rafmacalaba/datause-encoder-data huggingface dataset:
result is not robust. The evidence concerning the link statistically insignificant when the change in government wages is considered as an explanatory variable.<sup>4</sup> The prominent role played by average government wages may be due to the presence of several state-owned enterprises in the BDF samples. Wage decisions in these firms could indeed be responsive to govemment pay policies. To assess the extent to which the labor costs of private sector firms are affected by government wages it would be necessary to have information on state ownership by sectors. Unfortunately, information of this sort is not available at this stage. The conclusion that changes in the SMIG do not have a significant impact on labor costs, on the other hand, should not be affected by the inclusion of data on state ownership by sectors.<sup>5</sup> The results in Tables 4 and 5 also suggest that labor costs in large firms are only weakly responsive to real shocks, as captured by the economy-wide fluctuations in output and the terms of trade. The coefficients associated to these two variables are not statistically significant in the case of CMte d'Ivoire. And only the changes in the terms-of-trade level appear to be relevant in the case of Senegal (the coefficient multiplying the change in economic activity is not robust to changes in the set of control variables). Real shocks could still affect average labor costs through the SMIG, because the latter is endogenous, as will be shown below. But the SMIG was found to have little or no influence on pay decisions by large firms. Real rigidity thus appears to be an important feature of average labor costs in the formal sector of the economy.

- 4 Microeconomic evidence on how labor costs reacted to the 1994 devaluation of the CFA Franc is ambiguous in this respect too. Using plant-level data for Cameroon, Barba Navaretti _et al._ (1996) show that the median wage fell by 11 percent compared to output prices between 1992-93 and 1994-95. But on the other hand the average wage increased by about 13 percent.

- _5_ Given the insignificance of changes in the SMIG over the whole sample, these changes could be positively correlated with changes in the labor costs of private firms only if they were at the same time negatively correlated with changes in the labor costs of state-owned enterprises, which is unlikely.

23
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# EXECUTIVE SUMMARY

1. The link between financial services and development is by now broadly acknowledged in economic literature.  Over the past years, the World Bank has explored the theme of access to financial services in various countries in Latin America, including Colombia.  The present report provides an initial overview of the state of financial access in Colombia, drawing on the limited available information to date.

2. Access to financial services has been declining in Colombia, as illustrated by decreasing levels of financial depth and intermediation and decreasing availability of basic financial services in relation to population size. In addition, a household survey in Bogotá finds that 61% of the adult population lacks access to any form of formal financial services, such as checking or savings accounts, payment services or loans.

3. An overall decline in financial intermediation, especially following the banking and fiscal crisis in the second half of the 1990s offers a partial explanation for the low level of financial access in Colombia. The level of intermediation was at 85% in 2003, down from 116% in 1998. the number of current and savings accounts per inhabitant has also been decreasing in the last four years. In light of lower risk investment opportunities in form of government bonds, Colombia’s main financial intermediaries, private banks, have limited incentives to increase their level of intermediation and provide services to low income clients. With the recovery of the financial system, the challenge today is to increase intermediation overall, and especially, intermediation to the poor.

4. A brief supply side analysis shows that Colombia’s banks provide costly services mainly catered towards high income clients. Existing fees and costs of checking, savings and loan services average 5-10% of a monthly minimum wage, making them hard to afford for low income clients. In addition, high minimum balance requirements create strong barriers to access even for basic financial services such as savings accounts. The situation is exacerbated by multiple documentation requirements resulting from strict anti-money laundering regulation which reduces the eligibility of low income clients for formal financial services.

5. Financial exclusion is associated with lower and more uncertain incomes, lower education and closer links to the informal sector. A household survey conducted in Bogotá for this study reveals that 70% of the unbanked earn less than one minimum wage per month, are three times more likely to be unemployed than the banked, and have lower education levels. The unbanked save and borrow largely in the informal sector. At the same time, however, high home ownership rates show that the unbanked have the capacity to build assets, demonstrating that they have ‘bankable’ characteristics.

15
strategy, resistance to endosulfan could create serious problems in controlling armigera in future.

