rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Acemoglu, Daron and Fabrizio Zilibotti (1997) “Was Prometheus Unbound by Chance? Risk, Diversification, and Growth.” _Journal of Political Economy_ 105(4): 709-751.

Acharya, Viral, and Philipp Schnabl (2010) “Do Global Banks Spread Global Imbalances? Asset-Backed Commercial Paper during the Financial Crisis of 2007–09.” _IMF Economic Review_ 58(1): 37-73.

Avdjiev, Stefan, Leonardo Gambacorta, Linda S. Goldberg, and Stefano Schiaffi (2017) “The Shifting Drivers of Global Liquidity.” Federal Reserve Bank of New York Staff Report 819, June.

Baker, Scott R., Nicholas Bloom, and Steven J. Davis (2015) “Measuring Economic Policy Uncertainty.” NBER Working Paper 21633, October.

Calvo, Guillermo, Leonardo Leiderman, and Carmen Reinhart (1993) “Capital Inflows and Real Exchange Rate Appreciation in Latin America: The Role of External Factors.” _IMF Staff Papers_ 40(1): 108-151.

Chinn, Menzie D. and Hiro Ito (2006) “What Matters for Financial Development? Capital Controls, Institutions, and Interactions.” _Journal of Development Economics_ 81(1): 163-192.

Chuhan, Punam, Stijn Claessens, and Nlandu Mamingi (1998) “Equity and bond flows to Latin America and Asia: the role of global and country factors.” _Journal of Development Economics_ 55: 439-463.

Calderón, César, and Megumi Kubota (2009) “Does Financial Openness Lead to Deeper Domestic Financial Markets?” Policy Research Working Paper 4973, World Bank, Washington, DC.

Calderón, César, and Megumi Kubota (2012) “Gross Inflows Gone Wild: Gross Capital Inflows, Credit Booms and Crises.” The World Bank Policy Research Working Paper Series WPS 6270, Washington, DC: The World Bank.

Calderón, César, and Megumi Kubota (2019) “Ride the wild surf: An investigation of the drivers of surges in capital inflows.” _Journal of International Money and Finance_ 92: 112-136.

Calderón, César, and Klaus Schmidt-Hebbel (2008) “Business Cycles and Fiscal Policies: The Role of Institutions and Financial Markets.” Working Paper 481, Central Bank of Chile, Santiago, Chile.

Cardarelli, Roberto, Selim Elekdag, and M. Ayhan Kose (2010) “Capital inflows: Macroeconomic implications and policy responses.” _Economic Systems_ 34(4): 333-356.

Doguwa, Sani. I., Mohammed M. Tumala and Olufemi I. Ajibola. (2014) “Destination Sectors and Originating Economies of Nigeria’s Private Foreign Assets and Liabilities in 2012.” _CBN Journal of Applied Statistics_ Vol. 5 No.1.

Fernandez-Arias, Eduardo and Peter J. Montiel (1996) “The Surge in Capital Inflows to Developing Countries: An Analytical Overview.” _The World Bank Economic Review_ 10: 51–77.

Forbes, Kristin J., and Francis E. Warnock (2012) “Capital Flow Waves: Surges, stops, flight and retrenchment.” _Journal of International Economics_ 88: 235-251.

Ghosh, Atish R., Mahvash S. Qureshi, Jun Il Kim, and Juan Zalduendo (2014) “Surges.” _Journal of International Economics_ 92: 266-285.

17
# **1. Introduction**

The impacts of international migration on development in the sending countries, and especially the effects on remaining household members, are increasingly studied. Empirical analysis is needed because the effect of migration on development in source communities is _a priori_ unclear. Migrant-sending households and their communities can benefit from remittance inflows, which now make up 30 percent of total financial flows to the developing world, but earnings and other household inputs that migrants would have generated locally are lost. Hence this is a growing area of the literature; for example, out of the 392 journal articles and working papers with remittances as a title or keyword, 60% were published since 2006.<sup>1</sup> Even more studies are likely in future as new survey data become available and labor mobility increases in response to growing international wage gaps, rising demand for services, divergent trends in youth and elderly populations in developed and developing countries, and catch up from the previously “everything but labor” nature of globalization in the post-World War II era (Pritchett, 2006).

