rafmacalaba/datause-encoder-data huggingface dataset:
and measures, was followed. But we are unable to do this analysis because of data limitations, especially on earlier strategies. Instead, we analyze whether the strategies in our sample have an impact on the development of regulatory policy, while including as many strategies as possible in the analysis. The quantitative analysis of the impact of strategies on regulatory reforms focuses on three indicators from the World Bank’s Doing Business (DB) database that are relevant for financial sector development: getting credit, enforcing contracts, and resolving insolvency. The coverage of the DB database starts in 2004, so this part of the analysis considers a more limited period than in our extensive historical database on strategies. Nevertheless, this exercise can provide some insight into whether the more recent strategies have affected the implementation of regulatory policies in the financial sector.

# **4. What do the estimation results suggest?**

# **4.a. Strategies help increase financial depth**

Financial deepening, particularly the ratio of deposits to GDP, is positively affected by the presence and the quality of a strategy. The coefficients of all four strategy variables (the _dummy_ and _index_ strategy indicators for both the _impulse_ and _shift_ variables—see above) are significant and positively related to the change in the deposits-to-GDP ratio (Table 2), thus showing that the presence and quality of a strategy have an important impact on financial deepening. The estimated relationships between our four strategy indicators and the change in the credit-to-GDP ratio are positive, but the coefficients are not significantly different from zero. One possible explanation is that the ratio of credit to GDP is an ambiguous measure of sustainable financial deepening because it also can be a predictor of crises, and in particular a rise in credit to GDP from an already high level can increase instability. Also, increases in credit-to-GDP ratios can 19
the strongest association with both measures of nutritional outcome, followed by food share and 1- SSEXR. The same broad pattern can be observed when we focus on the lower end of the distribution.

**Table 7: Height-for-age Z-score (Tanzania)**

|||**Model 1**<br>|**- Without C**<br>|**ontrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Unc<br>|onditionalqu<br>|antile regress<br>|ion<br>|
|||10th|25th|50th|75th|
|Per capita kcal|-0.004|0.015|-0.005|-0.006|0.041|
||(0.053)|(0.090)|(0.058)|(0.060)|(0.072)|
|1-SSR|0.005|-0.023|-0.021|0.034|-0.011|
||(0.047)|(0.073)|(0.052)|(0.056)|(0.065)|
|1-SSEXR|0.049|0.042|0.098*|0.125**|0.032|
||(0.046)|(0.074)|(0.052)|(0.056)|(0.064)|
|HDDS|0.147***|0.172**|0.131**|0.136**|0.200***|
||(0.045)|(0.078)|(0.053)|(0.058)|(0.066)|
|Per capita||||||
|expenditure|-0.002|-0.066|-0.035|0.017|0.110|
||(0.054)|(0.099)|(0.061)|(0.063)|(0.074)|
|Food share|-0.149***|-0.176***|-0.178***|-0.133**|-0.110|
||(0.046)|(0.057)|(0.047)|(0.058)|(0.072)|

|||**Mode**<br>|**l 2 - With Co**<br>|**ntrols**<br>||
|---|---|---|---|---|---|
|VARIABLES|OLS|Un<br>|conditionalqu<br>|antile regres<br>|sion<br>|
|||10th|25th|50th|75th|
|Per capita kcal|0.001|0.057|0.014|-0.056|0.022|
||(0.058)|(0.095)|(0.063)|(0.065)|(0.079)|
|1-SSR|-0.028|-0.045|-0.046|0.030|-0.040|
||(0.048)|(0.079)|(0.054)|(0.058)|(0.067)|
|1-SSEXR|-0.001|0.045|0.082|0.097*|-0.042|
||(0.048)|(0.080)|(0.055)|(0.057)|(0.065)|
|HDDS|0.067|0.164*|0.084|0.023|0.103|
||(0.051)|(0.086)|(0.059)|(0.063)|(0.076)|
|Per capita expenditure|-0.027|-0.063|-0.053|-0.061|0.053|
||(0.059)|(0.109)|(0.069)|(0.074)|(0.083)|
|Food share|-0.070|-0.137**|-0.143***|-0.083|-0.006|
||(0.047)|(0.068)|(0.055)|(0.063)|(0.078)|

Controlling for the confounding factors, none of the OLS estimates of food security measures are statistically significant for either height-for-age or weight-for-age. At the lower end of the distribution (at the 10<sup>th</sup> quantile), HDDS and food share are significant for height-for-age, whereas only food share is significant for weight-for-age.

