rafmacalaba/datause-encoder-data huggingface dataset:
measure because it reflects better permanent income and is less subject to short-term fluctuations than current income. In addition, the general experience is that expenditure is typically collected with greater accuracy than income. This is an important point because the income modules assume well over half of ali questions in the CILSS. In other words, dropping the income modules would yield a much simpler and leaner instrument which would reduce only marginally the value of the data from the point of view of analyzing welfare and poverty. We would argue that in the context of a typical low-income developing country it is not a top priority for welfare and poverty analysis to iAentify household savings. In the context of linking welfare changes to the macroeconomic environment, the key information is not so much the level of housel' )ld income, but the composition of income and how it is changing when the external economic environment is changing.

For that purpose, it is feasible to introduce in a survey a line of qualitative questioning to obtain information on the pattern of income, and how it is changing over time, without going into the quantitative detail needed to build up total income. For example, questions could be asked of the sort "How much does wage income contribute to total household income?", with the answer codes indicating ranges such as "less than 25%", "between 25% and 50 %", etc. To capture change, questions could be added such as "Is your wage income now higher, lower or the same relative to twelve months ago?" or "Has the importance of wage income increased, decreased, or remained about the same in your household's total income?" An approach along these lines is currently being tested in a dozen African countries, as part of the administration of Priority Surveys (see Grootaert and Marchant, 1991).

Accepting then, that most welfare and poverty analysis can usefully be based on household expenditure and that this should be the focus of data collection, there still remain unresolved issues relating to the quality of expenditure data. A main query pertains to the optimal reference and recall periods for each expenditure category.2/ 2/The reference period is the period over which the respondent is asked to report. The recall period is the time interval over which the respondent is asked to search his/her memory, at any given interview. In a one-visit survey, the two periods 7
32

**Table 5. Economic Growth and the Role of Aid, RER Misalignment, and Financial Development in the World (cont.)**

||**[1]**<br>**[2]**|**[3]**|
|---|---|---|
|RER*Official Development Assistance|-0.0015<sup>**</sup><br>(2.38)|-0.0018<br>*<br>*<br>(3.55)|
|Official Development Assistance*Peace Onset|0.0002<br>(0.54)||
||<sup>**</sup>|*<br>|
|Official Development Assistance*Post-conflict 1|0.0008|0.0008<br>*|
||(3.75)|(9.04)|
||<sup>**</sup>|*|
|Official Development Assistance*Post-conflict|0.0018 <br>(2.84)|0.0010<br>*<br>(6.37)|
||<sup>**</sup>|*|
|RER Misalignment*Financial Depth|0.0321|0.0279<br>*|
|_Period Shifts_|||
||<br>|*|
|Intercept (base period: 1975-79)|-0.1132 **<br>-0.0881**|-0.0781<br>*|
|1980-84|-0.0180<sup>**</sup><br>-0.0197<sup>**</sup>|-0.0176<br>*<br>*<br>|
|1985-89|-0.0173<sup>**</sup><br>-0.0202<sup>**</sup>|-0.0181<br>*<br>*<br>|
|1990-94|-0.0215<sup>**</sup><br>-0.0213<sup>**</sup>|-0.0225<br>*<br>*|
|1995-99|-0.0307<sup>**</sup><br>-0.0276<sup>**</sup>|-0.0294<br>*<br>*|
|2000-04|-0.0273<sup>**</sup><br>-0.0235<sup>**</sup>|-0.0251<br>*<br>*|
|No. Countries /No. Observations|78/363<br>78/363|78/363|
|**SPECIFICATION TESTS (P-Values)**|||
|(a) Sargan Test|0.70<br>0.94|0.89|
|(b) Serial Correlation :|||
|Second-Order|0.30<br>0.20|0.23|

Note: Numbers below coefficients are the corresponding robust standard errors. * (**) denotes statistical significance at the 10 (5) percent level. Sample: Cross-country panel data consisting of non-overlapping 5-year averages spanning 1970-2004. Estimation method: GMM-IV system estimator
-17-

income are distributed more, equally. Row (3) of Table 9 shows that the correlation ratio between source income and total income inequality is highest for two income sources: land rent and other agricultural income. By contrast, inequality in imputed land rent is poorly correlated with total income inequality and inequality in agricultural wages is very weakly correlated with total agricultural income inequality.

