rafmacalaba/datause-encoder-data huggingface dataset:
TABLE OF CONTENIS

**I1**

## **I. INTRODUCTION** ...............

## **U.** FRAMEWORK FOR MACROECONOMIC POLICY **ANALYSIS.**

2

|Overview|**2**.....<br>:|
|---|---|
|Behavioral Relationships, Accounting Rules md Flow-of-Funds Consistency|3<br>|
|The Monetary Sector: the demand for fimancial assets.|3<br>|
|Government revenue...............<br>...............|5..<br>S|
|Government expenditure ...............|_6_|
|The balance of payments ...............|6|
|National accounts ...............|7|
|Miscellaneous.........................................<br>|..... 8|
|Flow of Funds Consistency ................................|8|

# III. POLICY SOLUTIONS .10

|Introduction .10||
|---|---|
|The Economics of the Exchange Rate Solu.on .10<br>||
|The Econouics of the Interest Rate Solution .13||
|The Economics of Monetizing the Fiscal Defcit .15||
|The Economics of Joint Policy Action.|.16|
|Summary .18<br>IV. POLICYSIMLATIONS .18||
|The Base Projections ... .<br>........................................|18|
|Exchange Rate Solution .........................................|22|
|TheInterestRate Solution .....................................|26|
|Monetization<br>.......|29|
|lointExchange Rate and Interest Rate PolicyAction.ii*v***@******|*****30|
|Joint Exchange Rate and Interest Rate Policy Action and Monetzati.on<br>.34||
|Summary of Simulation Results**.**.............<br>.......<br>..|.<br>34|

# V. LIMITATIONS AND FUTURE EXTENSIONS ..........

37

## ANNEXES

|Annex I: Descripdon of Model .<br>.............................|40|
|---|---|
|<br>Annex 11: Fiscal Deficits and Macroeconomic<br>Consistency ....................|49|
|<br>Annex III: Import Elasdcities in Kenya: 1968 and Beyond|56|
|<br>Annex IV: Key Assumptions and Targets Input Worksheets and Output Tables|66|
|<br>Annex V: Time Bound Formulae .....................<br>I|100|

# REEEESNCS .103
sample. In Table 1, it relies on comparing outcomes between observations in cells D and F with observations in cells E and G.<sup>19</sup> We include sampling weights to ensure results are representative of the population of cash transfer beneficiaries.

Second, we compare outcomes between households that are non-beneficiaries of cash transfers in villages assigned or not to BCC. We obtain estimates of BCC local spill-overs based on the following regression: This specification relies on comparing outcomes between observations in cells B and C in Table 1. We include sampling weights to ensure results are representative of the population of non-beneficiaries in cash transfer villages.

In addition to reporting robust (White-Huber) clustered standard errors and related asymptotic p-values for coefficients in equations (1) and (2), we also report the exact p values from a randomization-t test. Following Young (2018), the exact p-values are obtained from 2,000 simulations and take into account the stratified randomization.<sup>20</sup> While in principle the exact p-values could be more conservative since our design involves clustered randomization, the size of the experiment is not small and in practice the exact p values are close to the asymptotic p values. We also report joint test of significance of treatment effects based on Young (2018).

# _<u>4.2 Impacts of Cash Transfers</u>_

The study is also designed to assess the relative effect of the behavioral change component and cash transfers in improving parenting practices and children’s human capital. We can estimate the relative impacts of the two interventions on predicted beneficiary households based on baseline data.

As discussed above, the registry census and targeting data collection took place in the treatment groups. However, the baseline survey included all the variables necessary to estimate the proxy-means targeting score for all households in the baseline sample. On that basis, we can replicate the targeting process and

> 19 We also provide a robustness check by considering actual beneficiaries from the baseline sample only (comparing observations in cells D and E in Table 1).

> 20 We report the randomization-t test as Young (2018) shows it is superior to the alternative randomization-t statistic. Furthermore, he shows that there are little changes in rejection rates beyond 2,000 simulations.