25
Keeping in mind that sample sizes are very small and adherence is only reported and not observed, the data shows that adherence to antiretroviral treatment has not significantly decreased, as the vast majority of those for which it is measured stayed adherent between the two waves of the survey. This result tends to confirm other (medical) studies of adherence, which find no consistent link between it and the socio-economic status, in particular income. However, those whose income was negatively affected by the crisis have seen a less rapid progression or a deterioration of their CD4 count, compared to those whose income did not change.

19
# **References:**

- Ades, Alberto and Rafael Di Tella (1997). “National Champions and Corruption: Some Unpleasant Interventionist Arithmetic”, _The Economic Journal 107_ , pp.1023-1042.

- Ahlin, Christian (2000). Corruption, Aggregate Economic Activity and Political Organization. University of Chicago, processed.

- Bardhan, Pranab and Dilip Mookherjee (2000). “Corruption and Decentralization of Infrastructure Delivery in Developing Countries”, _Institute for Economic Development Working Paper No. 104_ , Massachusetts: Boston University.

- Becker, Gary S. (1968). “Crime and Punishment: An Economic Approach”, _Journal of Political Economy_ 76(2), pp.169-217.

- Becker, Gary S. and George J. Stigler (1974): “Law Enforcement, Malfeasance, and Compensation of Enforcers”, _Journal of Legal Studies_ 3, pp.1-18.

- Blanchard, Olivier and Andrei Shleifer (2000). “Federalism with and without Political Centralization: China verses Russia”, _NBER Working Paper 7616_ , Cambridge, Mass.: National Bureau of Economic Research.

- Boadway, R., Roberts, S., and Shah, A. (2000). “Fiscal Federalism Dimensions of Tax Reform in Developing Countries,” in Perry, G., Whalley, J., and McMahon, G. (eds.) _Fiscal Reform and Structural Change in Developing Countries, Vol. 1._ New York: St. Martin’s Press.

- Carbonara, Emanuela (2000). “Corruption and Decentralization”, _Dipartimento di Scienze Economich, Working Paper No. 342/83_ , Universita degli Studi di Bologna.

- Crook, Richard and James Manor (2000). “Democratic Decentralization”, _OED Working Paper Series No. 11_ , Washington, DC: World Bank.

- Evans, Peter and James Rauch (1997). “Bureaucratic Structure and Economic Performance in Less Developed Countries”, _IRIS Working Paper_ 175, Maryland: IRIS.

- Fiszbein, Ariel (1997). “ The Emergence of Local Capacity: Lessons from Colombia”, _World Development_ 25 (7), pp.1029-1043.

- Goel, Rajeev K. and Michael A. Nelson (1998). “Corruption and Government Size: A Disaggregated Analysis”, _Public Choice_ 97(1-2), pp. 107-120.

- Grier, Robin (1997). “The Effect of Religion on Economic Development: A Cross National Study of 63 Former Colonies”, _Kyklos_ 1, pp.47-62

- Huther, Jeff and Anwar Shah (1998). “A Simple Measure of Good Governance and Its Application to the Debate on Fiscal Decentralization”, _World Bank Policy Research Working Paper Series No. 1498_ ,  Washington, DC: World Bank.

- Huther, Jeff and Anwar Shah (2000). “Anti-corruption Programs and Policies: A Framework for Evaluation”, _World Bank Policy Research Working Paper Series No. 2501_ , Washington, DC: World Bank.

- Isham, Jonathan, Daniel Kaufmann, and Lant H. Pritchett (1998). “Civil Liberties, Democracy, and the Performance of Government Projects”, _The World Bank Economic Review_ 11(2), pp.219-242.

