rafmacalaba/datause-encoder-data huggingface dataset:
pressures towards increased inequality may be intensifying in many regions in the world.<sup>18</sup> Furthermore, the losses and gains in agriculture wages exhibit strong regional patterns: real wages of unskilled farmers rise in Latin America, the Middle East, and East Asia & Pacific, while declining in other developing regions, and, much more strongly, in high income countries.

**Figure 6 Most countries gain from the removal of agricultural distortions**

<!-- Start of picture text --> Percent change in real consumption<br>-4.0 -2.0 0.0 2.0 4.0 6.0 8.0 10.0<br>Ecuador<br>Lithuania<br>New Zealand<br>Bulgaria<br>Nigeria<br>Rest of North Africa<br>Colombia<br>Rest of Western Europe<br>Argentina<br>Mozambique<br>Slovakia<br>Estonia<br>Czech Republic<br>Brazil<br>Morocco<br>Slovenia<br>Poland<br>Russia<br>Rest of East Asia<br>Vietnam<br>Nicaragua<br>Hungary<br>Malaysia<br>Thailand<br>Zimbabwe<br>Latvia<br>Rest of LAC<br>Korea<br>Indonesia Global liberalization of trade in agriculture and food products<br>Rest of ECA<br>Kazakhstan Global liberalization of all trade<br>Philippines<br>Middle East<br>EU 15<br>Bangladesh<br>Romania<br>Turkey<br>Australia<br>Japan<br>Zambia<br>Canada<br>Rest of Sub-Saharan Africa<br>Tanzania<br>Taiwan<br>Pakistan<br>India<br>Hong Kong and Singapore<br>United States<br>Egypt<br>South Africa<br>Other South Asia<br>Sri Lanka<br>Mexico<br>China<br>Rest of West & Central Africa<br>Chile<br>Madagascar<br>Uganda<br>Senegal<br>Kyrgystan<br><!-- End of picture text --> _Note:_ The black bars show the percent increase in consumption (at pre-reform prices) due to the removal of trade distortions in agriculture and food products (excluding beverages and tobacco). The grey bars show the additional gains in consumption due to the removal of all remaining trade barriers. The combined length of the two bars shows the consumption gains from a full global trade reform. _Source:_ Authors’ simulations with the LINKAGE model.

> 18 Note that trends in consumption per capita are unlikely to be representative of the welfare of agricultural households, since their weight in total consumption is low due to limited incomes and high incidence of poverty.

14
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2706 Household Income Dynamics<br>in Rural China|Jyotsna Jalan<br>Martin Ravallion|November 2001|C. Cunanan<br>32301|
|WPS2707 Financial Intermediary Development<br>and Growth Volatility: Do<br>Intermediaries Dampen or Magnify<br>Shocks?|Thorsten Beck<br>Mattias Lundberg<br>Giovanni Majnoni|November 2001|A. Yaptenco<br>38526|
|WPS2708 Accountability and Corruption:<br>Political Institutions Matter|Daniel Lederman<br>Norman Loayza<br>Rodrigo Reis Soares|November 2001|P. Soto<br>37892|
|WPS2709 Explaining Leakage of Public Funds|Ritva Reinikka<br>Jakob Svensson|November 2001|H. Sladovich<br>37698|
|WPS271 0 Breaking up the Collective Farm:<br>Welfare Outcomes of Vietnam's<br>Massive Land Privatization|Martin Ravallion<br>Dominique van de Walle|November 2001|C. Cunanan<br>32301|
|WPS2711 Identifying Class Size Effects in<br>Developing Countries: Evidence from<br>Rural Schools in Bolivia|Miguel Urquiola|November 2001|H. Sladovich<br>37698|
|WPS27'2 Contract Risks and Credit Spread<br>Determinants in the International<br>Project Bond Market|Mansoor Dailami<br>Robert Hauswald|November 2001|W. Nedrow<br>31585|
|WPS2713 Causes of Inequality in Health:<br>Who You Are? Where You Live?<br>Or Who Your Parents Were?|Adam Wagstaff<br>Pierella Paci<br>Heather Joshi|November 2001|H. Sladovich<br>37698|
19

