rafmacalaba/datause-encoder-data huggingface dataset:
year lagged value of the ratio of credit to the private sector to GDP as a measure of credit availability (DC_GDPt-1). As shown in Table 4, these macroeconomic indicators are highly correlated. Therefore, we try alternative specifications and find that our results are robust to the exclusion of the ratio of credit to the private sector to GDP.

Our next set of explanatory variables includes indicators of the business and legal environment. First, we include the “Credit information index” (CreditInfo) from the World Bank’s Doing Business Indicators, which measures the scope, access and quality of credit information available through either public or private bureaus. We expect factoring to be larger in countries with quick and comprehensive credit information on many buyers. Next, we include “Creditor Rights” (CreditorRts), which is an index aggregating four indicators of creditor rights from Djankov, McLiesh, and Shleifer (2005).<sup>6</sup> We also include from the World Bank’s Doing Business Indicators the “Cost of enforcing contracts as a % of debt” (Enforce_Debt), which measures the cost of going through court procedures as a percentage of the debt value (a higher value indicates a weaker contract environment). It is difficult to predict the sign of these two variables, since on the one hand, factoring can substitute for collateralized lending in countries with weak collateral laws and contract enforcement (since the receivables are sold, rather than collateralized), but on the other hand, factoring requires strong creditor rights for the factor to collect from the buyer in the case of default.

Our regression results are shown in Table 5. In column 1 we include country fixed effects and in all columns (except column 6) we include year fixed effects. Since our business environment variables are constant over time, in column 6 we use country

> 6 Our results are robust to the substitution of creditors rights with the broader “Legal rights index”, which is measures the degree to which collateral and bankruptcy laws facilitate lending (Doing Business, 2004).

13
**Table 4.2: Estimated effect on hours worked from a US$100 increase in exports per** **<u>worker in Cambodia</u>**

|VARIABLES|**Mean weekly**<br>**hours.**|**Mean weekly**<br>**hours, Male**|**Mean weekly**<br>**hours, Female**|
|---|---|---|---|
|Foreign demand shock|0.0342|0.0197|0.0497*|
|(USD 100), 2014-2009|(0.0221)|(0.0175)|(0.0290)|
|Observations|183|183|183|
|R-squared|0.504|0.520|0.482|
|Foreign demand shock|0.00933**|0.00681**|0.0110**|
|(USD 100), 2019-2009|(0.00459)|(0.00341)|(0.00557)|
|Observations|185|185|185|
|R-squared|0.419|0.516|0.333|
|Foreign demand shock|0.00751|0.00626|0.00848|
|(USD 100), 2019-2014|(0.00528)|(0.00424)|(0.00612)|
|Observations|183|183|183|
|R-squared|0.041|0.169|0.013|

Note: Controls are not exhaustive yet. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The table contains the results from a single, exactly-identified OLS instrumental variable regression for 2009, 2014, 2019, and 2020 Cambodia Socioeconomic Survey (CSES) data.

In sum, the increase in Cambodian exports arising from trade policies in the last few decades correlates with improving local labor market outcomes. First, the decrease in informality rate has proven to be related with increases in trade exposure. Moreover, in those districts where trade exposure is higher, workers (especially women) tend to be working more, which might help mitigate gender disparities (at least in terms of participation). These findings are all supported by the literature and classical theories that increases in trade are beneficial for local labor market outcomes.

# **5. Conclusions**

In Cambodia, the trade liberalization policies of recent decades have coincided with one of the strongest growth rates among developing countries. Moreover, structural transformation from agriculture to other industries—largely provoked by increases in trade—boosted job creation, triggered productivity gains, and helped reduce poverty and inequality.