26
0.101) when including the northern region dummy in the specification. This is because there is more religious heterogeneity in the north than in the south. Column 5, where we exclude the northern dummy, confirms that parents seem to behave less cooperatively in the coastal region.

# **Table 4**

# **Determinants of cooperativeness in the public good game**

||Dum<br>|my equal to<br>|**Dependen**<br>1 if parent i<br>0 oth<br>|**t variable:**<br>nvested in th<br>erwise<br>|e group acco<br>|unt,<br>|
|---|---|---|---|---|---|---|
||Probit<br>(1)|Probit<br>(2)|Probit<br>(3)|Probit<br>(4)|Probit<br>(5)|Probit<br>(6)|
|Age|-0.000|-0.000|-0.000|-0.000|-0.000|-0.001|
||(0.927)|(0.807)|(0.813)|(0.866)|(0.813)|(0.692)|
|Female|-0.012|-0.010|-0.003|-0.006|-0.000|-0.003|
||(0.584)|(0.659)|(0.886)|(0.793)|(0.991)|(0.886)|
|Wealth index|0.019|0.022|0.028*|0.026*|0.030**|0.022|
||(0.219)|(0.152)|(0.053)|(0.083)|(0.037)|(0.110)|
|Years of schooling|0.003|0.004|0.003|0.004|0.004|0.002|
||(0.375)|(0.304)|(0.356)|(0.304)|(0.324)|(0.564)|
|Member of a social organization||-0.040|-0.046|-0.047|-0.048|-0.035|
|||(0.611)|(0.565)|(0.555)|(0.550)|(0.648)|
|Participated in community activities||-0.033|-0.035|-0.033|-0.035|-0.038|
|||(0.293)|(0.266)|(0.287)|(0.267)|(0.236)|
|Generalized trust||0.021|0.020|0.021|0.024|-0.002|
|||(0.541)|(0.557)|(0.542)|(0.473)|(0.962)|
|Thinks village members would work together||-0.002|-0.003|-0.004|-0.003|-0.000|
|||(0.827)|(0.790)|(0.727)|(0.750)|(0.987)|
|N. of good friends among parents||<br>0.007|<br>0.004|<br>0.006|<br>0.001|<br>-0.003|
|||(0.509)|(0.680)|(0.552)|(0.883)|(0.726)|
|N. of relatives among parents||0.062**|0.060**|0.062**|0.053**|0.047|
|||(0.026)|(0.029)|(0.023)|(0.047)|(0.101)|
|Rural area|||-0.029|-0.031|-0.018|-0.021|
||||(0.411)|(0.384)|(0.611)|(0.537)|
|Standard deviation of wealth (school-level)|||-0.096|-0.096|-0.079|-0.075|
||||(0.121)|(0.126)|(0.204)|(0.213)|
|Religious heterogeneity (district-level)|||-0.055*|-0.052|-0.062*||
||||(0.078)|(0.101)|(0.081)||
|Northern region||||-0.029|||
|||||(0.410)|||
|Coastal area|||||-0.081**<br>(0.029)||
|Tirana area|||||-0.074<br>(0.171)||
|Mountain area|||||-0.038||
||||||(0.439)||
|District fixed effects|No|No|No|No|No|Yes|
|_Joint significance of geographical areas (p-value)_|_-_|_-_|_-_|_-_|_0.142_|_-_|
|_Joint significance of districtf.e.(p-value)_|_-_|_-_|_-_|_-_|_-_|_0.000_|
|Observations|1,800|1,800|1,800|1,800|1,800|1,800|

Note: Robust standard errors have been clustered at the school level. P values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. We report marginal effects of continuous variables and the effect of a change from 0 to 1 for dichotomous variables. In all regressions we control for the four geographical areas dummy variables; Tirana is the excluded geographical area.

17
cost, so that the budget share allocated to the CCT increases.