# CHAPTER 5: LJPLICATIONS FOR FUTUR AGRICTULRAL CREDrT LENDING

27. Targetting lending for Agricultural Credit<sup>projects may become warranted when It follows</sup> significant changes that have occurred<sup>In the economy and which are expected to have substantlal</sup> Impact on the rural sector. The recent<sup>Bank Policy paper on financlal sector operations reads:22</sup> "in many developing countries, poor informatlon,<sup>unfamillarity with modern financial</sup> Instruments and Institutions, lack of lender<sup>experlence and expertise In evaluating certaln</sup> activitles or new technologies, absence of<sup>confidence between borrowers and lenders,</sup> uncertalntles regarding the permanence of<sup>economic reforms, or simply traditlon may limit</sup> market response to the opportunitles opened<sup>and demands made by financial sector reform</sup> and broader economic adjustment. Properly<sup>designed and complemented by necessary pollcy</sup> reforms, targeted credit can act as a catelyst<sup>to overcomo such obstacles, opening market</sup> access to previously excluded groups and<sup>sectors, such as microenterprises and small</sup> farmers, thereby promoting the development of<sup>credit markets."</sup> It has to be, however, substantiated that full<sup>reliance on market forces Is not preferable, and that,</sup> In the absence of the proposed Intervention<sup>In the financlal market, the supply response or the</sup> adoptlon of now technologies vWouId be unacceptably slow. In the circumstances<sup>outilned above,</sup> agricultural credit projects serving as a<sup>catalyst In growth should be accompanied by the removal</sup> of distorted policies that inhlbit demand<sup>for agriculture Investments,</sup> Introducing a specific targetted line of credit aimed at speeding<sup>up supply response can be a complementary measure to</sup> the removal of price controls on agricultural<sup>products which hindered market-Induced lending to the</sup> rural sector. Slmilarly, directed credit<sup>can be useful when the banking system Is considered to be</sup> responding too slowly to other significant<sup>reforms such as changes In the terms of trade of the</sup> agricultural sector due to trade llberalizatlon,<sup>a sharp correctlon of an overstated rate of exchange</sup> World Bnk Polce Guidb Finania Seco Ope0ton,<sup>**Apri 1991,**page9,paagrah 10.</sup>
**Table 2. Mean Economic Outcomes (in 2013 Dirhams) by Round and INDH**

||**Control**|**2008**<br>**Treatment**|<br>**Total**|**Control**|**2011**<br>**Treatment**|**Total**|**Control**|**2013**<br>**Treatment**|**Total**|
|---|---|---|---|---|---|---|---|---|---|
|**Income per capita**|4,778.63|4,798.42|4,788.7|5,134.98**|5,479.03**|5,313.89|5,068.43***|5,577.44***|5,333.93|
|**Log income per capita**|8.26|8.27|8.27|8.23***|8.34***|8.29|8.27***|8.37***|8.32|
|**Consumption per capita**|3,925.43|3,976.68|3,951.5|3,555.93***|4,045.50***|3,810.45|3,241.24|3,204.57|3,222.11|
|**Log consumption per capita**|8.26|8.27|8.27|8.23***|8.34***|8.29|8.27***|8.37***|8.32|
|**Household wealth per capita**|12,888.57*|11,784.68*|12,327.05|21,110.58***|27,028.18***|24,187.05|23,445.85***|28,067.55***|25,856.56|
|**Log household wealth per capita**|8.42**|8.47**|8.45|9.22***|9.32***|9.27|9.60***|9.71***|9.66|
|**N (observations)**|4,415|4,571|8,986|4,322|4,680|9,002|4,118|4,490|8,608|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ Tests for equality of means across treatment and control estimated in each round with *p < 0.1; **p < 0.05; ***p < 0.01.

12
Bianco, A. M. & Martínez, E., 2009. Robust testing in the logistic regression model. _Computational Statistics & Data Analysis,_ 53(12), pp. 4095-4105.

Bloem, J., 2021. Aspirations and Real Estate Investments in Rural Myanmar. _Journal of Economic Inequality._ Boateng, E. S. & Löwe, A., 2018. _Aspirations matter: what young people in Ghana think about work,_ s.l.: Overseas Development Institute.

Breda, T., Grenet, J., Monnet , M. & Van Effenterre, C., 2020. Can female role models reduce the gender gap in science? Evidence from classroom interventions in French high schools. _IZA Discussion Paper 13163._ Carling, J., 2017. How Does Migration Arise?. In: _Ideas to Inform International Cooperation on Safe, Orderly and Regular Migration._ Geneva: IOM.

Cramer, J., 2007. Robustness of Logit Analysis: Unobserved Heterogeneity and Mis-specified Disturbances. _Oxford Bulletin of Economics and Statistics,_ Volume 69, pp. 545-555.

