rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Total Factor Productivity and Electricity

|TFP estimation procedure:|OLS|Olley/Pakes|
|---|---|---|
|Days with power outages|-0.002<br>[0.002]|-0.005<sup>**</sup><br>[0.003]|
|Share of firms that own a<br>generator|0.662<sup>***</sup><br>[0.255]|0.634<sup>**</sup><br>[0.296]|
|Exporter|-0.035|0.021|
||[0.044]|[0.052]|
|Age of the firm|0.009<br>[0.018]|0.029<br>[0.023]|
|Size of the firm|0.028<br>[0.046]|0.041<br>[0.056]|
|Foreign owned|0.169<sup>***</sup><br>[0.053]|0.150<sup>**</sup><br>[0.060]|
|Location in Capital Region|0.173<sup>***</sup><br>[0.051]|0.194<sup>***</sup><br>[0.059]|
|Country dummies|Yes|Yes|
|Industrydummies|Yes|Yes|
|Observations|985|880|
|Adjusted R-squared|0.30|0.47|
|Robust standard errors in parentheses.<br>statistical significance at the 10%, 5%<br>The services related variables are used|*, **, *** ind<br>and 1% level<br>in regional a|icate<br>s, respectively.<br>verages.|

# **Financial Services**

With respect to the financial sector, we have separate information on two key functions of banks. The first role of banks that we analyze is to facilitate payment transactions domestically and internationally. We proxy the efficiency with which payment transactions are conducted by using information from the Enterprise Surveys on the number of days required to clear both a domestic and an international payment transaction. For example, clearing a foreign currency wire takes between 3 days in Mauritius and almost 8 days in Ethiopia. Clearing a domestic currency wire takes an average of 4 days in Tanzania. While this is an admittedly specific element of banking transactions, it is likely to proxy the overall transaction efficiency of locally available banking services.

The results in table 4 show a significant positive relationship between firm productivity and the efficiency of the banking system in facilitating payment transactions, as measured by the speed of payment processing. The relationship holds regardless of whether we focus on the processing of domestic currency payments or international transactions, and regardless of which of the two productivity estimates we use. Even when the two measures are introduced together, both remain significant at the 5% level. In other words, firms in regions where banks operate more efficiently are on average more productive than firms located in regions with low banking efficiency.<sup>5</sup>

> 5 It should be recalled that the regressions include country and industry fixed effects, so that we are not picking up systematic differences in banking efficiency across these two dimensions.

8
Javorcik, B.S., Ç. Özden, M. Spatareanu, and C. Neagu. 2011. “Migrant Networks and Foreign Direct Investment.” _Journal of Development Economics_ 94: 231–41. Kerr, W.R. 2008. “Ethnic Scientific Communities and International Technology Diffusion.” _Review of Economics and Statistics_ 90: 518–-37.

Kugler, M., and H. Rapoport. 2007. “International Labor and Capital Flows: Complements or Substitutes?” _Economics Letters_ 94: 155–62.

___. 2011. “Migration and Cross-Border Equity Portfolio Flows.” CID Working Paper No. 223. Harvard University.

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Leblang, D.. 2010. “Familiarity Breeds Investments: Diaspora Networks and International Investment.” _The American Political Science Review_ 104: 584–600. Martin, P., and H. Rey. 2004. “Financial Super-Markets: Size Matters for Asset Trade.” _Journal of International Economics_ 64: 335–61.

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Rauch, J.E., and A. Casella. 2003. “Overcoming Informational Barriers to International Resource Allocation: Prices and Ties.” _The Economic Journal_ 113: 21–42.

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Wall Street Journal. 2016, “The Bank of China Takes Manhattan,” C. Cui and D. Huang.

17
|2006||-Nationalization of water<br>services in greater Buenos<br>Aires||
|---|---|---|---|
|2007|Election of President<br>Cristina Fernandez de<br>Kirchner|-Government intervention in<br>the statistical agency<br>(INDEC) for inflation<br>methodology (January)||
|2008||-“Unification” of pension<br>system with assets of private<br>funds transferred to the state<br>-Airline nationalized|Start of global financial<br>crisis/”Great Recession”|
|2009|||“Great Recession”|
|2010|Death of former<br>President Nestor<br>Kirchner|Second phase of debt<br>exchange with creditors<br>(about two-thirds of holdouts<br>from 2005). Only 7 percent of<br>debt now held byholdouts.|Non-oil commodity prices<br>start falling|
|2011|Re-election of<br>President Cristina<br>Fernández de Kirchner|||
|2012||- Import restrictions<br>-Reform of Central Bank<br>Charter<br>-Nationalization of the YPF<br>oil company|US, Japan and EU file<br>complaint with WTO<br>against Argentina for import<br>restrictions|
|2014||-New Supply Law<br>-Agreement reached with<br>Paris Club creditors<br>- Default on restructured debt<br>following court ruling in New<br>York(July)|-WTO rules against<br>Argentina<br>-End of “RUFO” clause on<br>exchanged debt (end-<br>December)|
|2015|-Death of Alberto<br>Nisman (AMIA<br>investigator)<br>-Elections in October<br>result in victory for<br>Macri|-Nationalization of railways<br>-Exchange rate reunification<br>in December|-Oil price collapse|
|2016||-Deal reached with holdout<br>creditors<br>-Reduction in export taxes<br>-Public utility price increases||