Finally, there is no evidence of resistance to monocrotophos, quinalphos (although tolerance to quinalphos had increased to 5-9 fold in the 1990/91 season, as compared to 2-4 fold in the 1989/90 season) and methomyl in any of the field strains examined so far. Thus, reported difficulties with these pesticides may reflect inadequacies of the techniques used in the application of pesticides in general.

# Summary

The evidence available from the resistance monitoring studies points to the fact that resistance of Heliothis to SPs is increasing in all parts of the country--partly due to increased local selection pressures (due to increased use of this group of pesticides) and partly due to the reduction of susceptible refugia (arising from policy induced changes in relative agricultural prices). There are two important economic imRlications flowing out of this. The first implication of increasing resistance (almost all over India, attributable to the overuse of pesticides) is to increase the continuing losses due to increased Heliothis damage. The second major implication is that RF values in some parts of the country are at levels that are entirely consistent with field failures,i.e., it seems likely that indiscriminate pesticide use has increased substantially the Rrobability of catastrophic crop devastation of the sort that occurred in 1987/88. That field 54
# the Coordination of Humanitarian Affairs (OCHA).

We take advantage of these features to causally identify the _marginal_ effect of the barriers’ placement on employment, wages and working time of Palestinians in the West Bank over the 2000-2009 period. In order to do so we compute a yearly index of barriers’ closeness to the individual worker using a refined spatial unit of analysis and examine its impact on labor market outcomes at the same spatial level. The results suggest a significant negative effect of the barriers on the probability to be employed as well as on hourly wages. These effects are almost entirely accounted for by one major type of barriers, i.e. check-points, while the other barriers have a much less significant impact. In particular placing one check-point one minute away from a locality reduces its residents’ probability of being employed by 0.5 percentage points and their hourly wage by 5.2%. The check-points have also an impact on the quantity of labor supplied by workers, decreasing the number of days worked in a month while increasing the number of hours per working day.

These estimates should be interpreted only as the _marginal_ (rather than the _total_ ) effects of closures on the labor market, i.e. the difference between localities which are affected by the closures to a greater or a lesser extent according to their distance to the closures. As the restrictions to mobility affect the entire West Bank economy (e.g. IMF, 2010, World Bank, 2007 and 2010, UNCTAD, 2011), it is not possible to obtain a counterfactual of localities actually unaffected by the closures, which would be necessary to compute the _total_ effects of closures. This problem is typical of studies estimating conflicts’ economic impact within countries to the extent that even the residents of more peaceful regions are typically also adversely affected by civil war disruptions (Blattman and Miguel, 2010). In this sense the _marginal_ effect we compute is necessarily an underestimation of the _total_ effect of the closures. In addition, our estimates do not take into account other dynamic labor market effects of the barriers, such as those on the ability to accumulate human capital.<sup>3</sup> Similarly internal closures are likely to raise the costs of attending schools for students. Such effects are likely to play out in the medium run and therefore are not captured by our analysis.

The analysis also tries to distinguish between the two main channels through which we

> 3 For example, Di Maio and Nandi (2012) provide evidence that external closures increase child labor and reduce school attendance in the West Bank.

3
The return on admitted assets is generally quite poor, although there has been considerable improvement in recent years. The reported average return is below 7% and has risen slightly over the past few years. However, insurance companies have made significant unrealized capital gains in their holdings of listed equities and in their property investments.

# **Financial Condition**

The financial condition of the Tunisian insurance industry as a whole is very poor. However, the private companies appear to be in reasonably strong financial position (at least on the basis of their reported but unaudited results), while two public sector companies -- El Ittihad and Lloyd Tunisien -- suffer from a serious capital deficiency and a persistent accumulation of losses. DGA data for 1992 show that cumulative losses suffered by four companies (the above plus two mutual companies) amounted to 57.9 MD. These exceeded the total paid up capital and reserves of the industry, which amounted to 52.5 MD. Thus, the industry as a whole operated in 1992 with a negative equity of 5.4 MD (Table 6). This represented a small improvement over 1990 when accumulated losses amounted to 44.9 MD, the reported total equity of the industry was 38.1 MD and the overall shortfall was 6.8 MD. The private companies, the state-owned STAR and the three mutual companies reported positive equity. But in the case of STAR and the mutual companies, the level of equity was well below the required solvency standards imposed by the new insurance code.

