rafmacalaba/datause-encoder-data huggingface dataset:
|Average of dummy variables for quarters<br>-97<br>**-265**|**98**<br>**-110**|176,141|
|---|---|---|
|p-values for test of equalitywithpreviousyear<br>_0.00_|_0.00_<br>_0.00_||
|Sources: As for Table 1.|||
|Notes: The change in expenditure is obtained as the average of the quarterly effect<br>-265 = (-187.93 – 321.07 – 351.89 – 197.34)/4, where the right-hand-side numbers c|s; for instance,<br>ome from Table 1. The p-v|alues are|
|based on t-tests of the equality between the sums of quarterly dummy variables of|one year compared to the n|ext.|

Small households saw a significant rise in expenditure levels in 2010, but most of the other changes do not show up as statistically significant when the data are disaggregated by household size. Households that were net sellers of crops did not see any particularly low expenditure in

**Table 3.  Measuring the Impact of the 2008-09 Recession on Real Expenditure per capita by region and household size**

||Real|Change i|n real expenditu|re per capita|compared||
|---|---|---|---|---|---|---|
||expenditure||to 200|7:Q1||Number of|
||per capita|2007|2008<br>_baht/quarter_|2009|2010|households|
|All Thailand|4,068|-291|**-1058**|**393**|**-439**|176,141|
||||_0.00_|_0.00_|_0.00_||
|Memo: nominal exp/cap|4,248|1|**32**<br>_0.00_|**385**<br>_0.00_|**391**<br>_0.00_|176,141|
|Region 1: Bangkok|7,973|85|**3,906**|**1,299**|**3,461**|10,520|
||||_0.00_|_0.00_|_0.00_||
|Region 2: Center|4,686|-156|**-423**|**215**|**-267**|51,442|
||||_0.01_|_0.00_|_0.00_||
|Region 3: North|3,226|-105|**-622**|**-123**|**-439**|43,389|
||||_0.00_|_0.00_|_0.00_||
|Region 4: Northeast|2,926|-198|**-846**|**-140**|**-613**|45,521|
||||_0.00_|_0.00_|_0.00_||
|Region 5: South|4,164|39|137|174|375|25,269|
||||_0.48_|_0.81_|_0.20_||
|Urban|6,037|-132|-59|**107**|111|108,690|
||||_0.63_|_0.04_|_0.96_||
|Rural|3,184|-32|**-528**|**70**|**-391**|67,451|
||||_0.00_|_0.00_|_0.00_||
|Very poor (deciles 1-2)|1,321|-8|-24|**5**|**-27**|20,546|
||||_0.07_|_0.01_|_0.00_||
|_Memo: % very poor_||_21.9_|_21.1_|_19.5_|_17.6_||
|Poor (deciles 1-4)|1,690|-13|**-71**|**3**|**-50**|48,355|
||||_0.00_|_0.00_|_0.00_||
|Non-poor (deciles 5-10)|5,654|-92|-146|**127**|-1|127,786|
||||_0.32_|_0.00_|_0.10_||
|Household size: 1|7,972|-189|347|-113|**546**|24,825|
||||_0.11_|_0.12_|_0.03_||
|Household size: 2|5,355|-153|112|86|**342**|43,083|
||||_0.06_|_0.83_|_0.03_||
|Household size: 3|4,389|-21|17|109|168|41,391|
||||_0.72_|_0.31_|_0.52_||
|Household size: 4|3,848|-90|-91|**100**|-47|34,129|
||||_0.77_|_0.02_|_0.07_||
|Household size: 5|3,367|79|47|152|96|17,879|
||||_0.89_|_0.25_|_0.55_||
|Household size: 6 and up|2,899|-54|53|165|94|14,834|
||||_0.28_|_0.20_|_0.42_||

