rafmacalaba/datause-encoder-data huggingface dataset:
63 THEORETICAL ANALYSIS Qc - housing consumption under rent controls

-

- PmQm estimated rent in the absence of controls, also denoted Rm

- PcQc observed controlled rent, also denoted Rc

b - price elasticity of dcmand.

In the special case where the price elasticity of demand, b, is equal to -1, the expression b/(b+l) is undefined. But it can be shown that in this special case the benefit can be expressed using natural logarithms as: Benefit - PmQm [log (PmQc) - log(PmQm)] + PmQm - PcQc [2] These two related equations will be the centerpiece of the empirical analysis in the next chapter. The benefit may be thought of as composed of two parts. The first is comprised of the two terms to the right of the brackets in equations [1] and [2]. This is simply the additional spending on non-housing goods brought about by paying a rent Rc (=PcQc) rather than Rm (-PmQm). This - simple difference between market and controlled rents, Rm Rc, is often used as an approximation to tenant benefits from the imposition of controls. But this simple benefit measure does not take into account how households value changes in housing consumption in addition to changes in disposable income. The second, comprising the terms in parentheses and brackets in the two equations, depends on the difference in housing consumption with and without rent controls. But - whereas in the simple benefit measure (Rm Rc) an extra dollar of non-housing is counted as being worth exactly one dollar to the tenant, in the benefit measures [1] and [2] extra housing is discounted based on the tenant's relative preference for housing vis- a-vis other goods.

The measures in [11 and [2] do not include all possible costs and benefits to tenants. For example, rent control may increase transactions costs for tenants, including search costs (Clark, 1982), and increase waiting time for housing units (the cost of which to tenants may be considerable, see Willis, 1984). All of these will reduce the benefits to tenants, but the full system may also increase the bundle of property rights, such as security of tenure, enjoyed by tenants thus increasing their benefits in this area. The above measures [1] and [2] are then better approximations of benefits than Rm - Rc, but they are still approximatiot.s.

The cost imposed on landlords is straightforwardly approximated by PmQc - PcQc, or the difference between controlled and market rents for the unit inhabited by the tenant. This measure of cost to landlords does not include losses from prior accelerated depreciation of the unit. However, this could be regarded as a saving in maintenance costs, whiclh would generate benefits elsewhere, perhaps equal to the opportunity cost forgone. The cost to landlords would also include losses from the uncompensated transfer of property rights to - renters. Thus, the true costs to landlords may therefore exceed the (PmQc PcQc) estimates.
# Who Should Be at the Top of Bottom-Up Development? A Case Study of the National Rural Livelihoods Mission in Rajasthan, India

Shareen Joshi (Georgetown University) Vijayendra Rao (World Bank)

## JEL Codes:  D70, O10, D72

Keywords: Community Driven Development, Decentralization, Self-Help Groups, India Acknowledgements: The authors are indebted to Radha Khan for supervising the qualitative fieldwork, Ritwik Sircar and Anirvan Chowdhury for outstanding research assistance, SRIIMRB for collecting the quantitative data for this study, the Government of Rajasthan for sharing valuable data, PRADAN for valuable initial conversations, and the staff of PRADAN, PEDO, SERP and the Rajasthan SHG federations for their support. The authors are grateful to Georgetown University and the South Asia Food and Nutrition Security Initiative for their financial support. For excellent comments and conversations we thank Radha Khan, Sanjay Sharma, Irfan Nooruddin, Milan Vaishnav, Anders Olofsgard and participants of the India Politics Workshop in Washington DC.
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10. "I am not confident that the state authorities protect my person and my property from criminal actions" To what degree do you agree with this statement?

Now 10 years ago (1) fully agree E C (2) agree in most cases 7 [7 (3) tend to agree bi X (4) tend to disagree 5 [J _(5)_ disagree in most cases [ 0 (6) strongly disagree [7 L <!-- Start of picture text --> 90<br>80<br>70<br>|l<br>50<br>4<br>30<br>20*<br>20<br>10<br>-<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br>Now<br><!-- End of picture text -->

## **Percentage of firms who ticked (1), (2), or (3) for question 10**

In most countries the business community feels that authorities do not adequately guarantee their personal safety and enforce their property rights. In Latin America, Africa, the CIS, and the CEE almost 80 percent of entrepreneurs reported that they did not feel confident that the state authorities would protect their person and property from criminal actions. Even in the developed countries half of the respondents (on average) did not trust government in this dimension. Furthermore in all regions, except for Asia, entrepreneurs reported that the security of property and personal safety had decreased over the last decade (over the last 5 years in the case of the transition economies).

