rafmacalaba/datause-encoder-data huggingface dataset:
empirical analysis of the effects of decentralization on economic development and growth in Turkish provinces using cross-sectional and panel data approaches. Our panel dataset consists of 67 provinces over a period from 1976 to 2001. We examine specifically if variations in local decentralization across these provinces and across time have had a significant impact on economic development and growth. We find mixed results from our analysis of decentralization in Turkish provinces. First, creation of new provinces by separation from existing provinces seems to have had no significant impact on development or growth in those existing provinces. On the other hand, decentralization through increase in the number of local governments per capita seems to have had a negative effect on the level economic development.

# **2. Literature on Decentralization**

Decentralization is seen as an important avenue for efficiency gains by enabling a direct link between local provision of services and local tastes (Oates 1972, 1993). It is then expected that decentralization helps promote economic growth. Numerous studies examined empirically the relationship between decentralization and economic growth.<sup>1</sup> Among these Davoodi and Zou (1998) used a panel of 46 developed and developing countries for the period 1970-1985 and found a negative relationship between fiscal decentralization and growth in developing countries and no significant relationship for the developed countries. China has been a popular case study due to its sweeping fiscal reforms to decentralize since late 1970s. Zhang and Zou (1998) examined a panel of 28 Chinese provinces during the period 1980-1992 and found a negative relationship

> 1 Other aspects of fiscal decentralization were also examined. See, for example, Oates (1985) for the impact of fiscal decentralization on public sector size, De Mello (2000) for a cross-country study on fiscal decentralization and fiscal balances, and Neyapti (2006) for a recent empirical study on revenue decentralization and inequality.

2
- Moriconi, S., G. Peri, and R. Turati (2022). Skill of the immigrants and vote of the natives: Immigration and nationalism in european elections 2007–2016. _European Economic Review 141_ , 103986.

- Orefice, G. (2015, February). International migration and trade agreements: The new role of PTAs. _Canadian Journal of Economics 48_ (1), 310–334.

- Ortega, F. and G. Peri (2013, 01). The effect of income and immigration policies on international migration. _Migration Studies 1_ (1), 47–74.

- Santos-Silva, J. M. C. and S. Tenreyro (2006, November). The Log of Gravity. _The Review of Economics and Statistics 88_ (4), 641–658.

19
being. The questionnaire for the husbands covered the labor supply of household members, income from labor, time use, household expenses on items usually bought by male members (e.g., khat, cigarettes, transport, etc.), intra-household decision making, personality traits, and mental health and well-being.

Subsets of modules from the household survey were administered as part of the midline weekly surveys. These modules covered time use, expenditures, food diversity and security, school participation, program knowledge, and public works delegation. For women who delegated the public works opportunity to another household member, the interview collected self-reported information on the women and their delegees’ mutually agreed upon income-sharing rule.

_Weekly Surveys on Employment and Intra-household Transfers_ A key innovation in our data collection was to improve measurement of the working status of the target population in a setting with volatile and irregular rates of self-employment and casual labor. We based our survey instruments on the labor diaries developed by Dupas et al. (2020). The weekly surveys were administered to both the female beneficiary and her husband, if present (95 percent of beneficiaries were married), for three consecutive weeks both at midline and at endline. Enumerators visited each household once a week and asked whether the respondent had worked, the amount of time worked, and the type of work performed for each of the seven days prior to the interview. Work is defined as time on any income-generating activity. In one of the three weeks, the female beneficiary was also asked about the labor force participation and earnings of other adult household members (excluding husbands) in the seven days prior to the interview.<sup>10</sup> In addition, the weekly survey included a special section on intra-household transfers. Specifically, the section documented whether any transfers had occurred between the woman and the rest of household members as well as the amounts of the transfers in either direction. The module was intended to measure potential intra-household reallocation of income in response to the intervention, i.e., whether the women engaging in public works handed over their income to their husbands.

