rafmacalaba/datause-encoder-data huggingface dataset:
We are able to shed light on this issue by following the labor-market trajectories of workers leaving firms that exit the market. To follow them, we use our linked employer employee panel datasets. Workers’ trajectories can be tracked as long as they remain formally employed, and as long as the characteristics of the firms in which they gain employment can be identified. Given the features of the dataset, an exit of workers from the registry can be interpreted as evidence that the workers are either no longer employed, or that they are employed in informal work.

Table 9 reports the transitions of workers who were employed in firms that exited after the 2016 minimum-wage increase. The vast majority of workers from exiting firms had not been formally reemployed by the end of 2016 (Table 9, Panel a). Only 15 percent had found another formal job, meaning that 85 percent were either unemployed, or employed informally one year after the minimum wage rose. Workers who were employed in large, formal firms that exited are more likely to have found other formal employment, compared with workers who were employed in microenterprises.

Table 9, Panel b, restricts the transition matrix to the minority of workers who had found other formal employment by the end of 2016. The panel exhibits evidence for the upward mobility of some workers who transitioned into larger firms. Among workers who had been employed in microenterprises prior to the wage increase, and who had found formal employment by the end of 2016, more than two-thirds found employment in non-microenterprises. Meanwhile, only limited, downward mobility is evident among formal-sector workers who had been employed in large firms that exited after the minimum-wage increase. More than 80 percent of these workers had been reemployed by other large formal firms by the end of 2016. This indicates a certain level of positive reallocation among workers who had found other formal jobs.

The EIS data do not allow us to observe what happened to workers who were employed by firms that wound up existing, and who were no longer formally employed after the minimum wage increased. We do not know whether these workers transitioned into informal employment, became unemployed, or dropped out of the labor force. To provide indicative evidence on this question, we complement the EIS data with microdata from the Turkish Household Labor Force Survey (HLFS) published by the Turkish Statistical Institute, a nationally representative household survey that is available annually from 1998 to 2018. The survey asks all employed individuals whether they are registered with social security institutions through their primary employment. This allows 26
# **References**

Aghion, P. and P. Howitt (2007), “Capital, Innovation and Growth Accounting”, _Oxford Review of Economic Policy_ Vol. 23, No 1, pp. 79-93.

Araujo, J.T., M. Brueckner, M. Clavijo, E. Vostroknutova and K. Wacker (2014), _Benchmarking the Determinants of Economic Growth in Latin America and the Caribbean_ , World Bank.

De la Torre, A. and A. Ize (2019). “Accounting for Growth: An Open Macro Perspective. <mark>” Mimeo.</mark> Fortin, N., T. Lemieux and S. Firpo (2010), “Decomposition Methods in Economics”, NBER WP16045.

Hausmann, R. et al. (2014). _The Atlas of Economic Complexity: Mapping Paths to Prosperity_ , MIT Press Books.

Hausmann, R. J. Hwang and D. Rodrik (2005). “What you Export Matters.” NBER WP11905.

Ize, A. (2019a). “A Note on Mexico’s Growth”, _Estudios Economicos,_ <mark>vol. 34(1), pp. 123-155.</mark> Ize, A. (2019b). “El Modesto Crecimiento de México: Una Descomposición Macroeconómica Contable”, _Revista de Economía Mexicana, UNAM_ (forthcoming).

Lederman, D. and W. Maloney (2012). _Does What You Export Matter? In Search of Empirical Guidance for Industrial Policies_ . World Bank.

Mandel, B. (2011). “The Dynamics and Differentiation of Latin American Exports”. Federal Reserve Bank of New York Staff Report 508.

Pages, C., ed. (2010). _The Age of Productivity_ . Inter-American Development Bank, Washington, D.C.

Prados de la Escosura, L. (2007). “When did Latin America Fall Behind,” in _The Decline of Latin American Economies: Growth, Institutions and Crises_ , edited by S. Edwards, G. Esquivel, G. Márquez, The University of Chicago Press (by NBER).