**Proposition 5.** _Suppose t_<sup>_∗_</sup> _c_<sup>_>_0</sup><sup>_undertheCDFF_(1)(</sup><sup>_y, μ_)</sup><sup>_._</sup> _Consider F_<sup>(2)</sup> ( _y, μ_ ) _s.t. F_<sup>(1)</sup> ( _y|s_<sup>_∗_</sup> = 1) _(weakly) FOSD F_<sup>(2)</sup> ( _y|s_<sup>_∗_</sup> = 1) _and F_<sup>(2)</sup> ( _y|s_<sup>_∗_</sup> = 0) _(weakly) FOSD F_<sup>(1)</sup> ( _y|s_<sup>_∗_</sup> = 0) _, with one holding strictly. Further suppose that average behavioral responses and the share of children enrolled are the same under both F_<sup>(1)</sup> _and F_<sup>(2)</sup> _at t_<sup>_∗_</sup> _c_<sup>_._</sup> _Then under F_<sup>(2)</sup> ( _y, μ_ ) _, the optimal local reform starting from t_<sup>_∗_</sup> _c_<sup>_istoincreasetc._</sup>

# _Proof._ See Appendix A.8.

Proposition 5 states as we shift the distribution of parent incomes for the schooling households to the left (i.e., we make the schooling households poorer), the targeting benefit becomes more important, making it optimal (locally) to increase the CCT (and, in turn, reduce the UCT). Shifting the distribution of parent incomes for the schooling households is useful to think about, as it is closely related to the degree of parental income inequality. Because schooling is a normal good (i.e.,<sup>_<u>∂s</u>_</sup> _∂y_<sup>_∗≥_0),ifwereducetheparental</sup> incomes of the schooling households, loosely speaking, we lower the degree of parental income inequality. Conversely, as we increase the parental incomes of the schooling households, we increase the degree of parental income inequality. Then, by Proposition 5, as we increase (decrease) parental income inequality, we would expect the targeting benefit to fall (rise) relative to the exclusion cost, and, hence, the share of the budget allocated towards the CCT to also fall (rise). From this we conjecture that moving from our current set-up where the planner allocates money to a predetermined set of poor households (note, this is the typical - most CCT and UCT programs are offered only to a poorer subset of the population), to a scenario where the planner allocates money to the entire population (i.e., a universal CCT or UCT), would work to severely reduce the targeting benefit relative to the exclusion cost.

Finally, we consider how changing the size of child incomes affects the optimal CCT. As _yc_ increases (holding all else equal), the discrete loss in household consumption that results from sending a child to school increases, thus increasing the targeting benefit. However, making a formal statement as to how the optimal CCT changes with _yc_ proves difficult as changing _yc_ changes the set of enrolled households. Increasing _yc_ will result in fewer households sending their child to school, with this reduction likely coming from poorer (lower parental income) households. This will work in favor of reducing the targeting benefit, making a comparative static in _yc_ unfeasible.

15
Table 3 Descriptive Statistics: Household Characteristics

||Non-Club<br>Members|Club<br>Members|
|---|---|---|
|_HEAD_|||
|Female|6.05%<br>|7.69%<br>|
|Unmarried|8.37%|9.81%|
|Primary education incomplete or no education|77.61%|68.70%|
|Primary education complete|11.40%|18.04%|
|Secondary education incomplete|8.37%|9.81%|
|Secondary education complete|2.22%|2.65%|
|Tertiary education|0.40%|0.80%|
|Number of months away from HH|0.34|0.22|
|Weak health|19.47%|20.42%|
|_HOUSEHOLD_|||
|Household Size|5.26|6.02|
|Prop. members age 8-12|11.45%|13.33%|
|Prop. members age 12-18|10.97%|11.04%|
|Prop. members age 18-45|37.22%|36.58%|
|Prop. members age 46+|12.95%|10.05%|
|Land holding - in acres: Median|3|3.5|
|_PER CAPITA EXPENDITURE - in Mk_|||
|Food-Bev|41,954|43,133|
|Clothing-Footwear|4,100|4,163|
|Housing-Utilities|15,601|13,612|
|Furnishing|2,951|3,728|
|Health|828|884|
|Transport|4,231|3,991|
|Recreation|429|476|
|Education|813|911|
|Number of obs.|991|377|

Note: Own calculations based on Malawi Integrated Household Survey (2004/05). Mk stand for Malawi Kwanchas.