38
estimates of the effect of fuel subsidies on fishing because our results are robust to excluding vessels built after these regulations would have affected vessel construction decisions (Section 5.3). But they do prevent us from interpreting differences in gross tonnage distances as due entirely to the 2016-2020 fuel subsidy policy. Among vessels built on or after 2015, the median one-time renovation and reconstruction subsidy received is $703,166, compared to the median annual fuel subsidy of $223,380 (both 2022 USD). Examining these distributions is nonetheless informative for assessing our focus on vessels built before 2016 in Sections 5.1 and 5.2.1.

Figure 5 plots the normalized gross tonnage distance distribution for all vessels, regardless of whether their engine power ceiling is binding, because the fuel subsidy policy may affect the characteristics of all vessels.<sup>26</sup> Vessels built before 2016 are discontinuously more likely to have gross tonnage just _below_ their gross tonnage threshold (Figure 5a). By contrast, there is no discontinuous difference at the threshold in the density of vessels built on or after 2016 (Figure 5b). The t-statistic on the difference in these two discontinuities is 2.2. Firms seem to have responded to the regulator’s suite of regulations by reconstructing larger vessels, including vessels that will be eligible to receive larger fuel subsidies. Note that there is no discontinuous difference in the density of binding vessels built before 2016 (Figure A8).

# **5.3 Robustness checks**

If less-subsidized vessels fish less because of the 2016 to 2020 fuel subsidy policy, then the Stable Unit Treatment Value Assumption (SUTVA) would be violated. One way this SUTVA violation could occur is if more-subsidized vessels decreased the fish biomass available for less-subsidized vessels to catch. We consider this scenario by plotting log monthly fishing hours between 2012 and 2020 for binding and non-binding vessels above and below their nearest gross tonnage thresholds (four groups). If the SUTVA assumption fails, we would expect to see a decrease in fishing hours between 2016 and 2020 among the two groups of non-binding vessels and among binding vessels below their nearest gross tonnage threshold.

> 26We created these figures with the rdplotdensity command from the rddensity package (Cattaneo et al., 2021). We used all default options with one exception: we set the local polynomial order used to construct the bias-corrected density estimators as 2 (default = 3). This change causes the confidence intervals to be conventional rather than bias-corrected.

30
- 2 -

Survey of Income and Program Participation found that of 18.8 million poor people in 1990, more than 5 million had moved out of poverty by 1991, while more than 6 million had become poor (Shea, 1995). Looking at a three-year panel for Pakistan, Alderman and Garcia (1993) found that very few households remained poor or non-poor for each of the three years. For Africa, Grootaert and Kanbur (1995) found that in Cote d'Ivoire, 30% to 45% of households improved their living standard from one year to the next, over the 1985-88 period when the Ivorian economy was in severe recession. A significant number of these households managed to escape poverty. For example, in 1987-88, the two years with the largest economic decline, 26.7% of households who started off as very poor improved their poverty class to either midpoor or non-poor, and 19.3% of mid-poor households escaped poverty.

Such results indicate significant mobility, into and out of poverty, and reveal a dynamism among the poor which policy should stimulate. To do so, it is necessary to know what characteristics differentiate those who escape poverty from those who remain poor. Grootaert and Kanbur found that region of residence and socio-economic status were important factors, but they did not investigate the role of other household characteristics, especially asset endowments such as human and physical capital. The present paper aims to fill this void.

In the next section, we present the data source and methodology for the inquiry. Sections 3 & 4 contain the empirical results, respectively for urban and rural areas, and a concluding section presents some policy implications.

