rafmacalaba/datause-encoder-data huggingface dataset:
It was further argued that inflationary pressures in the period 1985-1989 originated mainly in the investment sector. Traditionally, it was the consumer sector where most observers would seek the emergence of inflationary pressures in view of its institutional characteristics. Our argument is different. Even though the investment sector was strictly controlled and the emergence of open inflation was extremely difficult, the scope for inflationary pressures was - wide. The pressures themselves arose from a mixture of factors poor investment planning (micro and macro) and high priority given to investments and social consumption.

The discussion of official price indices was focused on the consumer price index as a leading proxy of inflation measurement and producer prices. Various sensitivity tests performed by the Federal Statistical Office have revealed that official price indices were not significantly affected by the statistical methodology adopted in their construction. The hypothesis of hidden and suppressed inflation had been addressed through a number of approaches. Econometric models in the disequilibrium framework, aiming to specify the extent of excess demand, are heavily dependent on the model specifications and are also difficult to interpret. The favorite indicator of suppressed inflation has been the savings variable. Several variants of this have been computed for the 1980s, and they indicate the presence of some "forced" savings, albeit not substantial. This important conclusion is in line with other tests of suppressed inflation such as the conjuncture tests.

Estimates of hidden inflation are also identified and reviewed. While there is a general agreement about the presence of hidden inflations in the period 1985-1989, there is no consensus about the rate. Estimates range from 0.5 percent to 2 percent in the consumer market and about 3 percent in industry. The different ranges of inflation in the consumer and producer sectors were not atypical in CPEs, in view of the market segmentation noted above.

The most remarkable feature of all the inflation indicators is the relatively low level of inflation in Czechoslovakia, in comparison to most CPEs. We believe that this reflects a better macroeconomic policy. This relatively favorable development had important consequences for the introduction of price liberalization in 1990 and 1991, because prices were liberalized in the presence of a relatively smaller inflationary gap.

The price liberalization of 1990 and 1991 did indeed lead to acceleration of inflation throughout 1990 and to substantial increase in the price level in January 1991. Since then, the rate of inflation has been slowing down and has stabilized at less than 1 percent per month. This paper identifies major causes of inflation escalation (inflationary gap, removal of subsidies, devaluations and expectations) as well as the reasons behind the recent progress in controlling inflation.

As is common, empirical studies of this kdnd have a number of shortcomings. The authors are well aware of many problenm; pertaining to computations of various inflation 23
**Figure 1.  Results Chain with an Application to Education Services**

|Program<br>Objectives|Inputs<br>|Intermediate<br>|Outputs|Outcomes|Impact|Reach|
|---|---|---|---|---|---|---|
|Improve<br>quantity,<br>quality,<br>and<br>access to<br>education<br>services|Educational<br>spending by<br>age, sex,<br>urban/rural;<br>spending by<br>grade level,<br>teachers,<br>staff,<br>facilities,<br>tools,<br>books,<br>regulations|Enrollments,<br>student-<br>teacher ratio,<br>class size|Achievement<br>scores,<br>graduation<br>rates, drop-<br>out rates|Literacy<br>rates,<br>supply of<br>skilled<br>professio<br>nals|Informed<br>citizenry,<br>civic<br>engagement,<br>enhanced<br>international<br>competitive<br>ness|Winners and<br>losers from<br>government<br>programs|

Source: Shah (2005b) Output-based grants must have conditions on outputs as opposed to outcomes, as outcomes are subject to influence by factors beyond the control of a public manager. Public managers should be held accountable only for factors under their control. Outcome-based conditions diffuse enforcement of accountability for results. Since the grant conditions are concerned with service delivery performance in terms of quality of output and access, the manager is free to choose the program and inputs to deliver results. To achieve those results, he or she faces positive incentives by grant conditions that encourage alternate service delivery mechanisms by contracting out, outsourcing, or simply encouraging competition among government and non-government providers. This can be done by establishing a level playing field through at par financing, by offering franchises through competitive bidding, or by providing rewards for performance through benchmarking or yardstick competition. Such an incentive environment is expected to yield a management paradigm that emphasizes results-based accountability to clients with the following common elements:

- Contracts or work program agreements based on pre-specified outputs and performance targets and budgetary allocations.

