation of the impact of the several minimum wage increase scenarios on Romania's labor market outcomes. This approach enables us to estimate the broader economy-wide effects and the more granular micro-level consequences of different policy options.

# Methodology and Assumptions

**We rely exclusively on administrative tax data for the microsimulation.** The administrative tax data comprises a series of 12 monthly datasets. Considering that income evolves throughout the year, we work with one of the year's final months. Hence, we have opted to use data from November 2021. Notably, we exclude December 2021 due to the potential seasonality caused by annual bonuses during that month. Some additional robustness checks were performed using EU-SILC and matched administrative tax data, as presented later in this section.

**As mentioned above, the administrative tax data does not provide direct information on the skill level of the taxpayers.** Therefore, we use the employer's NACE sectoral code as a proxy for skill level. We assume that people working in the following sectors are skilled workers: information and communication; financial and insurance; professional, scientific, and technical activities; public administration and defense, education; human health and social work activities; arts, entertainment and recreation, activities of extraterritorial organizations and bodies. The remaining taxpayers are classified as unskilled. The share of skilled workers in our sample is 35.97%. In the EU-SILC 2021, where the information on skill level is provided, the share of skilled employees is 32.91%. Therefore, our proxy is close to the true distribution of skilled and unskilled workers in the Romanian labor force. However, the similarity on the aggregate level does not guarantee the similarity on the sector level. In EU-SILC data, we can identify high-skill workers in all sectors of the economy. Using administrative tax data, we can only use the sector as a proxy of skill level and thus assume that skilled workers do not work in some sectors.

**In this study, we utilize labor demand elasticities based on international research to model the potential impact of changes in the minimum wage on employment and wages.** We quantify the “disemployment” effect by examining the proportion of workers earning wages between the existing and proposed minimum wage who transition to a non-employment status with zero wages (referred to as "losers").

32
<!-- Start of picture text --> 10,000<br>Fo)<br>uv<br>7,500 nDQD<br>5,000 =co}2FtOo<br>2<br>— 2,500 os<br>sS=<br>ato)<br>@w 10,00012: Bs)<br>>3<br>37,500 &<br>5 =<br>Ss 3<br>3 5,000 =<br>58<br>2£ 2,500 a<br>e<br>as= fo} =<br><=58 10,0007,500 Bs)3©a<br>5,000 =a=<br>8<br>3<br>2,500 2<br>a<br>fo}<br>2015 2031 2051 2015 2031 2051 2015 2031 2051 2015 2031 2051 2015 2031 2051<br><!-- End of picture text --> <!-- Start of picture text --> 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100<br>Year<br>Ml eEast Asia & Pacific MM Europe & Central Asia MB High Income<br>Mi Latin America & Caribbean [fj Middle East & North Africa!) South Asia<br>MM) Sub-Saharan Africa<br><!-- End of picture text --> <!-- Start of picture text --> onstant Protection Levels |Constant Abs. Flood Risk| |Constant Rel. Flood Risk Risk intolerance CBA Protection<br>10,000<br>D<br>&)<br>ae)<br>7,500 RS<br>ro<br>&<br><!-- End of picture text -->

<!-- Start of picture text --> 5,000 2co}&<br>az<br>—SsS 2,500 <=<br>Fra} 0<br>anwo 10,000 ee]<br>=, 8<br>on ~<br>3 7,500 a<br>5 :<br>fo} 2g<br>% 5,000 Ss<br>83<br>28<br>S 2,500 a<br>2 =<br>sg= ie} e<br>5810,000 D<br><%<br>7,500 ee)oa<br>=a<br>5,000 =co}oO<br>2,500 Zz=<br>a<br>ie}<br>2015 2031 2051 2015 2031 2051 2015 2031 2051 2015 2031 2051 2015 2031 2051<br><!-- End of picture text --> <!-- Start of picture text --> 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100 2030 2050 2100<br>Year<br>(Ml East Asia & Pacific Ml Europe & Central Asia — High Income<br>{Latin America & Caribbean fj Middle East & North Africa) South Asia<br>(Sub-Saharan Africa<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 12: Simulated Paths of Income Shares<br>(A.1) Output (A.2) Consumption (A.3) Wages<br>10.2 9.7 9.5<br>Baseline<br>Counterfactual 9.4<br>10.1 Data 9.6<br>9.3<br>10 9.5 9.2<br>9.4 9.1<br>9.9<br>9<br>9.3<br>9.8 BaselineCounterfactual 8.9 BaselineCounterfactual<br>9.7 9.2 Data 8.8 Data<br>1970 1980 1990 2000 2010 2020 2030 1970 1980 1990 2000 2010 2020 2030 1970 1980 1990 2000 2010 2020 2030<br>Year Year Year<br>(B.1) non-ICT Capital (B.2) ICT Capital (B.3) ICT Share<br>9.5<br>11.6 Baseline 9 0.04<br>Counterfactual<br>11.5 Data 0.035<br>8.5<br>11.4<br>0.03<br>11.3 8 BaselineCounterfactual BaselineCounterfactual<br>Data 0.025 Data<br>11.2 7.5<br>0.02<br>11.1<br>7<br>11 0.015<br>6.5<br>1970 1980 1990 2000 2010 2020 2030 1970 1980 1990 2000 2010 2020 2030 1970 1980 1990 2000 2010 2020 2030<br>Year Year Year<br>(C.1) Routine Share (C.2) Non-Routine Share (C.3) Labor Share<br>0.36 0.66<br>0.38 0.65<br>0.34<br>0.36 0.64<br>0.34 0.32 0.63<br>0.32 Baseline Baseline 0.62<br>Counterfactual 0.3 Counterfactual<br>0.3 Data Data 0.61<br>0.28 0.6<br>0.28<br>0.59 Baseline<br>0.26 0.26 0.58 CounterfactualData<br>0.24<br>1970 1980 1990 2000 2010 2020 2030 1970 1980 1990 2000 2010 2020 2030 1970 1980 1990 2000 2010 2020 2030<br>Year Year Year<br>lny lnc lnw<br>lnk lnict sc<br>sr snr sl<br><!-- End of picture text --> _Notes:_ The figures contrast the simulated effects of the declining ICT price (red line), a counterfactual simulation in which the ICT price is held constant at its 1968 level (orange line), and corresponding counter parts in the data (blue line). The shaded area indicates the “in-sample” period.

comparable with the data.

Figure 12 illustrates the simulated transition paths, and Table 5 presents a comparison of the long run implications in the baseline simulation and the counterfactual. At around 1980, the price of ICT capital starts falling rapidly. In response, agents accumulate ICT capital, and the path of ICT starts diverging from its counterfactual. Since non-ICT capital is complementary to _x_ , the accumulation of ICT capital raises the returns to non-ICT capital, and the stock of non-ICT capital increases as well. As a result of ICT and non-ICT capital accumulation, output increases relative to its counterfactual. The higher capital stocks also raise the marginal product of labor, resulting 30