# **4.2. Baseline balance and attrition**

> 10 It is important to keep this change in survey design in mind when looking at changes in the levels of employment and earnings from baseline to midline or endline, as this change would impact the four groups (AD) equally. Indeed, we observe a much higher and similar rate of employment for women in control households at midline and endline, suggesting that the weekly surveys did capture volatile work better than the standard 7-day recall design.

12
Policy Research Working Paper 4943

# **Abstract**

This paper introduces the updated and expanded version of the Financial Development and Structure Database and presents recent trends in structure and development of financial institutions and markets across countries. The authors add indicators on banking structure and financial globalization. They find a deepening of both financial markets and institutions, a trend concentrated in highincome countries and more pronounced for markets than for banks. Similarly, the recent increase in cross-border lending and debt issues has been concentrated in highincome countries, while low and lower-middle income countries have experienced an increase in remittance flows. Low net interest margins, rising profitability and declining stability in high-income countries’ banking sectors characterize the recent financial sector boom in high income countries leading up to the global financial crisis of 2007.

This paper—a product of the Finance and Private Sector Team, Development Research Group—is part of a larger effort in the department to understand financial development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ademirguckunt@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
**Table 3.3: Household heads with no monetary income in rural Mexico,** **<u>Selected years 1992-2002</u>**

||**19**<br>|**92**<br>|**19**<br>|**96**<br>|**20**<br>|**02**<br>|
|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|**Urban**|**Rural**|
|**Household heads with no income**|||||||
|(a) Total Household Heads|13,152,597|4,183,553|15,252,501|4,667,367|18,588,744|5,595,519|
|(b) No income heads|694,848|229,130|866,784|232,449|1,059,990|234,401|
|(b)/(a)|5.28|5.48|5.68|4.98|5.70|4.19|
|Average Age|61.39|61.91|60.09|61.77|61.61|64.05|
|Male|51.87|68.72|58.09|64.12|46.07|71.69|
|Female|48.13|31.28|41.91|35.88|53.93|28.31|
|**Education**|||||||
|No education|30.05|67.29|29.35|55.37|25.01|50.00|
|Primary Incomplete|36.18|28.91|32.59|32.02|34.18|34.07|
|Primary Complete|21.22|2.93|23.52|11.28|23.52|13.52|
|Secondary Complete|6.15|0.65|6.35|1.13|8.52|2.35|
|Higher Education|6.40|0.21|8.18|0.21|8.77|0.06|
|**Labor status**|||||||
|Employed|7.56|22.91|5.13|30.92|9.31|25.93|
|Unemployed|7.88|1.30|15.24|4.63|5.83|3.64|
|Inactive|84.56|75.79|79.63|64.45|84.86|70.42|
|Source: Calculations based on ENIGH 1|992-2002. No|terural is ar|eas with less|than 2,500i|nhabitants.||

# **3.1 Poverty Depth**

Although Mexico has made substantial progress since 1996 in reducing the share of the rural population living in extreme poverty, the problem remains broad and deep. The P0 measures the proportion of people below a certain poverty line but takes no account of how far they are below that line—the degree of poverty—or whether they are becoming even poorer. To address the situation of the poorest and to evaluate whether their economic situation has improved, the poverty gap (or P1) measure is used. In rural areas with less than 15,000 inhabitants the P1 poverty measure reveals that the extreme poverty depth fell 10 percentage points to 12 percent during 1996-2002 (Figure 3.3 and Table 3.1). However, the poverty gap measure reveals that poverty depth remained virtually unchanged in the 1992-2002 period.

The regional poverty gap figures reveal that there are also large regional differences in the depth of poverty in rural localities (Figure 3.4 and Table 3.1). In the North and Capital regions P1 was below 5 percent while in the Center, Gulf, and South P1 reached more than 15 percent in areas with less than 15,000 inhabitants in 2002. During 1992-1996 the poverty gap increased the most in the regions where poverty was less deep in 1992, such as the Capital and North regions where P1 reached 17 and 16 percent, respectively. The depth of poverty was at its highest for all the regions in the middle of the 1990s.