This paper expands on the Cambodian trade liberalization literature by using the most recent data available to examine the effects of trade policies on trade flows, and how increasing 26 | P a g e
|Table 1: Growth and red<br>(Shapleya|istribution decom<br>pproach -per capita|position of poverty c<br>consumption)|hanges|
|---|---|---|---|
||Bangladesh|Peru|Thailand|
||2000 vs 2010|2004 vs 2010|2000 vs 2009|
|**Headcount rate FGT(0)**||||
|t0|0.489|0.491|0.239|
||(0.012)|(0.002)|(0.002)|
|t1|0.315|0.330|0.098|
||(0.009)|(0.002)|(0.001)|
|Actual%|-0.173|-0.161|-0.141|
||(0.015)|(0.003)|(0.002)|
|Growth|-0.158|-0.138|-0.142|
||(0.016)|(0.004)|(0.003)|
|Redistribution|-0.016|-0.024|0.001|
||(0.015)|(0.003)|(0.003)|
|Contributions to the decline in FGT(0)||||
|Growth|91%|85%|101%|
|Redistribution|9%|15%|-1%|
|**Poverty gap FGT(1)**||||
|t0|0.128|0.164|0.055|
||(0.005)|(0.001)|(0.001)|
|t1|0.065|0.093|0.018|
||(0.002)|(0.001)|(0.000)|
|Actual%|-0.062|-0.071|-0.036|
||(0.005)|(0.001)|(0.001)|
|Growth|-0.056|-0.059|-0.037|
||(0.006)|(0.002)|(0.001)|
|Redistribution|-0.006|-0.011|0.000|
||(0.006)|(0.002)|(0.001)|
|Contributions to the decline in FGT(0)||||
|Growth|90%|84%|103%|
|Redistribution|10%|16%|0%|

<mark>Source: Own estimations based on Peru's ENAHO 2005 -2009, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.</mark> <mark>Standard errors for a two-sided 95% confidence interval shown in parentheses.</mark> 28
# **Table A.3.1. Conditional correlations of grades and absenteeism with aspired and expected education as dummies (completing a postsecondary degree vs. secondary or below)**

||**Stand**|**ardized average**|**grades**|**Number of s**|**chool days misse**<br>**month**|**d in the past**|
|---|---|---|---|---|---|---|
||(1)|(2)|(4)|(5)|(6)|(8)|
|Aspiring to Postsecondary (dummy)|**0.204*****|||**-0.337*****|||
||(0.01)|||(0.03)|||
|Expecting Postsecondary (dummy)||**0.086*****<br>(0.01)|||**-0.167*****<br>(0.02)||
|There is a difference between aspired and|||**0.087*****|||**-0.069*****|
|expected educational level(dummy)*|||(0.01)|||(0.02)|
|Self-esteem|-0.004|-0.006|-0.001|-0.051***|-0.047***||
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)||
|Sense of belonging|0.064***|0.067***|0.067***|-0.103***|-0.107***|-0.053***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|Perseverance|0.042***|0.045***|0.048***|-0.029**|-0.032***|-0.108***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|Growth mindset|0.146***|0.151***|0.154***|-0.091***|-0.098***|-0.038***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|Effort beliefs|<br>0.053***|<br>0.055***|<br>0.055***|<br>0.002|<br>-0.001|<br>-0.105***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|Learning orientation|0.124***|0.131***|0.132***|-0.096***|-0.106***|-0.002|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|Performance-avoidance orientation|-0.058***|-0.060***|-0.060***|0.038***|0.041***|-0.112***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.01)|(0.01)|
|Age (with decimals)|-0.101***|-0.105***|-0.111***|0.146***|0.152***|0.043***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Being a girl (dummy)|0.580***|0.602***|0.600***|-0.361***|-0.395***|0.163***|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|
|Living in urban area (vs. rural)|<br>-0.113***|<br>-0.107***|<br>-0.089***|<br>-0.113***|<br>-0.118***|<br>-0.401***|
||(0.01)|(0.01)|(0.01)|(0.03)|(0.03)|(0.02)|
|Living in Java (vs. in Sumatra)|0.039***|0.032***|0.022***|0.011|0.019|-0.152***|
||(0.01)|(0.01)|(0.01)|(0.03)|(0.03)|(0.03)|
|Constant|0.997***|1.164***|1.238***|-0.342|-0.593***|0.039|
||(0.08)|(0.08)|(0.07)|(0.21)|(0.21)|(0.21)|
|Observations|54,639|54,615|54,610|55,039|55,014|55,009|
|R-squared|0.24|0.23|0.23|0.04|0.04|0.04|

_Source_ : Authors’ calculations based on the 2018 survey “Getting to Know 8th Grade Students.” _Note_ : *2,024 students (5 percent of the sample) have higher expectations than aspirations. Conditional correlations are Ordinary Least Squares regressions. Yellow and blue highlights show significant correlations at least at the 95 percent level for the variables of interest and covariates, respectively. Grades are standardized at the school level, with a mean of zero and a standard deviation of one. Measures of socioemotional skills are standardized for the entire sample. Standard errors are clustered at the school level.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 35
PotLfI(CY RlSFARCH WORKING PAPER 2487

# Summary findings

Countries with more developed financial sectors experience fewer fluctuations in real per capita output, consumption, and investment growth. But the manner in which the financial sector develops matters.