The data in Table 9 serve to explain why unequal landownership does not play a dominant role in agricultural income inequality in -- this sample. While the source income weights for landownership measured by combining the values for imputed land rent and land rent - - generally exceed those for other agricultural income, the source ginis and correlation ratios for these two income sources are on the whole less than those for other agricultural income. In this sample other agricultural income (returns to labor and crop profit) represents the main source of agricultural income inequality. This is because other agricultural income (returns to labor and crop profit) has a large share of total agricultural income, a large source gini and is highly correlated with total agricultural inequality.

# 5. Conclusion

This paper has examined the sources of income inequality in rural Pakistan. Using three-year time series data from 727 households in three provinces, this study decomposed both the sources of overall income inequality and the sources of agricultural income inequality. Two central findings emerge from the paper. First, the study shows that of the five sources of total rural income -- agricultural,
# **VII.   CONCLUSIONS**

In this paper, we find that a tighter fiscal stance in Brazilian states, measured by changes in the cyclically-adjusted primary balance, does not seem to increase inequality over the period 1995-2011. This conclusion contrasts to the results of several papers that analyze the impact of fiscal consolidations on inequality at the national level for OECD countries (Ball and others, 2013). All measures of inequality considered in this paper came from the same survey and therefore are not subject to “spurious” design and implementation effects that can confound a cross-country analysis of heterogeneous surveys.

Our results also suggest that revenue increases in Brazilian states were not associated with increases in inequality. In addition, reductions in primary expenditures do not seem to have had deleterious impacts on inequality measures. Some further disaggregation indicates that revenue increases due to revenue transfers to states are linked to decreases in inequality, whereas changes in investment expenditure are positively linked to inequality measures. Several robustness checks performed confirm that there is no evidence that fiscal adjustment at the sub-national level is positively linked to inequality.

The different conclusions obtained relative to the rest of the literature could be explained by differences in the institutional setting and in the structural characteristics of Brazilian states relative to OECD economies, such as the importance of transfers of revenues. The paper does not attempt to establish the precise mechanism at play, but possible differences driving the result include: higher initial levels of inequality; lesser reliance on progressive taxation; the absence of extensive social safety nets and other automatic stabilizers; scope to significantly improve the efficiency of public spending and the quality of public services; and the regressive nature of some forms of public expenditure at the state level.

Furthermore, fiscal adjustment at the state-level might also have been achieved through efficiency gains both on the expenditure-side, but also in terms of revenue collection with no discernible impact in terms of increasing inequality. In addition, greater fiscal discipline could also have improved the investment climate within states, which facilitated job creation and investment.

Future research could focus on drilling down on the mechanism linking the fiscal stance and inequality dynamics. This would be important to ascertain where the relationship is likely to be maintained over time as the country macroeconomic and social conditions evolve, thus informing policy making more precisely. A central message, however, is that the results linking fiscal adjustment to an increase in inequality in advanced economies cannot be easily generalized to developing countries, given the Brazilian experience.
# **5. Results**

# **5.1 Relative deprivation in the income space**

In this section, we look at the spatial divide between urban and rural areas in Morocco using standard measures of inequality and relative deprivation. The purpose of this section is to assess the urban-rural divide in Morocco and to illustrate the use of the Yitzhaki (1979) relative deprivation measure and its relation with the Gini index.

The sharp inequality in living standards across urban and rural areas is a very apparent feature of the Moroccan economy. According to a recent World Bank study (2010a), the poverty rate in rural areas is almost three times as high as in urban areas (14.5 percent and 4.8 percent respectively). This is likely the largest urban-rural gap in the Middle East and North Africa region and one of the largest in the world (World Bank 2010b).