- Incentives for replacement of lifelong rotating employment with contractual appointments with task specialization.

- Local autonomy and managerial flexibility but accountability for results to own residents (citizens).

- Redefinition of public sector role as purchaser but not necessarily provider of public services.

- Adoption of the subsidiarity principle—that is, public sector decisions made at the level of government closest to the people, unless a convincing case can be made not to do so.

22
# **Acknowledgements**

The study was conducted under the partnership between the World Bank Living Standards Measurement Study and the Stanford University Center on Food Security and the Environment (FSE) for developing and testing approaches for using satellite measurements, in combination with ground data, to estimate plot‐level crop yields in smallholder production systems. The technical assistance to the 2015 and 2016 rounds of MAPS: Methodological Experiment on Measuring Maize Productivity, Soil Fertility and Variety was provided through the World Bank Living Standards Measurement Study (LSMS) “Minding the (Agricultural) Data Gap” Methodological Research Program, funded by UK Aid. The implementation of MAPS I (2015) was financed by the World Bank LSMS – Minding the (Agricultural) Data Gap Research Program, the Global Strategy to Improve Agricultural and Rural Statistics, led by the Food and Agriculture Organization of the United Nations (FAO), and the CGIAR Standing Panel on Impact Assessment. The implementation of MAPS II (2016) was financed by the World Bank Innovations in Big Data Analytics Program, the World Bank Trust Fund for Statistical Capacity Building – Innovations in Development Data Window, and the CGIAR Standing Panel on Impact Assessment. In MAPS I and MAPS II, Terra Bella provided free high‐resolution satellite imagery for the MAPS remote sensing tasking area for research purposes. The technical partners included the World Agroforestry Center on soil fertility measurement, and the CGIAR Standing Panel on Impact Assessment on maize variety identification. MAPS I and MAPS II were both implemented using the World Bank _Survey Solutions_ Computer‐Assisted Personal Interviewing (CAPI) platform. The research team would like to thank the dedicated management and field staff of Uganda Bureau of Statistics regarding fieldwork implementation; Mr. Wilbert Drazi Vundru for Survey Solutions programming, fieldwork supervision and survey data quality control; and Ms. Madeline Lisaius for help with image processing.

22
cost as the main barrier to using formal childcare (European Commission 2016).<sup>4</sup> The average net childcare costs amount to 17 percent of middle-income two-earner couples’ median full-time earnings (OECD 2021). Relatively less information is available on the costs of childcare in LMICs, but existing studies suggest that childcare expenses are significant relative to families’ earnings in many countries.<sup>5</sup> Clark et al. (2019) show that high childcare costs discourage households in Kenya from using formal childcare facilities, which negatively affects mothers’ participation in the labor market. Similar findings exist for Mozambique and Liberia (Bhatkal, 2014; Lokshin et al., 2000). Conversely, in rural Colombia, small monthly fees for community-based childcare centers more than tripled women’s employment and increased their work hours by 75 hours per month (Attanasio and Vera-Hernandez, 2004).

The willingness to use formal childcare services and the potential for childcare regulation to impact FLFP also depend on the strength of social norms related to gender roles and women’s work, and on the quality of childcare. According to the latest wave of the World Value Survey, 41 percent of respondents globally agree or strongly agree with the statement that “A pre-school child suffers with a working mother” and 51 percent agree or strongly agree with the statement that “On the whole, family life suffers when a woman works full time.” Although existing studies on the impact of childcare quality on women’s

> 4Another study from 27 EU member states highlights that approximately 25 percent of women who do not work or work part-time report that childcare is unaffordable (Mills et al., 2014).

> 5For example, a survey conducted in Edo state, Nigeria, in 2005 found that the average cost of a private childcare center kindergarten for one child was more than half the salary of someone earning the minimum wage (Olubor, 2009). A study of peri-urban areas in Accra (Ghana), Johannesburg (South Africa), Lagos (Nigeria), and Nairobi (Kenya) found that the average cost of a preschool child was one-quarter to nearly half of a person’s monthly spending (Bidwell et al., 2014). In contrast, Latin American countries with broad public sector involvement in childcare have programs that are more affordable.