The relative importance of banks in the financial system is important in explaining consumption and investment volatility. The proportion of credit provided to the private sector best explains volatility of consumption and output.

Denizer, Iyigun, and Owen generate their main results using fixed-effects estimation with panel data from 70 countries for the years 1 956-98.

Their general findings suggest that the risk management and information processing provided by banks may be especially important in reducing consumption and investment volatility. The simple availability of credit to the private sector probably helps smooth consumption and GDP.

This paper-a product of the Poverty Reduction and Economic Management Sector Unit, Europe and Central Asia Region-is part of a larger effort in the region to understand the links between finance and macroeconomic volatility. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Irina Partola, room H4-346, telephone 202-473-5759, fax 202-522-2751, email address ipartolaC@Lworldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may he contacted at cdenizer@Cworldbank.org, murat.iyigun@;frb.gov, or aowen@'hamilton.edu. November 2000. (28 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An ohjective 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 he cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the vieu of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
compared to smaller countries at comparable levels of per-capita income.<sup>4</sup> This is the case for all other populous countries it is compared with in graphs below. What is striking, however, is that Pakistan’s average trade-to-GDP ratio for the period 2007-09 was smaller than what it was a decade earlier (1997-99), in contrast to the shares of its peers. In 1997-99, Pakistan’s trade-toGDP ratio was as high as China’s, and much higher than India’s. Ten years on, shares of both China and India almost doubled while Pakistan’s fell.

**Figure 3**

**Openness to trade**

<!-- Start of picture text --> Openness to Trade 9799 Openness to Trade 0709<br>MYS<br>MYS<br>VNM<br>THA<br>VNM THA<br>EGYCHN<br>EGY TUR BGD IND IRNTUR<br>BGD INDPAK IRN USA PAK BRA USA<br>BRA<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Log of GDP per capita (PPP, av. 9799) Log of GDP per capita (PPP, av. 0709)<br>Source: Authors’ calculations based on data from WDI.<br>Table 1<br>Export Composition by Market<br>– Selected Economies-<br>Region 2009 CAGR(%)*<br>Exports (US$ '000) % of Total 00-07 00-09<br>EU 4,416,240 31.1 10.3 -7.0<br>USA 3,345,885 23.6 7.3 -6.4<br>CHINA 1,632,546 11.5 7.5 -3.1<br>BRAZIL-RUSSIA-INDIA 588,617 4.1 21.1 1.0<br>TURKEY 618,949 4.4 30.6 7.9<br>REST OF WORLD 3,596,671 25.3 13.7 -9.8<br>Total 14,198,908 100 10.7 -6.3<br>CHN<br>250 250<br>200 200<br>150 150<br>100 100<br>Trade to GDP (%), 9799 Trade to GDP (%), 0709<br>50 50<br>0 0<br><!-- End of picture text -->

* Compound average growth rate

Source: COMTRADE indication of the dependence of domestic producers on foreign demand and of domestic consumers and producers on foreign supply. There is a concave relationship between trade openness and per capita income: countries tend to trade more as incomes rise, but at a decreasing rate.

> 4 An OLS regression (using cross-country data from WDI) of trade/GDP on the log of per capita income (PPP current $), its squared value, cost of exporting (using a sub-indicator from Doing Business 2008-2010) and population, gives a predicted trade share of 65 percent. The actual trade share of less than 32 percent is therefore extremely low.

5
mathematically equivalent. According to one presentation, the entitlement to state i can be written as fonows: 6 entitlementi standard financial assistance + special revenue needsi + special expenditure needs3 - assessed needs met by specific purpose transfersi

## where

## standard financial assistance = an equal per capita grant

The amount of standard financial assistance is determined based on the difference between the total expenditures and revenues of the states, and adjusted for the center's resource availability for transfer. The objective of the standard financial assistance is to close the vertical fiscal imbalance (the fact that the states' total expenditure is higher than their total revenue) for the states as a whole, without adjusting for the specific needs arising from individual states' revenue and expenditure situations.