A decomposition of economic inequality using the MHYS 2009-2010 data illustrated the extent to which the rural-urban gap is a substantial contributor to Morocco’s overall economic inequality. We decompose economic inequality into its between-group and within-group components following Bourguignon (1979) and Shorrocks (1984). Between-group inequality reflects the mean differences across the rural and urban populations; within-group inequality reflects the inequality within the rural and urban sub-groups. Table 3 shows only slightly higher inequality within urban areas as compared to within rural areas. However, the between-group inequality across the rural-urban areas explains roughly a third of the national inequality. Using slightly older datasets from Morocco, the World Bank (2010b) arrives at similar conclusions for expenditures. The sharpness of Morocco’s rural-urban divide is particularly evident when put in the context of other countries. For example, Shorrocks and Wan (2008) show the range of expenditure based between-group (rural-urban) inequality, corrected for spatial price differences, to typically range from 5 to 15 percent of overall inequality. The rural-urban sharp divide in Morocco is thus very high by international standards.

**Table 3: Inequality (Gini) decomposition in between and within inequality**

||**Expenditure**|
|---|---|
|Total inequality|0.352|
|Urban inequality|0.331|
|Rural inequality|0.326|
|Within-group inequality|0.184|
|Between-group inequality|0.109|
|Between as a share of total|30.9%|

Source: MHYS 2009-2010. (*) Per capita expenditure are spatially adjusted for rural-urban price differences 13
Combining the effect of the Family 500+ and the increase in the change in the tax-free allowance leads to a further reduction in poverty and inequality. Most of the change results from the introduction of the Family 500+ program, as shown in Table 7, with inequality declining by an additional 0.2 to 0.5 Gini points and extreme poverty by 0.3 to 0.6 percentage points, depending on the size of the allowance. The Figure 10. Progressivity and impact of direct taxes and <u>contributions under alternative reforms</u>

|0000<br>0.010<br>0.020<br>0.030<br>0.040<br>0.050<br>0.060<br>0.070<br>0.080<br>Kakwani Index<br>gressive <----------------><br>Progressive|||0000<br>0.005<br>0.010<br>0.015<br>0.020<br>0.025<br>0.030<br>0.035<br>Marginal contribution|
|---|---|---|---|
|.<br>2014<br>Baseline<br>Re|Includes<br>Family<br>Family<br>500+ and|Family<br>500+ and|.|
||500+<br>Tax-free|Tax-free||
||amount|amount||
||PLN 5000|PLN 8000||
|Kakwani|Redistributive effect (G|ini points)||
|Source: WorldBankestimate|s based on HBS2014.|||

cost of these programs ranges from an additional PLN 7.5 (0.4 percent of GDP) to PLN 19 million (1.1 percent of GDP) relative to the cost of Family 500+ program alone. This estimate takes into account an expected increase in consumption and therefore in indirect taxes (under the assumption that households consume a similar basket of goods).

Table 7. Impact of Family 500+ and Alternative Tax Allowance Reforms

||Market<br>Income|Market income<br>+ contributory<br>pensions|Disposable<br>Income|Consumabl<br>e Income|Final<br>Income|Estimated Fisc|al Cost|
|---|---|---|---|---|---|---|---|
||(1)|(2) = (1) +<br>contributory<br>pensions|(3) = (2) -<br>direct taxes -<br>contributions<br>+ direct<br>transfers|(4) = (3) -<br>indirect<br>taxes|(5) = (4)<br>+ inkind<br>transfers|(million PLN per<br>year)|(percent<br>of GDP)|
|**Gini**||||||||
|202014 Baseline|||0.3428|0.3547|0.2821|||
|1) Family 500+|||0.3133|0.3270|0.2704|22,181|1.3%|
|2) PIT allowance at PLN 5000|05265|03759|0.3397|0.3517|0.2799|8,873|0.5%|
|3) PIT allowance at PLN 8000<br>Sim|.|.|0.3356|0.3477|0.2768|22,582|1.3%|
|4) Family 500+ & PIT allowance at PLN 5000|||0.3107|0.3245|0.2682|29,702|1.7%|
|5) Family 500+ & PIT allowance at PLN 8000|||0.3076|0.3214|0.2652|41,172|2.4%|
|**Extreme Poverty**(percent of population)||||||||
|202014 Baseline|||5.5%|8.9%||||
|1) Family 500+|||3.4%|5.9%||||
|2) PIT allowance at PLN 5000|272%|60%|5.1%|8.5%||||
|3) PIT allowance at PLN 8000<br>Sim|.|.|4.8%|7.9%||||
|4) Family 500+ & PIT allowance at PLN 5000|||3.2%|5.6%||||
|5) Family 500+ & PIT allowance at PLN 8000<br>Source: Own estimates based on HBS 2014.|||3.1%|5.3%||||

