rafmacalaba/datause-encoder-data huggingface dataset:
least in the near term.<sup>2 4</sup> Table 9 which summarizes the assistance given to the crises countries by the international community, is consistent with this view. Note that for the countries where the crises was most serve, Indonesia, Korea and the Philippines, the magnitude of the aid was very large, 17, 12.4 and 10.8 per cent of GDP, respectively. Granted, the international community expects that much of this aid will be repaid. And it is very difficult to quantify the value of the transfer implicit in the below market rates at which these loans are made. Still the large magnitude of the assistance programs is consistent with the view the affected governments were not in a position to make fiscal adjustments quickly enough to make credible a commitment not to monetize the prospective deficits associated with their failing banking systems.

Table 9: Assistance from International Community

|||(%of GDP)|||
|---|---|---|---|---|
||IMF|<br>Multilateral*|Bilateral|Total|
|Indonesia|4.6|3.7|8.7|17.0|
|Korea|4.5|3.0|5.0|12.4|
|Phillipines|1.7|||1.7|
|<br>Thailand|2.5|1.7|6.7|10.8|

*World Bank and Asian Development Bank Source: Garcia (1998)

We now present evidence on the role that central banks played in supporting financial institutions after the currency crises. Figure 5 displays central bank credit to deposit money banks, expressed as a ratio of the monetary base, from 1995 on. With the exception of the Philippines, this ratio increased dramatically after the crises. For example, in Korea the ratio climbed from roughly one to 3 and half times the base, while in Thailand it increased from less than 10 percent to 24In a previous version of this paper we briefly reviewed the plans of the different countries as of the last quarter of 1998 to finance the costs associated with their failing bank systems.

35
# 1. Introduction

Between 2010 and 2018, the share of global migrants increased by 25 percent, with 9 in 10 international migrants crossing borders due to economic reasons (World Bank, 2019). While these numbers suggest that migration increasingly becomes an important strategy for individuals to improve their livelihoods, economists are often puzzled why there are not as many migrants (e.g. Bryan et al., 2014). Standard economic theory suggests that migrants move from regions with high labor supplies and low demand to regions with lower labor supply will higher demand (Harris and Todaro, 1970). Yet, episodes of migration have not reflected the predictions of equilibrium-based migration theory, leading migration research to consider other potential factors such as social interactions (Radu, 2008). Moreover, migration is an important poverty alleviation tool with significant potential welfare gains. By doubling the number of young immigrants from developing countries to high income countries, the estimated global annual income gain could be USD 1.4 trillion (World Bank, 2018). Migration can benefit both migrants (e.g., via access to better jobs) and their family members (e.g., via remittances). Recent studies estimate that remittances have a positive effect on economic growth in developing countries that have less advanced financial markets (Sobiech, 2019). However, in 2018, only 3.5 percent of the global population were migrants. Understanding the drivers of migration is important not only to shed light on this puzzle, but also because migration can be an important engine for growth and development—offering substantial gains for the migrants, and both the sending and destination countries.

One of these potential gains can be in the form of expanding women’s employment opportunities. In 2019, only 33.6 percent of women in lower-middle income countries participated in the labor force (ILOSTAT, 2019). With an important and increasing share of migrants being women (Fleury, 2016), gender can play a distinct role in the migration decision and process. Women and men are subject to different social norms, which have repercussions on their likelihood and experience of migration (Bastia & Haagsman, 2020). However, the migration literature has long been gender-blind (Donato & Gabaccia, 2015) and existing studies that focused on women’s migration focused on non-labor drivers of migration, such as, marriage and family reunification (Ghosh, 2009). But women are also likely to migrate for economic reasons. For instance, Afsar (2011) shows that in India, many women migrate to find overseas employment in order to escape poverty, and Botea et al. (2018) find that in Lesotho women migrate to cope with economic shocks. Studies also show that single, higher educated women (Richter and Taylor, 2008), and those with social networks have higher likelihood of migration as documented by Fleury (2016). Understanding gender-specific barriers or determinants to migrate may 2
- 17 -

used to deal with this problem. First, lagged infrastructure variables are adopted. Second, the contemporaneous values of infrastructure are used but instrumented by their lagged values if they are available. In the latter case, the lagged infrastructure variables are additional instrumental variables in the usual 3SLS setting. On the other hand, both weather and exchange rate variables are treated as exogenous.

**Commodities** . To estimate coffee demand and supply, three coffee-related products are sampled from _Eurostat_ : coffee and coffee substitutes (SITC3-071), cocoa (do. 072), and chocolate and food preparations containing cocoa (do. 073). Tea is another major farm product in Africa, but it is technically differentiated in production and processing; thus tea is not included. In the case of dairy, again, three milk-related products are chosen from the _WITS COMTRADE_ database: milk and cream (SITC3-022), butter and other fat of milk (do. 023), and cheese and curd (do. 024). The advantage of pooling data from more than one subcategory is that it allows us to control for unobservable country-specific characteristics in both supply and demand equations, while maintaining a reasonable assumption of a common production function.