4
# 1 Introduction

Despite the emphasis placed by micro...nance organizations on lending to female business owners, evidence from three recent randomized controlled trials has cast doubt on the ability of capital alone to grow female-operated microenterprises (de Mel et al 2008, Banerjee et al 2010, and Karlan and Zinman 2010).<sup>1</sup> The three experiments were all run in South and Southeast Asian countries: Sri Lanka, India and the Philippines. In Sri Lanka, the capital was provided as grants, while in India and the Philippines, capital was provided by increasing the availability of microloans. In Sri Lanka and the Philippines, the lack of returns in female-owned enterprises contrasted with evidence of positive returns in male-owned enterprises.

One possible interpretation is that female-owned microenterprises in these countries are already operating at their e¢ cient level of capital, which just happens to be very low for most ...rms. Emran et al. (2007), for instance, argue that many of the women drawn into self-employment have low e¢ cient scale and are only self-employed because of labor market imperfections. Labor market imperfections for women may be particularly strong in societies like those in South Asia, as evidenced by low labor force participation rates among women. This raises the question of whether capital might be more successful in growing female-owned microenterprises in other areas of the world.<sup>2</sup> In much of Africa, for example, female labor force participation rates are higher than in Asia, and women are more integral to household income generation. It is therefore possible that the scope for female ...rm growth from more capital is higher in Africa.

An alternative explanation is that the small scale of many female-owned ...rms is in fact not e¢ cient, but instead arises from a lack of separation between household and business decisionmaking and from ine¢ ciencies in the way people allocate assets between them. One form of ine¢ ciency can arise from self-control problems, leading individuals to not undertake productive investments today that have large payo¤s in the future (Banerjee and Mullainathan, 2010; Du‡o et al, 2010). A second form of ine¢ ciency can come from ine¢ cient intra-household allocation of resources (Udry, 1996; Somville, 2011) or pressure to share with others in ones social network (Charlier, 1999; Platteau, 2000; di Falco and Bulte, 2009). Either of these cases can cause a

> 1 Bjorvatn et al. (2011) also ...nd no e¤ect of capital grants on either male of female small businesses in Tanzania, but their sample is restricted to non-credit constrained entrepreneurs so it is unclear how relevant these ...ndings are for microenterpreneurs in general.

> 2 This is related to the more general issue of external validity, a common refrain in recent debates about what the profession is learning from randomized experiments (e.g. Banerjee and Du‡o, 2009; Ravallion, 2009; Deaton 2010).

2
**Table 3:** Average Job Flows in the 1990s, Overall and by Region, Total Economy

|**OVERALL**||||||
|---|---|---|---|---|---|
|Variable|Obs|Mean|Std. Dev.|Min|Max|
|Job Creation Rate|1048|0.147|0.067|0.000|0.647|
|Job Destruction Rate|1048|0.131|0.062|0.000|0.419|
|Net Employment Growth|1048|0.015|0.065|-0.299|0.419|
|Job Reallocation Rate|1048|0.278|0.112|0.000|0.875|
|Excess Job Reallocation Rate|1048|0.231|0.098|0.000|0.732|
|Job Creation Rate (Entry)|1048|0.055|0.043|0.000|0.357|
|Job Destruction Rate (Exit)|1048|0.046|0.029|0.000|0.216|
|**OECD**||||||
|Job Creation Rate|448|0.127|0.046|0.033|0.288|
|Job Destruction Rate|448|0.127|0.060|0.029|0.411|
|Net Employment Growth|448|0.000|0.046|-0.282|0.148|
|Job Reallocation Rate|448|0.254|0.096|0.072|0.57|
|Excess Job Reallocation Rate|448|0.223|0.085|0.058|0.472|
|Job Creation Rate (Entry)|448|0.045|0.030|0.003|0.195|
|Job Destruction Rate (Exit)|448|0.045|0.028|0.000|0.216|
|**LAC**||||||
|Job Creation Rate|300|0.148|0.061|0.033|0.431|
|Job Destruction Rate|300|0.140|0.066|0.041|0.419|
|Net Employment Growth|300|0.008|0.053|-0.214|0.286|
|Job Reallocation Rate|300|0.288|0.114|0.086|0.785|
|Excess Job Reallocation Rate|300|0.248|0.103|0.066|0.732|
|Job Creation Rate (Entry)|300|0.056|0.040|0.000|0.227|
|Job Destruction Rate (Exit)|300|0.053|0.032|0.003|0.152|
|**TRANSITION**||||||
|Job Creation Rate|300|0.174|0.088|0.000|0.647|
|Job Destruction Rate|300|0.128|0.061|0.000|0.385|
|Net Employment Growth|300|0.046|0.087|-0.299|0.419|
|Job Reallocation Rate|300|0.303|0.123|0.000|0.875|
|Excess Job Reallocation Rate|300|0.227|0.109|0.000|0.608|
|Job Creation Rate (Entry)|300|0.070|0.056|0.000|0.357|
|Job Destruction Rate (Exit)|300|0.039|0.025|0.000|0.135|