Losers and Winners in Recession: Thailand 2008-09 17
Table 2

|Cent|ral Govem<br>(in per|ment Debt/Exp<br>centage point|ort Ratios<br>s)||
|---|---|---|---|---|
||19222|1931|1933|93|
|Hcavy Defaulter,s|||||
|Brazil|153|163|215|127|
|Bulgaria|263|288|na|416|
|Chile|102|327|842|573|
|Colombia|58|101|123|67|
|Costa Rica|96|130|174|158|
|El Salvador|106|153|183|157|
|Germany|7|34|62|42|
|Greece|415|750|633|474|
|Guatemala|65|97|162|123|
|Hungary|124|251|347|256|
|Poland|135|212|472|355|
|Uruguay|147|185|212|148|
|Unweighted Averages:|139|224|311|241|
|Light Defaulters|||||
|Argentina|49|73|113|81|
|<br>Australia|162|684|621|585|
|Austria|78|157|310|274|
|Belgium|87|111|190|240|
|Canada|44|82|98|107|
|Czechoslovakia|26|48|119|98|
|Denmark|45|56|57|54|
|Finland|52|128|74|51|
|France|na|na|22|18|
|Italy|12|17|28|30|
|<br>Japan|69|132|76|57|
|<br>New Zealand|278|454|400|350|
|Nicaragua|30|42|53|46|
|<br>Norway|107|165|133|118|
|<br>Spain|43|95|137|157|
|<br>Venezuela|_5_|1|1|1|
|Unweighted Averages:|72|150|152|142|

Notes: na denotes not available. War debts and reparations are excluded for France. Figures for Australia starting in 1931 include state debts assumed by the federal authorities. Those for Brazil and Chile include selected state and local debts assumed by the federal authorities.

Source: League of Nations, Annual Statistical Yearbook (various issues).

11
David, 1993). However, it now appears that the key driving force behind changes in per capita food consumption is changes in real incomes—the same increases in real incomes that drive the urbanization process (Satterthwaite, McGranahan and Tacoli, 2010).

# **3.1. Cereal Equivalent (CE) Measures of Food**

Some scholars argue that the dietary shift from crop based to animal based products may increase total food demand sharply relative to supply, due to the inefficient conversion of plant based feeds (typically cereals<sup>8</sup> ) into animal based foods.<sup>9</sup> Yotopoulos (1985) argues that the supply of cereals available for food may decline as developed and middle-income countries consume cereals disproportionately as feed, raising world prices of cereals. He suggested that this “Food-Feed Competition” may have contributed to the world food crisis of 1972-74. More recently, a number of scholars have explored the implications of this dietary shift on agricultural resources and the environment (e.g., Elferink and Nonhebel, 2007; Garnet, 2009; GerbensLeenes, Nonhebel and Ivens, 2002ab; Gerber, Steinfeld, Henderson, Mottet, Opio, Dijkman, Falcucci and Tempio, 2013; Steinfeld et al. 2006; Williams, Audsley and Sandars, 2006; Wirsenius, 2003; Wirsenius, Azar and Berndes, 2010). For instance, Wirsenius (2003) argues that the idea of competition for grains between animals and humans does not capture fully the resource implications of dietary change, since the core issue is the competition for land rather than competition for consumption of cereals. Analyzing comprehensively total feed<sup>10</sup> and land requirements in their land-minimizing model, Wirsenius et al. (2010) suggest that greater feed-

> 8 About 34 percent of cereals were consumed indirectly in the form of animal feed in 2009 (FAOSTAT).

> 9 Yotopoulos (1985) suggests calorie-equivalent grain-meat conversion ratios vary from 2:1 for poultry to 7:1 for grain-fed beef, estimates that do not appear to take into account indirect use, such as for breeding animals, considered by Rask and Rask (2011).

> 10 In addition to the edible-type crops (e.g., cereals, starchy roots, sugar crops and oil crops), Wirsenius (2003) considers other types of feeds such as the use of forage crops, pastures, and by-products/residues.

8
of developed and developing countries. Such indicators include monetization variables, such as M2 or M3 to GDP, or financial depth indicators, such as private credit (outstanding claims of financial institutions on the private sector) to GDP. Later papers have added indicators of the size and liquidity of stock markets, albeit available for fewer countries and shorter time periods. Indicators for the efficiency and competitiveness of financial systems, non-bank financial institutions such as institutional investors and, most importantly, the outreach of financial systems, are available for only a few countries and often do not have a time dimension.<sup>4</sup> Within-country studies allow researchers to utilize more micro-based data or focus on specific policy interventions or reforms.