14
# **Micro Efficiency and Macro Growth**

# **Introduction**

In trying to understand the rapid output growth of East Asian ‘miracle’ countries, Krugman (1994), Young (1995), and others were engaged in an interesting debate on whether capital accumulation or total factor productivity growth best explained the high and sustained output growth of these countries. Their conclusion that capital accumulation was most important was based on macroeconomic data analysis in a factors‐of‐production approach to sources of growth. Others have found that t he growth of output is strongly correlated with productivity growth in developed and developing economies as reported by Kehoe and Prescott (2002) and Solimano and Soto (2004), and this co-movement appears to be stronger the longer is the time period considered.

This analysis would have been even more interesting if these researchers looked at microeconomic evidence for the same set of countries for the same time period to re‐confirm their results. This would have also enabled them to understand the differences in TFP in each of the East Asian countries and its impact on growth. It is possible that some countries are propelled by physical and human capital while others by TFP. For example, Collins and Bosworth (2003) analyzed data from 84 countries for the period 1960 to 2000 and found that capital accumulation and productivity growth both contribute to output growth. At the global level, they found that the contribution of capital and productivity is roughly equal but there have been substantial variations in their relative contribution across countries and time.

Growth analysis has primarily been a macroeconomic subject with its emphasis on contribution of capital, labor or human capital to output growth and the role of total factor productivity (see Collins and Bosworth 1996, Hu and Khan, 1997, Sarel 1997, Sala-i-Martin 2004, Hall and Jones, 1999, Easterly and Levine 2001). These studies provided varying conclusions. Mankiw, Romer and Weil (1992) found that physical and human capital accounted for 80 percent of variation in percapita income across countries, while Klenow and Rodriquez-Clare (1997) emphasize productivity growth as accounting for 90 percent of income per capita variation, while Easterly and Levine (2001) emphatically state that it is not factor accumulation.

Before the recent availability of micro‐level databases, the macroeconomic approaches to study of productivity suffered many shortcomings, such as measuring productivity as residual in macro‐level production functions or treating it as a measure of technical change. In particular, total factor productivity is measured as a residual and as growth is determined by many factors. Moreover, accounting framework should not be used for causes of growth as many factors are endogenous to the growth process.