29
**Graph 2.1: Average residual maturity of public debt (in years)**

<!-- Start of picture text --> 9.2<br>8.6 8.5<br>6.1<br>4.1<br>3.9<br>3.7 3.5 3.6<br>3.3<br>2.9 3<br>2.7<br>2.4 2.5 2.5<br>1.5<br>1.1 1.2 1.1<br><!-- End of picture text --> Source: Salvemini (1992:78) The sale of 12-month Treasury bills was effected by a uniform price auction since 1962, while 3 and 6-month bills were issued by public subscription. In 1975, a reform of the auction mechanism for Treasury bills allowed the Bank of Italy to participate on an equal footing with other market agents (banks and non-banks). The central bank was committed to act as a residual buyer for unsubscribed bills. A floor price was fixed by the Treasury for each auction.

Although the reform was an important first attempt to bring more participants to government securities auctions, its problems were embedded in both the floor price mechanism<sup>6</sup> and the residual role left to the Bank of Italy. This put an informal ceiling on the level of interest rates, weakening the central bank’s discretion in monetary policy.<sup>7</sup> The floor price basically made monetization partially automatic - if the price set by the Treasury differed from the price determined by supply and demand, the Bank of Italy was obliged to choose between refusing to finance the government or monetizing the debt. The financing of the Treasury occurred through a special overdraft facility that was limited to 14 percent of the yearly budget.<sup>8</sup> Only if the government exceeded the ceiling, did the central bank have a choice to interrupt the financing.

The impact of the growing monetization of public debt is clearly reflected in the relative importance of different types of investors (Table 2.1). The share of the Bank of Italy rose from 12 percent of total debt in 1966 to 48 percent in 1975. Banking institutions were able to maintain a more or less constant share of a rapidly growing debt at around 35 percent, although this fell to 32 percent in 1975. In contrast, the “economy and foreign investors”, which is defined as a

> 6 The floor price for the 6 and 12-month Treasury bill auctions was abolished in 1989, while that for medium and long-term CCTs and BTPs was abolished in 1992.

> 7 Important reforms were made in order to grant full independence to the Central Bank through various monetary control techniques, starting in the 1980s. (Santini 1997: 290-291).

> 8 The interest rate applied to the overdraft facility was also very favorable, and fixed at 1%. This was a basic reason for the massive utilization of the facility by the Treasury (Scarpelli 2001).

10
# **Chapter 3: Stocking up**

## **<mark>Step-by-step Implementation Guidance</mark>**

Step 1: Preparing the material. It is best to keep <u>your records in a special book (your inventory book). Any booklet or notebook will do.</u> Step 2: Figure out your most profitable products. Select the five to seven most profitable products, since it will be easier to track every single product’s stock-out (step 3), demand (step 4), compare its requirements for storage, capital, and transport relative to other products (step 5), and compare relevant prices, product quality, availability, and supplier schedules (step 6). Step 3: Get an idea about stock-outs. Take the ledger where you listed your most profitable products in (step 1) and start taking note of how often you run out of stock for these products. If you ask your customers how much exactly they were going to buy, you will have to note this down in a certain unit. For example, if a customer asks for 4 bottles of soy sauce, you can add 4 tallies in the row of that product in your records. Label the third column with “Unit” and the fourth column with “Stock-outs”. In the fourth column you will keep the tallies for stockouts of all your main products. The examples in the HB illustrate all this with typical goods sold.

Step 4: Get an idea of your inventory. Add the following two new column labels: label column 5 with “Items sold” and column 6 with “Inventory”. In column 5, you will keep count of the number of items sold for each of the products using a tally in just the same way you did with stock-outs. Step 5: Identify constraints (storage, capital, transport, limited shelf-life, manpower). The HB proposes product-specific solutions for typical problems with stocking-up (e.g. lack of storage space, size of vehicle, and time) Step 6: Compare suppliers (quality, prices, availability, schedules). This is important in order to avoid undesirable outcomes and to get the best prices. The HB explains how <u>to do this.</u> Step 7: Come up with a stock-up schedule.