**Market definition.** As a potential market for coffee and cocoa, we focus on the European import market. Europe is the main export destination for many African producers, and European countries are largely dependent on imports for coffee and cocoa. The potential market size is defined as the total imports of EU25 from the world. While European consumers are supposed to purchase one unit of coffee or cocoa from extra-EU countries, the outside option would be to buy coffee and cocoa from intra-EU countries.<sup>15</sup> Both intra- and extra-EU trade data are available in _Eurostat_ .

> 15 The available data do not distinguish imports from outside the region and re-exports within the region. But the data show that about 80 percent of total coffee imports of EU member countries are associated with extraEU imports.
# **Table 8. Measuring the Impact of the Middle Office Directly Reporting to the Board on Investment Policies**<sup>**18**</sup>

|Independent variable|(1)<br>Number of<br>eligible assets|(2)<br>Number of<br>eligible<br>currencies|(3)<br>Estimated risk of<br>the portfolio|(4)<br>Investment<br>horizon – Total<br>tranched and<br>untranched<br>portfolio (months)|
|---|---|---|---|---|
|Middle office reports to the board|2.854***<br>(0.797)|3.098**<br>(1.373)|-0.045<br>(0.262)|1.304<br>(12.801)|
|Government Effectiveness|0.421|1.043|0.229|14.347**|
||(0.403)|(0.710)|(0.150)|(7.044)|
|Reserves to GDP (%)|4.065**|5.072*|-0.765|-5.020|
||(1.547)|(2.748)|(0.772)|(24.300)|
|Short-term external debt stocks, % of reserves|0.000|-0.000|-0.000|0.019*|
||(0.000)|(0.000)|(0.000)|(0.010)|
|Current account balance (% of GDP)|0.059|-0.055|-0.003|-0.616|
||(0.039)|(0.068)|(0.013)|(0.585)|
|Number of currency crisis (1971–2017)|0.255|0.774*|-0.065|0.564|
||(0.219)|(0.390)|(0.076)|(3.843)|
|Constant|3.182***|2.745|2.005***|21.414|
||(0.995)|(1.732)|(0.384)|(16.198)|
|Observations|83|85|63|48|
|R-squared|0.287|0.149|0.082|0.303|
||Standard errors in par<br>*** p<0.01, ** p<0.05,|entheses<br>* p<0.1|||

**_Organization of Reserve Management Operations and Investment Policy._** We find support that how institutions organize their reserve management operations matters for the investment policy of central banks. Central banks in which the front, middle, and back office are in the same department have, on average, significantly shorter investment horizons. As macrovolatility, levels of reserves, and the broader governance environment may influence the investment horizon of central banks' reserve operation, we include these in the regression as control variables (see Table 9).

> 18 Each of the columns in this table refers to a different regression with the column name as dependent variable (number of eligible asset classes, number of eligible currencies, estimated risk of the portfolio, and investment horizon).

16
||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Other**<br>**Servic**<br>**es**|2.01|30.23|20.58|12.76|10.67|10.64|10.61|10.58|1.77|1.73|0.00|

_Table B. 11. NACE (Statistical classification of economic activities in the European Union) codes of the sectors of economic activity that make up the 20 sectors for the_

|**Sector**|**Description NACE**|
|---|---|
|**Agriculture**|A01,A03|
|**Forestry**|A02|
|**Mining**|Β|
|**Food Manufacturing**|C10,C11,C12|
|**Textile**|C13,C15|
|**Wood and Paper**|C16,C17,C18|
|**Chemical and Plastic Products**|C19--C23|
|**Metal Products**|C24,C25|
|**Machinery and Equipment**|C26--C33|
|**Energy**|D|
|**Construction**|F|
|**Trade**|G45--G47|
|**Accommodation and Food Services**|I|
|**Transportation**|H49--H53|
|**Banking-Financing**|K64--K66|
|**Real Estate**|L68|
|**Public Administration**|O|
|**Education**|P|
|**Health**|Q|
|**Other Services**|E,J58-63,M69-75,N,R,S,T,U|