# **II. Data Source and Methodology**

The data for this analysis come from the C6te d'Ivoire Living Standards Survey (CILSS) which was conducted annually from 1985 to 1988. Each year the survey covered a representative sample of 1,600 households, and collected detailed information on employment, income, expenditure, assets, basic needs and other socio-economic characteristics of the household. Over the four years, coverage and methodology of the data collection were held constant so that results are comparable over time (see Grootaert, 1986, for more details on the CILSS).
# **Appendix B: List of High Quality Studies of Craft and Coping Jobs**

## **EDUCATION**

|_Co_<br>_Typ_|_untry Context_: OCED<br>_e of study_: Observational<br>|**Findings**<br>||
|---|---|---|---|
||**_Positive_**|**_Neutral_**|**_Negative_**|
|1.|Atkinson et al.(2004)|1. Figlio & Winicki(2005)|1. Clotfelter et al.(2004)|
|2.|Clotfelter et al.(2008)|2. Jacob & Levitt(2003)|2. Eberts et al.(2002)|
|3.|Clotfelter, Ladd,& Vigdor<br>(2007)|||
|4.|Cooper & Cohn(1997)|||
|5.|Dee & Keys(2004)|||
|6.|Figlio & Kenny (2007)|||
|7.|Goldhaber & Brewer(2000)|||
|8.|Jacob(2005)|||
|9.|Ladd(1999)|||
|10.|Lavy (2008)|||
|11.|Lavy (2009)|||
|12.|Woessman(2010)|||

|_Country Context_: OCED<br>_Type of study_: Experimental|||
|---|---|---|
||**Findings**||
|**_Positive_**|**_Neutral_**|**_Negative_**|
|No studies|No studies|1. Fryer(2011)|
|||2. Goodman and Turner(2010)|
|||3. Springer et al(2010)|
|_Country Context_: Developing<br>_Type of study_: Observational|||
||**Findings**||
|**_Positive_**|**_Neutral_**|**_Negative_**|
|1. Kingdon & Teal(2008)|No studies|No studies|

|_Country Context_: Developing<br>_Type of study_: Experimental|||
|---|---|---|
||**Findings**||
|**_Positive_**|**_Neutral_**|**_Negative_**|
|1. Duflo et al(2007)|1. Glewwe et al(2010)|1. Kremer & Chen(2001)|
|2. Muralidharan & Sundararaman<br>(2009)|||
|3. Jain & Narayan(2011)|||

37
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**International Labour Organization, The.** 2013. _Measuring informality: A statistical manual on the informal sector and informal employment._ International Labour Office Geneva.

- **Kathuria, Sanjay, Arti Grover, Viviana Maria Eugenia Perego, Aaditya Mattoo, and Pritam Banerjee.** 2019. _Unleashing E-Commerce for South Asian Integration: International Development in Focus series._ World Bank.

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**Maloney, William F.** 2004. “Informality revisited.” _World development_ , 32(7): 1159–1178.

28
# Contents

## **1 Introduction**

|**1Intr**|**oduct**|**ion**<br>**4**|
|---|---|---|
|**2The**|**Labo**|**rMarketinMorocco**<br>**6**|
|2.1|Basic|Structure..........................<br>7|
|2.2|Emplo|yment,Unemployment,andtheReturnstoEducation.<br>8|
||2.2.1|Sectoraldistributionoftheworkforce.....<br>. . . .<br>.<br>8|
||2.2.2|Publicsectoremployment...... . . . . . . . . .<br>.<br>9|
||2.2.3|Unemployment....... . . . . . . . . . . . . . .<br>.<br>9|
||2.2.4|ReturnsonEducation...... . . . . . . . . . . .<br>.<br>11|
|2.3|Regul|atoryandInstitutionalFeatures...... . . . . . . .<br>.<br>12|
||2.3.1|MinimumWageRegulation<br>. . . . . . . . . . . . . . .<br>12|
||2.3.2|HiringandFiringRegulations...... . . . . . . .<br>.<br>13|
||2.3.3|InstitutionalJobMatchingProcesses.....<br>. . . .<br>.<br>14|
||2.3.4|NonwageLaborCosts....... . . . . . . . . . .<br>.<br>14|
||2.3.5|TradeUnions. . . . . . . . . . . . . . . . . . . . . . .<br>15|
|2.4|Wage|Flexibility. . . . . . . . . . . . . . . . . . . . . . . . . .<br>16|
|2.5|Dome|sticandInternationalLabor Migration.....<br>. . . .<br>.<br>17|
||2.5.1|Rural-UrbanMigration....... . . . . . . . . . .<br>.<br>17|
||2.5.2|InternationalMigration....... . . . . . . . . . .<br>.<br>17|
|2.6|Const|raintsandChallenges....... . . . . . . . . . . .<br>.<br>17|