## **<mark>Tips</mark>**

If you ran out stocks for a product, get used to asking your customer “Do you want something else?”. That way, you may find out about demand for products the customer would not have mentioned otherwise.

Reacting to price fluctuations, discounts, and product availability: If price changes are not communicated to you directly by your supplier, you can set a day in the week on which you update those price. Also, keep an open eye on discounts. In our talks with small and mid-sized shop owners, every one of them emphasized this practice as key for success. Sometimes it is your local supermarket which runs a discount offer for cooking oil or a local supplier who needs to get rid of his stock in eggs (and so decreases prices).

Do keep an eye on your suppliers’ delivery times and stockouts. Do not hesitate to change to a more reliable partner. Retailers like you have emphasized that getting the best prices and being attentive to bulk discounts of their suppliers are keys to reducing stocking-up costs.
refugees will create opportunities for the entry of new firms (Foster et al., 2008). It leads to the hypothesis that there was in increase in the number of new firms, in particular in the sectors that depend on informal labor.

Third, because the refugees have better information about the refugees’ needs for products, some of them may exploit their entrepreneurial abilities to setting up new firms. While there is no easy way to measure to what extent refugees are able to transfer their financial capital from Syria to Turkey, it seems safe to assume that their more liquid capital arrives with them. Indeed, their networks and social capital certainly do. It leads to the hypothesis that the refugee crisis increased the number of foreign-owned firms.

# **4 Methodology**

The primary equiaton of interest is given by equation 1 where _Yit_ is the province level outcome, _Rit_ is the number of refugees in the province, _Pi_ are province fixed effects and _Tt_ are year fixed effects. Subscript _i_ indicates individual provinces and _t_ years. The standard criticism to the OLS estimator is that the resulting estimate of _ρ_ in equation 1 is likely to be biased if refugees choose areas with better prospects Dustmann et al. (2008).

We deal with the potential endogeneity problem using the two methods previously introduced in analyzing the impact of Syrian refugees on the labor market. The first method is to follow Del Carpio and Wagner (2015) and to exploit the presence of six border crossings between Syria and Turkey to construct a weighted distance variable to instrument the number of refugees in Turkey at the province level using data compiled by Erdo ̆gan (2014) for the year 2014. The second method is to assume that the region nearest to the Syrian border where the initial refugee camps were located is the treatment region which allows for an estimation using the difference-in-differences method as in Ceritoglu et al. (2017).

7
- 13 -

# **IV. MAIN ESTIMATION RESULTS AND POLICY IMPLICATIONS**

The ordinary least squares (OLS) regression is performed for Equations (1) and (2), separately. First, with the actual precipitation data during the actual project period, it is shown that precipitation causes delays, but not cost overruns (Table 2).<sup>8</sup> The coefficient of ln _Rain_<sup>_t_</sup> is estimated at 0.326, which is statistically significant in project delay equation. By contrast, the coefficient is not significant in the cost overrun equation. This is consistent to our prior expectations, because the sampled rural road works are fairly technically simple. Therefore, contractors are normally not allowed to claim for additional costs, unless there is a major change in specifications. On the other hand, when heavy rain or storms, regardless whether anticipated or not, happen, the contractors’ likely response seems to be just to delay the project.

When the actual precipitation is decomposed into the three factors, it is found that the anticipated precipitation, _rain_<sup>_e_</sup> , explains project delays. The coefficient is estimated at 0.493, which is statistically significant. It can be interpreted that if future precipitation were anticipated based on the backward-looking expectation, project delays could be avoided to a certain extent. The project delay is also explained by precipitation during the extended period. The coefficient is 1.633. In the cost overrun equation, none of the decomposed factors are statistically significant. Again, contractors are unlikely to claim for any cost adjustment because of weather in our case.

> 8 In general there is a view that an additional incentive mechanism is needed particularly for large-scale projects, such as the midcourse review process where the contractual performance would be reviewed periodically and contractors would be penalized if they do not meet the intermediary targets. To tighten the contractor incentives and avoid ex post contract adjustments, the similar type of mechanism has been used in some of the road projects in Nepal in recent years.