46
**-50-**

|LEWIS, B.<br>1981|KenXa. Chicago: University of Chicago Press<br>C.<br>Invisible Farmers: Women and the Crisis in Agriculture.<br>Washington, DC: Agency for International<br>Development,<br>Office of<br>Women in Development.|
|---|---|
|MATT, L.<br>1979|A Report on Women's Cooperatives in the North West and South West<br>Provinces. Yaounde: US Peace Corps.|
|McCULLOCH<br>1954|, M., H. LITLEWOOD and I. DUCAST<br>Peoples of the Central Caseroons. London: International<br>African<br>Institute.|
|MIDENO||
|1984a|Baseline Socio-Economic<br>Survev. North West Province. Cameroon.<br>1984. Bamenda, Cameroon: North West Development<br>Authority.|
|MIDENO||
|1984b|Baseline Yields of Arabica Coffee. Maize. and Beans. North West<br>Province. 1983. Bamenda, Cameroon: North West Development<br>Authority.|
|MIDENO||
|1984c|f:tension Service Survey: Socio-economic<br>Background of Maize<br>Contact Farmers. 1984. Report No. 1 Basenda, Caseroon: North West<br>Development Authority.|
|MIDENO||
|1985a|Extension Service Survey: Maize Package Adoption. 1984. Report No.<br>2. Bamenda, Cameroon: North West Development Authority.|
|MIDENO||
|1985b|Mid-Term Review: Extension and Extension Training Progrm.<br>Banenda, Cameroon: North West Development Authority.|
|MIDENO||
|1985c|Adeotive Research Section: Internal Mid-Project Review. Bamenda,<br>Caneroon: North West Development Authority.|
|MIDENO||
|1987a|Studies and Dissertations,<br>Memorandum ETA/8/87. Bamenda:<br>Provincial Delegation of Agriculture.|
|MIDENO||
|1987b|Technical DescriRtion of Project Cogoonents. Banenda, Cameroon:<br>North West Development Authority.|
|MIDENO||
|1988a|Village Extension Worker Pewrformance<br>Study. Bamenda, Cameroon:<br>North West Development<br>Authority.|
Policy ReseaRch WoRking PaPeR 4742

# **Abstract**

When assessing the impact of changes in food prices on poverty, it is important to consider food producers (who may benefit from an increase in prices) as well as consumers (who loose out when the price increases), with a focus on poor consumers and producers. In the case of rice in Liberia however, the impact of a change in price is not ambiguous because a large share of the rice consumed is imported, while the rice locally produced is used mostly for auto-consumption. An increase in the price of rice will result in higher poverty in the country as a whole (even if some local producers will gain from this increase), while a reduction in price will reduce poverty. Furthermore, because rice represents a large share of food consumption, any change in its price is likely to have a large impact on poverty. Using data from the 2007 CWIQ survey, the paper finds that an increase or decrease of 20 percent in the price of rice could lead to an increase or decrease of three to four percentage points in the share of the population in poverty.

This paper—a product of the Development Dialogue on Values and Ethics, Human Development Network—is part of a larger study by the Africa Chief Economist Office and the Development Dialogue on Values and Ethics on the impact of the food price crisis in Africa and the policy responses available to governments. This research was started in the Africa PREM department and benefits from funding from the Africa Region Regional Studies Program as well as the Belgium and Luxemburg Poverty Reduction Partnerships. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at qwodon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Appendix Figure 1C - Italy The main variables during 1997-2010 for firms from Italy**

<!-- Start of picture text --> 9.00% Gross Savings 14% Gross Capital Formation<br>8.00% 12%<br>7.00% 10%<br>6.00%<br>8%<br>5.00%<br>6%<br>4.00%<br>4%<br>3.00%<br>2%<br>2.00%<br>0%<br>6%<br>Excess Savings Non-Operating Assets<br>45%<br>4% 40%<br>35%<br>2%<br>30%<br>25%<br>0%<br>20%<br>-2% 15%<br>10%<br>-4%<br>5%<br>0%<br>-6%<br>75% Leverage 14% Liquidity Holdings<br>70% 12%<br>65% 10%<br>60% 8%<br>55% 6%<br>50% 4%<br>45% 2%<br>40% 0%<br>Median<br>Asset-Weighted Average<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 19... 19... 19... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20... 20...<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Excess Savings, Gross Savings, Gross Capital Formation, Non-Operating Assets, Leverage, and Liquidity Holdings, during the period 1997-2010, according to median and asset-weighted average, for the sample of manufacturing firms from Italy.
<!-- Start of picture text --> Figure 7B. Asset prices during global recessions and recoveries<br>(index)<br><!-- End of picture text --> <!-- Start of picture text --> Global Recessions<br>House Prices Equity Prices<br>105 140<br>(Index) (Index)<br>100<br>120<br>95<br>100<br>90<br>85 80<br>-4 -3 -2 -1 0 1 2 -4 -3 -2 -1 0 1 2<br>Global Recoveries<br>House Prices Equity Prices<br>112 220<br>(Index) (Index)<br>200<br>108<br>180<br>104<br>160<br>140<br>100<br>120<br>96<br>100<br>92 80<br>-2 -1 0 1 2 3 4 -2 -1 0 1 2 3 4<br>1975 1982 1991 2009<br>Average of global recessions<br><!-- End of picture text --> Note: Time 0 denotes the year of a global recession (shaded in gray). All variables are at annual frequency. All variables are market-weighted by gross domestic product in US dollars, including all advanced and emerging market economies. Panels on global recessions are index numbers equal to 100 one period before the global recession year. Panels on global recoveries are index numbers equal to 100 in the global recession year.