As the number of micro‐level (firm or plant‐level) empirical studies, focusing on productivity increased during the past decade, they have started to raise many questions on understanding of aggregate growth. The studies showed that aggregate productivity growth is closely linked to 2
|**Table 2: Initial village and household charact**|**eristics by rese**|**rvation status of t**|**he village**||
|---|---|---|---|---|
|**Variables**|**Total**|**Village re**|**served?**|**Test for equality**|
||**Mean**|**Yes**|**No**||
|**Initial Village Characteristics**|||||
|Population in 1999|3870|3381|4088|0.81|
|Share of population Hindu (%)|86.60|84.49|87.53|0.87|
|Share of population Muslin (%)|6.62|6.92|6.48|0.19|
|Net area sown (acres)|1190|1181|1193|0.06|
|Net Area irrigated under Canal/Stream (acres)|323|221|370|1.24|
|Access to Bank|0.71|0.72|0.71|0.13|
|Distance to Mandi(km)|12.69|12.52|12.76|0.12|
|Distance to retail market (km)|8.67|8.87|8.57|0.22|
|Distance to nearest Town (km)|14.38|14.64|14.27|0.23|
|Distance to nearest Pucca Road (km)|2.45|2.22|2.56|0.45|
|Share of village with st. lights|0.49|0.42|0.53|1.43|
|<br>Share of villages with PCO|0.33|0.28|0.35|1.04|
|<br>Share of villages with trained dai|0.56|0.55|0.57|0.26|
|Share of villages with male health worker|0.47|0.35|0.52|2.31**|
|Share of villages with female health workers|0.61|0.52|0.65|1.81*|
|Share of villages with Anganwadi|0.82|0.80|0.83|0.32|
|Share of scheme building drinking water|0.24|0.23|0.25|0.35|
|Share of scheme building road|0.18|0.21|0.17|0.81|
|Share of villages with food for work programs|0.48|0.52|0.46|0.90|
|Daily male agricultural casual wage (Rs./day)|49.86|49.85|49.87|0.01|
|Daily female agricultural casual wage (Rs./day)|31.59|32.58|31.35|0.76|
|<br>No. of observations|233|71|162||
|**Initial Household Characteristics**|||||
|Members < 14|1.93|1.92|1.93|0.24|
|members 14-60|3.97|4.04|3.95|1.15|
|Members > 60|0.42|0.40|0.43|1.35|
|Head's years of education|4.92|5.00|4.89|0.65|
|Head completed primary|0.51|0.51|0.51|0.17|
|<br>Head completed middle school|0.31|0.31|0.31|0.16|
|Head's age|49.50|48.97|49.70|1.53|
|<br>Female head|0.05|0.06|0.05|1.3|
|Area of cropland(acres)|2.75|2.78|2.74|0.21|
|Value of physical assets (Rs.)|43127|41358|43810|0.9|
|Value of total assets (Rs.)|52564|51829|52848|0.34|
|Household total income (Rs.)|15549|15380|15615|0.21|
|<br>Share of crop income|0.52|0.53|0.52|0.44|
|Share of agriculture|0.67|0.68|0.66|1.22|
|Share of off-farm income (Rs.)|0.32|0.31|0.33|1.18|
|<br>Household total expenditure (Rs.)|37769|37807|37755|0.04|
|Household consumption (Rs.)|9274|9367|9239|0.45|
|No. of observations|4,275|1,191|3,084||

Note: Distance to railway station, bus stand, post office, telephone office, weekly market, and all the other schemes between the reserved and unreserved groups are not statistically significant. The column „test for equality‟ reports the |t|-statistic for the test of equality of means.

21
# **Understanding the Geographical Distribution of Stunting in Tanzania: A Geospatial Analysis of the 2015–16 Demographic and Health Survey**

# **Background**

Stunting, defined as being more than two standard deviations below the World Health Organization (WHO) Child Growth Standards median,<sup>1</sup> arises from a range of factors including low breastfeeding rates and malnutrition among mothers, inadequate health care or poor access to safe water and sanitation leading to a range of acute or subclinical infections in early life. Globally, around 155 million children are estimated to be stunted, and international targets have been established to reduce global stunting by 40% by 2025<sup>2,3</sup> as one component of achieving the SDG goal 2.2 of eliminating all forms of malnutrition by 2030.<sup>4</sup> However, the current average annual rate of reduction in stunting of 2.3% falls short of the 4% required if this goal is to be met.<sup>5</sup> Recent World Bank estimates suggest that the per capita income penalty incurred among contemporary workforces as a result of failure to eliminate stunting in their childhood amounts, on average across all countries, to around 7% of gross domestic product (GDP) per capita. In Sub‐Saharan Africa and South Asia, these figures rise to around 9% to 10%.<sup>6</sup> Several studies have shown that stunted growth during childhood entraps the future of children in a vicious circle of recurrent diseases, reduced human development<sup>7,8,9</sup> and lower earnings.<sup>10,11</sup> This, in turn, can increase the likelihood of being poor and even lead to the transmission of poverty to the next generation.<sup>12,13</sup> Like many low‐income countries, stunting is widespread in Tanzania. An estimated 2.7 million children in Tanzania are stunted, making the country home to the third highest population of stunted children in Sub‐Saharan Africa, after Ethiopia and the Democratic Republic of Congo. Although multisectoral efforts to reduce stunting rates have yielded some progress (with prevalence among children under 5 declining from an estimated 42% in 2010<sup>14</sup> to 34% in 2016<sup>15</sup> ), there is a renewed call to accelerate this progress and further reduce stunting nationwide through a host of nutrition‐specific and nutrition‐ sensitive interventions, particularly focusing on the first 1,000 days of life.<sup>16,17</sup> These multisectoral interventions—spanning the health, education, social protection, water supply, and sanitation sectors—are intended to converge at the local level for maximum effectiveness.<sup>18</sup> As part of these efforts, there is an increasing recognition of the need to understand the geographic heterogeneity of stunting across the country at a granular level. This granularity supports strategies that are optimally 2
**Table A11**