16
20

# _Border Pricesfor Kerosene (local curencyper liter)_

|CXuhy|1980|_1981_|1982|1983|1984|1985|_1986_|1987|1988|1989|
|---|---|---|---|---|---|---|---|---|---|---|
|Columbia|13.27|14.80|17.06|18.97|24.11|31.30|27.08|33.78|40.78|_56.50_|
|Gmn.s|S.89|5.75|5.6|_5.04_|8.89|12.47|13.33|23.76|30.27|41.76|
|ndia|1.94|2.66|2.90|2.97|2.61|2.81|1.82|1.80|2.23|2.59|
|Indonsia|160.S1|19S.56|195.S3|261A3|233.29|240.71|138.99|211.17|223.18|246.20|
|Maloayd|_0.6_|0.71|_0.69_|0.67|0.53|0.54|0.28|0.32|0.35|0.38|
|_&3OOcO._|0.96|1.59|1.85|2.09|2.03|2.29|1.32|1.16|1.26|1.32|
|Pakista|2.42|3.04|3.64|3.85|3.23|3.45|2.33|2.39|2.70|2.97|
|Philppins|1.92|_2AS_|2.52|3.20|3.80|4.03|221|2.64|2.79|3.02|
|Thuailnd|S.24|_6.75_|6.80|6.61|5.38|5.89|2.85|3.30|3.35|3.57|
|aIbty|20.09|36.56|53.30|71.56|92.70|129.17|10.08|132.89|248.56|367.30|
|Zinbabwe|0.17|0.22|0.24|0.31|0.31|0.39|0.26|0.24|0.30|0.36|

Shadow exhag rae for 1980 through 1983 assuned to be IUSS-20 cedis, official exchange rates thereafter.
11 as follows: (i) For individuals with at least 12 years of education, we assume children start school at age 6 and experience equals age – years of education – 6; (ii) For individuals with less than 12 years of education, we assume that experience before age 18 is inconsequential and experience equals age minus 18.

**Model.** For individual _I_ and sample _t_ , we first use OLS to estimate the following model for _each_ country one by one, first for the whole population of 18-67 year-old workers, and then separately for 18-67 year-old male and female workers _separately_ :

where the dependent variable is the log of monthly earnings ( _lnWit_ ). Experience is categorized into seven bins ( _expite_ ). The bins are [5-9 years] (which we call 5), [10-14] (10), [15-19] (15), [20-24] (20), [25-29] (25), [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We include the number of years of education ( _eduit_ ) and sample fixed effects ( _θt_ ) to capture country-year-sample unobservables.<sup>21</sup> Note that having separate regressions for males and females has the advantage of making the sample fixed effects gender-specific. Finally, we omit samples without at least 10 observations in each bin. **Profiles.** The coefficients of the experience dummies help construct wage-experience profiles for developing countries ( _G_ ) and developed countries ( _D_ ) (high-income countries in 2017 according to the World Bank classification). To obtain these profiles, we obtain the average wage differential for each country and construct the mean wage differentials for each group weighted by the population of each country in 2017. As shown in Fig. 1, in developed countries, a worker with 30 years of experience earns 80% more than a worker with zero experience. For developing countries, the difference is 45%. Note that differences in the profiles for males and females will be discussed later. **Returns.** Measures of the returns to experience should take the integral below the profiles. For each bin one by one (5, 10, etc.), we estimate an annualized return. We then take the mean of the annualized returns across the seven bins to obtain the _mean annualized return_ throughout the experience distribution. More precisely, for individuals belonging to bin _e_ , we obtain the _bin-specific annualized return_ as _rete_ = (( _βe_ + 1)<sup>(1</sup><sup>_/e_)</sup> _−_ 1) _∗_ 100, with _βe_ being the estimated coefficient for bin _e_ in eq. (3). For this subgroup of individuals in the society, it tells us by how many percentage points wages 21Most countries having several samples, we use individual weights divided by the size of the sample.
Udry 2005), the change in agricultural labor contracts after the Green Revolution (Hayami and Kikuchi 1981), and the emergence of forest user groups (Tachibana et al. 2001). A common finding among these studies is that as resource scarcity increases, stakeholders (resource users, group leaders, etc.) reform their institutions to facilitate more efficient resource utilization.<sup>1</sup> Our finding adds to this group, insisting on the importance of price but, at the same time, indicating its possible adverse effect on institutional change. Second, similar to existing studies’ findings, we confirm the importance of social capital, homogeneity, resource characteristics, and an exit option (in our case, individual irrigation) for the evolution and survival of successful institutions (Ostrom 1990, 2000, 2010). We would like to emphasize the importance of an exit option as a factor that underlies the decline of collective management. This point will become increasingly important because exit options in rural areas are becoming more widely available under the economy’s rapid industrialization and globalization.