13
# **Tables**

|Table 1. Human populations in five countries of East Africa in 2000<br>and 2050 (UNPD, 2008)|10|
|---|---|
|Table 2. Past and projected trends in consumption of meat and milk<br>in developing and developed countries. From Steinfeld et al. (2006)<br>and FAO (2006).|12|
|Table 3. Bovines, sheep and goats, pigs and poultry numbers for the<br>reference run, by region (billion head). Rosegrant et al., 2009.|18|
|Table 4. Grazing intensities in rangeland systems to 2030 and 2050<br>for the reference run, by region (TLU per ha). Rosegrant et al., 2009.|20|
|Table 5. Consumptive water use for 2000 and 2050 in Km<sup>3</sup>per year.<br>IMPACT model simulations (Rosegrant et al., 2009).|24|
|Table 6. Global land use change between 1987 and 2006 (1000 km<sup>2</sup><br>per year) (GEO4, 2007).|29|
|Table 7. Selected land use returns in some forest nations. Source: M<br>Grieg-Gran cited in Eliasch (2008).|38|
|Table 8. Relative importance, geographic spread, and level of impact<br>of different livestock-related processes affecting biodiversity (Reid et<br>al., 2009).|44|
|Table 9. Livestock's contribution to greenhouse gas emissions<br>(Steinfeld et al., 2006).|53|
|Table 10. Livestock-related measures for mitigating greenhouse gas<br>emissions, their mitigative effect on individual gases, degree of<br>scientific confidence in the mitigative effect, and the potential impact<br>on sustainable development. From Smith et al. (2007).|65|

5
convenience, Table 1 summarizes the choice of explanatory variables included in the baseline specification of each study.

All four studies estimate this equation using the SYS‐GMM estimator for dynamic panels with a short time dimension, developed by Holtz‐Eakin, Newey and Rosen (1988), Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998) (see also Roodman (2009) for an extensive discussion of this estimator and its implementation in Stata). Following the seminal application to cross‐ country growth empirics by Caselli, Esquivel and Lefort (1996), this methodology has been widely used in the empirical literature on the determinants of growth across countries. The estimator optimally combines inferences from GMM estimation of Equation (1) in levels and in differences, using appropriate lagged levels and differences of right‐hand‐side variables as internal instruments. Specifically, the SYS‐GMM procedure estimates the “levels” Equation (1) jointly with a first‐differenced version of Equation (1): where ∆ is the first‐difference operator, i.e. ∆��� ���� ������ for any variable � . The rationale for differencing Equation (1) is that it eliminates the unobserved country‐specific effect, �� , from the differenced equation This addresses the problem that �� is by construction correlated with lagged income on the right‐hand‐side, and moreover �� might also be correlated with the other explanatory variables of interest. However, differencing introduces a new challenge, which is that the differenced error term, ∆��� , now is mechanically correlated with lagged growth, ∆����� , since the latter includes ����� which from Equation (1) is depends on ����� , which in turn is included in the differenced error term. This problem is addressed with an appropriate choice of internal instruments, as discussed next.

SYS‐GMM estimation of Equations (1) and (2) commonly relies on the following standard identifying assumptions. The first assumption is that the idiosyncratic component of the error term, ��� , is uncorrelated over time. From Equation (1), ��� depends on current and lagged values of the error term, but not future values, so this implies that �������∆�����0 for s �2 , i.e. second and higher lags of log income per capita are valid instruments in the differenced equation. The second assumption is that ��������������0 for ��0 , i.e. the explanatory variables are predetermined with respect to the shock to growth. This implies that second and higher lags of the explanatory variables are valid instruments in the differenced equation. The third assumption, required to identify the levels equation, is that lagged 8
|**National sample**<br>**Table 5. Food sec**|Mean<br>**urity measure**|25th<br>**s across the**|50th<br>**distribution**|75th|
|---|---|---|---|---|
|Real value of per capita monthly<br>food consumption|929|612|810|1101|
|Daily per capita caloric intake|2601|2030|2441|3006|
|Food consumption score|61|46|61|74|
|**Agricultural households**|Mean|25th|50th|75th|
|Real value of per capita monthly<br>food consumption|911.29|606.95|801.34|1088.28|
|Daily per capita caloric intake|2586.81|2026.04|2419.25|2964.53|
|Food consumption score|61.59|48.00|62.00|74.00|
|**Nonagricultural households**|Mean|25th|50th|75th|
|Real value of per capita monthly<br>food consumption|951.86|619.16|821.26|1121.48|
|Daily per capita caloric intake|2619.47|2033.54|2470.99|3044.94|
|Food consumption score|60.07|44.00|59.50|75.00|
|See Table 4 notes.|||||