The remainder of the paper is structured as follows. Section 2 illustrates the identification problem, which is at the center of the finance and growth literature, using the example of a simple Ordinary Least Squares (OLS) estimation of regression (1). Section 3 discusses instrumental variable (IV) approaches using cross-sectional and panel data. Section 4 discusses time-series approaches and section 5 differences-in-differences techniques. Section 6 discusses the use of firm- and household-level data and the methodological challenges this implies. Section 7 concludes and looks forward to new research directions.

# **2. Correlation vs. causality -- the identification problem**

Goldsmith (1969) was the first to empirically show the positive correlation between financial development and GDP per capita, using data on the assets of financial intermediaries relative to GNP and data on the sum of net issues of bonds and securities plus changes in loans relative to GNP for 35 countries over the period 1860 to 1963. Such a correlation, however, does not control for other factors that are associated with economic growth and might thus be driven by other country characteristics correlated with both finance and growth. Second, such a correlation does not provide any information on the direction of causality between finance and growth. The early finance and growth literature has therefore used standard cross-country OLS regressions that

> 4 See Beck, Demirguc-Kunt and Levine (2000) for an overview of different cross-country indicators of financial development and Beck et al. (2008) for a discussion of the different dimensions of financial development, such as depth, efficiency and reach. See World Bank (2007) for a discussion of financial outreach indicators.

3
icy uncertainty but EMDEs saw even higher levels of uncertainty in the early 2000s when many economies were adjusting to inflation targeting frameworks and bringing down inflation expectations.

This paper makes a number of contributions to the literature. First, it extends the analysis of the impact of fiscal and monetary policy uncertainty in a VAR model beyond the United States to 54 economies, including 32 advanced economies and 22 EMDEs. As such, this paper is the first to provide a perspective on policy uncertainty in both advanced economies and EMDEs. Second, the VAR model used in this paper generates country and global fiscal and monetary policy uncertainty from a unified framework and provides the most comprehensive analysis of fiscal and monetary policy uncertainty to date. It also an alternative to the common of uncer- provides important approach measuring tainty adopted in the literature following the work of Baker et al. (2016), and in line with Jurado et al. (2015). Third, it is the first to try and explicitly account for other factors that may drive uncertainty by removing transitory events and data outliers that may contaminate uncertainty measures. Finally, this paper provides a perspective on the evolution of monetary and fiscal policy uncertainty during the COVID-19 pandemic.

# **2 Related literature**

This paper contributes to a growing literature on the importance of policy uncertainty in driving macroeconomic outcomes and on measuring this uncertainty.

## **2.1 Measures of policy uncertainty**

There are four broad approaches to measuring uncertainty: textual analysis, market data, surveys, and models with time-varying volatility. The most prolific source of measuring this uncertainty started with Baker et al. (2016) who use newspaper coverage frequency (textual analysis) to develop an economic policy uncertainty (EPU) measure. The au4
# **Table 9 What are the Correlates of Migrant Participation in the New Rural Collective Medical Insurance Program**

# Dependent Variable: Participation (Yes=1, No=0)

Probit Marginal Effects

|Regressors|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Age|0.001|-0.001|-0.001|-0.003|-0.004|-0.005|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|Age-Squared|-0.000|-0.000|-0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of Schooling|-0.034***<br>(0.003)|-0.018***<br>(0.003)|-0.035***<br>(0.003)|-0.019***<br>(0.003)|-0.018***<br>(0.003)|-0.015***<br>(0.003)|
||-0.188***|-0.132***|-0.186***|-0.130***|-0.112***|-0.087***|
|Post-Secondary Education|(0.028)|(0.030)|(0.028)|(0.030)|(0.032)|(0.033)|
|Married|0.103***<br>(0.022)|0.095***<br>(0.023)|0.110***<br>(0.022)|0.100***<br>(0.023)|0.088***<br>(0.024)|0.090***<br>(0.024)|
|Female|-0.045***|-0.035**|-0.034**|-0.027*|-0.029*|-0.026|
||(0.015)|(0.015)|(0.016)|(0.016)|(0.017)|(0.017)|
|Rural Hukou||0.397***<br>(0.015)||0.396***<br>(0.015)|0.396***<br>(0.016)|0.378***<br>(0.016)|
|Currently Employed|||0.074***|0.056**|-0.323|-0.253|
||||(0.026)|(0.027)|(0.276)|(0.545)|
|Participates in Medical||||||-0.341***|
|Insurance through Employer||||||(0.022)|
|Self-Employed|||||0.584***|0.520|
||||||(0.232)|(0.450)|
|_Alternate Controls_|||||||
|Industry|no|no|no|no|yes|yes|
|Work Unit Ownership|no|no|no|no|yes|yes|
|Observations|5,116|5,116|5,116|5,116|5,116|5,116|
|Average Participation|0.463|0.463|0.463|0.463|0.463|0.463|