**Table 6**

**Aggregate Balance Sheet of Insurance Industry, 1992**

|**Assets**|MD|%|
|---|---|---|
|Government Bonds|147.1|24.8|
|Equities|49.5|8.3|
|Properties|33.0|5.6|
|Liquid Assets|59.6|10.0|
|Personal Loans|2.9|_0.5_|
|Claims on Reinsurers|67.2|11.3|
|Premiums Receivable|86.1|14.5|
|Other Assets|90.9|15.3|
|Cumulative Losses (4 cos)|57.9*|9.7|
|Total|594.2|100.0|
|**Liabilities**|||
|Equity|48.3*|8.1|
|Insurance Reserves|412.9|69.5|
|Due to Reinsurers|45.9|7.7|
|Due to Government|37.5|6.3|
|Other Liabilities|49.6|8.4|
|Total|594.2|100.0|

* The industry as a whole has a negative net equity because of the large cumulative losses of four companies.

Source: Ministry of Finance, Direction Generale des Assurances 12
**−** **<u>Table 2: Agricultural Trade Protection Regressions NTM I</u>**

|**Year Dep. Var.**||**Constant**|**M/Y **|**X/Y **|**%PACEXP**|**RANKPACEXP**|_N_<br>_R_<sup>2</sup>|Adj. _R_<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|
|1993 NTM|est<br>_t_|0.212<br>**1.981**|0.009<br>0.599|−0.270<br>−0.724|2.694<br>**2.782**||31 0.267|0.185|
|1993 NTM|est<br>_t_|−0.084<br>−0.518|0.013<br>0.932|−0.344<br>−0.961||0.754<br>**3.167**|31 0.312|0.236|
|1996 NTM|est<br>_t_|0.953<br>**12.226**|0.024<br>1.613|−0.445<br> **−2.034**|0.425<br>0.515||33 0.292|0.219|
|1996 NTM|est<br>_t_|0.756<br>**6.538**|0.026<br>**1.914**|−0.468<br> **−2.329**||0.408<br>**2.239**|33 0.391|0.328|
|1999 NTM|est|0.884|0.008|−0.265|0.644||38 0.161|0.087|
||_t_|**9.436**|0.395|−0.620|0.463||||
|1999 NTM|est<br>_t_|0.848<br>**6.443**|0.010<br>0.498|−0.303<br>−0.725||0.114<br>0.568|38 0.164|0.090|
|1993 PRICE|est<br>_t_|0.092<br>0.936|−0.001<br>−0.089|0.006<br>0.018|2.264<br>**2.553**||31 0.206|0.118|
|1993 PRICE|est<br>_t_|−0.130<br>−0.852|0.003<br>0.193|−0.063<br>−0.186||0.586<br>**2.615**|31 0.214|0.126|
|1996 PRICE|est<br>_t_|0.000<br> **−2.327**|0.000<br>0.048|0.000<br>0.018|0.018<br>**12.444**||33 0.845|0.829|
|1996 PRICE|est<br>_t_|0.000<br>−0.671|0.000<br>0.773|−0.001<br>−0.770||0.001<br>**1.695**|33 0.107|0.014|
|1999 PRICE|est<br>_t_|0.000<br>**−1.903**|0.000<br>0.044|0.000<br>−0.004|0.023<br>**7.169**||38 0.611|0.577|
|1999 PRICE|est<br>_t_|0.000<br>−0.502|0.000<br>0.939|−0.001<br>−0.936||0.001<br>**1.698**|38 0.100|0.021|
|1993 THREAT|est<br>_t_|0.015<br>0.276|0.004<br>0.466|−0.093<br>−0.487|1.989<br>**4.012**||31 0.397|0.330|
|1993 THREAT|est|−0.019|0.008|−0.197||0.226|31 0.109|0.010|
||_t_|−0.183|0.848|−0.858||1.478|||
|1996 THREAT|est<br>_t_|0.993<br>**12.014**|0.023<br>1.510|−0.446<br>**−1.921**|−1.872<br> **−2.138**||33 0.312|0.241|
|1996 THREAT|est<br>_t_|0.801<br>**5.732**|0.020<br>1.214|−0.379<br>−1.559||0.229<br>1.040|33 0.232|0.152|
|1999 THREAT|est<br>_t_|0.938<br>**9.527**|0.008<br>0.363|−0.264<br>−0.589|−2.335<br>−1.599||38 0.182|0.110|
|1999 THREAT|est|0.878|0.001|−0.125||−0.041|38 0.121|0.044|
||_t_|**6.121**|0.064|−0.274||−0.188|||