Sachs <mark>, J. and A. Warner (2001). "The Curse of Natural Resources".</mark> _<mark>European Economic Review</mark>_ <mark>, vol. 45, issue 4-6, pp. 827-838.</mark> Shapiro, A. and F. Mandelman (2014). “Remittances, Entrepreneurship, and Employment Dynamics over the Business Cycle.” FRB Atlanta Working Paper, No. 2014-19.

Rodrik, D. (2008). “The Real Exchange Rate and Economic Growth”, _Brookings Papers on Economic Activity_ , 39:2 (Fall), pp. 365-439.

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Consider the behavior of H countries supporting MF for L countries in this case.<sup>8</sup> The welfare function related to fossil-fuel consumption in the L country, in the absence of MF support from the outside, is specified as: _RL_ is fossil-fuel consumption in carbon-equivalent units in this country _, γ_ is a fixed technology parameter, _p_ = the fossil fuel price, and _vL_ = the “co-benefit” to the L country from reducing its carbon emissions from its own mitigation policy.<sup>9</sup> (3.1) represents a simple “linear-quadratic” relationship (where utility or profit for the L economy is a function of fossil fuel consumption only), valid over a (limited) range for _R_ relevant here, and resulting in simple results in particular for cases with small or moderate changes in GHG emissions.<sup>10</sup> (Under the PA, even conditional mitigation targets are for most L countries likely to be implemented using moderate carbon prices, at least in the starting phases.) The optimal fossil-fuel consumption of the L country before receiving MF from donor countries is found from maximizing (3.1) with respect to _RL_ : The L country is assumed to itself implement a mitigation level _vL/γ_ as part of its unconditional NDC plan, by imposing its own national carbon tax _vL_ .

Consider an H country or country bloc, which provides MF payments to an L country.<sup>11</sup> Define _RLC_ as the optimal fossil fuel consumption in the L country, given support payments from the H country to the L country in return for additional GHG mitigation. Due to a “missing additionality” problem, discussed below, support payments are however assumed to go also to units of mitigation

> 8 The presentation of analytical examples builds largely on Mundaca and Strand 2019a.

> 9 Such co-benefits could be significant (see Nemet et al. 2010; Jiang et al. 2013; Parry et al. 2015; Rao et al. 2016, 2017; Pigato et al. 2019).

> 10 Mathematically, (3.1) represents a second-order Taylor expansion of the country’s net production function, highly precise for small changes in outcome variables but often less precise for larger changes.

> 11 We say nothing about how a “country bloc” (or “climate club”; Nordhaus 2015) is conceptualized or formed.

8
|||||||||passive<br>follower|
|---|---|---|---|---|---|---|---|---|
|Haiss,<br>Sumegi|2006|29<br>European<br>countries|1992-<br>2004|Real GDP/<br>employee|Life prem.,<br>P&C prem.,<br>total prem.|Cross country<br>panel|Physical<br>capital,<br>human<br>capital,<br>interest rate,<br>inflation|Weak<br>causality from<br>life insurance<br>to growth.|
|Arena|2008|55<br>countries|1976-<br>2004|Real GDP/<br>capita<br>growth|Life, non-life<br>penetration|Dynamic<br>panel GMM|Private<br>credit, SX<br>turnover,<br>openness,<br>gov’t cons,<br>inflation,<br>human<br>capital, terms<br>of trade<br>change|Significant<br>causal effect<br>from<br>insurance to<br>GDP growth..<br>Non life at all<br>stages of<br>development,<br>life later.|
|Vadlamannat<br>i|2008|India|1980-<br>2006|Real GDP/<br>capita|Life, non life,<br>total,<br>penetration||Pop. growth,<br>savings,<br>lending,<br>inflation,<br>various<br>reform<br>measures/<br>inc.<br>dummies.|Strong supply<br>leading –<br>reinforced by<br>reforms.|
|Curak,<br>Loncar and<br>Poposki|2009|10 UE<br>transition<br>countries<br>–<br>unbalance<br>d panel|1992-<br>1997|GDP/<br>capita<br>growth|Life prem.,<br>P&C prem.,<br>total prem.|OLS after<br>differencing,<br>2sls|GDP/ capita,<br>private<br>credit, SX<br>capitalization<br>, investment,<br>education,<br>openness,<br>inflation|Insurance<br>development<br>promotes<br>economic<br>growth|
|Adams,<br>Andersson,<br>Andersson<br>and<br>Lindmark|2009|Sweden|1830-<br>1998|Ln Real<br>GDP/<br>capita|Real bank<br>credit, real<br>total<br>premiums per<br>capita<br>(insurance<br>density)|Granger<br>causality with<br>lags||Insurance is<br>supply leading<br>for economic<br>growth and<br>bank lending<br>with a 4 year<br>lag. Banks<br>have neutral<br>effect on<br>growth.|
|Avram,<br>Nguyen,<br>Skully|2010|93<br>countries|1980-<br>2006|Real GDP/<br>capita<br>growth|Density and<br>penetration,<br>life non life|Cross section<br>regression,<br>Dynamic|Bank credit,<br>SX turnover,<br>in. GDP/<br>capita,gov’t|Significant<br>supply leading<br>based on<br>density, but|