Dalton, P. S., Ruschenpohler, J. & Zia, B., 2018. Determinants and Dynamics of Business Aspirations : Evidence from Small-Scale Entrepreneurs in an Emerging Market. _Policy Research Working Paper No. 8400._ Davey, F. & Stoppard, J., 1993. Some factors affecting the occupational expectations of female adolescents. _Journal of Vocational Behavior,_ pp. 43, 235–250.

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Dustmann, C. & Okatenko, A., 2024. Out-migration, wealth constraints, and the quality of local amenities. _Journal of Development Economics,_ Volume 110, pp. 52-63.

Du, X. & Wong, B., 2019. Science career aspiration and science capital in China and UK: a comparative study using PISA data. _International Journal of Science Education._ Esipova, N., Pugliese, A. & Ray, J., 2018. _More than 750 Million Worldwide Would Migrate if They Could._ [Online] Available at: https://news.gallup.com/poll/245255/750-million-worldwide-migrate.aspx Evans, R., 2010. Children’s Caring Roles and Responsibilities within the Family in Africa. _Geography Compass,_ Volume 4, pp. 1477-1496.

Favara, M., 2017. Do Dreams Come True? Aspirations and Educational Attainments of Ethiopian Boys and Girls. _Journal of African Economies,_ p. 26(5): 561–583.

27
- -

iv (d) Financ .l PolicX. Part of the user cost of capital is the required return for the marginal source of finance. In an international context, finance may be raised from retain earnings, home-country debt and host-country debt. To the extent that real interest rates are unequal across countries, user costs of capital also vary, depending on the country in which capital is raised. In addition, the debt-equity ratio ia also relevant, since the two sources of finance are not taxed equally.

(e) Net Foreign Tax Credit Position. Taxes ultimately paid at home also depend on firms' net foreign tax credit position. Excess foreign tax credits enable a firm to remit additional foreign-source income with little or no further taxation at home. A deficient foreign-t.xcredit position has the opposite effects. The net foreign-tax-credit position of a firm, in turn, depends on the relative tax rates of the home and host countries as well as the extent to which the credit applies. Some capital exporters allow global crediting, in which credits arising from one host country may be applied to the income originating from another country. In other cases, foreign tax credits may be country-specific.

# 2. National Welfare

# (a) Developing Countries in General

Tax policy may fail to serve national interest if it is based entirely on traditional, one-country fremework. This section draws on the multi-country analysis presented above to indicate the scope and constraints faced by a developing country.

An important feature of policy-making in an international environment is the strategic behavior of the parties concerned.
# **Table 5. Services Linkages to Total Exports, 2011**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|**(10)**|**(11)**|**(12)**|**(13)**|**(14)**|**(15)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Primary|3.3|0.3|1.7|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|5.4|
|Energy|0.4|39.7|4.8|0.9|0.0|0.0|0.0|0.7|0.0|0.0|0.0|0.1|0.0|0.0|45.7|
|Manufacturing|0.3|2.2|11.6|0.6|0.0|0.1|0.0|0.3|0.0|0.0|0.0|0.1|0.0|0.0|14.7|
|Services|**1.0**|**16.4**|**7.7**|**9.2**|**0.0**|**0.6**|**0.5**|**4.3**|**0.3**|**0.2**|**0.2**|**2.6**|**0.1**|**0.2**|**34.3**|
|Electricity, gas, & water|0.0|0.1|0.1|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.3|
|Construction|0.0|1.0|0.5|0.7|0.0|0.5|0.0|0.1|0.0|0.0|0.0|0.1|0.0|0.0|2.2|
|Trade, distribution, & hotels|0.5|8.0|3.8|1.5|0.0|0.1|0.4|0.8|0.0|0.0|0.0|0.1|0.0|0.0|13.8|
|Transport|0.1|2.7|1.3|3.1|0.0|0.0|0.0|3.0|0.0|0.0|0.0|0.0|0.0|0.0|7.3|
|Communication|0.0|0.3|0.1|0.3|0.0|0.0|0.0|0.0|0.2|0.0|0.0|0.0|0.0|0.0|0.6|
|Finance|0.1|1.6|0.8|0.4|0.0|0.0|0.0|0.1|0.0|0.2|0.0|0.0|0.0|0.0|2.9|
|Insurance|0.0|0.1|0.0|0.2|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.0|0.0|0.3|
|Other business services|0.1|2.4|1.1|2.7|0.0|0.0|0.0|0.3|0.0|0.0|0.0|2.4|0.0|0.0|6.4|
|Other consumer services|0.0|0.0|0.0|0.1|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.1|
|Other services|0.0|0.1|0.0|0.2|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.2|0.3|
|Backward/ Demand|4.9|58.6|25.8|10.7|0.0|0.8|0.5|5.4|0.3|0.3|0.2|2.9|0.2|0.3|100.0|