Source: Own calculations based on harmonized firm-level database.

# **3.2 Firm Dynamics Play a Major Role in Total Job Flows**

The second main stylized fact emerging from our analysis of job flows is the strong contribution of the creative destruction process. Indeed, entering and exiting firms account 10
_35_ <!-- Start of picture text --> Filgure D.1. Asia: Concentration of The Poor in Rural Areas<br>..............................................................................................................................<br>, 100<br>*j80<br>60<br>0o 404 40<br>20<br>0<br>Pahistan Inaonesia Thailand<br>India Philppines Malaysia<br>X-Axis<br><!-- End of picture text -->

# Characteristics of the Rural Poor

The largest roncentrations of the rural poor are among the landless (45 percent) and among tenants and marginal landowners (46 percent). Livestock constitutes the most important source of income for the rural poor (28 percent), followed by crop income (24 percent) and nonfarm microenterprise (20 percent) Cable 3.3). Rural poverty alleviation programs would have to focus on these principal sources of income to lift the poor out of poverty. Indirecty, of course, a credit program for agriculture would increase crop incomes as well as income from farm labor. However, credit programs for rural microenterprises and to support consumption for the very poor would still be needed.

Table 3.3 ncome Sources of the Rural Poor (percentage)

|6~.......................|Share of incme<br>**....**.**.........**<br>. . i . . . . ............. .......|
|---|---|
|Livestock|28|
|Crops|24|
|Nonfarm enterprise|20|
|Wages and salaries|18|
|Remittances|7|
|Other sources|3|

Source Aly Alp Ercelawn, 'Poverty in Rural Palkstan: A Study of Villages" In I. Nabi, The Ouality of Life in Pakistan: Studies in Social Sector Economics (Vanguard), 1986.
Committee, National Institute of Urban Affairs (1995), Planning Commission (1983), ORG (1989), National Policy on Education (1986). We have used current expenditure norms for water supply, sewerage/sanitation and solid waste management on the basis of the National Institute of Urban Affairs (NIUA)\, as this is the most recent study on these norms (NIUA, 1995). The norms on roads and street lights are not specified in this study. We have referred to the most recent study available on these norms by PricewaterhouseCoopers(2001). The norms on O&M used from different sources for our analysis are tabulated in Table A 1 in the Appendix.

**Table 10. Estimated Expenditure Needs (Per Capita) of ULBs (Rs, 2004-05)**

|**UA**|**Expendit**<br>|**ure Need Revenue**<br>|**Expe**<br>|**nditure Need**<br>**Capital**<br>|**Expend**<br>|**iture Need Total**<br>|
|---|---|---|---|---|---|---|
||Central<br>City|Non Central City<br>Median,<br>(Maximum,<br>Minimum)|Central<br>City|Non Central<br>City<br>Median,<br>(Maximum,<br>Minimum)|Central<br>City|Non Central<br>City<br>Median,<br>(Maximum,<br>Minimum)|
|Delhi|1,236||618||1,854||
|Chennai|995|865|517|450|1,513|1,314|
|||(2,996, 465)||(1,558, 242)||(4,555, 706)|
|Hyderabad|1098|637|285|166|1,383|802|
|||(1,125, 238)||(293, 62)||(1,418, 299)|
|Kolkata||862||129||991|
|||(862, 730)||(129, 109)||(991, 839)|
|Pune|2,673|2,665|1,149|1,146|3,822|3,811|
|||(2,743, 2,581)||(1,179, 1,110)||(3,922, 3,691)|

**Source: Authors’ Computations**

It is interesting to note that

- Pune records the highest expenditure needs (capital, revenue and total), both for central and non central cities. But this is mainly caused by higher other expenditure needs estimated.