# - - special revenue needsi = Pi **(R/Ys)(YfPs** Yi/Pi) = Pi (RJP.)[l  (Yi/Pi)/(Y3<sup>/Ps)]</sup>

where Pi is the population of state i, Rs is the total revenue of all the states, Y, is the total tax bases of all the states, RJYS is the national average effective tax rate (standard tax effort), P. is the country's population, Y)P, is national average per capita tax base (standard tax capacity), Y, is the tax base in state i, and Pi is the population of state i, and YI/P3<sup>is per capita tax base of state i (own tax capacity). If</sup> _(YiIPi) f(YRJP)_ < 1, that is, state i's tax capacity is lower than the national average, then the state will receive a positive entitlement as special revenue needs, and vice versa.

Special expenditure needs of state i is the sum of the needs of many expenditure categories of that state. In each category, the need is calculated using the following formula:

## Pi (Ef/P.) (yi-l)

where Pi is the population of state i, Es is the total expenditure of all the states, Es/P. is per capita standard expenditure. y, is the category disability ratio of state i, which measures the extend to which state i's need differs from the standard. Generally, a state's category disability ratio is calculated by combining (usually by multiplying but sometimes by adding) individual disability factors which express relevant cost influences as a ratio of the Australian average. The general formula for most individual disability factors can be written as:

## Yi = disability factor of state i = (xf/Pi)/(x)PJ)

**6** See Comnmonwealh Grants Commission (1996), pp.65-66.

10
# **Figure 1: Identifying the Agricultural Population in the Global Income Distribution**

<!-- Start of picture text --> Poverty Line<br>Global Income Distribution, 2000<br>Global Agricultural<br>Population<br>Global Non-Agricultural<br>Population<br>1 3 5 7 9<br>Monthly household per capita income (1993 PPP, log)<br>.4<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> Three facts about the agricultural sector determine the welfare effects of a globalscale removal of agricultural distortions: (1) the proportion of the world population whose real incomes depend on the agricultural sector; (2) the initial position of the agricultural population in the global income distribution; and (3) the dispersion of incomes among the agricultural population. Using the GIDD dataset, Figure 1 shows a kernel density for the global income distribution of household per capita income/consumption and kernel _densities_ for incomes/consumption of the population in and out of the agricultural sector, respectively.<sup>6</sup> The area below the kernel _density_ for the agricultural sector is equal to 0.45, showing that 45 percent of the world population relies on agriculture for its livelihood. The distribution of the agricultural population is located to the left of the non-agricultural distribution implying that households in the agricultural sector earn, on average, lower incomes than their counterparts in other sectors. In Purchasing Power Parity (PPP) US dollars, the average agricultural household’s per capita monthly income is 65 dollars, just 20 percent of the 329 dollars of per capita income earned by the average households in the non-agriculture group, see Table 1. The differences in shape between the two distributions corroborates what Kuznets hypothesized more than 50 years ago, i.e. incomes in the traditional sector are less welfare indicator is converted to 1993 international prices by multiplying the outcome of the previous calculations by the 1993 PPP exchange rate.

> 6 The distributions for the agricultural and non-agricultural populations are not, strictly speaking, density functions since the area below the curve do not add to 1. The densities of the agricultural and nonagricultural population had been rescaled so that the area under the curve represents the proportion of the world population within these two groups.

4
7

# **Figure 1 Network Code Processes**

<!-- Start of picture text --> Day before Day after<br>Non-daily gas day Gas day gas day<br>License Gas flow Network i. Measurement<br>from Ofgas nornination i operation i<br>Framework . ! Scheduling > Flexibility Allocation<br>agreement market<br>Customer Renomination Gas<br>and site trading and GsDaily balance<br>registration balancing calculation<br>Capacity booking Flexibiiity icing<br>and trading bids Invoicing<br>- -- . .. .. ; ~~~~~~~~(monthly)<br>,........... .......................<br>U.K. Link<br>Shipper 'British Gas ,<br>TransCo<br>Slhippe fiunction BGfTfunction<br>Srrce 1oGT 1996<br><!-- End of picture text --> _Stage I. Non-daily processes, pe7formed before the gCas day_

- Shippers must obtain a shippers license from Ofgas and then establish a framework agreement with BGT.

