rafmacalaba/datause-encoder-data huggingface dataset:
negatively correlated with both, demographic and geographical outreach. Column (3) shows no statistically significant relationship between the rate of inflation and any of our outreach measures. In contrast, indicators of physical infrastructure are positively associated with banking sector outreach, possibly because good infrastructure reduces the costs of opening and operating branches (though there are many other unmeasured factors which may also drive the result). Column (8) shows that, after controlling for the effects of income and population density, there is no significant correlation between absence of violence and neither of our measures of outreach. Contrary to our previous results, concentration is not significantly associated with either measure of branch penetration. Finally, in column (10) we show that a regulatory barrier such as requiring approval by the financial regulator to open a branch is not significantly associated with lower branch penetration.<sup>36</sup> In sum, the estimates in Tables 19 – 23 yield interesting results. The first one is obvious, GDP per capita is strongly positively associated with all measures of financial inclusion; it explains a large fraction of the cross-country variation. Second, after controlling for per capita income, variables which reflect the institutional and development conditions and that are largely outside of the control of financial regulators are also associated with a broader penetration of financial services. More specifically, measures of physical infrastructure, such as phone line penetration and electricity usage, are positively associated with all four measures of deposit penetration, loan penetration, and geographic and population based branch penetration. Lower inflation is also positively associated with deposit penetration. While these results probably confound the direct impact of better infrastructure or inflation control with other aspects of economic development and macroeconomic management, these results highlight the fact that some of the factors which affect the ability of policy makers to set conditions for greater financial inclusion are outside of their direct control. Third, there is also a significant relationship between measures of deposit and loan penetration and variables which might plausibly be within the control of financial regulators and policy makers. In particular, financial infrastructure, as measured by better

> 36 Cross-country analysis among richer countries shows that requiring branch approval is correlated with lower branch penetration. This relationship is statistically significant even after controlling for income, population density, and other factors.

34
> **Calculating the “discount factor” Z.** The “discount factor” Z differs among the different products for Banrural. For microfinance and agriculture lending, it was less than one, indicating that Banrural achieves its social development objectives by reaching low-income groups. For housing and consumer loans, Banrural’s discount factor was higher than one, indicating that it has not adequately channeled loans to the targeted low-income groups (see table 10).

**Table 10. Calculating the “Discount Factor” Z**

||**Microfinance**|**Agricultural**|**Housing**|**Consumer**|**Total**|
|---|---|---|---|---|---|
|**Summation of loan values**||||||
|**(Q millions)**|466|339|384|1,502|3,211|
|**Weighted average summation of**||||||
|**loan values (Q millions)**|158|114|117|107|886|
|**_OI_**<sup>_w_</sup>|0.34|0.34|0.31|0.07|0.28|
|**_OI_**<sup>_nw_</sup>|0.33|0.33|0.33|0.33|0.33|
|**Z**|0.98|0.99|1.09|4.68|1.94|

# **5. Conclusions and Potential Policy Implications**

# **Conclusions**

This paper has illustrated the usefulness of the SDI as a performance measurement parameter for public banks. The objective was to test and develop a methodology that is better suited to assess the performance of DFIs by applying performance assessment criteria that take into account both the social objective and the subsidies received.

The SDI, which was originally developed for measuring the sustainability of DFIs, can and should be applied to measure the level of subsidy that a DFI is “consuming” for each financial product that is extended to a specific clientele (for example, low-income farmers). It could serve governments, enlighten donors and managements of DFIs that are seeking to lower or eliminate their subsidy dependence, or justify the continued benefit from welltargeted and effectively delivered subsidies. In addition, the SDI provides policy makers with an explicit measurement of the costs incurred by the government in supporting the DFI, thus enabling a better comparison with other development interventions.

> **_The OI can be used to measure the level of achievement of the DFI’s social objectives._** The newly developed OI is a flexible parameter that captures the level of DFI’s outreach with respect to the designated target group. Although in this paper the OI is used to measure outreach in terms of loans extended to various income groups, its ease of use allows its adaptation to other social policy objectives, such as lending to remote regions. A clear measure of outreach is essential, particularly because critics of DFIs claim that these institutions have been “captured” by special interest groups or that they have sought to address the same clients that commercial banks either serve or are ready to serve (if those banks had access to the same concessionary resources).