26
effects of the recession on tax revenue, turned the U.S. government budget from a surplus of almost 1 percent of GDP in 2000 to a deficit of 4.5 percent<sup>41</sup> of GDP in 2008 (Figure 41). All else equal, this increase in the budget deficit implies a fall in public sector savings and national savings, and a corresponding swing of 5.5 percentage points of US GDP in the current account.

<!-- Start of picture text --> Figure 41: US Budget Receipts, Outlays, and Surpluses or Deficits<br>20% 2%<br>18%<br>1%<br>16%<br>0%<br>14%<br>-1%<br>12%<br>10% -2%<br>8%<br>-3%<br>6%<br>-4%<br>4%<br>-5%<br>2%<br>0% -6%<br>Surplus or Deficit(−) Receipts Outlays<br>On-budget receipts and outlays as a % of GDP<br>On-budget surpluses (+) or deficits (-) as a % of GDP<br><!-- End of picture text --> Source: Office of Management and Budget.

64. To summarize, loose monetary policy that was enacted to counteract the burst of the I.T. bubble and the 9/11 attacks created conditions of excess liquidity in which the housing and equity bubble could develop. This bubble increased households‘ wealth and consumption. As a result, the U.S. economy boomed. However, U.S. domestic production was not able to meet the increase in domestic consumption, which led to current account deficits with countries providing consumer goods to the U.S., including oil-exporting countries and consumer goods-exporting countries. As China had become the major producer of labor-intensive processed consumer goods by 2000, the United States ran an increasingly large deficit with China, which in turn ran trade deficits with many other East Asian economies that provided intermediate products to China.

> 41 Office of Management and Budget (2010). The deficit is defined here without Social Security funds.

48
Using this method and _<u>τ</u>_<sup>_~~l~~_</sup> _JA_<sup>’s that depend on 2016 estimates of �</sup><sup>_τ k_</sup> _JA_<sup>, we calculate</sup> that final expenditure in Puerto Rico would be $1.4 billion (about 0.8 percent) lower in 2016 without the JA. Table 6 decomposes this value into burdens on particular types of final expenditure; consumption spending would be $692 million (about 1.1 percent) lower per year, or $203 per citizen annually. The highest burden is on investment, which could be maintained at existing levels with 3 percent lower expenditures if the JA were removed. This implicit tax on investment implies that the policy imposes dynamic losses on PR that are likely much more consequential than our static CV estimates.

Table 6: Compensating Variation, Jones Act Removal, 2016

||Share in<br>Final Demand|Total CV<br>(millions of $U.S.)|Per Capita<br>($U.S.)|% Change<br>vs No JA|
|---|---|---|---|---|
|Final Demand|100.0%|1,398|410|0.8%|
|Consumption|36.7%|692|203|1.1%|
|Exports|46.0%|291|85|0.4%|
|Investment|8.2%|403|118|3.0%|
|Local Government|6.6%|0|0|0.0%|
|Municipal Government|1.6%|0|0|0.0%|
|Federal Government|0.9%|0|0|0.0%|

Note: CV estimates are calculated using NAICS code level weighted averages of the estimated JA-tariff equivalents. All figures are in 2016 dollars; per capita estimates use PR’s population in 2016. All calculations assume zero tariff equivalents for NAICS codes 3251, 3254 and 3390, the pharmaceutical sector.

Using the same approach, but considering the removal of 2016 U.S. MFN tariffs for all goods arriving from ROW partners that are not members of a preferential trade agreement with the U.S., we calculate the CV of removing such tariffs to be U.S.$150 million (about 0.1 percent of expenditures).<sup>45</sup> MFN tariffs on Puerto Rican households’ imports of non-PTA partner goods costs them approximately $94 million, or $28 per person per year. The implication is that the JA costs Puerto 45The tariffs we remove are trade-weighted averages of 2016 MFN tariffs imposed on non-U.S. PTA partners, assuming a value of zero for PTA partners.

39