The biggest difficulty in measuring impacts of migration on development is posed by selectivity issues. A common research strategy in this literature is to use household survey data to compare households who have had at least one member emigrate to those that have not. Such comparisons are complicated by a double-selectivity problem: first, households self-select into migration, and second, among households involved in migration, some send a subset of members with the rest remaining whilst other households migrate en masse.

In this paper we address these selectivity issues using the randomization provided by an immigration ballot under New Zealand’s immigration policy. We survey applicants to this random ballot and compare outcomes for the remaining household members of emigrants with those for members of similar households who were unsuccessful in the ballot. The policy rules determine which household members can accompany the principal migrant, providing an instrument to address the second selectivity issue. Since this migration channel has only recently opened, we measure the short-term impact of migration, which may change over time. The short term may be when household challenges are greatest, as they adapt to the absence of household members and have yet to receive large quantities of remittances.

The particular policy we focus on is the Pacific Access Category (PAC), which was established in 2001 and allows an annual quota of 250 Tongans to immigrate as permanent residents to New Zealand without going through the usual channels used for groups such as

> 1 Specifically, a search of EconPapers on RePEC (January 27, 2009) reveals that there were 17 papers in all of the 1980s, 49 in all of the 1990s, 88 between 2000 and 2005, and 87 in 2008 alone.

2
- ii -

|Determining the Size of the Grant . . . . . . . . . . . . . . . . . . .<br>..|. .. ........<br>. . . . . . . .<br>_56_|
|---|---|
|<br>Distribution of the Grant ...............................................<br>Measuring Capacity and Expenditure Need ....................................|_56_<br>_57_|
|VII. BORROWING ......................................................|58|
|Past Borrowing Pr<br>ac tic<br>e<br>s<br>...............................................|59|
|<br>Sources of Borrowing .................................................|60|
|VIII. PRIVATIZATION AND OWNERSHIP .......................................|61|
|AssetTansfers.............................|61|
|Privatization vs Entrepreneuriaism ..................................|62|
|<br>Timing of Asset Sales: Sales vs Give-aways ..................................|63|
|<br>Revenues from Privatization ..................................|64|
|IX. CONCLUSIONS ..................................|64|
|REFERENCES .................|84|

## List of Tables

|Table I Trends in Decentralization: Government Organization.|16|
|---|---|
|Table 2 Decntrlization: Scale and Size of Government..................|.............|
|Table 3 Reform and the Size of the Government Sector .22||
|Table 4 Macroeconomic Indicatorsi.2||
|Table_5_Tax Administration in Transition Socialist Economies ............................|28|
|Tabls 6 ExpenditWe Assignment to Subnational Authorities.................|.............<br>30|
|Tanle 7 A Possible Expenditure Assignment .......................................|8<br>3|
|Table 8 Revenue Assignments to Subnational Authorities...............|...............<br>44|
|Table 9 Structure of Subnational Govermment Finance .<br>.................................|1|
|Table 10 Subnational Government Borrowing.......................|................<br>59|
|Table 11 Privadzatioa Revenues: Ownership and Disposition<br>..|62|

# _Acknow_edeMents_

_We would like to thank those who commented on this paper, or who otherwise shared their insights on subnatlonal finance in the various countries, including Felix Jacob; Zelko Bogetdc, Robert Van Pulley, Thn Condon, Gabor Peteri, Shankar Acharya, Anwar Shah, Hen g-fiu Zou, Tony Pellegrin4 Jim Hicks, Luca Barbone, 7Thanos Cat sambas and Amaresha Bagchl. Research assistance of Rltu Nayyar is gratefully acknowledged. Susheela Jonnakuty providedexpert word prcessing. 7Thisis the firstin a seriesof papers under RPO BB67770M._
fluctuations in their incomes, as reported in the household surveys. For example, over