# _Question 11. Reliability of the judiciary_

Unreliable judiciaries can cause two forms of uncertainty: unreliable judiciaries offer no fair recourse against unlawful behavior between citizens and similarly unfair treatment of citizens by state officials is unclearly prosecuted by the judiciary. This undenmines property and contract rights.

18
# **Figure 6**

# **Value of U.S. Land Imports from Mexico by Mode**

<!-- Start of picture text --> Value ($millions)<br><!-- End of picture text --> <!-- Start of picture text --> 120,000<br>100,000<br>80,000<br>Truck<br>60,000<br>Rail<br>40,000<br>20,000<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Year<br><!-- End of picture text --> Source: http://www.bts.gov/publications/national_transportation_statistics/2005/html/table_01_54.html

**Table 2 – Costs of Crossing U.S.-Mexican Border (Northbound)**

||**Costs **<br>|**(US$)**<br>|**Time **<br>|**(hours)**<br>|
|---|---|---|---|---|
||**Low**|**High **|**Low**|**High **|
|**Mexican side**|||||
|Trucking Monterrey-Border|188|210|2.5|3.0|
|Unloads and reloads as needed|75|150|0.3|1.0|
|Drayage|75|150|||
|Mexican inspection|||0.0|2.0|
|**_Totals Mexico_**|**338**|**510**|**2.8**|**6.0**|
|**United States side**|||||
|Congestion, waiting time|||1.0|6.0|
|U.S. inspection|||0.0|4.0|
|Unloads and reloads as needed|||0.3|0.5|
|Trucking to Chicago|1,338|1,343|30.0|51.0|
|**_Totals US_**|**1,338**|**1,343**|**31.3**|**61.5**|
|**Total U.S. & Mexico**|**1,676**|**1,853**|**34.0**|**67.5**|

Source: Haralambides and Londono-Kent (2004, p.178).

23
Rey, P., and Stiglitz, J. E. (1993). Short-term contracts as a monitoring device. _NBER Working Papers_ 4514, National Bureau of Economic Research, Cambridge, MA.

Schiantarelli, F., and Sembenelli, A. (1997). The maturity structure of debt: Determinants and effects on firms’ performance: Evidence from the United Kingdom and Italy. _Policy Research Working Paper_ 1699. World Bank, Washington, DC.

Schiantarelli, F. and Srivastava, V. (1997). Debt maturity and firm performance: A panel study of Indian companies. _Policy Research Working Paper_ 1724. World Bank, Washington, DC.

Townsend, R. M. (1979). Optimal contracts and competitive markets with costly state verification. _Journal of Economic Theory_ , 21(2):265–293.

Vermoesen, V., Deloof, M., and Laveren, E. (2013). Long-term debt maturity and financing constraints of SMEs during the Global Financial Crisis. _Small Business Economics_ , 41(2):433-448.

25
the other less tradeable goods (except fresh meat) are similar in magnitude (with milk products higher in magnitude but imprecisely measured). In contrast, the price of rice, which is more easily traded is negatively related to saturation above this cutoff. Increasing the cutoff from 65 to 75 percent sharpens these patterns (although the effects for the more tradeable goods are more imprecisely measured). Reducing the cutoff value to 50 percent or estimating a linear model dampens the contrast between more and less tradeable goods somewhat. The results therefore support the notion that saturation leads to pressure on the prices on less tradeable goods, especially when it is high.

Unit values have known interpretive difficulties; most importantly they reflect both quality choices made by the household as well as the prices faced by it. This longstanding problem has been recently explored by <mark>McKelvey</mark> (2011) and Gibson and Kim (2019) who demonstrate that the traditional method of correcting price elasticity estimates for quality adjustments (Deaton, 1988) does not in fact result in elasticity estimates free of quality choices. Therefore, instead of pursuing a related correction, we first note that the main foods in our study are relatively homogenous and should not exhibit significant quality-income elasticities. Second, we note that Attanasio et al. (2013) showed that in Mexico, changes in unit values reasonably track changes in local prices.