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likelihood of crime and violence.”<sup>8</sup> To test our hypothesis, we re-estimate our model with a triple interaction term that captures the interaction between gendered laws, rule of law and the female dummy. Each of the variables is also included in the model as two-way interaction terms as well as individual variables. This is the same approach as was taken above to test the mediating impact of negative social norms.

The results presented in **Table 4** show that the triple interaction between legal equality, _Female_ and legal enforcement is positively and significantly related to account ownership for the regression in which equality is represented by the _Mobility_ , _Workplace_ , _Pay_ , _Marriage_ , _Entrepreneurship,_ and _Assets_ indicators, as well as the regression with the aggregate _WBL index_ . This supports **H2** : rule of law strengthens the positive relationship between legal equality and women’s financial inclusion. Our finding extends the claim of La Porta et al. (1997): higher regulatory quality also significantly increases women’s financial inclusion. This implies that in order to broaden financial access for women, economies must pursue their efforts to improve their governance and institutions, specifically through strengthening the rule of law, including enforcement of financial contracts and financial regulatory oversight.

# **4.2.2. Investigating the channels**

The Global Findex data set also includes cross-country data on self-reported reasons for not having a formal account, making it possible to identify specific barriers to financial inclusion. Each respondent can answer whether one or several of the proposed barriers contributes to restricting her/his access to account ownership. More specifically, respondents were asked: “Please tell me whether each of the following is a reason why you, personally, do not have an account at a bank or another type of formal financial institution?”. The reasons considered are: _Too far away_ , _Too expensive_ , _Lack of documentation_ , _Lack of trust_ , _Lack of money_ , _Religious reasons_ , _Family member has one_ , _Cannot get one_ or _No need for financial services_ . **Appendix A** provides a more detailed description of each response.

Investigating the motives for financial exclusion are especially relevant for studies focusing on the role of institutional frameworks. Indeed, individuals can either be voluntarily or involuntarily financially excluded (de Koker and Jentzsch, 2011). The 2014 Global Financial Development Report (World Bank, 2013) defines voluntary exclusion as the result of religious or cultural impediments, or lack of interest in financial services. In contrast, involuntary

> 8 <u>https://info.worldbank.org/governance/wgi/Home/downLoadFile?fileName=rl.pdf</u> [Accessed: November 29th, 2022].

13
consumption). This requires panel data. For this to work, interview months must also differ across waves for a sufficient number of households since otherwise the effect of the month of interview cannot be identified. While less prone to bias, fixed effects estimation comes at the expense of precision, making it a more stringent test of seasonality. The availability of three waves of household panel data in Tanzania, each conducted throughout the year and with many households interviewed in different months across waves, enables this study to follow the fixed‐effects estimation route.

Second, seasonal factors are estimated on a limited number of observations for each month (three in the empirical application of this paper). The Monte Carlo results presented above in connection with price seasonality indicate that seasonal gaps estimated in short time series may be biased upwards. It is reasonable to suppose that this result will extend to panels with short time dimension. The more parsimonious trigonometric specification is therefore also explored here: Fixed effects estimation of (9) is the preferred specification. For comparison, the estimated seasonal gap using the dummy variable approach (with and without fixed effects) will also be reported. To control for sources of consumption seasonality that are unrelated to the production cycle, such as the effect of specific demand shifters related to religious events and holidays (e.g. Ramadan and Christmas), a December dummy, to account for possible additional Christmas expenditure, and a Ramadan dummy are added to the vector of controls _xhw_ .