25
Table 5 shows the results of the PPML regressions for the full sample as well as for sub-samples in different eras of the international financial system. In line with our main hypothesis, bilateral economic and financial exposure is significantly correlated with bilateral rescue lending across all subsamples, both when using measures of trade and banking linkages. The same is true for political allegiances (UN voting similarity, post-WWII) and for the importance of a shared colonial history. We also find that geographic distance is a statistically significant predictor of bilateral rescue lending. It enters all regressions models with a negative coefficient and thus suggests that informational asymmetries and enforcement issues play an important role in impeding official financial lending in crisis times.

Table 6. Gravity models of bilateral rescue lending: Different econometric models

||Dep. v|ariable: Bilateral rescue l|ending|
|---|---|---|---|
||Probit<br>(1)|OLS<br>(2)|PPML<br>(3)|
|Trade exposure|0.24***<br>(0.03)|0.40***<br>(0.06)|0.43***<br>(0.10)|
|Distance|-0.13*<br>(0.07)|-0.46***<br>(0.13)|-0.29*<br>(0.15)|
|UN voting|-0.34<br>(0.41)|1.59***<br>(0.57)|3.77***<br>(0.71)|
|Former colony|0.35***<br>(0.13)|1.67***<br>(0.21)|0.92***<br>(0.20)|
|Constant|3.08**|-0.99|6.64***|
||(1.30)|(1.23)|(1.17)|
|Observations|13778|3639|13778|
|R-squared||0.600|0.742|
|Creditor FE||||
|Debtor FE||||
|Episode FE||||
|Controls||||

_Note:_ The dependent variable in the probit model is a dummy variable indicating the provision of a bilateral rescue loan, the dependent variables in the OLS and PPML model are log real bilateral commitments. All explanatory variables enter as pre-crisis values. All three models include creditor, debtor and episode fixed effects. Robust standard errors are clustered at the creditor-debtor dyad. ***, **, and * indicate statistical significance at the 1%, 5% and 10% level. See appendix table A2 for a list of included countries.

34
of both the theoretical and the empirical literature on entry into and exit out of export markets. In Section 4, we discuss the econometric specification of a hazard model that incorporates country and product specific factors that influence export survival and present the empirical results including a battery of robustness checks. Section 5 concludes.

# **2. Data and Patterns of Export Survival**

In this paper, we use a novel data set that is more comprehensive than those used in previous studies of trade flow survival in a number of important dimensions. We investigate trade flows at the 5 digit level of the SITC from 82 exporting countries to 53 importers. This amounts to a total of 2,861,394 export lifetime spells.<sup>2</sup> The level of product detail in our data is finer than that of previous studies. We consider 1271 products (mineral oils are excluded) whereas the dataset of Blyde (2008) has 625 products and that of Besedes and Prusa (2007) has 380.3 Hence, it is interesting to see if the conclusions of these previous studies hold for more detailed products but we do feel that the more detailed product data better captures the dynamics of entry and exit into exporting. At a high level of aggregation, the results may be distorted by the fact that different product sub-categories classified into one group may be subject to different survival patterns.

Our coverage of exporters is broader than previous studies with the explicit intention of including a wider range of countries with differing income levels and in particular to include more Sub-Saharan African countries. Blyde (2008) has 47 high and middleincome exporters and no low-income countries whereas Besedes and Prusa (2006b) investigate the export survival patterns of 46 countries, only one of which, Madagascar, is a low-income country. Our data set covers 82 exporters including 22 low-income countries and 22 countries from Sub-Saharan Africa.

> 2 We treat an export occurrence as a flow in excess of $1000 but do test the sensitivity of our conclusions to flow size.

> 3 Besedes and Prusa (2006b) and Nitsch (2007) use very detailed data at or close to the tariff  line level but only analyse the duration of exports to a single market, the US and Germany respectively.