We nevertheless carry out two robustness checks on these results that aim to minimize potential quality variation. First, we calculate median village unit values after trimming values that are smaller or greater than the village mean unit value plus or minus 3 standard deviations. Second, we calculate alternative median unit values only taking into account households in the top villagelevel quartile of the distribution of per capita consumption expenditures. Even in highly saturated villages, these households should largely be nonbeneficiaries and therefore their food quality choices unaffected by the income transfer from program participation.

The results, reported in Annex 2, are consistent with our main findings. In fact, the point estimates for fresh fish, fresh meat, and even fruit are larger than the point estimates based on unit values from the entire sample. Finally, we estimate the same regression specification using the price data from the experimental sample. Exploiting the cross-sectional variation in saturation in the treatment arm of the experimental sample, we find a coefficient of 0.117 on the interaction 20
pollution better if they had multiple air monitoring stations and monitoring equipment. An EPB head notes: We would like to control heavy metal pollution in waste water as we know that some companies pollute it but we simply cannot afford the monitoring equipment. We have pointed this out to the provincial leaders for many years but only got approval for it this year. (Interview, September 2011, Shanxi, municipality EPB head, quoted in Kostka (2013)).

In summary, local EPB leaders receive mixed signals: they are asked to fully implement binding environmental targets but these demands by upper-level governments are not always matched with a corresponding increase in financial resources.

# _3.4 Limits on Public and Private Participation_

Another barrier at the local level is the limited opportunities that exist for participation of local non-state actors to improve environmental management. In the following, this problem is referred to as the “participation gap” (Kostka and Mol, 2013). Although there is thin evidence that participation of non-state actors leads directly to better environmental outcomes (Bulkeley & Mol, 2003; Newig & Fritsch, 2009), there are many other benefits of participation to environmental policy (Beierle, 1999). A high degree of participation by non-state actors is seen to be important in educating the public; it incorporates both public values and preferences into decision-making; it fosters trust in institutions; it reduces conflict; and it makes decisions more cost-effective (Beierle, 1999; Kostka and Mol, 2013). Public participation in environmental policy-making and implementation can also enhance the legitimacy of these policies (Kostka and Mol, 2013).

Participation in China’s environmental policymaking has traditionally been structured and institutionalized within the state and party systems, for instance, through the China People’s Political Consultative Conference (PPCC) at various levels (Kostka and Mol, 2013). These mechanisms of participation – characterized by restricted opportunities for participation and limited access to decision makers – continue to define Chinese environmental policy-

**Figure 4: Structural Context of Actors**

_Source: Li and Higgins, 2013_ making (Kostka and Mol, 2013). Environmental guidelines and measures are initiated, driven, and executed by the government. _Figure 4_ summarizes how government officials from the EPB and other government agencies interact with and respond to pressures from industry, public, non-governmental organizations, and the mass media.

29
**Table 3. Relaxing the Exclusion Restrictions:  Conley et al.  Bound Estimates**

|||**Lower**<br>**Bound**|**Upper**<br>**Bound**|
|---|---|---|---|
|**ln(Landgini 2012)**||||
|ln(km of railroad)|θ ε[-0.01*_β_, 0.01*_β_]|0.097|0.399|
||θ ε[-0.05*_β_, 0.05*_β_]|0.023|0.502|
||θ ε[-0.1*_β_, 0.1*_β_]|-0.082|0.636|
|ln(dist. to GQ)|θ ε[-0.01*_β_, 0.01*_β_]|0.028|0.445|
||θ ε[-0.05*_β_, 0.05*_β_]|-0.033|0.541|
|Predicted Market Access 1|θ ε[-0.01*_β_, 0.01*_β_]|0.129|0.353|
||θ ε[-0.05*_β_, 0.05*_β_]|0.121|0.364|
||θ ε[-0.1*_β_, 0.1*_β_]|0.110|0.378|
|Predicted Market Access 2|θ ε[-0.01*_β_, 0.01*_β_]|0.140|0.371|
||θ ε[-0.05*_β_, 0.05*_β_]|0.131|0.383|
||θ ε[-0.1*_β_, 0.1*_β_]|0.120|0.398|
|**ln(Landless 2012)**||||
|ln(km of railroad)|θ ε[-0.01*_β_, 0.01*_β_]|0.441|1.549|
||θ ε[-0.05*_β_, 0.05*_β_]|0.157|1.991|
||θ ε[-0.1*_β_, 0.1*_β_]|-0.263|2.563|
|ln(dist. to GQ)|θ ε[-0.01*_β_, 0.01*_β_]|-0.068|0.876|
|Predicted Market Access 1|θ ε[-0.01*_β_, 0.01*_β_]|0.427|1.091|
||θ ε[-0.05*_β_, 0.05*_β_]|0.402|1.124|
||θ ε[-0.1*_β_, 0.1*_β_]|0.370|1.165|
|Predicted Market Access 2|θ ε[-0.0001, 0.0001]|0.375|1.022|
||θ ε[-0.05*_β_, 0.05*_β_]|0.351|1.053|
||θ ε[-0.1*_β_, 0.1*_β_]|0.320|1.092|