59
Policy Research Working Paper 5935

# **Abstract**

This paper uses a novel dataset on United States food import refusals to show that reputation is an important factor in the enforcement of sanitary and phytosanitary measures. The strongest reputation effect comes from a country’s own history of compliance in relation to a particular product. The odds of at least one import refusal in the current year increase by more than 300 percent if there was a refusal in the preceding year, after controlling for other factors. However, the data are also suggestive of the existence of two sets of spillovers. First, import refusals are less likely if there is an established history of compliance in relation to other goods in the same sector. Second, an established history of compliance in relation to the same product by neighboring countries also helps reduce the number of import refusals. These findings have important policy implications for exporters of agricultural products, especially in middle-income countries. In particular, they highlight the importance of a comprehensive approach to upgrading standards systems, focusing on sectors rather than individual products, as well as the possible benefits that can come from regional cooperation in building sanitary and phytosanitary compliance capacity.

This paper is a product of the Growth and Competitiveness Unit, World Bank Institute. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Jmaur@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **3. Measuring Informality in Brazil**

Many commentators express a belief that the informal sector in Brazil is large and growing, despite trade liberalization and successful macroeconomic stabilization over the last 15 years. For example, Carneiro (1997) reports that, although in the late 1980s the Brazilian economy appeared to stagnate, unemployment rates remained low and total employment (formal and informal) continued expanding. In 1988, prior to trade liberalization and at a time of chronic price instability, the Brazilian statistical agency (IBGE) estimated that 13% of GDP was generated in the informal or unregistered economy. Within the service economy this proportion was estimated as high as 30%, suggesting that the overall estimate may well have been conservative (Carneiro, 1997).

The achievement of macroeconomic stability in Brazil that followed the successful adoption of the Plano Real in 1994 might have removed some of the pressure for economic activity to migrate into the informal sector. On the other hand trade liberalization in the early 1990s increased competitive pressures in the tradable sectors of the economy. But recent research finds little or no relationship in Brazil between trade liberalization and informal employment, defined according to labor contract status (Goldberg and Pavcnik, 2003). Furthermore constitutional reform in 1988 may have simultaneously decreased flexibility in the formal labor market (Barros and Corseuil, 2001). So the emerging pattern of development in the informal sector may be complex.

12
25 adopts the value that appears under the heading 'scenarios'). The EU average country is assumed to behave as determined by the current EU Average growth determinant values (see Table 6).

**Table 9: Components of the Index Z.**

||Degree of<br>Openness|Fiscal<br>Policy &<br>Size of<br>Governmen|Monetary<br>Policy & other<br>Financial Sector<br>t<br>Policies|Price<br>System<br>Distortion|Rule<br>of<br>Law|Average|
|---|---|---|---|---|---|---|
|EU**Average**|4.52|2.45|4.11|4.11|3.93|3.82|
|Czech Republic|4.50|3.00|4.50|3.50|4.50|**4.00**|
|Hungary|3.00|2.50|3.00|3.50|3.50|**3.10**|
|Poland|3.00|2.75|2.00|3.50|3.50|2.95|
|Slovakia|4.00|2.25|3.00|3.00|3.00|3.05|
|Slovenia|2.00|2.75|3.50|2.50|2.50|2.65|
|**CEE**Average|3.30|2.65|3.20|3.20|3.40|3.15|

Note: The Index of Policy Z for CEE countries is based on the original IEF Index while the EU Average is based on the Modified IEF Index.

# Difference in Growth Paths

The goal in this sub-section is to identify how much higher is the growth rate of GDP per capita of CEE countries vis-a-vis that of the EU average country and, in particular, the behavior of the difference in growth rates under alternative scenarios. These "differential" growth paths are calculated solving iteratively equation [xv] in Appendix 4. As before, we assume in these exercises that the EU average country maintains the current EU Average growth determinants 19 (see Table 6)

**19** Our analysis is based on differences in growth rates of per capita GDP. To obtain the growth rate of the economy we must add the growth rate of the labor force (about half a percentage point a year) and the EU growth rate of GDP per capita (say, for example, 1.5 percentage points a year). The EU growth rate of GDP per capita can be viewed as reflecting the exogenously given technological progress and the growth that results from the current growth determinants of the EU average country. Given our current coefficient estimates, the latter explains growth rates in the EU average country of about one percentage point.