## **3** A Quantitative Framework

- A Quantitative Framework **20** 3.1 Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.1 Rural Production . . . . . . . . . . . . . . . . . . . . . 20 3.1.2 Urban Informal Production ..... . . . . . . . . . . 21 3.1.3 Production of public goods and services .... . . . . 22 3.1.4 Urban Formal Private Production ..... . . . . . . . 22

- 3.2 Wages, Employment, Migration and Skills Acquisition .... . 23 3.2.1 Rural Wages, Employment, and Internal Migration . . 23 3.2.2 Urban Unskilled Wages, Employment, and Unemployment ......... . .. . . .. . .. . . .. . .. . . 25

- 3.2.3 Urban Skilled Wages, Employment and Unemployment 28 3.2.4 Skills Acquisition ...... . . . . . . . . . . . . . . . 30 3.2.5 International Labor Migration ..... . . . . . . . . . 31

- 3.3 Supply and Demand . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 External Trade ....... . . ......... . . . . . . . . 34

2
occur unconsciously. Norms also serve to coordinate the behavior of individuals in a group. Coordinated behavior has the effect of providing the group with a sense of shared values, which further reinforces the underlying values, attitudes and behavior. Figure 7 illustrates the relationship between values, behavior and norms.<sup>15</sup>

# **Intergroup dynamics and exclusion**

Individuals are often classified, by themselves and by others, as belonging to a group or groups. This ‘grouping’ plays an important role in the normative process of social exclusion. When conducting surveys and censuses, researching social phenomena, and executing policies and programs, governments, for example, rely heavily on classifying people into social groups. Yet social groups are not natural phenomena; social groups are constructed by social processes and, as such, are often the product of dominant power relations in society. Defining differences and similarities between people is at the heart of ‘grouping’. This sorting of people into ‘similar’ and ‘different’ exists across all societies and is innate in people (Tajfel and Turner 1985; Crocker and Lutsky 1986).

> 15 The diagram has been developed based on the VBN (Values-Behavior-Norms) theory proposed by Schwartz (1994) and the description of ‘ritualization’ of gender norms in the classroom found in Moscheta, MacNamee and dos Santos, (2011).

22
teria) are not applicable. One practical result in Qu and Perron (2007) is that the limited distribution of the estimates of the break-points is not affected by the imposition of valid restrictions on the parameters. Hence, a non-linear VAR model with a number of lags, say p, can be considered as a valid restriction of a non-linear VAR model with p+1 lags, if the break-dates in the two models are significantly unchanged. We use this result to decide on the number of lags in the application section.<sup>5</sup>

# **3. House Prices Macro-Link in the US: Evidence of Instability**

Under this section we apply the Qu and Perron’s algorithm to the US macrodata. We consider a VAR Model with three endogenous variables: real gross domestic product (GDP) or real private consumption (C), real house prices (HP) and the Fed Funds rate (I), as a proxy for the monetary policy stance. Closed specifications accounting for the house prices macro-relationship have been used by Iacoviello (2005), Goodhard and Hoffman (2007, 2008), Oikarine (2009), and Calza, Monacelli, and Stracca (2013) among others.

We work with two different VAR specifications, one including GDP and the other private consumption, to examine whether the reduction in credit constraints in the US could have modified only the impact of housing prices on consumption, but not its effect on overall economic activity.

Our data is in quarterly frequency from 1960q1 to 2009q3. GDP, private consumption, and house prices are measured in logarithms, with the log values detrended via the first-differencing filter.<sup>6</sup> Private consumption and house prices are deflated using the consumer price index. The Fed Funds rate is the average value in the first month of each quarter. The house price series comes from the Shiller real home price index. Figure 1 shows the data series used.

- _3.1 Application of the Qu and Perron’s Algorithm in a VAR with no Linear_ . _Constraints_

The Qu and Perron’s procedure to identify breaks is applied to the following model:

> 5An alternative method of lag selection in a non-linear VAR is proposed in Kurozumi and Tuvaandorj (2011). The authors discuss a modified AIC and BIC to be used in the case of a multivariate model with multiple structural changes.

> 6 We use the first-differencing filter instead of a two-sided moving average filter (for example, Hodrick-Prescott or Baxter-King) in order to test for Granger causality in Section 4. Indeed, it is well known in the econometric literature that the use of a two-sided moving average filter biais the Granger causality test towards a false finding of Granger causation (see for instance Cogley 2008).