Impact of new road on sanitation

|Open Defecation|Latrine in Premises|Pit Latrine - with slab|Pit Latrine - without slab|
|---|---|---|---|
|New road<br>0.013|-0.010|0.019|-0.014|
|(0.038)|(0.036)|(0.017)|(0.012)|
|Control group mean<br>0.891|0.104|0.019|0.011|
|N<br>1775|1775|1775|1775|
|R2<br>0.26|0.27|0.10|0.08|
|_∗p <_0_._10_,∗∗p <_0_._05_,∗∗∗p <_0_._01||||

Notes: The Total Sanitation Campaign (TSC) is stated to have “aimed to transition rural households from open defecation to use of on-site pit latrines” (Spears, 2015). The program began construction of latrines in 2001. The outcomes considered here are 2011 Population Census measures of (in order) percentages of households who report: open defecation; the existence of a latrine within premises; an in-house pit latrine with slab or ventilated improved pit; and an in-house pit latrine without slab/open pit. The sample has been restricted to villages with population within the optimal bandwidth (84) of 1,000, the threshold used by the TSC. The sample of states here come from our main PMGSY specification. The specification includes baseline village-level controls for amenities and economic indicators, as well as district-cutoff fixed effects (see Section V for details). Heteroskedasticity robust standard errors are reported below point estimates.
<!-- Start of picture text --> nExchange_2010 -0.045 -0.19 0.11<br>0.50<br>nPerimeter_2010 0.011 0.11<br>0.25<br>Fullnes_2010 -0.035 0.24 0.15<br>Circuity_avg_2010 0.021 -0.14 0.00<br>ection_density_2010 -0.19 0.16 0.3<br>-0.25<br><!-- End of picture text --> <!-- Start of picture text --> Street_density_2010 -0.22 0.2 0.29<br>-0.50<br>Sprawl_2010 0.049 -0.25 -0.17<br>ge oe ge<br>we? a wy<br>er on \e<br>x we<br><!-- End of picture text -->
- c) Persons, who receive a temporary disability benefit and no longer have their job contract;

- d) Foreign citizens, who live in Albania (law no. 108/2013, “Për të huajt”);

- e) Persons, who closed their activity and are no longer working, but are unemployed (the benefit calculation starts from the period being contributed to the social insurance system as an employee.

Duration of the entitlement to the unemployment benefit:

|**Insurance record**|**Duration of the entitlement**|
|---|---|
|**At least 12 months(since the last benefit)**|3 months|
|**At least 5years(since the last benefit)**|9 months|
|**More than 10years(since the last benefit)**|12 months|

In case of the second request for unemployment insurance (when having an insurance record of 1, 5 or 10 years) the duration of the entitlement is reduced to 3 months only. In addition, if the recipient of the unemployment benefit is employed during the period of the benefit, the unemployment benefit is taken intermittently for a period of (i) within 6 months of persons entitled a 3 months unemployment benefit (ii) within 18 months for persons entitled a 9 months unemployment benefit, (iii) within 24 months for persons entitled a 12 months unemployment benefit, but not more than 91 days for those who have an insurance record of at least 1 year, 275 days for those who an insurance record of at least 5 years and 365 days for those who have an insurance record of at least 10 years.

## **_Income test:_**

Not applicable.