Finally, understanding the extent to which seasonality in food prices transmits to seasonality in food consumption is the third core question of interest. It is natural to suppose that seasonality in food consumption is a consequence of seasonality in the prices of subsistence foods – see Dercon and Krishnan (2000), Dostie, Haggblade, and Randriamamonjy (2002) and Ellis and Manda (2012). High food prices generate both negative income and substitution effects for net food purchasers. For net food sellers, greater price seasonality induces larger fluctuations in income. A larger share of their income depends on food production and their sales are often 11
less cohesion. The increase in solidarity is less apparent among refugees where the majority (53%) stated that the crisis had no effect on solidarity.

**Figure 18: Levels of trust, by group (June) (%)**

<!-- Start of picture text --> How much to do you trust people in your How much do you trust your neighbors?<br>town/village 76<br>54<br>5557 50<br>43 46 4242 IDPs<br>39 IDPs<br>24 23 Refugees<br>14 Refugees<br>6 7 3 4 Returnees 6 3 1 1 0 Returnees<br>A lot of Trust No trust No trust A lot of Trust No trust No trust<br>trust at all trust at all<br>How much do you trust people from other How much do you trust religious leaders?<br>ethnic groups? 73<br>67<br>6260 51<br>49<br>43 37 IDPs<br>IPDs 30<br>25 19 Refugees<br>16 Refugees<br>9 6 6 6 8 1 Returnees 4 5 2 1 4 Returnees<br>A lot of Trust No trust No trust A lot of Trust No trust No trust<br>trust at all trust at all<br><!-- End of picture text --> Source: Listening to Displaced People Survey, 2014.

Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time.

Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North.<sup>20</sup>

> 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. The secession of the North is stated as an option on how to resolve the conflict

25
# **Figure 17b**

<!-- Start of picture text --> Share of households with diversified or specialized (Africa)<br>income portfolios, by expenditure quintiles<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>Diversified Specialized in On-farm Activities Specialized in non-agric wage<br>Specialized in agric wage Specialized in self-employment Specialized in non-labor income<br>Specialized in transfers<br>NER 11 MAL 04 MAL 11 MAD 93 GHA 92 UGA 06 GHA 98 UGA 10 UGA 11 GHA 05 TZA 09 KEN 05 NGA 04 NGA 10<br>100<br>80<br>60<br>Share (%)<br>40<br>20<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Notes: Surveys sorted by increasing per capita GDP.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 18. Figure 19.<br>Non-Agricultural Wage Specialization by Per Capita GDP On-farm Specialization by Per Capita GDP<br>ETH 12<br>NGA 04<br>MAD 93GHA a 92<br>MAL 04 GHA b 98 TZA 09 NGA 10<br>MA L 11 UGA 10GHA c 05<br>NER 11 UGA 06 UGA 11 KEN 05<br>UGA 11<br>UGA 06 KEN 05<br>ETH 12 NER 11 MAL 04 MAL 11 MAD 93 GHA 92 G HA UGA 10 98 GHA 05 TZA 09 NGA 04 NGA 10<br>6 6.5 7 7.5 8 6 6.5 7 7.5 8<br>Log Per Capita GDP (PPP, COnstant 2005, $) Log Per Capita GDP (PPP, Constant 2005, $)<br>Average Specialization Quintile 1 Average Specialization Quintile 1<br>Quintile 3 Quintile 5 Quintile 3 Quintile 5<br>Notes: 1. Fitted curve is the quadratic prediction of the share of non-agricultural wage specialized households on per capita GDP, Notes: 1. Fitted curve is the quadratic prediction of the share of on-farm specialized households on per capita GDP,<br> for the respective quintile. for the respective quintile.<br>2. Surveys sorted by increasing Per Capita GDP. 2. Surveys sorted by increasing Per Capita GDP.<br>40 100<br>80<br>60<br>20<br>40<br>20<br>Share of Specialized Households (%) Share of Specialized Households (%)<br>0 0<br><!-- End of picture text -->