_Source_ : World Bank Trade Value Added Database and Francois et al, 2013 _Note_ : Column headings are as follows: (1) Primary; (2) Energy; (3) Manufacturing; (4) Services; (5) Electricity, Gas, and Water; (6) Construction; (7) Trade, Distribution, and Hotels; (8) Transport; (9) Communication; (10) Finance; (11) Insurance; (12) Other Business Services; (13) Other Consumer Services; (14) Other Services; (15) Forward/Supply **.**
Bolivariana de Venezuela, Ecuador and Brazil. The aggregate context is thus one of weakening overall demand for the sector, but where the weakened currency increased competitiveness against imports. Real sales of domestic production were then fairly flat over our study period, falling 0.12 percent between 2013 and 2016.<sup>8</sup>

# **3. The Intervention**

The program was implemented by the National Productivity Center (Centro Nacional de Productividad, CNP), which is a Colombian non-profit institution with the mandate to contribute to increase productivity, innovation and competitiveness of Colombian businesses. CNP originally was funded and supported by Japanese technical cooperation and has been the recipient of training and in-house technical assistance to develop capabilities in implementing managerial consulting services such as Lean, Six-Sigma, etc. During its 15 years of experience CNP has developed a model of operation that has allowed it to support more than 4,000 Colombian companies in different areas of management, innovation, productivity and competitiveness. CNP used two types of consultants for the intervention. The first were lead consultants, who were long-term employees of CNP with more than 10 years experience, and experience managing teams. They led area consultants, who had to have had at least 5 years experience, and specialized in a particular focus area such as logistics or finance. The direct cost of implementation of this program was approximately US$2.4 million.

# **3.1 Diagnostic phase**

All firms, including the control, received a diagnostic as the first phase. This was implemented on a rolling basis between June and October 2013. The diagnostic was carried out by a team of 6 consultants, consisting of a lead consultant and five specialists, one for each area (Logistics, Human Resources, Finance, Marketing and Sales, and Production). The diagnostic began with an opening meeting with top and middle management, and then each area specialists would have five days of meetings with the responsible manager in the firm for their area to evaluate the 141 individual management practices that form Anexo K. This forms the baseline management practices measure. The consultants would also examine the firms’ key performance indicators for the last three years (to the extent records existed), and work with the leader to finish with a report

> 8 Export data and sales data are from DANE and are for the CIIU sector 2930 “Manufacturing of parts, pieces, and accessories for automobiles and their motors”.

8
retrospective section of LiTS data, which asks individuals about past events between 1989 and 2006<sup>5</sup> , we find evidence that adaptability is lower for negative events such as economic distress or labor market shock. Moreover, unpleasant events in the most recent past, in our case these are events which took place between 2000 and 2006, have higher impacts. This supports peak/end rule phenomenon suggested by Kahneman and Thaler (2005). Third, we find significant evidence that reluctant respondents on average are less satisfied with their lives. Reported life satisfaction is lower if the emotional state is negative during interview. We analyze this in greater detail in section 3.

Social capital such as trust, participation in civic groups and financial stability are associated with higher satisfaction. Finally, we find significant regional variation in reported life-satisfaction across European, Balkan, lower and middle income Commonwealth of independent states (CIS). This shows the significance of Soviet legacy and variation in reform trajectories since transition. We also find nuanced stories about life satisfaction separately for European countries, Balkan countries, low income and middle income CIS.

The rest of the paper is organized as follows. In section 2 we briefly discuss methodological issues of estimating a subjective well-being function. In section 3 we introduce our main arguments based on illustrative summary statistics and graphs. Section 4 discusses empirical model and key findings. In section 5 we provide a number of sensitivity analysis validating empirical outcomes. This is followed by a concluding discussion at the end.

# **2. Methodology**

The analysis of life-satisfaction (or happiness) in mainstream economics relates to the maximization of the utility function even though there is mixed evidence that happiness is sufficiently measured by happiness (Clark, et al 2008). That said, the relevance of