Notes (for both panels): Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. The sample includes 5116 migrant working age adults, aged 18-60. The sample is drawn from the five cities common to the 2005 and 2010 CULS survey rounds (Fuzhou, Shanghai, Shenyang, Wuhan and Xian). All models include city dummy variables. Models (5) and (6) control for 17 industries and 8 work unit ownership types.

36
Altogether, Table 8 shows that except for the export of goods and non-factor services<sup>17</sup> , all the indicators of trade reform improved significantly between the two periods. One can speculate that greater imports forced domestic businesses to increase their efficiency of production in order to become more competitive, since they lost the price protection afforded by tariffs. More advanced technology was perhaps also introduced as reflected in the increased amount of capital importation, thus contributing to further reductions in the cost of production.

**Table 8**

||In<br>|dices oftrad<br>|ereform<br>|Trade<br>|Trade-tax<br>||
|---|---|---|---|---|---|---|
|Period|TFP|Imp/GDP|Imp(cap)/GDP|ratio|ratio|Exp/GDP|
|1980-89|-2.37|14.7|3.9|26.7|46.6|12|
|1995-03|7.78|27.0|6.5|35.2|23.2|8.2|

Trade ratio: (Exports and imports of goods and services) / GDP Trade tax ratio: Trade tax / total tax A second set of quantitative indices measure financial reform (Table 9). These include the ratios of broad money (M2) and private sector credit to GDP, as well as the real interest rate. The M2 share of GDP rose from 14% during the 1980-89 period to 16% during 1995-2003. At the same time the private sector credit share rose from 7% to 9% while real interest rates dropped from 9% to 8.5%. One may surmise that financial reforms brought about a financial deepening that made economic resources available at a lower cost (Kanimba, 2004). These resources were channeled from the financial system to capital investment and helped improve the overall productivity of the economy. It should be noted, however, that previous studies have produced mixed results on the economic effects of financial development. Amiruddin et al. (2007) concluded that 21
residents of a county received IDs. After controlling for village-fixed effects and village-specific trends, we identify the change in cost of migrating by exploiting differences in the timing of access to IDs and the non-linearity in the relationship between the annual change in the village migrant share and the time since IDs were distributed. To ensure that IDs were not distributed in response to demand for ID cards, we show that the timing of ID card distribution is not related to exogenous rainfall shocks affecting both earnings in the local economy and migrant labor supply. We further show that the timing of ID distribution is not systematically related to changes in variables proxying for time-varying local policies, which may affect the returns to labor or self-employment locally, or to time-varying proxies reflecting local administrative capacity, which could be related to village leader responsiveness to local demand for IDs.

To better identify differences in rate of migrant network growth across villages, we interact the non-linear function of years since IDs were distributed with the variance of county rainfall. Under the assumption that a village fixed effect controls for inherent riskiness of the local environment, the interaction facilitates identification by allowing for differences across villages in how IDs affect the growth of migrant networks. We also examine the plausibility of this expanded set of instruments.

The paper proceeds as follows. In section 2 we provide additional background on rural-urban migration in China and introduce the RCRE Household and Village surveys used in the analyses. Section 3 introduces the household model which provides a framework for the empirical methodology discussed in section 4. In section 5, we present our results and a final section concludes.