# I. THE STUDY SITE AND DATA

The Zhanghe Irrigation System (ZIS) is located to the north of the Yangtze River in Hubei Province in central China (figure 1). This system is typical of large-scale Chinese irrigation systems and is the province’s most important base of commodity grain production (Dong et al. 2004; Mushtaq et al. 2006). The total area is 554,000 ha, of which approximately 160,000 ha are irrigated. About 80% of the irrigated area lies in the hilly region and the local topography is rolling in many parts. This geographical feature makes farmers’ field activities, including irrigation practices, less observable to each other beyond their immediate neighbors. With the utilization of irrigation and adequate rainfall, ZIS farmers cultivate rice as their main crop during the mid-season (May to September). Rapeseed and winter wheat are commonly cultivated during the winter.

The main water source in the ZIS is the Zhanghe Reservoir, which was built on a tributary of the Yangtze River (figure 2). The reservoir was designed for irrigation, domestic water supply, industrial use, and hydropower generation. A sharp increase in water demand for nonagricultural purposes has increased agricultural water scarcity; the water share allocated to agriculture has dropped from 80% to 25% over the past 25 years (Moya et al. 2004).

> 1 Hayami and Ruttan (1985) conceptualize this mechanism as a theory of induced institutional innovation.

4
supply, with no major impact on activity choice for males 15 years or older, suggests the presence of gender differences in the use of remittances across (and possibly within) households.

# _B. Data_

I mainly use two household level data sets in this paper: Survey of Living Conditions (SLC) and Labor Force Survey (LFS). The SLC is the annual nationwide survey that is implemented to establish baseline measures of household characteristics and then to monitor the impact of government programs on health, education and nutrition. The SLC includes detailed information on the total amount of expenditure for food and non-food items, total amount of income from the sources other than labor. The SLC is linked to the LFS to have both living condition and labor information for the same individual. The sample size is about 2,000 households and 7,000 individuals each year.<sup>17</sup> In the annual LFS, each member of the household is asked questions regarding his or her employment status. Employed persons are asked about hours worked, work experience, training, education, employment status and gross average income over the last twelve months. Unemployed persons are asked the duration of and reason for their unemployment, the job search, work experience, training, education, employment status, and income. Persons outside the labor force were asked about previous work experience, training, education, employment status, and income.<sup>18</sup> Jamaica’s SLC and LFS do not provide a longitudinal individual or household data that would be needed to study the effect of remittances on labors decisions over time. However, the data retains the same geographical clusters in the sample for three or four years. In the same cluster, the exactly same households are sampled every year. Although it is not possible to trace each household's behavior over time, we can trace the aggregate indicators of households at the cluster level because the member households in the cluster do not change over time as long as the cluster remains effective in the survey. A pseudopanel data can be constructed from the indicators at the cluster level over time, using the clusters as the new unit of the sample.

> 17 World Bank (2002)

> 18

> PIOJ and STATIN, various issues

9