# _Stochastic dominance analysis_

To take a more systematic approach to characterizing the association between sources of income, specialization and welfare, we use measures of stochastic dominance. For each of four of the African countries, covering six data sets—Malawi (2011), Niger (2011), Tanzania (2009 and 2010) and Uganda (2010 and 2011)—we first compared sources of income. We then look at income diversification and specialization income generation strategies by both total household income and by per capita expenditure. We include the latter as expenditure is considered the gold standard in terms of measuring household level welfare. In each case we calculated pairwise tests of stochastic dominance (see Appendix Figure A1 for an example), and we rank sources of income (or income 18
# **Market Integration and Structural Transformation in a Poor Rural Economy**

**Måns Söderbom**<sup>**1**</sup> **Bob Rijkers**<sup>**2**</sup>

## **JEL Classification:** O13, O14, O18.

**Key words:** Market integration, trade, structural change, rural transformation

**Acknowledgements:** We thank Arne Bigsten, Luc Christiaensen, Paul Collier, Stefan Dercon, Catalina Gutierrez, Helena Hwang, Varun Kshirsagar, Alice Kuegler, Peter Lanjouw, Josef Loening, Remco Oostendorp, Pierella Paci, Johan Stennek, Ana Revenga and Francis Teal for helpful comments and discussions.

1Department of Economics, School of Business, Economics and Law, University of Gothenburg, E-mail: mans.soderbom@economics.gu.se 2The World Bank, Washington D.C. E-mail: brijkers@worldbank.org
a consistent number of observations across estimates.<sup>8</sup> The resulting sample consisted of 1,924 respondents.

The sample includes individuals between the ages of 18-30, who lived in Karachi at the time of data collection. It is balanced in terms of gender (54 percent men, 46 percent women), but the age distribution is concentrated at the tails.<sup>9</sup> Approximately 16 percent of respondents were 18 years-old and 19 percent were 30 years-old, whereas other ages (19 to 29 years) each represented only 4-8 percent of the sample. The respondents were more educated than the national average. While the primary net enrollment rate in Pakistan is 67.7 percent and the secondary enrollment rate is 38.5 percent,<sup>10</sup> 100 percent of the sample reported having completed primary and 87.5 percent reported having completed secondary school (further, within the latter, 31 percent had a bachelor’s and almost 6 percent had a master’s degree). Approximately 33 percent of the respondents reported working, another 29 percent reported studying, and 15 percent reported doing both. Most respondents had a lower monthly household income (less than 30,000 Pakistani rupees) compared to the national average (35,662 Pakistani rupees or approximately US$230).<sup>11</sup> Given the sensitivities about asking participants to disclose their ethnic background, the study participants were asked about their “native language” instead of ethnicity. This was then treated as a proxy for ethnic affiliation. As a result, ethnic diversity in the sample might be underreported, especially if some respondents were unsure about the survey’s emphasis on “native language” and mistakenly reported a language they knew how to speak. Further, it is possible for younger individuals living in a cosmopolitan urban setting such as Karachi not to speak the native language associated with their ethnic identity. The literature also suggests that individuals might choose to misrepresent their identity, consciously or subconsciously, for security concerns, politics, social identity, self-understanding or other reasons (see discussion in Taş et al., 2014). With these caveats in mind, most respondents in the sample, 77 percent, reported Urdu as their native language, and thus were assigned to the Muhajir ethnic group.

> 8 At the beginning of the survey, some questions were not coded as compulsory and thus, some participants did not complete all the survey questions. These observations were later dropped to maintain consistency across the samples used for the analysis.

> 9 Age was self-reported, and the facilitators did not check identity cards. It is possible for the sample age range to be wider than 18-30 years if the clustering of respondents at the two tails is caused by those younger than 18-years-old overstating their age and those older than 30-years-old understating their age to participate in the study.

> 10 World Development Indicators (WDI), World Bank, 2018.

> 11 Household Integrated Economic Survey (HIES) 2015-16.

9