**Figure 4: Distribution of Shocks**

<!-- Start of picture text --> Inconx shocks<br>.3 88 [2I.8Rsia<br>'S i i 7.66 .9S 1 .71h 95-96 N9<br>0 so1o N 0 N0 0 0 0 N 0.2 0<br>o 9 ea '' 'la -O So c (%) 0 1 0 N 0 10 I <<br>Size of Shock (%/)<br>Expenditure shocks<br>10.8 -1<br>0 5.9 the mean expndtue ws -. 3 i Ru s i ad 4 1 i Poan. oc inccteorisavdifeen8.2 I5f e tRussia<br>fall.9 o lo t la h l w lol0o i o 4 Poland o lo a0 4 W<br>_i__ Size of Shockicms<br><!-- End of picture text --> Note: The figure shows the distribution of the change in real equivalent income and real equivalent expenditure between the referunec month and the same month one year later. The reference month is the month in which the household was observed in wave 2 of the survey. In Russia, wave 2 was fielded in November and December of 1995 while in Poland wave 2 was fielded between January and December of 1994. Income and expenditure shocks are in deviation of the national mean. The mean income shock was -4.0% in Russia and 1.8% in Ploland while the mean expenditure shock was -8.3% in Russia and 4. 1% in Poland. Since categories have different sizes, these plots do _not_ show distribution functions.

40% of the population in Russia either sees their income increase to more than double, or fall to less than half, while in Poland this fluctuation happens to slightly more than 10% of the population. Fluctuations in reported expenditures are only slightly smaller than those in incomes.

At first blush, these fluctuations in economic fortunes appear to be huge, but many of these changes might reflect transitory events that have a relatively small effect on underlying well-being of the population. In terms of our fashion metaphor, suppose we observe an individual who usually wears cheap clothes but who today wears expensive ones. This could be a persistent change that affects her underlying fashion 21
To summarize, the effects of Basic Protection, human capital, modern infrastructure, Refined BE, some aspects of the agglomeration environment (i.e., being in a big city, informal competition) tend to have effects on firm growth that are invariant to a country’s level of development. The effect of bank financing on Lgrowth is also income-invariant. Some BE elements work better in lower-income countries: access to bank financing has a positive association with LPgrowth, but only in low- and lower-middle-income countries; capacity agglomeration has a pronounced effect on LPgrowth only in low-income countries. Interestingly, trade credit access plays a positive role only in lower-middle-income countries.

# **6. Conclusions**

In this paper we have used comprehensive firm-level data which cover 709 cities in 128 countries to investigate the association between the business environment (BE) and firm growth, both in terms of employment growth and labor productivity growth. Unlike previous literature, we mainly focus on the city-industry-level BE with the advantage of being able to control for country and industry fixed effects. We examine a relatively comprehensive list of factors, including Basic BE (consisting of Basic Protection, human capital, infrastructure, and access to finance), Refined BE (consisting of entry and exit barriers, labor regulation, and tax burdens), and the agglomeration environment, which has been largely ignored by the BE literature.

When assuming homogeneous effects using the globally pooled data, Basic Protection, modern infrastructure (i.e., the internet), and access to finance prove to be important for firm growth, as does the agglomeration environment. Less important are electricity and Refined BE. Further investigations, however, reveal strong heterogeneity of the BE effects.

To begin with, the environment which is amenable to the growth of high-growth firms is dramatically different to that for other firms. For the latter group, Basic Protection and capacity agglomeration are important. In contrast, high-growth firms benefit relatively more from modern infrastructure, access to finance, lower Land Access Obstacle and lower Tax Burden Obstacle, and from being in big cities. High-productivity-growth firms also benefit significantly more from trade credit networks.

Small firms tend to need a stronger Basic and Refined BE than large and medium-sized enterprises, with some exceptions. Small firms benefit relatively more in terms of employment growth than larger firms when there are better Basic Protection, adequate availability of human 26
not find evidence of a significant link between the amount of labor farmers allocate to their plots (including both family and hired labor) and participation in extension services (see Table A12 in Appendix D). Thus, our data seem to provide no empirical support for this concern.

# **5 Conclusion**

In this paper, we study demand for advice in an agricultural setting. We first lay out a novel channel to explain farmers’ demand for agricultural extension services. Second, we provide empirical evidence that is consistent with implications of the model. The empirical work makes use of a large recently collected panel dataset of farmers from three countries in Sub-Saharan Africa.