4
# **1. Introduction**

Jamaica is considered to have had relative success in developing a sound market economy and reducing poverty over the last decade and a half. The Jamaican economy has undergone significant changes since the beginning of the 1990s. The country has followed the regional Latin America and the Caribbean (LAC) trend and has shifted from an industrial economy to a service oriented one. The contribution of the industrial sector to national GDP fell from 40% in 1992 to 30% in late 1998, while the contribution of the service sector increased from 52% to 62%.<sup>1</sup> The agriculture sector remained stable during the same period. Hence, while at the beginning of the 1990s Jamaica was relatively more oriented to the industrial sector than the average LAC country, it has converged with the rest of the region in becoming a more service oriented economy. This trend is commonly observed in industrial countries. The composition of employment across sectors followed a similar trend.

Jamaica, however, has suffered from labor market problems that are common to other small LAC countries: A shrinking labor force and high unemployment. Employment has grown slowly in the last decade and a half, and domestic labor force participation decreased during the same period. In fact, Jamaica is one of the few countries in LAC in which labor force participation rates have had a negative impact on the growth of labor supply. Also, the negative impact of net migration on the growth of labor supply in Jamaica has been one of the strongest in the region.<sup>2</sup> Labor force participation rates have been declining steadily for both men and women in Jamaica.

Along with the declining participation rates, Jamaica has experienced double digit unemployment for most of the 1990s and the early 2000s. On average, the unemployment rate for women has been twice as high as the rate for men. Not surprisingly, femaleheaded households account for about two-thirds of the households in poverty.

The recently-stagnated economy of Jamaica poses a puzzle in that high rates of unemployment have persisted even when real wages have been increasing for more than a decade. The real wage increased from less than J$900 to more than J$2,000 during the

> 1 World Bank (2006a)

> 2 World Bank (2006c)

2
# **Table 2: Global FDI Outflows $US Millions**

||1989<br>Value<br>(%)|1990<br>Value<br>(%)|1991<br>Value<br>(%)|1992<br>Value<br>(%)|1993<br>Value<br>(%)|1994<br>Value<br>**(%)**|
|---|---|---|---|---|---|---|
|World Total|217,584 (100.0)|238,448 (100.0)|189,175 (100.0)|180,949 (100.0)|222,171 (100.0)|222,254 (100.0)|
|Industrial<br>Economies|207,164 (95.2)|228,685 (95.9)|181,896 (96.2)|161,130 (89.0)|192,959 (86.9)|189,280 (85.2)|
|Europe|123,717|141,700|113,112|103,009|103,693|113,921|
|United States|29,000|32,690|29,130|34,790|68,978|45,640|
|Japan|44,160|48,050|30,740|17,240|13,714|17,938|
|Developing<br>Economies|10,420 (4.8)|9,763 (4.1)|7,279 (3.8)|19,819 (11.0)|29,136 (13.1)|32,907 (14.8)|
|Africa|892|1412|897|319|843|686|
|Latin America|950|4,508|1,834|2,259|0|1,900|
|Asia|8,967|8,363|5,425|16,736|28,315|30,306|
|East Asia_(sum of_|932 (0.5)|1,492 (0.7)|611 (0.3)|884 (1.0)|988 (1.1)|831 (0.4)|
|_below)_<br>Singapore|882|1,352|444|748|767|653|
|<br>Thailand|50|140|167|136|221|178|
|China|780(0.4)|830 (0.3)|913 (0.5)|4,000 (2.3)|4,400 (2.0)|2,000 (0.9)|

_Source: IMF BOP Yearbook, UN World Investment Report_ Asian developing countries have consistently received the largest percentage of FDI, and they are rapidly becoming a new source of FDI. More than 50 percent of developing country FDI inflows have been to Asia, and about 80 percent of the developing country FDI outflows originates in Asia, especially the newly industrialized economies in the region. Within Asia, some East Asian developing countries are attracting significant amounts of FDI. In particular, China and Vietnam are emerging as major FDI destinations. In recent years, most incremental increases in FDI in East Asia went to China.

## **The Trend of FDI in China**

Foreign direct investment started modestly in China after the passage of the 1979 Law on Joint Ventures. The Law permitted foreign investment and defined equity joint ventures. Still, the legal environment for FDI was not well-defined, and little attention was directed to attracting foreign investors. In 1986 and 1987 more serious attention was given to providing investment incentives. The Provisions for the Encouragement of Foreign Investment were promulgated in October 1986 and their implementing regulations announced over the next year. Partly as a result, FDI flows into China have been growing continuously ever since (Table 3). By 1992 China was receiving almost a quarter of total FDI inflows to developing countries, and today the share is 40 percent. Foreign investment has expanded to become by far the single most important source of external capital for China, surpassing the combination of bilateral development assistance and borrowing commercially and from international organizations.