# 2 Background

## 2.1 Rural-Urban Migration in China

Over the 1990s, rapid growth in the volume of rural migrants moving to urban areas signalled that a dramatic change in the nature of China’s labor market was taking place. Estimates using the 1 percent sample from the 1990 and 2000 rounds of the Population Census and the 1995 one percent population survey suggest that the inter-county migrant population grew from just over 20 million in 1990 to 45 million in 1995 and 79 million by 2000 (Liang and Ma, 2004). Surveys conducted by the National Bureau of Statistics (NBS) and the Ministry of Agriculture include more detailed retrospective information on past short-term migration, and suggest even higher levels of labor migration than those reported in the census (Cai, Park and Zhao, 2007).

Before labor mobility restrictions were relaxed, households in remote regions of rural China faced 3
marriage. People living in urban districts are generally more exposed to information campaigns promoting the use of condoms and there is higher availability in urban areas. As expected, being currently and formally married is negatively and significantly associated with condom use in the last intercourse with the spouse. However, married women (both currently and formerly married) are less likely than single women to use a condom when they have extramarital sex. This is a potential source of vulnerability which should be taken into consideration for prevention efforts. The same result was found in Burkina Faso, Cameroon and Kenya (De Walque 2006). Before the end of the apartheid regime in South Africa, about 60% of the Lesotho male workforce was employed in the mining industry in South Africa. During this period anecdotal evidence suggests that some married women started to engage in sexual relationships with other partners. In the Demographic and Health Survey, 11.6% of married women report having had extramarital sex in the last 12 months. This is substantially higher than in other African countries (De Walque, 2006)<sup>1</sup> . The existence of long-term extra-marital relationships might increase the trust between partners and therefore the use of condoms might not appear necessary anymore.

The probability of using a condom inside and outside the marriage increases with educational achievement. The relationship is robust, but statistically significant only for individuals with secondary education or more. As expected, wealth positively affects condom use, both for sexual intercourse with the spouse and with other partners.

# **4.3 Fidelity in the last 12 months**

This section shows the results of the likelihood of having had extra-marital sex in the last year.

[Insert table 6] The dependent variable we used in table 6 is equal to one if an individual had extramarital sex during the last 12 months. We include only currently married or

> 1 It is not clear whether this is due to an actual difference in behavior or to a difference in reporting.

14
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# **1 Introduction**

The marginal value of consumption is very high for poor households. For this reason, one expects households in low-income countries to be especially mindful of ways to arrange purchases so as to maximize consumption per dollar. This paper studies the surprising, contrary finding that many households in Tanzania purchase non-perishable goods in small increments, multiple times, over a two-week period. If price schedules were linear and transaction costs minimal, frequent purchasing of small quantities would have no impact on the budget set. If bulk discounts were available, but only for large quantities representing several weeks or months of consumption, there could be many reasons not to buy in bulk. However, we find that many items exhibit bulk discounts over modest quantity ranges. Households appear to be systematically over-paying for consumption goods in order to maintain a pattern of frequent, small-quantity purchases.

The goal of this paper is to better understand this phenomenon. We aim to answer two questions. First, how much consumption is foregone because of small-quantity purchasing? Second, why do households make purchases in this way?

To answer the first question we analyze transaction diaries maintained by 1,493 Tanzanian households over a two-week period. Diary respondents recorded the date, quantity, price, and detailed description of every purchase. From these data we calculate the total quantity purchased at the household-item level over two weeks, the total amount paid, and the counterfactual cost if the household had purchased the total quantity in one transaction. This last step makes use of expenditure schedules that we estimate from the data (and validate with market price surveys). We limit our analysis to 19 standardized items that are non-perishable over the relevant quantity and time ranges. The item list includes numerous staples: maize, rice, cooking oil, kerosene, onions, dried sardines.

We find that across items purchased multiple times over two weeks, the value of forgone consumption is equal to 8.7% of the value of expenditure. By simply bundling purchases made over a short period of time, the average household could spend almost 9% less on a range of important goods without reducing consumption.<sup>1</sup> There is substantial heterogene-

> 1This should not be thought of as a 9% return on a two-week investment, which could be re-invested for annualized return of over 900%. The implicit return to the household is limited by the bundle it purchases,

2