Notes:

1. OLS estimates. Sample is cross-section of 5-digit HS agricultural products for 93, 96, 99. NTM data (from TRAINS database) available for those years.

2. NTM=Coverage of imports with some Price-type (PRICE), Quant-type or Threat NTM (THREAT).

3. **Bold** and underline implies that corresponding estimate is statistical significance at 5%, **bold** implies that corresponding estimate is statistical significance at 10%.

4. %PACEXP=PAC spending as a fraction of total PAC spending for the cross-section. RANKPACEXP=Ranking by PAC spending (high rank implies relatively high PAC spending) scaled by sample size ( _N_ is the highest rank). So 1 ≥ RANKPACEXP ≥ 0 and monotonically increases with PAC spending.

48
strictly comparable because of differences in coverage, information from those surveys is helpful to identify correlates of vanilla farming that can help understand who the vanilla farmers are. Up to 200 vanilla-growing households were included in each survey.

Table 1 describes the geographical distribution of farming activities. Vanilla farming is concentrated on about 30,000 ha of plantations in the so-called SAVA zone (Sambava, Antalaha, Vohémar and Andapa) in the North-Eastern province of Antsiranana, with smaller numbers in the Toamasina province.<sup>5</sup> Table 1 Agricultural population by main crop and region, 1998

|Crop/Region<br>|Antanana<br>Sharein|rivo Mahaja<br>TotalNumber|nga Fianaran<br> of Producer<br>|tsoa Toamas<br>s (%)<br>|ina Antsiran|ana Toliar<br>|y<br>Number of<br>Producers<br>|Share in<br>total<br>(%)<br>|
|---|---|---|---|---|---|---|---|---|
|Rice<br>|22.4<br>|11.1|26.7|18.3|12.9|8.7|2,804,244|30.9|
|Other food|<br>40.4|7.0|14.9|13.4|5.8|18.5|4,402,227|48.5|
|crops<br>|||||||||
|Sub-total<br>food crops|33.4|8.6|19.5|15.3|8.6|14.7|7,206,471|79.5|
|Sugar cane|7.3|18.0|32.5|21.3|10.6|10.2|422,267|4.7|
|Cotton|0.0|25.0|0.0|8.2|4.1|62.7|20,783|0.2|
|Tobacco|22.6|24.0|33.4|5.0|2.2|12.8|82,244|0.9|
|Peanuts|27.0|8.7|30.4|5.9|2.0|26.1|575,794|6.3|
|Sub-total|18.6|13.7|30.9|11.8|5.3|19.7|1,101,088|12.1|
|ind. crops|||||||||
|Coffee<br>|1.2|3.2|41.2|32.8|19.9|1.6|480,897<br>|5.3|
|Vanilla|0.0|3.4|0.5|17.1|79.0|0.0|93,796|1.0|
|Pepper<br>|0.0|0.9|70.0|11.3|17.6|0.1|54,230|0.6|
|Cloves|0.0|3.7|7.3|80.6|8.4|0.0|127,908|1.4|
|Cocoa|0.0|1.2|0.0|3.9|94.9|0.0|5,883|0.1|
|Sub-total|0.8|3.1|32.2|37.1|25.6|1.0|762,714|8.4|
|export crops|||||||||
|Total|28.9|8.7|22.0|16.7|9.6|14.1|9,070,273|100|

Source: World Bank (2001) Because of its labor-intensive characteristics, vanilla is ill-suited for large-scale plantations. The number of farms involved in vanilla production fluctuates between 50,000 and 100,000.<sup>6</sup> Interestingly, Figure 3 shows that, compared to the average across all agricultural activities, concentration in vanilla farming is less than the EPM’s average (compare with annex table A1). This may be due to

> 5 Press sources report that vanilla production has also recently started in the island’s South-East region (Manakara, Vohipeno, Farafangana, Vangaindrano) as a response to the collapse of coffee prices, but is still embryonic at around 100 ha. See http://www3.clicanoo.com.