Notes: (1) θ is the direct effect of an instrument on the outcome variable. The lower and upper bounds are the estimated effects of market access on the relevant measure of land inequality given that θ belongs to a specified interval. (2) Predicted Market Access refers to estimated fitted values from a zero stage regression. This allows us to estimate a just identified IV model using predicted market access as a single instrument. (3) Predicted Market Access 1 is from a zero stage with both railroad length and distance to GQ. Predicted Market Access 2 is from a the zero stage with the three instruments chosen by the Lasso: distance to GQ interacted with slope, distance to GQ interacted with elevation, and railroad length interacted with crop suitability.
# Table 5: Probit Estimates of Growth events

|Dependent variable: Dummy f|or the timing|of growth ev|ents||||
|---|---|---|---|---|---|---|
||Probit|Probit|Probit|**Probit**|Logit|Logit|
|Col.|(1)|(2)|(3)|**(4)**|(5)|(6)|
|_FEit (fiscal event dummy)_|0.038**|0.045**|0.054***|**0.060*****|0.054***|0.058**|
||0.021|0.023|0.022|**0.022**|0.021|0.018|
|_FEit *_<br>,<br>_FE_<br>_FE_<br>_t_<br>_t_<br>_n_<br>_def_<br>+|-|-|0.019***|**0.019****|0.017***|0.018**|
||||0.007|**0.009**|0.006|0.009|
|_WWit (Trade liberal. dummy)_|-0.061**|-0.069**|-0.055*|**-0.060****|-0.049**|-0.049**|
||0.024|0.021|0.025|**0.023**|0.025|0.023|
|_TOTit (Terms of trade dummy)_|0.129*|0.132**|0.122*|**0.121****|0.119|0.113*|
||0.086|0.080|0.084|**0.078**|0.085|0.069|
|_DCt_ _(developed-country dummy)_|0.005|-|0.007|**-**|0.006|-|
||0.020||0.020||0.018||
|Year effects|yes|yes|yes|**yes**|yes|yes|
|Sample|All|non HI|All|**non HI**|All|non HI|
|Obs.|1127|706|1127|**706**|1127|706|
|Pseudo R2|0.08|0.10|0.9|**0.11**|0.09|0.11|
|observed proportion<br> ofGEit=1|12.5%|11.6%|12.5%|**11.6%**|12.5%|11.6%|

Estimation by probit. Coefficients are marginal probabilities evaluated at the sample means. Numbers below coefficients are robust standard errors. See text for definition of variables. HI stands for “High Income” countries as defined by the World Bank, July 2007.

***, **, * indicates significant at the 1%, 5%, and 10% level respectively. We allow for a five-year lag between a change in the underlying determinant and a growth event. The timing of the growth event is the three year window centered on the initiation dates.

_Source: Authors’ computation, see Annex A.2._ Before commenting on the results, one should caution about the endogeneity problems, especially of the fiscal event dummy. It could be that in countryevents when growth is anticipated to be unusually high, one might think that policy-makers would increase discretionary public spending (simultaneous bias if this increase occurs with a decreasing associated deficit). Unfortunately, we lack appropriate instruments, so the results should be interpreted accordingly.