3
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22
altemating conditional expectations (Breiman and Friedman (1985)). More importantly perhaps, the sample size used in this study is small, mostly due to the lack of available data on poverty rates. The lack of a sufficient number of observations prevents the use of more advanced regression techniques, such as the GMM method used by Hansen and Tarp (2001) in a different context, which would allow to control at the same time for both unobserved country-specific characteristics and endogeneity. Moreover, the available data on both sides of the regression equation are not highly reliable. As is well known, the aggregate measure of poverty used in this study is based on survey data; but there are large differences across countries in measuring poverty from these micro data (related most notably to differences in definitions of income or consumption), which create potentially serious comparability problems. I have used 'effective' measures of trade and financial integration, but the data are not without problems. Using average import tariffs does not account for the existence of non-tariff barriers (which themselves are very difficult to measure with any degree of precision). An altemative option to measure trade openness might be to use the new index of trade restrictiveness compiled by the IMF, which is based on a variety of published and unpublished sources (see Lankes (2002)); and as noted earlier, the share of assets of the domestic financial system held by foreign banks could be a complementary measure of financial globalization. In both cases, however, the number of observations available remains an issue.

I also found that fixed effects (which were not reported here to save space) are important and statistically significant in many cases, suggesting that countryspecific factors are important in determining the behavior of poverty rates. There is therefore a risk of misspecification that may persist despite my effort to control adequately for various determinants of poverty. More generally, parameter heterogeneity is a key problem in the type of cross-country, growth-poverty regressions presented in this study. Indeed, an implicit assumption in this type of regressions is that the parameters are constant across countries, that is, that all countries follow the same underlying model relating growth and poverty. If one is 40
# **V. Conclusions and Policy Insights**

**In Romania, the legally mandated minimum wage, established by the government in consultation with trade unions and employer organization** **<mark>s, varies across educational levels and economic sectors.</mark> Between 2007 and 2022, the Romanian nominal and real minimum wage experienced a substantial increase.** The minimum wage applies to full-time and part-time workers, adjusted proportionally based on working hours. While the minimum wage level is revised annually, the process lacks strict rules for determining the new level even though, over the last years, the government has relied on macroeconomic indicators to justify the minimum wage increases. Since 2019, three different levels have been implemented. The construction sector held the highest level, while education and experience determined rates for other workers. Starting from 2022, the separate minimum wage for employees with tertiary education has been naturally eliminated, as the government has kept it the same since its introduction in 2019. Over the years, Romania's gross minimum wages have significantly increased by an average annual real rate of 10% between 2007 and 2022, with some years experiencing more than 20% growth. This growth is notable compared to Romania's relatively low starting point and has led to substantial progress in real wages. At the outset of this period, Romania had one of the lowest ratios of the minimum wage to the median and mean wages in the EU. However, due to substantial increases in the minimum wage over the last decade, Romania now ranks among the top countries with a high ratio of the minimum wage to the median wage. Meanwhile, the ratio of the minimum wage to the mean wage positions Romania in the middle of the distribution.

**Setting and adjusting the minimum wage presents a complex challenge requiring a balanced approach.** While the process involves political deliberation and consultation with social partners, evidencebased decisions are crucial. Minimum wages should be established at levels that meet workers' and their families' needs, but also consider potential labor market and poverty impacts. Striking the right balance is essential: setting wages too low offers limited protection against low pay and poverty, while setting them too high can result in poor compliance and adverse employment effects. Regular inflation-adjusted changes provide predictability, but countries with hyperinflation may require more frequent updates. When well-designed, minimum wage policies can be part of a broader strategy to alleviate poverty through increased labor income, especially when the minimum wage approaches or matches living wages.

**When setting minimum wage levels, assessing adequacy and living wages is an important factor. In this paper, we construct estimates of real living wages for a representative poor Romanian household, relying on EC estimates of the consumption basket.** For effective comparisons, our estimates of living wage are derived from absolute estimates of essential needs rather than relative percentages of median income.

47