## **_Benefit amount:_**

Unemployment benefit amounts to 40% of the minimum wage established by the General Collective Agreement.

||**Before**<br>**1.8.2011**|**From**<br>**1.8.2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|---|---|---|---|---|---|---|---|
|**Minimum wage**||||||||
|**ALL/monthly**|19 000|20 000|21 000|22 000|22000|22000|22000|
|**Unemployment**||||||||
|**benefit**|6565|6 850|6 850|6 850|6 850|6 580|11 000|
|**ALL/monthly**||||||||

The recipient of this benefit, who also have dependent children not older than the age of 18, who study or are unable to work until they reach the age of 25, receive an additional family benefit for every dependent child equal to 5% of the unemployment benefit (2.5 – 5% of 11,000 ALL), but not more than 30%. This additional benefit is reduced to 50% if one of the parents is working or receives full pension.

# _Old-age Pension_

## **_Definition:_**

The age and insurance period for old-age pensions for the purposes of reaching the retirement age and completing the insurance period as specified in the Law “On Social Insurance in the Republic of Albania”, shall be increased in accordance with the provisions.

50
14 <!-- Start of picture text --> ... BaOX:7,Accommodat Local D iveity . .... .. ....<br>'Local:: goveriamen'15te .~~~~~~~~~~~VA . that co n Wide r:n:gof reali wimost Cities:<br>of 0 mllon eope,villages .Witlh 200bihabftantS, Auran esl ouae and rba ara s,sa<br>: , ~~~~~~~~~~~~~~~~~~...... . .. .. PA ...<br>.ocalities mrs rich, 'ome are poor somebavo strocj local comimmity spiit, s Ome v n Some<br>azu m by weflIntmb ioned wel-ftrane people thrb ncompetnt n orptofcas<br>.,., ~ ~ . ~ ::1 :.:s:.::.::.:.::-: S,-7 S,.S: ::::.. ::,::: .,..:::..,.. .. .,... .,.. ,<br>..... .. .%.'Sg :.govaenment v -:. erni ,s5; Sz>Gu::lu .: ocu n .. , .. S v ............. 1ffi~~~~~~...... t i , ....... t, . S<br>(Box 4)s,up and including accommodating ~'home-brewed informal-sectoz oluion.s t i .. ..<br>'<br>'S'e G ' . ,dcto and ,.lt ar d,atmna ,epnilte 'n Coom, altoug ' .s of .t,h::'e@m',<br>policy in: teru iof building roads, schools, etc. InE somc eorteX oilprdcn regons the tat oil.<br>''''" ' o* G,pniblt ~ G.o:': o ,.:.:;::':::: :n man, :s i ics as som of th.os poics<br>........ 6e * w2S ' 3.. offis of w :~. Gl ' G':... . . : .. ... . ..... . ........... : :::.a . ........ ,,,, e ''. . .<br>* =. = of ipl e l G, :: .: . " ' . :: ........................................... '::,.:*' ...... ...... .<br>, ., ,. , .... ..................... es~~~~~~~~~~~~~~~~~~~~~~~~~~~~.. G, ... .. .. :::f:-w<br>* ,~~~~~~~~~.i: gso.:.,: ..... .. ...... :*::j.., ................ .G .:::::','<br>..... z. o ' .E ........ ~~~~~~~~~~~~~~~~~~.......... ..... .. .. .. , .,_X0 . ,, .. .. .<br><!-- End of picture text -->

**into account place-specific information.d** Decentc **alized provision intended to overcome** these problems in turn has seldom succeeded and has given rise to its own problems - corruption, mismanagement, rent-seeldng, and so on. Indeed, there are probably as many papers in the literature decrying the limitations of fiscal and administrative decentralization as