> 6 Data from Madagascar’s EPMs suggests a figure around 90'000 with a substantial error margin because of imperfect stratification. A producer organization quotes 70'000 farmers in 2004.

9
Finally, we identify population and land with nodes on the network using the 964 cities nearest (i.e., within 30 kilometers) to any link with the transportation network, when all 68 projects are built in complement. Populations of these cities are as reported for 2015 by the UN Urbanization Prospects. In the average country, cities on the network comprise 24 percent of total population, though there is a range, with Singapore 97 percent, Turkey at 49 percent, and Cambodia at 11 percent, reflecting variation in domestic connectivity and urbanization in these countries, as well as the geographic scope of the Belt and Road itself. As shown in Figure 1, the Belt and Road passes through only portions of many countries. Our baseline specification focuses on welfare gains accruing only to these cities with direct connectivity to the international network. In discussion of the results however, we consider the implication of allocating additional population and land (e.g., from rural areas, or greater metropolitan areas around cities) to the nodes on the network.

# 3. THE MARKET ACCESS APPROACH TO VALUING THE BENEFITS OF IMPROVEMENTS TO AN INTERNATIONAL TRANSPORTATION NETWORK

The market access approach to quantify the welfare benefit of a transportation investment is elaborated by Donaldson and Hornbeck (2016) for the case of a domestic transportation network. Here, we focus on how to interpret the model in the context of an international transportation network, and the specific modifications we make to their model in order to apply the framework to the Belt and Road. The central mechanism in the model through which transportation investment affects welfare is price adjustment--when investment causes trade costs to decline between cities, consumers and firms in each city can buy goods more cheaply.

11
# **<mark>Table A6 – Impact of FD Law on Bank Lending to Firms Given Public Debt of Their Municipalities</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on bank lending to private firms in states and municipalities of varying ex ante public indebtedness.</mark> The variable _Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m_ . _Investmentf,b,m_ is the loan volume (in logs) for investment projects issued to firm _f_ , by bank _b_ in month _m_ . _Working Capitalf,b,m_ is the loan volume (in logs) for working capital issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _DebtMunicipalitym,16Q1_ measures the public indebtedness of municipality _m_ and is calculated as the municipal public debt per capita in 2016Q1. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in <u>Table A1 in the Appendix.</u>

|||Volumef,b,m<br>|||Investmentf,b,<br>|m<br>|W<br>|orking Capitalf<br>|,b,m<br>|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Postm*DebtStates,16Q1|0.018***|0.019***||0.049***|0.054***||0.019***|0.019***||
||(0.006)|(0.006)||(0.012)|(0.013)||(0.007)|(0.007)||
|Postm*DebtMunicipalitym,16Q1|-0.126***|-0.118***|-0.103***|-0.340***|-0.339***|-0.510***|-0.094***|-0.083***|-0.047**|
||(0.024)|(0.023)|(0.023)|(0.065)|(0.067)|(0.100)|(0.024)|(0.023)|(0.023)|
|Postm*DebtStates,16Q1*|0.073***|0.079***|0.103***|0.450***|0.446***|0.733***|0.081***|0.090***|0.103***|
|DebtMunicipalitym,16Q1|(0.022)|(0.022)|(0.029)|(0.063)|(0.064)|(0.099)|(0.023)|(0.023)|(0.030)|
|Observations|1,252,105|1,252,105|1,252,105|206,666|206,655|206,666|1,131,483|1,131,483|1,131,483|
|R-squared|0.803|0.803|0.803|0.870|0.870|0.871|0.799|0.800|0.800|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|State-Month FE|No|No|Yes|No|No|Yes|No|No|Yes|

71