> 5 Detail method of our calculation is given later, in section 3

5
# **Table 6: Ordered Probit and Probit Regressions**

Financial Vulnerability Regressions (Marginal Effects)

|||**finburd**|**en**<br>|||**finvul**|||**arrea**|**rs1**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**renter**|**Pseudo r**<sup>**2**</sup>|**t-test**|**mortgage**|**Pseudo r**<sup>**2**</sup>|
|_New EU Countries_|||||||||||
|Czech Republic|-0.0253|0.0737|0.08|0.69|0.0185 **|0.0531 ***|0.16|-4.63 ***|-0.0271 ***|0.17|
|Estonia|-0.1512 *|0.0040|0.12|-0.52|0.0171|0.0241 **|0.10|-0.40|-0.0369|0.15|
|Hungary|0.0437 ***|0.0825 ***|0.07|1.66 *|0.0444 ***|0.0687 ***|0.13|-1.30|-0.0878 ***|0.13|
|Latvia|0.0217 **|0.0518 **|0.09|-0.32|0.0566 ***|0.0769 ***|0.09|-0.75|-0.0344|0.09|
|Lithuania|-0.0118 ***|-0.0424 *|0.07|3.89 ***|0.0329|0.0289|0.08|0.09|-0.1195 **|0.34|
|Poland|-0.1706 **|0.0886 ***|0.08|-4.09 ***|0.0672 ***|0.0325 ***|0.09|1.23|-0.0615 **|0.13|
|Slovak Republic|-0.0539|0.0484|0.06|-0.88|0.0041|0.0774 ***|0.10|-5.76 ***|-0.0555 ***|0.15|
|Slovenia|0.1375 ***|0.0435|0.06|4.58 ***|0.0401|0.0261 **|0.09|0.38|-0.1278 **|0.16|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.0524 ***|0.0001|0.06|6.55 ***|0.0347 ***|0.0226 ***|0.14|0.65|0.0108|0.10|
|Belgium|-0.0525 ***|0.1200 ***|0.05|1.37|0.0258 ***|0.0752 ***|0.19|-5.29 ***|-0.0297 ***|0.16|
|Cyprus|0.0920 ***|-0.0448|0.10|6.47 ***|0.0892 ***|0.0136|0.10|2.72 ***|0.0760 **|0.11|
|Denmark|-0.0247||0.07||-0.0369 ***||0.24||-0.0048|0.24|
|Finland|0.0458 ***|0.0567 ***|0.06|1.54|0.0562 ***|0.1043 ***|0.17|-4.30 ***|-0.0426 ***|0.15|
|France|-0.0131 ***|0.0668 ***|0.04|0.85|0.0040|0.0643 ***|0.14|-5.59 ***|-0.0450 ***|0.11|
|Greece|0.0522 ***|0.0845 ***|0.06|1.67 *|0.2553 ***|0.1578 ***|0.16|2.72 ***|0.0206|0.18|
|Ireland|-0.0458 ***|0.2174 ***|0.09|-2.46 **|0.0164|0.0892 ***|0.26|-3.89 ***|-0.0494 **|0.20|
|Italy|0.2176 ***|0.0821 ***|0.08|13.94 ***|0.0925 ***|0.0987 ***|0.13|-1.41|-0.0491 ***|0.11|
|Luxembourg|0.0066|0.0661|0.08|3.64 ***|0.0082|0.0265|0.19|-2.07 **|-0.0133 *|0.19|
|Netherlands|-0.0897 ***||0.05||-0.0418 ***||0.13||-0.0265 ***|0.12|
|Portugal|0.1007 ***|-0.0261|0.10|8.50 ***|0.0311 **|0.0508 ***|0.10|-1.24|-0.0070|0.14|
|Spain|0.1106 ***|-0.0450 **|0.06|10.48 ***|0.0585 ***|0.0625 ***|0.09|0.29|-0.0080|0.09|
|Sweden|-0.0894 ***|0.0914 ***|0.07|-7.90 ***|0.0210|0.0935 ***|0.14|-7.46 ***|-0.0224 ***|0.11|
|United Kingdom|0.0256 ***|0.0782 ***|0.06|0.74|0.0216 **|0.1150 ***|0.21|-9.64 ***|-0.0602 ***|0.16|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

12
Countries: Asian and African Perspectives. Oxford Studies in Comparative Education. Didcot, UK: Symposium, 187–207.

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# **F Discussion of Mechanisms**

Table F.1. Effect of Gujarat Riots on Men’s Marriage Outcomes

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|
|---|---|---|
|Post X Riot|-0.861<sup>_∗∗∗_</sup>|0.028<sup>_∗∗_</sup>|
||(0.078)|(0.012)|
|Observations|56436|58122|
|R-squared|0.280|0.115|
|Control Average|24.35|0.0554|
|District Fixed Efects|Yes|Yes|
|Year Fixed Efects|Yes|Yes|
|Controls|Yes|Yes|

_Note_ : This table reports coefficients from specification 1. Standard errors are clustered at the state level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

44