The theoretical insights we provide on the determinants of farmers’ demand for agricultural advice and extension services center around the idea that participation in extension programs is only worthwhile if farmers devote sufficient attention to demanding, listening to, and implementing received advice. Based on this feature, we model farmers as rational decision makers facing a limited capacity to process information, causing attention to be a scarce resource. Our model gives rise to several interesting predictions. First, farmers’ decision to request extension services will depend on the amount of attention they are willing to devote to their agricultural production process, as opposed to other areas of life. Second, negative shocks to household income or wealth can result in larger demand for agricultural advice. Importantly, this holds even for shocks which do not directly affect optimal farming practices and thus do not constitute an immediate reason for farmers to request agricultural advice.

Overall, the model suggests a specific channel through which limited attention affects agricultural input choices and productivity, based on (imperfect) demand for advice. The derived insights complement the existing literature in this context and can contribute to explanations for low demand for extension service. Specifically, the implications of our model complement the insights of Banerjee and Mullainathan (2008), who argue that “people may not be able to fully attend to their jobs if they are also worrying about problems at home, and being distracted in this way reduces productivity” (p. 489). The model we propose explains how scarce attention translates into low productivity even if outside help is available at low monetary cost. On the other hand, the model implies a link between shocks and increased attention to agricultural production: Nonagricultural shocks can raise farmers’ marginal utility of additional income, consequently increasing their willingness to devote costly attention to agricultural production, leading to a higher probability that extension services are demanded. Given that participation in such programs translates into better production outcomes and eventually higher incomes, 26
# **TableA13. Factors affecting the poor household's probability to live in the GMA and,** **<u>once in the GMA, the probability to participate in the VAG/CRB</u>**

|||Probitmode|lsfortheh|ousehold's proba<br>Participate|bility to<br>in the|
|---|---|---|---|---|---|
|Variable|Variable description|Liveinthe|GMA|VAG/CR|B<sup>_a_</sup>|
|||(1)||(2)||
|park2|Kafue dummy variable|-1.284|***|-0.029||
|||(0.348)||(0.297)||
|park3|Lower Zambezi dummy|-0.442||0.131||
|||(0.349)||(0.236)||
|park4|Luangwa dummy|-0.633|*|-0.523|*|
|||(0.369)||(0.314)||
|hage|Age of the household head in years|-0.007|*|0.003||
|||(0.004)||(0.010)||
|fhhh|Female-headed household dummy|0.104||0.081||
|||(0.113)||(0.227)||
|maxedu|Education of the most educated household<br>|-0.022||0.028||
||member in years|(0.017)||(0.021)||
|c0to14|Number of children below 15 years|0.020||-0.067||
|||(0.029)||(0.071)||
|f15to60|Number of female members 15-60 years old|-0.023||0.414|***|
|||(0.057)||(0.145)||
|m15to60|Number of male members 15-60 years old|-0.004||-0.056||
|||(0.063)||(0.142)||
|m61plus|Number of members older than 60 years|-0.018||-0.290||
|||(0.109)||(0.328)||
|kroad|Distance to the nearest main road in km|0.012|**|0.000||
|||(0.005)||(0.005)||
|kbsch|Distance to the nearest basic school in km|0.004||-0.019||
|||(0.014)||(0.028)||
|kheal|Distance to the nearets health centre in km|0.000||-0.026|**|
|||(0.004)||(0.011)||
|vacast|Value of consumer durable assets in ZMK|-5.937||-3.690||
|||(3.705)||(8.503)||
|dcoop|Participation in cooperatives dummy|-0.449|**|1.032|***|
|||(0.181)||(0.326)||
|nproj|Number of projects in the community|0.012||0.018||
|||(0.041)||(0.037)||
|dcfnd|CRB funded dummy variable|<br>0.543|*|<br>1.599|***|
|||(0.284)||(0.296)||
|npart|Number of participants in the CRB/VAG|<br>0.158|**|<br>0.247|***|
|||(0.077)||(0.035)||
|cons|Intercept|0.725|**|-2.858|***|
|||(0.334)||(0.505)||

> _a_ Based only on the sub-sample of households that are located in the GMAs Level of significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

28
areas. In the agricultural context, Anderson (2001) reviews risk management instruments and discusses market failures and rationale for government intervention.