- Shippers purchase pipeline or storage capacity, or both, from BGT (capacity booking) or from other shippers in the secondary capacity market (capacity trading). BGT charges shippers for booked capacity monthly.

- Shippers register their customers and supply sites, or exit points, with BGT. Supply sites can be daily metered or non-daily metered. Typically, contract market customers have daily metered sites, and tariff market customers non-daily metered sites.

_Stage 2: Processes performed oh the day before the gas day_

- In the gas nomination process shippers inform BGT about the location, quantity, and calorific value of gas to be injected into and withdrawn from the system on the gas day (shippers can send their nomination up to one month in advance). Nomination must be finalized by 12:00 p.m. for withdrawal at daily metered sites, by 2:30 p.m. for use of storage, and by 3:00 p.m. for injection at the entry points. In the meantime BGT forecasts withdrawal at non-daily metered sites for each shipper and creates
sector-specific factors, including multilateral resistance. Finally, to ensure that our results are robust to the presence of zero trade flows in the dataset, we use a variation on the Poisson Pseudo-Maximum Likelihood approach of Santos Silva and Tenreyro (2006), in addition to standard OLS.

# **2 Mapping Road Networks in Europe and Central Asia**

As defined in this paper, the ECA region covers 27 countries stretching from the Czech Republic in the West to Russia (Siberia) in the East, and from Turkmenistan in the South to the Baltic States and Russia in the North (Table 1 and Figure 1). Anecdotal evidence suggests that the road network in this area is extensive, but of variable quality. This observation is particularly true in those countries where the post-Communist transition has been long and difficult. (See Molnar and Ojala 2003; ADB 2006; and Cadot et al. 2006 for further details.) Computerized maps and spatial analysis software make it possible to develop a detailed picture of road transport routes in the ECA region, as Buys et al. (2006) have done for SSA. We construct minimum-distance routes connecting 138 cities, i.e. all regional cities with a year 2000 population of over 300 000 people. In all, we have 9453 inter-city routes along 2411 individual arcs (Figure 1). For each route, we are able to identify the exact road distance traveled in each of the sample countries. For instance, the minimum distance route from Prague to Moscow includes 128.6km of road travel in the Czech Republic, 723.6km in Poland, 547.2km in Belarus and finally 454.4km in Russia.

We will be analyzing international trade data in the remainder of this paper. We need, therefore, to aggregate our intercity road distance data to the country level. To do that, we adopt the convention that the distance between two countries will be treated as the unweighted mean of the

- 5 -
4

# Basic Demographic, Employment, and Wage Statistics

Basic statistics about population, structure of employment, and wage level are important for understanding Senegal's labor markets. Moreover, they are also useful as background to the analytical work that follows in Chapters 5-7.

## 4.1 Ihe Demographic Situation

Demographic data for Senegal are derived primarily from the 1960.1961 and 1970-1971 demographic surveys, the nadonal census of 1976, the fecundity survey of 1978, and the labor migration survey of 1979. The data are of varied quality, aind the statistics presented below should be used with caution. (In 1987 Senegal undertook another national census, the results of which should be available in 1990.) According to the data, Senegal's population increased from 4,589 million in 1972 to,6,038 million in 1982. The annual rate of increase was a selAtively high 2.9 percent during the 1972-1982 period, with the result that 53 percent of the population was less than 20 years old in 1982. In 1982 the life expectancy at birth was 48 years (47 for men and 49 for women), and the average number of surviving children per family was estimated at 7.16-a large number resulting in part from a high incidence of polygamy. National statistics indicate that in 1982, 32 percent of married men were polygamous, while 48 percent of married women were members of polygamous households. The incidence of polygamy may have actually risen over the past twenty years (see Chapter 7).

As we indicated in Chapter 2, moat of the modem industrial activity is concentrated around Dakar. As a result, it is not surprising to find that 1,341 million people (22.2 percent of the population) reside in the Cap-Vert region (which contains Dakar) and that the population density in Cap-Vert is 75 times the national average.