_"I_ See Box 2. This argument assumes that it is more economical (requires lower tansactions costs) to aggregate information on local conditions and preferences **at** local than at central levels of government. This proposition may be correct, although it is (so _far s_ I low) unteste d, but it should be noted that it implies that ch informatio could be gathered **at** the center if sufficient effort was made to do so. Moreover, if the relative costs of acquiring information at different scales change over time - as seems likely - then the comparative advantage of carrying out different activities at different levels of government will also change (as emphasized by e.g. Breton, 1989).
Table 6 uses the country-level data set described above to re-estimate all of the models from table 4. The most noteworthy difference from the results in table 4 is that, although the net gains of making labor markets more flexible are still estimated to be larger for countries that currently have more rigid labor regulations, this coefficient is no longer statistically significant. The results from table 6 on creation, destruction, hiring, and firing, are qualitatively the same as in table 4. We therefore conclude that changes in the status of temporary workers do not drive the main results of the paper.

In summary, the results from tables 5 and 6 suggest that some of the effects of labor reform reported in table 4 may involve re-classifying workers from temporary to permanent. The result that making labor regulations more flexible will lead to larger increases in net employment in countries that currently have more rigid regulations appears somewhat more likely to be driven by these changes in status. Nevertheless there is no evidence that pro-market labor reforms would have a detrimental effect on net employment. The result that countries with more rigid labor regulations would experience larger increases both in hires and in fires is quite robust to excluding firms that employ temporary workers.

# 6 Conclusions

I find that labor-market reforms that make regulations more flexible will likely lead to an increase in aggregate employment, at least for permanent employees. Given the vast empirical literature on the effects of rigid labor regulations on employment, the results on net employment changes from the current paper can be viewed as yet another confirmation of the findings of a well-established literature.

The novelty of this paper is to study how net employment increases would be achieved after pro-market labor reform. I find that the post-reform increases in aggregate employment would be associated with increases in employee dismissals. Naturally, these increased dismissals would be more than compensated for by the post-reform increases in hires. Although I do not analyze productivity in this paper, it is logical to conjecture that pro-market labor reforms would lead to productivity gains as workers are more efficiently allocated to firms.<sup>7</sup> The first policy implication is that advocates for incumbent workers may be quite rational in opposing market-based labor reforms despite the gains to aggregate employment and productivity. To the extent

> 7 Recall that the Autor et. al. (2007), Besley and Burgess (2004) found negative affects of labor market regulations on productivity.

15
Table 4: Parallel trends: displaced TSC intensity uncorrelated with test scores in older children, IHDS

||(1)<br>(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|
|sample:|8-10 year olds|9-11 y|ear olds|8-11|year olds|
|Panel A: Rea|ding score|||||
|TSC latrines|2.418|-1.619||-0.523||
|per capita|(2.323)|(2.088)||(1.455)||
|TSC started|-0.0270||-0.0161||-0.00395|
||(0.0797)||(0.0942)||(0.0710)|
|_n_ (children)|6,580<br>6,580|6,169|6,169|8,213|8,213|
|Panel B: Mat|h score|||||
|TSC latrines|1.590|-2.297||-0.470||
|per capita|(1.685)|(2.178)||(1.422)||
|TSC started|-0.0631||-0.0436||-0.0217|
||(0.0577)||(0.0645)||(0.0469)|
|_n_ (children)|6,566<br>6,566|6,149|6,149|8,189|8,189|
|Standard errors clustered<br>from 0 at a two-sized 0.<br>fxed efects for districts <br>in that district, by that<br>displaced in various sa<br>trend|by survey PSU in pare<br>10 level. Only the rural <br> and for age, and an indi<br> year, a positive number<br>mples (see section 6.3) i<br> is uncorrelated with th|ntheses. No c<br> subsample o<br>cator for bei<br> of TSC latr<br>n order to de<br>e trend in th|oefcient is s<br>f the IHDS is <br>ng female. “T<br>ines had been<br>monstrate th<br>ese children’s|tatistically <br> used. Each<br>SC started”<br> built. TSC<br>at the withi<br> test scores.|signifcantly diferent<br> regression includes<br> is an indicator that<br> implementation is<br>n-district program|

45