Farmers in developing countries are vulnerable to multiple exogenous risks that may vary from those affecting a single person (idiosyncratic risk) to those that can devastate whole communities (systemic risk). As can be seen from Table 1, some of these risks are common for all households, while others are specific to agricultural production, which is inherently risky due to unforeseen systemic risk exposures to weather (production risk) and the pricing volatility of commodity markets (price risk). In addition, the sources of risk and their impact on the income of rural households can vary by farm size and farm type.

**Table 1.  Main sources of agricultural risks**

<!-- Start of picture text --> Micro Macro<br>Idiosyncratic Systemic<br>Production<br> Natural Hail Earthquake, floods,<br>drought<br> Diseases and pests Pest infestation Animal contagious<br>disease<br>Price Commodity, inputs,<br>exchange rates<br>Financial Interest rate,<br>Environmental Pollution, deforestation<br>Institutional Public subsidies,<br>Agricultural policy<br>Health Illness, injury, disability Epidemic<br>Property Fire, theft Earthquake, floods<br><!-- End of picture text --> Sources: Authors, adapted from Holzmann and Jorgensen (2000).

Farmers can use several strategies, whenever they are available, to deal with these multiple sources of risk. It is common to differentiate these strategies into two main categories: risk management and risk coping. While risk management strategies attempt to address the risk _ex ante,_ risk coping strategies are addressing it _ex post_ . One can also differentiate between technical and financial risk management approaches. For instance, an example of a technical risk management arrangement would be the selection of less risky agricultural production techniques, the use of preventive activities, or diversification of income sources. Financial protection can be obtained from insurance companies and capital markets. Examples of these risk management arrangements are presented in Table 2.

3
**Table 9.  The Composition of Tax Revenues at the County Level, 2003**

||National<br>Average for<br>counties +<br>townships|Henan<br>Provincial<br>Average|Huaxian|Sichuan<br>Provincial<br>Average|Shuangliu|
|---|---|---|---|---|---|
|Per capita Revenues (RMB)||529|269|637|698|
|VAT|18.3%|16.4%|8.2%|13.0%|12.2%|
|Business Tax|24.5%|21.4%|10.8%|21.2%|35.1%|
|EIT|6.5%|6.8%|2.6%|9.8%|5.9%|
|PIT|4.2%|3.2%|2.4%|4.1%|4.7%|
|Urban Maintenance and<br>Construction Tax(UMCT)|5.3%|4.8%|1.6%|7.5%|5.1%|
|Agricultural Taxes|15.9%|24.3%|50.4%|18.6%|11.4%|
|Other Revenues|25.2%|23.1%|23.8%|25.8%|25.4%|

Note: County level includes townships. National average includes counties and county-level cities, but excludes districts.

Source: Compendium of Fiscal Statistics for All Prefectures, Cities, and Counties, 2003.

# _Outside the budget_

Until the Rural Fee Reform was implemented, townships also depended on extra-budgetary fees and levies to finance as much as half of their public expenditures, but aggregate data is not available at the national level. A group of experts at the State Administration of Taxation estimated that legally prescribed fees and levies at the township and village levels totaled RMB 86 billion in 2000 (Yang et al. 2003). If these estimates are correct, fees and levies would have financed roughly one-third of public expenditures at the township level.<sup>21</sup> In addition, illegal fees might have added another RMB 40 billion, and unpaid, compulsory labor would have contributed an amount valued at RMB 59 billion to total township expenditures. Put another way, township finance drew from in-budget resources (own revenues and transfers) RMB 187 billion, and RMB 185 billion -- nearly an equal amount -- from extra-budgetary resources comprising fees, levies, and labor contributions. This aggregate picture appears to be consistent with data drawn from a survey of 50 townships, which show that in 2000, before the RFR, the average township drew RMB 92 from extrabudgetary and self raised funds, compared to tax revenues of RMB 119. Of course, township governments were (and are) not

> 21 This was equal to 50 percent of the budget expenditures of RMB 187 billion at the township level (CFY 2001).

18