Columns (1) and (2) in table 5 report the marginal coefficients corresponding to the estimation of (1) on the whole and on the “non high income” samples respectively. Hence, the reported coefficients give directly the change in the probability that a growth event occurs for a discrete change of the corresponding dummy variable from 0 to 1.

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Our results show that participation in farm activity and non-farm activity is interdependent. With strict liquidity constraints due to the shortage of land, poor households have strong incentives to diversify their income sources and to increase total income. Non-farm activity widens the occupation choice of the rural households, which is disproportionately in favor of the poor. Although the distribution of non-farm income is more unequal than that of farm income, with a higher participation rate of poor households, non-farm activity reduces rural inequality. As an alternative income source, non-farm income can be used to invest in farm activity and in human capital, which can further equalize rural income in the long run.

The non-farm sector can be a path out of poverty and inequality in rural china. It can absorb surplus agricultural labor and reduce the pressure on the demand for land, without creating excessive burdens for urban infrastructure. To enhance the participation and profitability of nonfarm activity, it is important to improve rural infrastructure, reduce market segmentation, and enlarge the potential of the rural areas; to ensure the pro-poor development of the non-farm sectors, it is critical to increase the capability of the poor households by improving universal basic education and by developing the credit and insurance market in rural areas; and to maximize the impacts of non-farm activity in rural inequality reduction, it is essential to strengthen the linkages between the farm and non-farm sectors, facilitate resource allocation, and enhance overall productivity.

# **References**

- Adams, R. H. J., 1989, Worker Remittances and Inequality in Rural Egypt, _Economic Development and Cultural Change_ 38(1), 45-71.

- Adams, R. H. J., 1994, Non-Farm Income and Inequality in Rural Pakistan: A Decomposition Analysis, _The Journal of Development Studies_ 31(1), 110-133.

- Adams, R. H. J., 1999, Non-farm Income, Inequality and Land in Rural Egypt, Policy Research Working Paper 2178, The World Bank.

- Adams, R. H. J. & J. J. He, 1995, Sources of Income Inequality and Poverty in Rural Pakistan, IFPRI Research Report 102.

- Aubert, C., 1995, Exode rural, exode agricole en Chine, la grande mutation?, _Espace Populations Société_ , 1995-2, 231-245.

- Banister, J. & J. R. Taylor, 1990, China: Surplus Labor and Migration, _Asia-Pacific Population Journal_ 4(4), 3-20.

> Barham, B. & S. Boucher, 1998, Migration, remittances, and inequality: estimating the effects of migration on income distribution, _Journal of Development Economics_ 55, 307-331.

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Using the estimated relationship between climate and farm specific variables and crop choice across current households, we measure the climate sensitivity of crop choice. As the agronomy literature indicates a non-linear relationship between climate (temperature and precipitation) and crop growth and by extension, crop revenues and climate, we model crop selection as a quadratic function of climate. Moreover, as climate is not uni-dimensional, we distinguish between seasonal temperature and precipitation. Following Greene (2003), climate sensitivities are estimated by the change in expected probability from the marginal change in climate variables. The estimated model is then used to predict marginal impacts of future climate change scenarios on African agriculture. We examine the marginal impact of climate on crop choice.

# **3. Data**

The data for this study were collected in 11 countries – Burkina Faso, Cameroon, Egypt, Ethiopia, Kenya, Ghana, Niger, Senegal, South Africa, Zambia and Zimbabwe – by national teams. In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. The districts are not representative of the distribution of farms in each country as there are more farms in more productive locations. In each chosen district, a survey was conducted of randomly selected farms. The sampling is clustered.

The number of surveys in each country varied, but a total of 9597 surveys were administered. Some farmers did not grow crops. Some surveys contained incorrect information about the size of the farm or area of cropland. Impossible values were judged to be missing. It is not clear what the sources of these errors were. They may reflect field errors due to a misunderstanding of the question, the units of measurement, or they may be intentional incorrect answers. Other surveys did not contain clear information on crop type and are therefore excluded. The final number of useable surveys for this analysis is 7296.<sup>4</sup> The distribution of surveys by country is shown in Table 1.

> 4 Kurukulasuriya and Mendelsohn (2006) was based on an analysis of 9063 surveys (complete dataset used in the Africa-wide Ricardian analysis).  However, only a fraction of the farmers chose to grow crops.

7