**Table 6. Effects of wheat flour price increases on food security**

||OLS|_25th quantile _<br>Uncondi|_50th quantile _<br>tionalQuantile R|_75th quantile_<br>egression|
|---|---|---|---|---|
|Log Real Value of Monthly Per Capita|-0.425***|-0.242***|-0.433***|-0.619***|
|Food Consumption|[0.0364]|[0.0437]|[0.0447]|[0.0571]|
|Lo Dail Per Caita Calorie Intake|-0.184***|-0.0866***|-0.187***|-0.279***|
|g y p|[0.0244]|[0.0290]|[0.0268]|[0.0340]|
|Lo Food Consumtion Score|-0.189***|-0.246***|-0.173***|-0.158***|
|g p|[0.0270]|[0.0508]|[0.0305]|[0.0319]|

Note: Coefficients and standard errors are from separate, population weighted regressions with dependent variable listed in the first column. Total observations: 20,483. OLS standard errors are corrected for clustering and stratification, UQR standard errors are clustered bootstrap (with replacement) estimates. *, **, and *** denote significance at 10%, 5%, and 1%, respectively.

29
and equity are balanced. Two-thirds of countries in the region had some form of STR for small businesses in March 2024, and in the remaining countries, small businesses that reach a certain size are expected to comply immediately with more complex corporate or personal income tax regimes. Among the countries with STRs, three stylized facts emerge about how they have been designed. Firstly, only 40 percent of countries apply a minimum turnover threshold, which means in the remaining countries, as soon as any business, no matter how small, makes any revenue, they are required to pay tax under the STR. Secondly, around 60 percent of countries rely solely on a percent of turnover for determining the amount of tax owed, whereas in the remaining countries, either a set fee or a combination of both is used. Thirdly, the relative size of tax obligations levied on small businesses varies considerably, with the profit rate that businesses would need to make for their level of taxation to be equal under the STR and CIT regimes ranging from 2 to 20 percent.

To shed light on the real-world implementation of an STR, we conduct a detailed examination of tax administrative data from the TOT regime in Kenya and identify three major shortcomings. Firstly, the minimum turnover threshold that aims to exempt the smallest businesses from paying tax is not binding in practice. On average, around 70 percent of businesses that pay TOT should be exempt as their declared turnover is below the minimum threshold (collectively, these businesses pay around one-third of the total amount of TOT collected). Survey data suggests that this is very likely due to a lack of knowledge by taxpayers and potentially due to a belief that filing a non-zero amount in tax will decrease the likelihood of being audited by the Kenya Revenue Authority (KRA). Secondly, efforts to mobilize revenue by increasing the amount of tax required from small businesses are partly offset by businesses lowering their declared turnover. Results from an event study show that, on average, in response to a three fold increase in the tax rate, businesses immediately decrease their declared turnover by around one-third, meaning the increase in revenue collection is less than proportional to the tax rate increase. Thirdly, requiring taxpayers to calculate a percentage of their monthly turnover to be paid in tax appears to be challenging 3
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27
over-quota tariff will be set at _commercil_ ECU1025/ton which will force up the price of over- quota bananas to an estimated ECU2332/ton see Borrell (1993). This price is clearly far above EU consumners' willingness to pay and they will switch to other fruit.

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14